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BigCode 1.35k

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd", "_____no_output_____" ] ], [ [ "Show me the first lines of the original file", "_____no_output_____" ] ], [ [ "df = pd.read_excel('/tmp/gonzalo_test/aseg.xls')\ndf.head()", "_____no_output_____" ] ], [ [ "Show me the region names containing 'Vent' or 'WM' or 'Hippo'", "_____no_output_____" ] ], [ [ "names = set([each for each in df['StructName'].tolist() \\\n if 'WM' in each \n or 'Vent' in each \n or 'Hippo' in each])\nnames", "_____no_output_____" ] ], [ [ "Reshape the table and show me the first lines", "_____no_output_____" ] ], [ [ "df = pd.DataFrame(df[df['StructName'].isin(names)], columns=['subject', 'StructName', 'Volume_mm3'])\ndf = df.pivot(index='subject', columns='StructName', values='Volume_mm3')\ndf.head()", "_____no_output_____" ] ], [ [ "Save it and success !", "_____no_output_____" ] ], [ [ "df.to_excel('/tmp/gonzalo_test/aseg_pivot.xls')", "_____no_output_____" ], [ "from IPython.display import Image\nImage(url='http://s2.quickmeme.com/img/c3/c37a6cc5f88867e5387b8787aaf67afc350b3f37f357ed0a3088241488063bce.jpg')", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/mohan-mj/Regression_Analysis/blob/master/Regression_Analysis_Chemical_Process.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "### The effect of temperature and reaction time affects the %yield. Develop a model for %yield in terms of temperature and time", "_____no_output_____" ] ], [ [ "import pandas as mypanda\nimport numpy as np\nfrom scipy import stats as mystats\nimport matplotlib.pyplot as myplot\nfrom pandas.plotting import scatter_matrix\nfrom statsmodels.formula.api import ols as myols\nfrom statsmodels.stats.anova import anova_lm", "_____no_output_____" ], [ "myData=mypanda.read_csv('datasets/Mult_Reg_Yield.csv')\nmyData", "_____no_output_____" ], [ "tmp=myData.Temperature\nyld =myData.Yield\ntime=myData.Time", "_____no_output_____" ] ], [ [ "##### check for relationship now", "_____no_output_____" ] ], [ [ "scatter_matrix(myData)\nmyplot.show()", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n \"\"\"Entry point for launching an IPython kernel.\n" ] ], [ [ "##### correlation between xs and y should be high", "_____no_output_____" ] ], [ [ "np.corrcoef(tmp,yld)", "_____no_output_____" ], [ "np.corrcoef(time,yld)", "_____no_output_____" ], [ "np.corrcoef(time,tmp)", "_____no_output_____" ], [ "mymodel=myols(\"yld ~ time + tmp\",myData)\nmymodel=mymodel.fit()\nmymodel.summary()", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n \"anyway, n=%i\" % int(n))\n" ] ], [ [ "##### check p value ==> only time is related to yield", "_____no_output_____" ] ], [ [ "mymodel=myols(\"yld ~ time \",myData).fit()\nmymodel.summary()", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n \"anyway, n=%i\" % int(n))\n" ], [ "pred=mymodel.predict()\nres=yld-pred\nres", "_____no_output_____" ], [ "#print(yld, res)", "_____no_output_____" ], [ "myplot.scatter(yld,pred)\nmyplot.show()", "_____no_output_____" ], [ "mystats.probplot(res,plot=myplot)\nmyplot.show()", "_____no_output_____" ], [ "mystats.normaltest(res)", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1334: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n \"anyway, n=%i\" % int(n))\n" ] ], [ [ "##### Implies it is normal", "_____no_output_____" ] ], [ [ "myplot.scatter(time,res)\nmyplot.show()", "_____no_output_____" ], [ "myplot.scatter(pred,res)\nmyplot.show()", "_____no_output_____" ] ], [ [ "##### random values /scattered plot means that the model is good.\nthere should not be any pattern in the plot. if pattern then there exists a better prediction using the method", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

[ [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/luizpavanello/cognizant_bootcamp_DIO/blob/master/project_python_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "#**Análise de Dados com Python e Pandas**", "_____no_output_____" ] ], [ [ "# Monta o drive no ambiente virtual permitindo acesso aos arquivos do drive\nfrom google.colab import drive\ndrive.mount('/content/drive')\n\n# Permite escolher um arquivo da máquina para upload no colab\nfrom google.colab import files\narq = files.upload()", "_____no_output_____" ], [ "from google.colab import drive\ndrive.mount('/content/drive')", "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] ], [ [ "###*Importando a biblioteca Pandas*", "_____no_output_____" ] ], [ [ "#importando a biblioteca Pandas\nimport pandas as pd", "_____no_output_____" ] ], [ [ "###*Lendo arquivos*", "_____no_output_____" ] ], [ [ "#Lendo CSV\ndf = pd.read_csv(\"/content/drive/MyDrive/Datasets/Gapminder.csv\", error_bad_lines=False, sep=\";\")", "_____no_output_____" ], [ "#Visualizando as 5 primeiras linhas\ndf.head()", "_____no_output_____" ] ], [ [ "###*Renomeando Colunas*", "_____no_output_____" ] ], [ [ "df = df.rename(columns={'country':'Country', 'continent':'Continent', 'year':'Year', 'lifeExp':'LifeExp', 'pop':'Population', 'gdpPercap':'PIB'})", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ] ], [ [ "###*Trabalhando com Linhas e Colunas do arquivo*", "_____no_output_____" ] ], [ [ "#Quantidade de linhas e colunas dentro do arquivo\ndf.shape", "_____no_output_____" ], [ "#Nome das colunas\ndf.columns", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ], [ [ "#Tipo de dado em ccada coluna\ndf.dtypes", "_____no_output_____" ], [ "#Últimas cindo linhas por padrao do arquivo (df.tail(10) → Últimas 10 linhas...)\ndf.tail()", "_____no_output_____" ], [ "#Média entre os dados das respectivas linhas e colunas\ndf.describe()", "_____no_output_____" ] ], [ [ "###*Trabalhando com Filtros*", "_____no_output_____" ] ], [ [ "df['Continent'].unique()", "_____no_output_____" ], [ "Oceania = df.loc[df['Continent'] == 'Oceania']\nOceania.head()", "_____no_output_____" ], [ "Oceania['Continent'].unique()", "_____no_output_____" ], [ "df.groupby('Continent')['Country'].nunique()", "_____no_output_____" ], [ "df.groupby('Year')['LifeExp'].mean()", "_____no_output_____" ], [ "df['PIB'].mean()", "_____no_output_____" ], [ "df['PIB'].sum()", "_____no_output_____" ] ], [ [ "# **Trabalhando com Planilhas de Excel**", "_____no_output_____" ], [ "### *Leitura dos Arquivos*", "_____no_output_____" ] ], [ [ "df1 = pd.read_excel(\"/content/drive/MyDrive/Datasets/Aracaju.xlsx\")\ndf2 = pd.read_excel(\"/content/drive/MyDrive/Datasets/Fortaleza.xlsx\")\ndf3 = pd.read_excel(\"/content/drive/MyDrive/Datasets/Natal.xlsx\")\ndf4 = pd.read_excel(\"/content/drive/MyDrive/Datasets/Recife.xlsx\")\ndf5 = pd.read_excel(\"/content/drive/MyDrive/Datasets/Salvador.xlsx\")", "_____no_output_____" ], [ "#Juntado todos os arquivos\ndf = pd.concat([df1, df2, df3, df4, df5])", "_____no_output_____" ], [ "#Exibindo as 5 primeiras linhas\ndf.head()", "_____no_output_____" ], [ "#Exibindo as 5 últimas linhas\ndf.tail()", "_____no_output_____" ], [ "df.sample(5)", "_____no_output_____" ], [ "#Verifincado o tipo de dado de cada coluna\ndf.dtypes", "_____no_output_____" ], [ "#Alterando o tipo de dado da coluna LojaID [int64 → object]\ndf['LojaID'] = df['LojaID'].astype('object')", "_____no_output_____" ], [ "df.dtypes", "_____no_output_____" ] ], [ [ "##***Tratando valores faltantes***", "_____no_output_____" ] ], [ [ "#Consultando linhas com valores faltantes\ndf.isnull().sum()", "_____no_output_____" ], [ "#Apagando as linhas com valores nulos\ndf.dropna(inplace=True)", "_____no_output_____" ], [ "#Apagando as linhas com valores nulos com base apenas em 1 coluna\ndf.dropna(subset=['Vendas'], inplace=True)", "_____no_output_____" ], [ "#Removendo linhas que estejam com valores faltantes em todas as colunas\ndf.dropna(how='all', inplace=True)", "_____no_output_____" ] ], [ [ "### ***Criando novas colunas***", "_____no_output_____" ] ], [ [ "#Criando a coluna de receita\ndf['Receita'] = df['Vendas'].mul(df['Qtde'])", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "df.tail()", "_____no_output_____" ], [ "df['Receita/Venda'] = df['Receita'] / df['Vendas']", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "#Retornando maior receita\ndf['Receita'].max()", "_____no_output_____" ], [ "#Retornando a menor receita\ndf['Receita'].min()", "_____no_output_____" ], [ "#nlargest\ndf.nlargest(3,'Receita')", "_____no_output_____" ], [ "#nsmallest\ndf.nsmallest(3, 'Receita')", "_____no_output_____" ], [ "#Agrupamento por cidade\ndf.groupby('Cidade')['Receita'].sum()", "_____no_output_____" ], [ "#Ordenando o conjunto de dados\ndf.sort_values('Receita', ascending=False).head(8)", "_____no_output_____" ] ], [ [ "# ***Trabalhando com datas***", "_____no_output_____" ] ], [ [ "#Transfomando a coluna de dataa em tipo inteiro\ndf['Data'] = df['Data'].astype('int64')", "_____no_output_____" ], [ "#Verificando o tipo de dado de cada coluna\ndf.dtypes", "_____no_output_____" ], [ "#Transformando a coluna de Data em Data\ndf['Data'] = pd.to_datetime(df['Data'])", "_____no_output_____" ], [ "df.dtypes", "_____no_output_____" ], [ "#Agrupamento por ano\ndf.groupby(df['Data'].dt.year)['Receita'].sum()", "_____no_output_____" ], [ "#Criado uma nova coluna com o ano\ndf['Ano_Venda'] = df['Data'].dt.year", "_____no_output_____" ], [ "df.sample(5)", "_____no_output_____" ], [ "#Extraindo o mes e o dia\ndf['mes_venda'], df['dia_venda'] = (df['Data'].dt.month, df['Data'].dt.day)", "_____no_output_____" ], [ "df.sample(5)", "_____no_output_____" ], [ "#Retornando a data mais antiga\ndf['Data'].min()", "_____no_output_____" ], [ "#Retornanoa data mais nova\ndf['Data'].max()", "_____no_output_____" ], [ "#Calculando a diferenca de dias\ndf['Diferenca_dias'] = df['Data'] - df['Data'].min()", "_____no_output_____" ], [ "df.sample(5)", "_____no_output_____" ], [ "#Criando a coluna de trimestre\ndf['Trimestre'] = df['Data'].dt.quarter", "_____no_output_____" ], [ "df.sample(5)", "_____no_output_____" ], [ "#Filtrando as vendas de 2019 do mes de janeiro\nvendas_jan_19 = df.loc[(df['Data'].dt.year == 2019) & (df['Data'].dt.month == 1)]", "_____no_output_____" ], [ "vendas_jan_19", "_____no_output_____" ] ], [ [ "# **Visualizacao de Dados**", "_____no_output_____" ] ], [ [ "df['LojaID'].value_counts(ascending=False)", "_____no_output_____" ] ], [ [ "### ***Gráficos***", "_____no_output_____" ] ], [ [ "#Gráfico de barras\ndf['LojaID'].value_counts(ascending=False).plot.bar();", "_____no_output_____" ], [ "#Gráfico de barras horizontais\ndf['LojaID'].value_counts().plot.barh();", "_____no_output_____" ], [ "#Gráfco de barras horizonatal\ndf['LojaID'].value_counts(ascending=True).plot.barh();", "_____no_output_____" ], [ "#Gráfico de Pizza\ndf.groupby(df['Data'].dt.year)['Receita'].sum().plot.pie();", "_____no_output_____" ], [ "#Total de vendas por cidade\ndf['Cidade'].value_counts()", "_____no_output_____" ], [ "#Adicionando um título e alterando o nome dos eixos\nimport matplotlib.pyplot as plt\ndf['Cidade'].value_counts().plot.bar(title='Total de vendas por Cidade')\nplt.xlabel('Cidade')\nplt.ylabel('Total de vendas');", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "#Alterando a cor do gráfico\nimport matplotlib.pyplot as plt\ndf['Cidade'].value_counts().plot.bar(title='Total de vendas por Cidade', color='green')\nplt.xlabel('Cidade')\nplt.ylabel('Total de vendas');", "_____no_output_____" ], [ "#Editando o Estilo\nplt.style.use('ggplot')", "_____no_output_____" ], [ "df.groupby(df['mes_venda'])['Qtde'].sum().plot(title = 'Total de Vendas')\nplt.xlabel('Mes')\nplt.ylabel('Venda')\nplt.legend();", "_____no_output_____" ], [ "df.groupby(df['mes_venda'])['Qtde'].sum()", "_____no_output_____" ], [ "#Selecionando apenas as vendas de 2019\ndf_2019 = df[df['Ano_Venda'] == 2019]", "_____no_output_____" ], [ "df_2019", "_____no_output_____" ], [ "#Total vendidos por mes\ndf_2019.groupby(df_2019['mes_venda'])['Qtde'].sum().plot(marker = 'v')\nplt.xlabel('Mes')\nplt.ylabel('Total de Produtos Vendidos')\nplt.legend();", "_____no_output_____" ], [ "#Histograma\nplt.hist(df['Qtde'], color='darkturquoise');", "_____no_output_____" ], [ "plt.scatter(x=df_2019['dia_venda'], y = df_2019['Receita']);", "_____no_output_____" ], [ "#Salvando em png\ndf_2019.groupby(df_2019['mes_venda'])['Qtde'].sum().plot(marker = 'v')\nplt.title('Quantidade de produtos vendidos x mes')\nplt.xlabel('Mes')\nplt.ylabel('Total de Produtos Vendidos')\nplt.legend()\nplt.savefig('grafico Qtde x mes.png');", "_____no_output_____" ] ], [ [ "# **Análise Exploratória**", "_____no_output_____" ] ], [ [ "plt.style.use('seaborn')", "_____no_output_____" ], [ "#Upload de arquivo\nfrom google.colab import files\narq = files.upload()", "_____no_output_____" ], [ "#Criando nosso DataFrame\ndf = pd.read_excel(\"/content/drive/MyDrive/Datasets/AdventureWorks.xlsx\")", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "#Quantidade de linhas e colunas\ndf.shape", "_____no_output_____" ], [ "#Verificando os tipos de dados\ndf.dtypes", "_____no_output_____" ], [ "#Qual a Receita total?\ndf['Valor Venda'].sum()", "_____no_output_____" ], [ "#Qual o Custo Total?\ndf['Custo'] = df['Custo Unitário'].mul(df['Quantidade']) #Criando a coluna de custo", "_____no_output_____" ], [ "df.head(1)", "_____no_output_____" ], [ "#Qual o custo Total?\nround(df['Custo'].sum(), 2)", "_____no_output_____" ], [ "#Agora que temos a receita, custo e total, podemos achar o Lucro Toal\n#Vamos criar uma coluna de Lucro que será Receia -Custo\ndf['Lucro'] = df['Valor Venda'] - df['Custo']", "_____no_output_____" ], [ "df.head(1)", "_____no_output_____" ], [ "#Total Lucro\nround(df['Lucro'].sum(), 2)", "_____no_output_____" ], [ "#Criando uma coluna com o total de dias para enviar o produto\ndf['Tempo_envio'] = df['Data Envio'] - df['Data Venda']", "_____no_output_____" ], [ "df.head(1)", "_____no_output_____" ], [ "#extraindo apenas os dias\ndf['Tempo_envio'] = (df['Data Envio'] - df['Data Venda']).dt.days", "_____no_output_____" ], [ "df.head(1)", "_____no_output_____" ], [ "#Verificando o tipo de coluna Tempo_envio\ndf['Tempo_envio'].dtype", "_____no_output_____" ], [ "#Média de tempo de envio por Marca\ndf.groupby('Marca')['Tempo_envio'].mean()", "_____no_output_____" ], [ "#Vaerificando se temos dados faltantes\ndf.isnull().sum()", "_____no_output_____" ], [ "#Agrupar por ano e Marca\ndf.groupby([df['Data Venda'].dt.year, 'Marca'])['Lucro'].sum()", "_____no_output_____" ], [ "#resetando o index\nlucro_ano = df.groupby([df['Data Venda'].dt.year, 'Marca'])['Lucro'].sum().reset_index()\nlucro_ano", "_____no_output_____" ], [ "#Qual o total de produtos vendidos\ndf.groupby('Produto')['Quantidade'].sum().sort_values(ascending=False)", "_____no_output_____" ], [ "#Gráfico Total de Produtos vendidos\ndf.groupby('Produto')['Quantidade'].sum().sort_values(ascending=True).plot.barh(title='Total Produtos Vendidos')\nplt.xlabel('Total')\nplt.ylabel('Produto');", "_____no_output_____" ], [ "#Selecionando apenas as vendas de 2009\ndf_2009 = df[df['Data Venda'].dt.year == 2009]", "_____no_output_____" ], [ "df_2009.head()", "_____no_output_____" ], [ "df_2009.groupby(df_2009[\"Data Venda\"].dt.month)[\"Lucro\"].sum().plot(title=\"Lucro x Mês\")\nplt.xlabel(\"Mês\")\nplt.ylabel(\"Lucro\");", "_____no_output_____" ], [ "df_2009.groupby(\"Marca\")[\"Lucro\"].sum().plot.bar(title=\"Lucro x Marca\")\nplt.xlabel(\"Marca\")\nplt.ylabel(\"Lucro\")\nplt.xticks(rotation='horizontal');", "_____no_output_____" ], [ "df[\"Tempo_envio\"].describe()", "_____no_output_____" ], [ "#Gráfico de Boxplot\nplt.boxplot(df[\"Tempo_envio\"]);", "_____no_output_____" ], [ "#Histograma\nplt.hist(df[\"Tempo_envio\"]);", "_____no_output_____" ], [ "#Tempo mínimo de envio\ndf[\"Tempo_envio\"].min()", "_____no_output_____" ], [ "#Tempo máximo de envio\ndf['Tempo_envio'].max()", "_____no_output_____" ], [ "#Identificando o Outlier\ndf[df[\"Tempo_envio\"] == 20]", "_____no_output_____" ], [ "df.to_csv('Project Python_Pandas.csv', index=False)", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "def compare_lists(head1, head2):\n temp1, temp2 = head1, head2\n \n while temp1 or temp2:\n if not temp1 or not temp2: return 0\n if temp1.data != temp2.data: return 0\n temp1 = temp1.next\n temp2 = temp2.next\n\n return 1", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Aerospike Java Client – Advanced Collection Data Types\n*Last updated: June 22, 2021*\n\nThe goal of this tutorial is to highlight the power of working with [collection data types (CDTs)](\"https://docs.aerospike.com/docs/guide/cdt.html\") in Aerospike. It covers the following topics:\n1. Setting [contexts (CTXs)](\"https://docs.aerospike.com/docs/guide/cdt-context.html\") to apply operations to nested Maps and Lists.\n2. Showing the return type options provided by CDT get/read operations.\n3. Highlighting how policies shape application transactions.\n\nThis [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html) requires the Aerospike Database running locally with Java kernel and Aerospike Java Client. To create a Docker container that satisfies the requirements and holds a copy of these notebooks, visit the [Aerospike Notebooks Repo](https://github.com/aerospike-examples/interactive-notebooks).", "_____no_output_____" ], [ "## Prerequisites\n\nThis Notebook builds on the material in the following notebooks:\n1. [Working with Lists](\"./java-working_with_lists.ipynb\") \n2. [Working with Maps](\"./java-working_with_lists.ipynb\")\n3. [Introduction to Transactions](\"./java-intro_to_transactions.ipynb\")\n\nIt uses examples based on those from [Modeling Using Lists](./java-modeling_using_lists.ipynb) and Working with Maps. If any of the following is confusing, please refer to a relevant notebook. ", "_____no_output_____" ], [ "# Notebook Setup", "_____no_output_____" ], [ "### Import Jupyter Java Integration \n\nMake it easier to work with Java in Jupyter.", "_____no_output_____" ] ], [ [ "import io.github.spencerpark.ijava.IJava;\nimport io.github.spencerpark.jupyter.kernel.magic.common.Shell;\n\nIJava.getKernelInstance().getMagics().registerMagics(Shell.class);", "_____no_output_____" ] ], [ [ "### Start Aerospike\n\nEnsure Aerospike Database is running locally.", "_____no_output_____" ] ], [ [ "%sh asd", "_____no_output_____" ] ], [ [ "### Download the Aerospike Java Client\n\nAsk Maven to download and install the project object model (POM) of the Aerospike Java Client.", "_____no_output_____" ] ], [ [ "%%loadFromPOM\n<dependencies>\n <dependency>\n <groupId>com.aerospike</groupId>\n <artifactId>aerospike-client</artifactId>\n <version>5.0.0</version>\n </dependency>\n</dependencies>", "_____no_output_____" ] ], [ [ "### Start the Aerospike Java Client and Connect\n\nCreate an instance of the Aerospike Java Client, and connect to the demo cluster.\n\nThe default cluster location for the Docker container is *localhost* port *3000*. If your cluster is not running on your local machine, modify *localhost* and *3000* to the values for your Aerospike cluster.", "_____no_output_____" ] ], [ [ "import com.aerospike.client.AerospikeClient;\n\nAerospikeClient client = new AerospikeClient(\"localhost\", 3000);\nSystem.out.println(\"Initialized the client and connected to the cluster.\");", "Initialized the client and connected to the cluster.\n" ] ], [ [ "# Create CDT Data, Put into Aerospike, and Print It", "_____no_output_____" ] ], [ [ "import com.aerospike.client.Key;\nimport com.aerospike.client.Bin;\nimport com.aerospike.client.policy.ClientPolicy;\nimport com.aerospike.client.Record;\nimport com.aerospike.client.Operation;\nimport com.aerospike.client.Value;\nimport com.aerospike.client.cdt.ListOperation;\nimport com.aerospike.client.cdt.ListPolicy;\nimport com.aerospike.client.cdt.ListOrder;\nimport com.aerospike.client.cdt.ListWriteFlags;\nimport com.aerospike.client.cdt.MapOperation;\nimport com.aerospike.client.cdt.MapPolicy;\nimport com.aerospike.client.cdt.MapOrder;\nimport com.aerospike.client.cdt.MapWriteFlags;\n\n\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.HashMap;\nimport java.util.List;\nimport java.util.Map;\n\n\n// Create whale migration list of tuples. \n\nArrayList<Value> whaleMigration0 = new ArrayList<Value>();\nwhaleMigration0.add(Value.get(1420));\nwhaleMigration0.add(Value.get(\"beluga whale\"));\nwhaleMigration0.add(Value.get(\"Beaufort Sea\"));\nwhaleMigration0.add(Value.get(\"Bering Sea\"));\n\nArrayList<Value> whaleMigration1 = new ArrayList<Value>();\nwhaleMigration1.add(Value.get(13988));\nwhaleMigration1.add(Value.get(\"gray whale\"));\nwhaleMigration1.add(Value.get(\"Baja California\"));\nwhaleMigration1.add(Value.get(\"Chukchi Sea\"));\n\nArrayList<Value> whaleMigration2 = new ArrayList<Value>();\nwhaleMigration2.add(Value.get(1278));\nwhaleMigration2.add(Value.get(\"north pacific right whale\"));\nwhaleMigration2.add(Value.get(\"Japan\"));\nwhaleMigration2.add(Value.get(\"Sea of Okhotsk\"));\n\nArrayList<Value> whaleMigration3 = new ArrayList<Value>();\nwhaleMigration3.add(Value.get(5100));\nwhaleMigration3.add(Value.get(\"humpback whale\"));\nwhaleMigration3.add(Value.get(\"Columbia\"));\nwhaleMigration3.add(Value.get(\"Antarctic Peninsula\"));\n\nArrayList<Value> whaleMigration4 = new ArrayList<Value>();\nwhaleMigration4.add(Value.get(3100));\nwhaleMigration4.add(Value.get(\"southern hemisphere blue whale\"));\nwhaleMigration4.add(Value.get(\"Corcovado Gulf\"));\nwhaleMigration4.add(Value.get(\"The Galapagos\"));\n\n\n\nArrayList<Value> whaleMigration = new ArrayList<Value>();\nwhaleMigration.add(Value.get(whaleMigration0));\nwhaleMigration.add(Value.get(whaleMigration1));\nwhaleMigration.add(Value.get(whaleMigration2));\nwhaleMigration.add(Value.get(whaleMigration3));\nwhaleMigration.add(Value.get(whaleMigration4));\n\n\n// Create Map of Whale Observations\n\nHashMap <Value, Value> mapObs = new HashMap <Value, Value>();\nHashMap <String, Integer> mapCoords0 = new HashMap <String, Integer>();\nmapCoords0.put(\"lat\", -85);\nmapCoords0.put(\"long\", -130);\nHashMap <String, Integer> mapCoords1 = new HashMap <String, Integer>();\nmapCoords1.put(\"lat\", -25);\nmapCoords1.put(\"long\", -50);\nHashMap <String, Integer> mapCoords2 = new HashMap <String, Integer>();\nmapCoords2.put(\"lat\", 35);\nmapCoords2.put(\"long\", 30);\n\n\nmapObs.put(Value.get(13456), Value.get(mapCoords1));\nmapObs.put(Value.get(14567), Value.get(mapCoords2));\nmapObs.put(Value.get(12345), Value.get(mapCoords0));\n\n\n// Put data in Aerospike, get the data, and print it\n\nString nestedCDTSetName = \"nestedset1\";\nString nestedCDTNamespaceName = \"test\";\n\nInteger whaleMigrationWriteFlags = ListWriteFlags.ADD_UNIQUE \n | ListWriteFlags.NO_FAIL \n | ListWriteFlags.PARTIAL;\nListPolicy whaleMigrationPolicy = new ListPolicy(ListOrder.UNORDERED, whaleMigrationWriteFlags);\nMapPolicy mapObsPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.DEFAULT);\n\nInteger whaleKeyName = 2;\nString listWhaleBinName = \"listwhalebin\";\nString mapObsBinName = \"mapobsbin\";\n\nBin bin1 = new Bin(listWhaleBinName, whaleMigration);\n\nKey whaleKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, whaleKeyName);\n\nRecord putDataIn = client.operate(client.writePolicyDefault, whaleKey,\n Operation.put(bin1),\n MapOperation.putItems(mapObsPolicy, mapObsBinName, mapObs)\n );\n\nSystem.out.println(listWhaleBinName + \": \" + whaleMigration + \"\\n\\n\" + \n mapObsBinName + \": \" + mapObs );", "listwhalebin: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\nmapobsbin: {13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 12345={lat=-85, long=-130}}\n" ] ], [ [ "# Using Contexts (CTXs) to work with Nested CDTs\nWhat are Nested CDTs and CTXs?\n", "_____no_output_____" ], [ "## What is a Nested CDT?\nThe primary use case of Key-Value Stores, like Aerospike Database, is to store document-oriented data, like a JSON map. As document-oriented data grows organically, it is common for one CDT (list or map) to contain another CDT. Does the application need a list in a map in a list in a map? Aerospike fully supports nesting CDTs, so that’s no problem. ", "_____no_output_____" ], [ "## What is a Context?\n\nA Context (CTX) is a reference to a nested CDT, a List or Map that is stored in a List or Map somewhere in an Aerospike Bin. All [List](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/ListOperation.html) and [Map Operations](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/MapOperation.html) accept an optional CTX argument. Any CTX argument must refer to data of the type supported by the operation. \n\nThe most common ways to access a CTX are to look up a Map CTX directly by its key within the Bin and to drill down within a List or Map by index, rank or value. A CTX can also be created within a List or Map. For more details, see the [CTX APIs](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/CDT.html). ", "_____no_output_____" ], [ "## Look up a Map CTX in a Bin by Mapkey\n\nUse the `mapKey` method to look up a CTX in a Map directly by mapkey. This works for a Map anywhere in a Bin.\n\nThe following is an example of finding a Map CTX in a Bin by Mapkey:", "_____no_output_____" ] ], [ [ "import com.aerospike.client.cdt.CTX;\nimport com.aerospike.client.cdt.MapReturnType;\n\nInteger lookupMapKey = 14567;\nString latKeyName = \"lat\";\n\nRecord whaleSightings = client.operate(client.writePolicyDefault, whaleKey, \n MapOperation.getByKey(mapObsBinName, Value.get(latKeyName), MapReturnType.VALUE, CTX.mapKey(Value.get(lookupMapKey)))\n );\n\nSystem.out.println(mapObsBinName + \": \" + mapObs );\nSystem.out.println(\"The \" + latKeyName + \" of sighting at timestamp \" + lookupMapKey + \": \" + whaleSightings.getValue(mapObsBinName));", "mapobsbin: {13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 12345={lat=-85, long=-130}}\nThe lat of sighting at timestamp 14567: 35\n" ] ], [ [ "## Drill down into a List or Map\nHere are the options to drill down into a CDT.\n\nDrilling down to a CTX in a List:\n* `listIndex`: Lookup list by index offset.\n* `listRank`: Lookup list by rank.\n* `listValue`: Lookup list by value.\n\nDrilling down to a CTX in a Map: \n* `mapIndex`: Lookup map by index offset.\n* `mapRank`: Lookup map by rank.\n* `mapValue`: Lookup map by value.\n\n\nThe following is an example of drilling down within a List and Map CTX:", "_____no_output_____" ] ], [ [ "import com.aerospike.client.cdt.ListReturnType;\n\n// CDT Drilldown Values\n\nInteger drilldownIndex = 2;\nInteger drilldownRank = 1;\nValue listDrilldownValue = Value.get(whaleMigration1);\nValue mapDrilldownValue = Value.get(mapCoords0);\n\n// Variables to access parts of the selected CDT.\n\nInteger getIndex = 1;\n\nRecord theRecord = client.get(null, whaleKey);\nRecord drilldown = client.operate(client.writePolicyDefault, whaleKey, \n ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listIndex(drilldownIndex)),\n ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listRank(drilldownRank)),\n ListOperation.getByIndex(listWhaleBinName, getIndex, MapReturnType.VALUE, CTX.listValue(listDrilldownValue)),\n MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapIndex(drilldownIndex)),\n MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapRank(drilldownRank)),\n MapOperation.getByIndex(mapObsBinName, getIndex, MapReturnType.VALUE, CTX.mapValue(mapDrilldownValue))\n );\n\nList<?> returnWhaleList = drilldown.getList(listWhaleBinName);\nList<?> returnObsList = drilldown.getList(mapObsBinName); \n\nSystem.out.println(\"The whale migration list is: \" + theRecord.getValue(listWhaleBinName) + \"\\n\");\nSystem.out.println(\"The whale name from the CTX selected by index \" + drilldownIndex + \": \" + returnWhaleList.get(0));\nSystem.out.println(\"The whale name from the CTX selected by rank \" + drilldownRank + \": \" + returnWhaleList.get(1));\nSystem.out.println(\"The whale name from the CTX selected by value \" + listDrilldownValue + \": \" + returnWhaleList.get(2) + \"\\n\\n\");\n\n\nSystem.out.println(\"The observation map is: \" + theRecord.getValue(mapObsBinName) + \"\\n\");\nSystem.out.println(\"The longitude of the observation from the CTX selected by index \" + drilldownIndex + \": \" + returnObsList.get(0));\nSystem.out.println(\"The longitude of the observation from the CTX selected by rank \" + drilldownRank + \": \" + returnObsList.get(1));\nSystem.out.println(\"The longitude of the observation from the CTX selected by value \" + mapDrilldownValue + \": \" + returnObsList.get(2));\n", "The whale migration list is: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\nThe whale name from the CTX selected by index 2: north pacific right whale\nThe whale name from the CTX selected by rank 1: beluga whale\nThe whale name from the CTX selected by value [13988, gray whale, Baja California, Chukchi Sea]: gray whale\n\n\nThe observation map is: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}}\n\nThe longitude of the observation from the CTX selected by index 2: 30\nThe longitude of the observation from the CTX selected by rank 1: -50\nThe longitude of the observation from the CTX selected by value {lat=-85, long=-130}: -130\n" ] ], [ [ "## Create a CTX Example\nIf the context for the operation does not yet exist, it can be created using the following methods.\n\nCreating a CTX in a List or Map:\n* `listIndexCreate`: Create list by base list's index offset.\n* `mapKeyCreate`: Create map by base map's key.\n\nThe following are examples of creating a list and map CTX and then writing data to the new CTX. ", "_____no_output_____" ] ], [ [ "ArrayList<Value> newWhaleMigration = new ArrayList<Value>();\nnewWhaleMigration.add(Value.get(1449));\nnewWhaleMigration.add(Value.get(\"sei whale\"));\nnewWhaleMigration.add(Value.get(\"Greenland\"));\nnewWhaleMigration.add(Value.get(\"Gulf of Maine\"));\n\nInteger whaleIndex = 5;\n\nHashMap <Value, Value> mapCoords3 = new HashMap <Value, Value>();\nmapCoords3.put(Value.get(\"lat\"), Value.get(95));\nmapCoords3.put(Value.get(\"long\"), Value.get(110));\n\n\nInteger newObsKey = 15678;\n\n\nRecord createCTX = client.operate(client.writePolicyDefault, whaleKey, \n ListOperation.insertItems(listWhaleBinName, 0, newWhaleMigration, CTX.listIndexCreate(whaleIndex, ListOrder.UNORDERED, true)),\n MapOperation.putItems(mapObsPolicy, mapObsBinName, mapCoords3, CTX.mapKeyCreate(Value.get(newObsKey), MapOrder.KEY_ORDERED))\n );\n\nRecord postCreate = client.get(null, whaleKey);\n\nSystem.out.println(\"Before, the whale migration list was: \" + theRecord.getValue(listWhaleBinName) + \"\\n\");\nSystem.out.println(\"After the addition, it is:\" + postCreate.getValue(listWhaleBinName) + \"\\n\\n\");\n\nSystem.out.println(\"Before, the observation map was: \" + theRecord.getValue(mapObsBinName) + \"\\n\");\nSystem.out.println(\"After the addition, it is: \" + postCreate.getValue(mapObsBinName));", "Before, the whale migration list was: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\nAfter the addition, it is:[[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine]]\n\n\nBefore, the observation map was: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}}\n\nAfter the addition, it is: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 15678={lat=95, long=110}}\n" ] ], [ [ "# Choosing the Return Type Options for CDTs\nOperations on CDTs can return different types of data, depending on the return type value specified. A return type can be combined with the INVERTED flag to return all data from the CDT that was not selected by the operation. The following are the [Return Types for Lists](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/ListReturnType.html) and [Maps](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/MapReturnType.html).", "_____no_output_____" ], [ "## Standard Return Type Options for CDTs\nAerospike Lists and Maps both provide the following return type options.\n\n* `COUNT`: Return count of items selected.\n* `INDEX`: Return index offset order.\n* `NONE`: Do not return a result.\n* `RANK`: Return value order. If the list/map is not ordered, Aerospike will JIT-sort the list/map.\n* `REVERSE_INDEX`: Return reverse index offset order.\n* `REVERSE_RANK`: Return value order from a version of the list sorted from maximum to minimum value. If the list is not ordered, Aerospike will JIT-sort the list. \n* `VALUE`: Return value for single item read and list of values from a range read.\n\nAll indexes are 0-based, with the last element accessible by index -1. \n\nThe following is an example demonstrating each possible return type from the same operation.", "_____no_output_____" ] ], [ [ "ArrayList<Value> lowTuple = new ArrayList<Value>();\nlowTuple.add(Value.get(1400));\nlowTuple.add(Value.NULL);\n\nArrayList<Value> highTuple = new ArrayList<Value>();\nhighTuple.add(Value.get(3500));\nhighTuple.add(Value.NULL);\n\nRecord between1400and3500 = client.operate(client.writePolicyDefault, whaleKey, \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.COUNT),\n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.INDEX), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.NONE), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.RANK), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.REVERSE_INDEX), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.REVERSE_RANK), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.VALUE) \n );\n\nList<?> returnWhaleRange = between1400and3500.getList(listWhaleBinName);\n\nSystem.out.println(\"The current whale migration list is: \" + postCreate.getValue(listWhaleBinName) + \"\\n\");\nSystem.out.println(\"For the whales who migrate between 1400 and 3500 miles...\");\nSystem.out.println(\"Return COUNT: \" + returnWhaleRange.get(0));\nSystem.out.println(\"Return INDEX: \" + returnWhaleRange.get(1));\nSystem.out.println(\"Return NONE: has no return value.\");\nSystem.out.println(\"Return RANK: \" + returnWhaleRange.get(2));\nSystem.out.println(\"Return REVERSE_INDEX: \" + returnWhaleRange.get(3));\nSystem.out.println(\"Return REVERSE_RANK: \" + returnWhaleRange.get(4));\nSystem.out.println(\"Return Values: \" + returnWhaleRange.get(5));", "The current whale migration list is: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine]]\n\nFor the whales who migrate between 1400 and 3500 miles...\nReturn COUNT: 3\nReturn INDEX: [0, 4, 5]\nReturn NONE: has no return value.\nReturn RANK: [1, 2, 3]\nReturn REVERSE_INDEX: [5, 1, 0]\nReturn REVERSE_RANK: [2, 3, 4]\nReturn Values: [[1420, beluga whale, Beaufort Sea, Bering Sea], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine]]\n" ] ], [ [ "## Additional Return Type Options for Maps\nBecause Maps have a replicable key/value structure, Aerospike provides options to return mapkeys or key/value pairs, in addition to value.\n\n* `KEY`: Return key for single key read and key list for range read.\n* `KEY_VALUE`: Return key/value pairs for items.\n\nThe following is an example demonstrating returning a key or key/value pair.", "_____no_output_____" ] ], [ [ "Integer latestObsRank = -1;\n\nRecord latestWhaleObs = client.operate(client.writePolicyDefault, whaleKey, \n MapOperation.getByRank(mapObsBinName, latestObsRank, MapReturnType.KEY),\n MapOperation.getByRank(mapObsBinName, latestObsRank, MapReturnType.KEY_VALUE)\n );\n\nList<?> latestObs = latestWhaleObs.getList(mapObsBinName);\n\nSystem.out.println(\"The current whale observations map is: \" + postCreate.getValue(mapObsBinName) + \"\\n\");\nSystem.out.println(\"For the most recent observation...\");\nSystem.out.println(\"Return the key: \" + latestObs.get(0));\nSystem.out.println(\"Return key/value pair: \" + latestObs.get(1));", "The current whale observations map is: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 15678={lat=95, long=110}}\n\nFor the most recent observation...\nReturn the key: 15678\nReturn key/value pair: [15678={lat=95, long=110}]\n" ] ], [ [ "## Invert the Operation Results for CDT Operations \nAerospike also provides the `INVERTED` flag for CDT operations. When `INVERTED` is “logical or”-ed to the return type, the flag instructs a list or map operation to return the return type data for list or Map elements that were not selected by the operation. This flag instructs an operation to act as though a logical NOT operator was applied to the entire operation. \n\nThe following is an example demonstrating inverted return values.\n", "_____no_output_____" ] ], [ [ "ArrayList<Value> lowTuple = new ArrayList<Value>();\nlowTuple.add(Value.get(1400));\nlowTuple.add(Value.NULL);\n\nArrayList<Value> highTuple = new ArrayList<Value>();\nhighTuple.add(Value.get(3500));\nhighTuple.add(Value.NULL);\n\nRecord between1400and3500 = client.operate(client.writePolicyDefault, whaleKey, \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.COUNT | ListReturnType.INVERTED),\n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.INDEX | ListReturnType.INVERTED), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.NONE | ListReturnType.INVERTED), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.RANK | ListReturnType.INVERTED), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.REVERSE_INDEX | ListReturnType.INVERTED), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.REVERSE_RANK | ListReturnType.INVERTED), \n ListOperation.getByValueRange(listWhaleBinName, Value.get(lowTuple), Value.get(highTuple), \n ListReturnType.VALUE | ListReturnType.INVERTED) \n );\n\nList<?> returnWhaleRange = between1400and3500.getList(listWhaleBinName);\n\nSystem.out.println(\"The current whale migration list is: \" + postCreate.getValue(listWhaleBinName) + \"\\n\");\nSystem.out.println(\"For the whales who migrate between 1400 and 3500 miles...\");\nSystem.out.println(\"Return INVERTED COUNT: \" + returnWhaleRange.get(0));\nSystem.out.println(\"Return INVERTED INDEX: \" + returnWhaleRange.get(1));\nSystem.out.println(\"Return INVERTED NONE: has no return value.\");\nSystem.out.println(\"Return INVERTED RANK: \" + returnWhaleRange.get(2));\nSystem.out.println(\"Return INVERTED REVERSE_INDEX: \" + returnWhaleRange.get(3));\nSystem.out.println(\"Return INVERTED REVERSE_RANK: \" + returnWhaleRange.get(4));\nSystem.out.println(\"Return INVERTED Values: \" + returnWhaleRange.get(5));", "The current whale migration list is: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine]]\n\nFor the whales who migrate between 1400 and 3500 miles...\nReturn INVERTED COUNT: 3\nReturn INVERTED INDEX: [1, 2, 3]\nReturn INVERTED NONE: has no return value.\nReturn INVERTED RANK: [0, 4, 5]\nReturn INVERTED REVERSE_INDEX: [4, 3, 2]\nReturn INVERTED REVERSE_RANK: [5, 0, 1]\nReturn INVERTED Values: [[13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula]]\n" ] ], [ [ "# Highlighting how policies shape application transactions\nEach data type operation has a write policy which can be set per CDT write/put operation to optionally:\n* Just-in-time sort the data being operated on. \n* Apply flags that instruct Aerospike’s transaction write behavior.\n\nCreate and set a MapPolicy or ListPolicy with the proper sort and write flags to change how Aerospike processes a transaction. ", "_____no_output_____" ], [ "## MapOrder and ListOrder, Just-in-time Sorting for an Operation \nBy default, Maps and Lists are stored unordered. There are explicit techniques to store a list or map in order. The Map data in this notebook is key sorted. Please refer to the code snippet creating the map data (above) for an example of this. There are examples of ordering lists in the notebook [Modeling Using Lists](./java-modeling_using_lists.ipynb). \n\nApplying a MapOrder or ListOrder has performance implications on operation performance. This can be a reason to apply a MapOrder or ListOrder when working with data. To understand the relative worst-case time complexity of Aerospike operations go [here for lists](https://docs.aerospike.com/docs/guide/cdt-list-performance.html) and [here for maps](https://docs.aerospike.com/docs/guide/cdt-map-performance.html). \n\nWhether to allow duplicates in a list is a function of ListOrder.\n\n**Note:** Aerospike finds that worst-case performance can be helpful in determining how to prioritize application use-cases against one another, but do not set realistic performance expectations for Aerospike Database. An example where they help is asking tough questions, like, “the worst case time complexity for operation A is X, is operation A important enough to do daily or just monthly in light of the other workloads that are more time sensitive?”\n", "_____no_output_____" ], [ "## Write Flags\nThe following are lists of [write flags for Lists](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/ListWriteFlags.html) and [Maps](https://docs.aerospike.com/apidocs/java/com/aerospike/client/cdt/MapWriteFlags.html). Beneath each are example transactions. \n\nA powerful use case for Aerospike is to group operations together into single-record atomic transactions using the `Operate` method. This technique is used above in this notebook. When applying transactions to data, there are common circumstances where:\n* All possible operations should be executed in a fault tolerant manner \n* Specific operation failure should cause all operations to fail\n\nWrite flags can be used in any combination, as appropriate to the application and Aerospike operation being applied.", "_____no_output_____" ], [ "### Write Flags for all CDTs\n* `DEFAULT`\n * For Lists, allow duplicate values and insertions at any index. \n * For Maps, allow map create or updates.\n* `NO_FAIL`: Do not raise an error if a CDT item is denied due to write flag constraints.\n* `PARTIAL`: Allow other valid CDT items to be committed if a CDT item is denied due to write flag constraints.\n\nThese flags provide fault tolerance to transactions. Apply some combination of the above three flags–`DEFAULT`, `NO_FAIL`, and `PARTIAL`–to operations by using “logical or” as demonstrated below. All other write flags set conditions for operations. \n\n**Note:** Without `NO_FAIL`, operations that fail due to the below policies will throw [either error code 24 or 26](https://docs.aerospike.com/docs/dev_reference/error_codes.html).", "_____no_output_____" ], [ "#### Default Examples", "_____no_output_____" ], [ "All of the above code snippets use a Default write flag policy. These operations are unrestricted by write policies.", "_____no_output_____" ], [ "#### No Fail Examples", "_____no_output_____" ], [ "All of the examples in the following sections show both an exception caused by a write flag, and then pair the demonstrated write flag with No Fail to show how the same operation can fail silently.", "_____no_output_____" ], [ "#### Partial Flag Example", "_____no_output_____" ], [ "Partial is generally used only in a transaction containing operations using the No Fail write flag. Otherwise, the transaction would contain no failures to overlook. The following example are a list and map transaction combining both failing and successful map and list operations. ", "_____no_output_____" ] ], [ [ "// create policy to apply and data to trigger operation failure\nInteger inBoundsIndex = 0;\nInteger outOfBoundsIndex = 20;\n\nHashMap <Value, Value> mapCoords4 = new HashMap <Value, Value>();\nmapCoords4.put(Value.get(\"lat\"), Value.get(0));\nmapCoords4.put(Value.get(\"long\"), Value.get(0));\n\nInteger existingObsKey = 13456;\n\nInteger listPartialWriteFlags = ListWriteFlags.INSERT_BOUNDED \n | ListWriteFlags.NO_FAIL \n | ListWriteFlags.PARTIAL;\nListPolicy listPartialWritePolicy = new ListPolicy(ListOrder.UNORDERED, listPartialWriteFlags);\n\nInteger mapPartialWriteFlags = MapWriteFlags.CREATE_ONLY \n | MapWriteFlags.NO_FAIL \n | MapWriteFlags.PARTIAL;\nMapPolicy mapPartialWritePolicy = new MapPolicy(MapOrder.KEY_ORDERED, mapPartialWriteFlags);\n\n\n// create fresh record\nInteger partialFlagKeyName = 6;\nKey partialFlagKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, partialFlagKeyName);\n\nBin bin1 = new Bin(listWhaleBinName, whaleMigration);\nRecord putDataIn = client.operate(null, partialFlagKey,\n Operation.put(bin1),\n MapOperation.putItems(mapObsPolicy, mapObsBinName, mapObs)\n );\nRecord partialDataPutIn = client.get(client.writePolicyDefault, partialFlagKey);\n\n\n// one failed and one successful operation for both list and map\nRecord partialSuccessOp = client.operate(null, partialFlagKey,\n ListOperation.insert(listPartialWritePolicy, listWhaleBinName, outOfBoundsIndex, Value.get(newWhaleMigration)),\n ListOperation.set(listPartialWritePolicy, listWhaleBinName, inBoundsIndex, Value.get(newWhaleMigration)), \n MapOperation.put(mapPartialWritePolicy, mapObsBinName, Value.get(existingObsKey), Value.get(mapCoords4)),\n MapOperation.put(mapPartialWritePolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords3))\n );\nRecord partialSuccessData = client.get(client.writePolicyDefault, partialFlagKey);\nSystem.out.println (\"Failed to add a 5th item.\\nSucceeded at changing the first item.\\n\");\nSystem.out.println (\"Original List: \" + partialDataPutIn.getValue(listWhaleBinName) + \"\\n\");\nSystem.out.println (\"Updated List: \" + partialSuccessData.getValue(listWhaleBinName) + \"\\n\\n\"); \nSystem.out.println (\"Failed to modify an exiting observation.\\nSucceeded at adding a new observation.\\n\");\nSystem.out.println (\"Original Map: \" + partialDataPutIn.getValue(mapObsBinName) + \"\\n\");\nSystem.out.println (\"Updated Map: \" + partialSuccessData.getValue(mapObsBinName) + \"\\n\\nFor more about the failed operations, see the examples below.\");\n\nBoolean partialExampleRecordDeleted=client.delete(null, partialFlagKey);\n", "Failed to add a 5th item.\nSucceeded at changing the first item.\n\nOriginal List: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\nUpdated List: [[1449, sei whale, Greenland, Gulf of Maine], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\n\nFailed to modify an exiting observation.\nSucceeded at adding a new observation.\n\nOriginal Map: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}}\n\nUpdated Map: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 15678={lat=95, long=110}}\n\nFor more about the failed operations, see the examples below.\n" ] ], [ [ "### Write Flags for Lists Only:\n* `INSERT_BOUNDED`: Enforce list boundaries when inserting. Do not allow values to be inserted at index outside current list boundaries.\n* `ADD_UNIQUE`: Only add unique values. ", "_____no_output_____" ], [ "#### Insert Bounded Example", "_____no_output_____" ] ], [ [ "// create policy to apply and data to break policy\nInteger outOfBoundsIndex = 20;\n\nListPolicy listInsertBoundedPolicy = new ListPolicy(ListOrder.UNORDERED, ListWriteFlags.INSERT_BOUNDED);\nListPolicy listBoundedNoFailPolicy = new ListPolicy(ListOrder.UNORDERED, ListWriteFlags.INSERT_BOUNDED \n | ListWriteFlags.NO_FAIL);\n\n// create fresh record\nInteger whaleBoundedKeyName = 7;\n\nBin bin1 = new Bin(listWhaleBinName, whaleMigration);\n\nKey whaleBoundedKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, whaleBoundedKeyName);\n\nclient.put(client.writePolicyDefault, whaleBoundedKey, bin1);\nRecord ibDataPutIn = client.get(null, whaleBoundedKey);\nSystem.out.println(\"Data in the record: \" + ibDataPutIn.getValue(listWhaleBinName) + \"\\n\");\n\n\n// fail for INSERT_BOUNDED\ntry {\n Record ibFail = client.operate(client.writePolicyDefault, whaleBoundedKey,\n ListOperation.insert(listInsertBoundedPolicy, listWhaleBinName, outOfBoundsIndex, Value.get(newWhaleMigration))\n );\n System.out.println(\"The code does not get here.\");\n} \ncatch(Exception e) {\n System.out.println(\"Out of Bounds Attempt 1: Exception caught.\");\n Record ibNoFail = client.operate(client.writePolicyDefault, whaleBoundedKey,\n ListOperation.insert(listBoundedNoFailPolicy, listWhaleBinName, outOfBoundsIndex, Value.get(newWhaleMigration))\n );\n Record ibNoFailData = client.get(client.writePolicyDefault, whaleBoundedKey);\n if(ibNoFailData.getValue(listWhaleBinName).equals(ibDataPutIn.getValue(listWhaleBinName))) {\n System.out.println(\"Out of Bounds Attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\\n\");\n }\n}\n\nRecord noIB = client.operate(client.writePolicyDefault, whaleBoundedKey,\n ListOperation.insert(listWhaleBinName, outOfBoundsIndex, Value.get(newWhaleMigration))\n);\nRecord noIBData = client.get(null, whaleBoundedKey);\nSystem.out.println(\"Without Insert Bounded, a series of nulls is inside the Bin: \" + noIBData.getValue(listWhaleBinName));", "Data in the record: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos]]\n\nOut of Bounds Attempt 1: Exception caught.\nOut of Bounds Attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\n\nWithout Insert Bounded, a series of nulls is insein the Bin: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, [1449, sei whale, Greenland, Gulf of Maine]]\n" ] ], [ [ "#### Add Unique Example", "_____no_output_____" ] ], [ [ "// create policy to apply\nListPolicy listAddUniquePolicy = new ListPolicy(ListOrder.UNORDERED, ListWriteFlags.ADD_UNIQUE);\nListPolicy listAddUniqueNoFailPolicy = new ListPolicy(ListOrder.UNORDERED, ListWriteFlags.ADD_UNIQUE \n | ListWriteFlags.NO_FAIL);\n\n// create fresh record\nInteger whaleAddUniqueKeyName = 8;\nBin bin1 = new Bin(listWhaleBinName, whaleMigration);\nKey whaleAddUniqueKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, whaleAddUniqueKeyName);\nclient.put(client.writePolicyDefault, whaleAddUniqueKey, bin1);\nRecord auDataPutIn = client.get(null, whaleAddUniqueKey);\n\n\n// successful ADD_UNIQUE operation\nRecord auSuccess = client.operate(client.writePolicyDefault, whaleAddUniqueKey,\n ListOperation.append(listAddUniquePolicy, listWhaleBinName, Value.get(newWhaleMigration))\n);\nRecord auSuccessData = client.get(null, whaleAddUniqueKey);\n\nSystem.out.println(\"Data after the unique add of \" + newWhaleMigration + \": \" + auSuccessData.getValue(listWhaleBinName) + \"\\n\");\n\n\n// fail for 2nd ADD_UNIQUE\ntry {\n Record auFail = client.operate(client.writePolicyDefault, whaleAddUniqueKey,\n ListOperation.append(listAddUniquePolicy, listWhaleBinName, Value.get(newWhaleMigration))\n );\n System.out.println(\"The code does not get here.\");\n}\ncatch(Exception e) {\n System.out.println(\"Non-Unique Add 1: Exception caught.\");\n Record auNoFail = client.operate(client.writePolicyDefault, whaleAddUniqueKey,\n ListOperation.append(listAddUniqueNoFailPolicy, listWhaleBinName, Value.get(newWhaleMigration))\n );\n Record auNoFailData = client.get(null, whaleAddUniqueKey);\n if(auNoFailData.getValue(listWhaleBinName).equals(auSuccessData.getValue(listWhaleBinName))) {\n System.out.println(\"Non-Unique Add 2: No operation was executed. Error was suppressed by NO_FAIL.\\n\");\n }\n}\n\nRecord noAU = client.operate(client.writePolicyDefault, whaleAddUniqueKey,\n ListOperation.append(listWhaleBinName, Value.get(newWhaleMigration))\n);\nRecord noAUData = client.get(null, whaleAddUniqueKey);\nSystem.out.println(\"Without Add Unique here, the tuple for a sei whale is there 2x: \" + noAUData.getValue(listWhaleBinName));", "Data after the unique add of [1449, sei whale, Greenland, Gulf of Maine]: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine]]\n\nNon-Unique Add 1: Exception caught.\nNon-Unique Add 2: No operation was executed. Error was suppressed by NO_FAIL.\n\nWithout Add Unique here, the tuple for a sei whale is there 2x: [[1420, beluga whale, Beaufort Sea, Bering Sea], [13988, gray whale, Baja California, Chukchi Sea], [1278, north pacific right whale, Japan, Sea of Okhotsk], [5100, humpback whale, Columbia, Antarctic Peninsula], [3100, southern hemisphere blue whale, Corcovado Gulf, The Galapagos], [1449, sei whale, Greenland, Gulf of Maine], [1449, sei whale, Greenland, Gulf of Maine]]\n" ] ], [ [ "### Write Flags for Maps Only:\n* `CREATE_ONLY`: If the key already exists, the item will be denied.\n* `UPDATE_ONLY`: If the key already exists, the item will be overwritten. If the key does not exist, the item will be denied.", "_____no_output_____" ], [ "#### Create Only Example", "_____no_output_____" ] ], [ [ "// create modify data and policy to apply\nHashMap <Value, Value> mapCoords4 = new HashMap <Value, Value>();\nmapCoords4.put(Value.get(\"lat\"), Value.get(0));\nmapCoords4.put(Value.get(\"long\"), Value.get(0));\n\nMapPolicy mapCreateOnlyPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.CREATE_ONLY);\nMapPolicy mapCreateOnlyNoFailPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.CREATE_ONLY \n | MapWriteFlags.NO_FAIL);\n\n// create fresh record\nInteger obsCreateOnlyKeyName = 9;\nKey obsCreateOnlyKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, obsCreateOnlyKeyName);\nRecord putDataIn = client.operate(client.writePolicyDefault, obsCreateOnlyKey,\n MapOperation.putItems(mapObsPolicy, mapObsBinName, mapObs)\n );\nRecord coDataPutIn = client.get(null, obsCreateOnlyKey);\n\n\n// success for CREATE_ONLY\nRecord coSuccess = client.operate(client.writePolicyDefault, obsCreateOnlyKey,\n MapOperation.put(mapCreateOnlyPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords3))\n);\nRecord coSuccessData = client.get(null, obsCreateOnlyKey);\nSystem.out.println(\"Created record and new key \" + newObsKey + \". The data is now: \" + coSuccessData.getValue(mapObsBinName) + \"\\n\");\n\n\n// fail for CREATE_ONLY\ntry {\n Record coFail = client.operate(client.writePolicyDefault, obsCreateOnlyKey,\n MapOperation.put(mapCreateOnlyPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords4))\n );\n System.out.println(\"The code does not get here.\");\n}\ncatch(Exception e) {\n System.out.println(\"Update attempt 1: Exception caught.\");\n Record coNoFail = client.operate(client.writePolicyDefault, obsCreateOnlyKey,\n MapOperation.put(mapCreateOnlyNoFailPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords4))\n );\n Record coNoFailData = client.get(null, obsCreateOnlyKey);\n if(coNoFailData.getValue(mapObsBinName).equals(coSuccessData.getValue(mapObsBinName))) {\n System.out.println(\"Update attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\\n\");\n }\n}\n\nRecord noCO = client.operate(client.writePolicyDefault, obsCreateOnlyKey, \n MapOperation.put(mapObsPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords4))\n);\nRecord noCOData = client.get(null, obsCreateOnlyKey);\nSystem.out.println(\"Without Create Only, the observation at 15678 is overwritten: \" + noCOData.getValue(mapObsBinName));\n\nBoolean createOnlyExampleRecordDeleted=client.delete(null, obsCreateOnlyKey);", "Created record and new key 15678. The data is now: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 15678={lat=95, long=110}}\n\nUpdate attempt 1: Exception caught.\nUpdate attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\n\nWithout Create Only, the observation at 15678 is overwritten: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}, 15678={lat=0, long=0}}\n" ] ], [ [ "#### Update Only Example", "_____no_output_____" ] ], [ [ "// create policy to apply\nMapPolicy mapUpdateOnlyPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.UPDATE_ONLY);\nMapPolicy mapUpdateOnlyNoFailPolicy = new MapPolicy(MapOrder.KEY_ORDERED, MapWriteFlags.UPDATE_ONLY \n | MapWriteFlags.NO_FAIL);\n\n// create Aerospike data elements for a fresh record\nInteger obsUpdateOnlyKeyName = 10;\nKey obsUpdateOnlyKey = new Key(nestedCDTNamespaceName, nestedCDTSetName, obsUpdateOnlyKeyName);\n\nRecord uoPutDataIn = client.operate(client.writePolicyDefault, obsUpdateOnlyKey,\n MapOperation.putItems(mapObsPolicy, mapObsBinName, mapObs)\n );\nRecord uoDataPutIn = client.get(null, obsUpdateOnlyKey);\nSystem.out.println(\"Created record: \" + uoDataPutIn.getValue(mapObsBinName) + \"\\n\");\n\n\n// fail for UPDATE_ONLY\ntry {\n Record uoFail = client.operate(client.writePolicyDefault, obsUpdateOnlyKey,\n MapOperation.put(mapUpdateOnlyPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords3))\n );\n System.out.println(\"The code does not get here.\");\n}\ncatch(Exception e) {\n System.out.println(\"Create Attempt 1: Exception caught.\");\n Record uoNoFail = client.operate(client.writePolicyDefault, obsUpdateOnlyKey,\n MapOperation.put(mapUpdateOnlyNoFailPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords3))\n );\n Record uoNoFailData = client.get(null, obsUpdateOnlyKey);\n if(uoNoFailData.getValue(mapObsBinName).equals(uoDataPutIn.getValue(mapObsBinName))){\n System.out.println(\"Create Attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\\n\");\n }\n}\n\nRecord noUO = client.operate(client.writePolicyDefault, obsUpdateOnlyKey, \n MapOperation.put(mapObsPolicy, mapObsBinName, Value.get(newObsKey), Value.get(mapCoords3))\n);\nRecord noUOData = client.get(null, obsUpdateOnlyKey);\n\n// success for UPDATE_ONLY\nRecord uoSuccess = client.operate(client.writePolicyDefault, obsUpdateOnlyKey,\n MapOperation.put(mapUpdateOnlyPolicy, mapObsBinName, Value.get(existingObsKey), Value.get(mapCoords4))\n);\nRecord uoSuccessData = client.get(null, obsUpdateOnlyKey);\nSystem.out.println(\"Using update only, the value of an existing key \" + existingObsKey + \" can be updated: \" + uoSuccessData.getValue(mapObsBinName) + \"\\n\");\n\nBoolean uoExampleRecordDeleted=client.delete(null, obsUpdateOnlyKey);", "Created record: {12345={lat=-85, long=-130}, 13456={lat=-25, long=-50}, 14567={lat=35, long=30}}\n\nCreate Attempt 1: Exception caught.\nCreate Attempt 2: No operation was executed. Error was suppressed by NO_FAIL.\n\nUsing update only, the value of an existing key 13456 can be updated: {12345={lat=-85, long=-130}, 13456={lat=0, long=0}, 14567={lat=35, long=30}, 15678={lat=95, long=110}}\n\n" ] ], [ [ "# Notebook Cleanup", "_____no_output_____" ], [ "### Truncate the Set\nTruncate the set from the Aerospike Database.", "_____no_output_____" ] ], [ [ "import com.aerospike.client.policy.InfoPolicy;\nInfoPolicy infoPolicy = new InfoPolicy();\n\nclient.truncate(infoPolicy, nestedCDTNamespaceName, nestedCDTSetName, null);\nSystem.out.println(\"Set Truncated.\");", "Set Truncated.\n" ] ], [ [ "### Close the Client connections to Aerospike", "_____no_output_____" ] ], [ [ "client.close();\nSystem.out.println(\"Server connection(s) closed.\");", "Server connection(s) closed.\n" ] ], [ [ "# Takeaways – CDTs Provide Flexible Document-Oriented Data Power\n\nAerospike Collection Data Types...\n1. facilitate complex data structures by supporting nesting through the use of contexts (CTXs)\n2. provide intuitive and flexible return types options from operations\n3. support policies that empower efficient and flexible transaction processing", "_____no_output_____" ], [ "# What's Next?", "_____no_output_____" ], [ "## Next Steps\n\nHave questions? Don't hesitate to reach out if you have additional questions about data modeling at https://discuss.aerospike.com/c/how-developers-are-using-aerospike/data-modeling/143.\n\nWant to check out other Java notebooks?\n1. [Intro to Transactions](./java-intro_to_transactions.ipynb)\n2. [Modeling Using Lists](./java-modeling_using_lists.ipynb)\n3. [Working with Maps](./java-working_with_maps.ipynb)\n4. [Aerospike Query and UDF](query_udf.ipynb)\n\n\nAre you running this from Binder? [Download the Aerospike Notebook Repo](https://github.com/aerospike-examples/interactive-notebooks) and work with Aerospike Database and Jupyter locally using a Docker container.", "_____no_output_____" ], [ "## Additional Resources\n\n* Want to get started with Java? [Download](https://www.aerospike.com/download/client/) or [install](https://github.com/aerospike/aerospike-client-java) the Aerospike Java Client. \n(https://www.aerospike.com/apidocs/java/com/aerospike/client/cdt/MapOperation.html).\n* What are Namespaces, Sets, and Bins? Check out the [Aerospike Data Model](https://www.aerospike.com/docs/architecture/data-model.html). \n* How robust is the Aerospike Database? Browses the [Aerospike Database Architecture](https://www.aerospike.com/docs/architecture/index.html).", "_____no_output_____" ] ] ]

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[ "BSD-2-Clause" ]

[ "BSD-2-Clause" ]

[ "BSD-2-Clause" ]

[ [ [ "!date", "Mon Nov 9 11:35:17 PST 2020\n" ] ], [ [ "# Merfish 10x comparison", "_____no_output_____" ] ], [ [ "import anndata\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.patches as mpatches\nimport scanpy as scanp\nfrom scipy.stats import ks_2samp, ttest_ind\nfrom scipy.sparse import csr_matrix\nfrom scipy import stats\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.manifold import TSNE\nfrom umap import UMAP\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import adjusted_rand_score\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom matplotlib import cm\nfrom scipy.spatial import ConvexHull\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import normalize\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport sys\nsys.path.append('/home/sina/projects/mop/BYVSTZP_2020/trackfig')\nfrom trackfig.utils import get_notebook_name\nfrom trackfig.trackfig import trackfig \n\nTRACKFIG = \"/home/sina/projects/mop/BYVSTZP_2020/trackfig.txt\"\nNB = get_notebook_name()\n\nfsize=20\n\nplt.rcParams.update({'font.size': fsize})\n%config InlineBackend.figure_format = 'retina'", "_____no_output_____" ], [ "unique_map = {'Astrocytes': \"Astro\",\n'Endothelial':\"Endo\",\n'SMC':\"SMC\",\n'L23_IT':\"L2/3 IT\",\n'VLMC': \"VLMC\",\n'L6_CT': \"L6 CT\",\n'L45_IT': \"L4/5 IT\",\n'L5_PT': \"L5 PT\",\n'L5_IT': \"L5 IT\",\n'Sst': \"Sst\",\n'L6_IT': \"L6 IT\",\n'Sncg': \"Sncg\",\n'L6_IT_Car3': \"L6 IT Car3\",\n'Vip': \"Vip\",\n'L56_NP': \"L5/6 NP\",\n'Pvalb': \"Pvalb\", \n'L6b': \"L6b\",\n'Lamp5': \"Lamp5\"}\n\ninv_map = {v: k for k, v in unique_map.items()}", "_____no_output_____" ], [ "cluster_cmap = {\n\"Astro\": (0.38823529411764707, 0.4745098039215686, 0.2235294117647059 ), # 637939,\n\"Endo\" : (0.5490196078431373, 0.6352941176470588, 0.3215686274509804 ), # 8ca252,\n\"SMC\" : (0.7098039215686275, 0.8117647058823529, 0.4196078431372549 ), # b5cf6b,\n\"VLMC\" : (0.807843137254902, 0.8588235294117647, 0.611764705882353 ), # cedb9c,\n\"Low Quality\" : (0,0,0),\n\"L2/3 IT\" : (0.9921568627450981, 0.6823529411764706, 0.4196078431372549 ), # fdae6b\n\"L5 PT\" : (0.9921568627450981, 0.8156862745098039, 0.6352941176470588 ), # fdd0a2\n\"L5 IT\" : (0.5176470588235295, 0.23529411764705882, 0.2235294117647059 ), # 843c39\n\"L5/6 NP\": \"#D43F3A\",\n\"L6 CT\" : (0.8392156862745098, 0.3803921568627451, 0.4196078431372549 ), # d6616b\n\"L6 IT\" : (0.9058823529411765, 0.5882352941176471, 0.611764705882353 ), # e7969c\n\"L6b\" : (1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e\n\"L6 IT Car3\" : (1.0, 0.7333333333333333, 0.47058823529411764 ), # ffbb78\n\"Lamp5\" : (0.19215686274509805, 0.5098039215686274, 0.7411764705882353 ), # 3182bd # blues\n\"Sncg\" : (0.4196078431372549, 0.6823529411764706, 0.8392156862745098 ), # 6baed6\n\"Vip\" : (0.6196078431372549, 0.792156862745098, 0.8823529411764706 ), # 9ecae1\n\"Sst\" : (0.7764705882352941, 0.8588235294117647, 0.9372549019607843 ), # c6dbef\n\"Pvalb\":(0.7372549019607844, 0.7411764705882353, 0.8627450980392157 ), # bcbddc\n}", "_____no_output_____" ], [ "def trim_axs(axs, N):\n \"\"\"little helper to massage the axs list to have correct length...\"\"\"\n axs = axs.flat\n for ax in axs[N:]:\n ax.remove()\n return axs[:N]", "_____no_output_____" ], [ "def split_by_target(mat, targets, target, axis=0):\n \"\"\"\n Split the rows of mat by the proper assignment\n \n mat = ndarray\n targets, length is equal to number of components (axis=0) or features (axis=1)\n target is a singular element from unique(assignments/features) \n \"\"\"\n if axis==0 and len(targets) != mat.shape[axis]: return -1\n if axis==1 and len(targets) != mat.shape[axis]: return -1\n \n mask = targets == target\n \n if axis==0:\n t_mat = mat[mask] # target matrix\n c_mat = mat[~mask] # complement matrix\n elif axis==1:\n t_mat = mat[:, mask] # target matrix\n c_mat = mat[:, ~mask] # complement matrix\n \n return (t_mat, c_mat)\n\ndef group_mtx_by_cluster(mtx, components, features, s2t, source_id=\"cell_id\", target_id=\"subclass_label\", by=\"components\"):\n \"\"\"\n mtx: ndarray components by features \n components: labels for rows of mtx\n features: labels for columns of mtx\n s2t: pandas dataframe mapping source (features or components) to a\n targets features(components) to group by\n target_id: column name in s2t to group by\n \"\"\"\n if target_id not in s2t.columns: return -1\n \n ncomp = components.shape[0]\n nfeat = features.shape[0]\n ntarget = s2t[target_id].nunique()\n \n if by ==\"features\": \n source = features\n elif by ==\"components\": \n source = components\n \n # Map the source to an index\n source2idx = dict(zip(source, range(len(source))))\n # Map the target to a list of source indices\n target2idx = (s2t.groupby(target_id)[source_id].apply(lambda x: [source2idx[i] for i in x])).to_dict()\n \n # array of unique targets\n unique = s2t[target_id].unique().astype(str)\n nuniq = unique.shape[0]\n X = np.zeros((nuniq, mtx.shape[1]))\n \n for tidx, t in enumerate(unique):\n # Grab the matrix indices corresponding to columns and source columns to group by\n source_indices = target2idx[t]\n #print(source_indices)\n \n # breaks generality\n sub_mtx = mtx[source_indices,:].mean(axis=0) # Sum on source indicies\n X[tidx,:] = sub_mtx # place summed vector in new matrix\n \n # Return matrix that is grouped by\n return (X, components, unique)", "_____no_output_____" ], [ "def nd(arr):\n return np.asarray(arr).reshape(-1)", "_____no_output_____" ], [ "mfish = anndata.read_h5ad(\"../../data/notebook/revision/merfish-updated.h5ad\")\n\nmfish.obs[\"tenx_subclass\"] = mfish.obs[\"subclass\"].apply(lambda x: unique_map.get(x, \"None\"))\nmfish = mfish[mfish.obs.tenx_subclass != \"None\"]", "_____no_output_____" ], [ "md = pd.read_csv(\"../../reference/10xv3_cluster_labels/sample_metadata.csv\", index_col = 0)\n\nmd[\"sex\"] = md[\"Gender\"].apply(lambda x: {\"Male\": \"M\", \"Female\":\"F\"}.get(x, \"X\"))\n\ntenx = anndata.read_h5ad(\"../../data/notebook/revision/10xv3_gene.h5ad\")\ntenx.obs[\"date\"] = tenx.obs.index.map(md[\"Amp_Date\"])\ntenx.obs[\"sex\"] = tenx.obs.index.map(md[\"sex\"])\n\ntenx = tenx[:,tenx.var.gene_short_name.isin(mfish.var.index)]\n\ntenx.var.index = tenx.var.gene_short_name.values\n#tenx = tenx[tenx.obs.eval(\"date == '11/29/2018'\").values] # males\n#tenx = tenx[tenx.obs.eval(\"date == '12/7/2018'\").values] # females\ntenx = tenx[tenx.obs.eval(\"date == '4/26/2019'\").values] # females and males\n#tenx = tenx[tenx.obs.subclass_label!=\"Low Quality\"]\n\n", "_____no_output_____" ], [ "md.groupby(\"Amp_Date\")[\"sex\"].value_counts()", "_____no_output_____" ], [ "print(tenx)\nprint(mfish)", "View of AnnData object with n_obs × n_vars = 35370 × 254\n obs: 'batch', 'cluster_id', 'cluster_label', 'subclass_label', 'class_label', 'subclass_id', 'class_id', 'date', 'sex'\n var: 'gene_name', 'gene_id', 'gene_short_name'\nView of AnnData object with n_obs × n_vars = 228355 × 254\n obs: 'fovID', 'fov_x', 'fov_y', 'volume', 'center_x', 'center_y', 'slice_id', 'sample_id', 'label', 'subclass', 'class_label', 'cell_id', 'tenx_subclass'\n var: 'n_iso'\n layers: 'X', 'log1p', 'norm'\n" ], [ "tenx.obs.subclass_label.value_counts()", "_____no_output_____" ], [ "mfish.obs.subclass.value_counts()", "_____no_output_____" ] ], [ [ "# Process", "_____no_output_____" ] ], [ [ "from sklearn.preprocessing import normalize", "_____no_output_____" ], [ "tenx.layers[\"X\"] = tenx.X\ntenx.layers[\"norm\"] = normalize(tenx.X, norm='l1', axis=1)*1000000\ntenx.layers[\"log1p\"] = csr_matrix(np.log1p(tenx.layers[\"norm\"]))", "_____no_output_____" ], [ "from sklearn.preprocessing import scale", "_____no_output_____" ], [ "%%time\nmat = tenx.layers[\"log1p\"].todense()\nmtx = scale(mat, axis=0, with_mean=True, with_std=True, copy=True)\ntenx.X = mtx", "CPU times: user 272 ms, sys: 110 ms, total: 382 ms\nWall time: 381 ms\n" ], [ "del mat", "_____no_output_____" ] ], [ [ "# Cluster comparisons", "_____no_output_____" ] ], [ [ "tenx = tenx[:,tenx.var.sort_index().index]\nmfish = mfish[:,mfish.var.sort_index().index]", "_____no_output_____" ], [ "tenx.var.head()", "_____no_output_____" ], [ "mfish.var.head()", "_____no_output_____" ], [ "mfish_mat = mfish.X\nmfish_ass = mfish.obs.tenx_subclass.values\n\ntenx_mat = tenx.X\ntenx_ass = tenx.obs.subclass_label.values", "_____no_output_____" ], [ "features = mfish.var.index.values\n\nunique = np.intersect1d(np.unique(mfish_ass), np.unique(tenx_ass))", "_____no_output_____" ], [ "%%time\nrvals = []\ntenx_x = []\nmfish_x = []\n\nfor uidx, u in enumerate(unique):\n mfish_t_mat, _ = split_by_target(mfish_mat, mfish_ass, u)\n tenx_t_mat, _ = split_by_target(tenx_mat, tenx_ass, u)\n \n \n mf = np.asarray(mfish_t_mat.mean(axis=0)).reshape(-1)\n t = np.asarray(tenx_t_mat.mean(axis=0)).reshape(-1)\n \n tenx_x.append(t)\n mfish_x.append(mf)\n \n r, p = stats.pearsonr(mf, t)\n rvals.append(r)\n print(\"[{} of {}] {:,.2f}: {}\".format(uidx+1, unique.shape[0],r, u) )", "[1 of 17] 0.79: Astro\n[2 of 17] 0.76: Endo\n[3 of 17] 0.84: L2/3 IT\n[4 of 17] 0.44: L5 IT\n[5 of 17] 0.78: L5 PT\n[6 of 17] 0.89: L5/6 NP\n[7 of 17] 0.90: L6 CT\n[8 of 17] 0.85: L6 IT\n[9 of 17] 0.83: L6 IT Car3\n[10 of 17] 0.89: L6b\n[11 of 17] 0.90: Lamp5\n[12 of 17] 0.86: Pvalb\n[13 of 17] 0.62: SMC\n[14 of 17] 0.84: Sncg\n[15 of 17] 0.90: Sst\n[16 of 17] 0.32: VLMC\n[17 of 17] 0.86: Vip\nCPU times: user 1.76 s, sys: 1.95 s, total: 3.7 s\nWall time: 3.69 s\n" ], [ "tenx_size = tenx.obs[\"subclass_label\"].value_counts()[unique]", "_____no_output_____" ], [ "fig, ax = plt.subplots(figsize=(10,7))\n\nx = tenx_size\ny = rvals\n\nfor i, txt in enumerate(unique):\n ax.annotate(i, (x[i], y[i]))\n ax.scatter(x[i], y[i], label=\"{}: {}\".format(i, txt), color=cluster_cmap[txt])\nax.set_ylim((0, 1))\nax.set_xscale(\"log\")\n \nax.set_xlabel(\"Number of 10xv3 cells\")\nax.set_ylabel(\"Pearson correlation\")\nax.legend(fontsize=15,loc='center left', bbox_to_anchor=(1, 0.5), markerscale=3)\nax.set_title(\"MERFISH v. 10xv3 gene subclass correlation\")\nplt.savefig(trackfig(\"../../figures/merfish-updated_10x_gene_subclass_size.png\", TRACKFIG, NB), bbox_inches='tight', dpi=300)\nplt.show()", "_____no_output_____" ], [ "# males\nmales = pd.DataFrame({\"subclass\": unique.tolist(), \n \"rvals\": rvals,\n \"size\": tenx.obs.subclass_label.value_counts()[unique]})", "_____no_output_____" ], [ "males", "_____no_output_____" ], [ "fig, ax = plt.subplots(figsize=(15,15), ncols=4, nrows=5)\nfig.subplots_adjust(hspace=0, wspace=0)\naxs = trim_axs(ax, len(unique))\nfig.suptitle('MERFISH v. 10xv3 gene subclass correlation', y=0.9)\n#fig.subplots_adjust(top=1)\n\nfor cidx, (ax, c) in enumerate(zip(axs, unique)):\n \n \n x = tenx_x[cidx]\n y = mfish_x[cidx]\n\n \n ax.scatter(x, y, label=\"{}: {:,}\".format(c, tenx_size[cidx]), color=\"k\", alpha=0.1)\n\n slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)\n\n minx = min(x)\n maxx = max(x)\n x = np.linspace(minx, maxx, 10)\n y = slope*x+intercept\n ax.plot(x, y, label=\"corr : {:,.2f}\".format(r_value**2), color=\"red\", linewidth=3)\n ax.legend(fontsize=15)\n ax.xaxis.set_ticklabels([])\n ax.yaxis.set_ticklabels([])\n ax.set_axis_off()\nfig.text(0.5, 0.1, '10xv3 scaled $log(TPM+1)$', ha='center', va='center', fontsize=30)\nfig.text(0.1, 0.5, 'MERFISH scaled $log(CPM+1)$', ha='center', va='center', rotation='vertical', fontsize=30)\n\nplt.savefig(trackfig(\"../../figures/merfish-updated_10x_gene_subclass_correlation_scatter.png\", TRACKFIG, NB), bbox_inches='tight',dpi=300)\nplt.show()", "_____no_output_____" ], [ "tenx[tenx.obs.subclass_label==\"L5 IT\"].obs.cluster_label.value_counts()", "_____no_output_____" ], [ "mfish[mfish.obs.subclass==\"L5_IT\"].obs.label.value_counts()", "_____no_output_____" ], [ "rvals", "_____no_output_____" ], [ "unique.tolist()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# تبدیل متن فارسی به فینگلیش\r\nدر طی این پروژه تیم نرم افزار برای کار با مدل تاکاترون 2 علاوه بر متن فارسی و صوت هر یک از جملات مورد بررسی در دیتاست به فینگلیش شده آن متن فارسی نیز نیاز دارند، بنابراین یکی از مراحل و کار هایی که باید انجام میشد تبدیل متن فارسی به فینگلیش بود برای این کار در طی دو مرحله کار پیش رفت.", "_____no_output_____" ], [ "## تبدیل دستی\r\nابتدا در حدود 5000 جمله فارسی به صورت دستی و در طی تلاش سه نفر کاملا به فینگلیش تبدیل شدند", "_____no_output_____" ], [ "## تبدیل اتوماتیک\r\nپس از تبدیل جملات طی پردازشی این جملات همگی به صورت دیتاستی از واژگان تبدیل شدند که شامل 38367 لغت فارسی و مطابق آنها به همین تعداد لغت فینگلش شده وجود داشت.\r\n", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Random sampling of parameters", "_____no_output_____" ], [ "(c) 2019 Manuel Razo. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT). \n\n---", "_____no_output_____" ] ], [ [ "import os\nimport itertools\nimport pickle\nimport cloudpickle\nimport re\nimport glob\nimport git\n\n# Our numerical workhorses\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\n\n# Import library to perform maximum entropy fits\nfrom maxentropy.skmaxent import FeatureTransformer, MinDivergenceModel\n\n# Import libraries to parallelize processes\nfrom joblib import Parallel, delayed\n\n# Import matplotlib stuff for plotting\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport matplotlib as mpl\n\n# Seaborn, useful for graphics\nimport seaborn as sns\n# Increase DPI of displayed figures\n%config InlineBackend.figure_format = 'retina'\n\n# Import the project utils\nimport ccutils\n\n# Find home directory for repo\nrepo = git.Repo(\"./\", search_parent_directories=True)\nhomedir = repo.working_dir\n\n# Define directories for data and figure \nfigdir = f'{homedir}/fig/MaxEnt_approx_joint/'\ndatadir = f'{homedir}/data/csv_maxEnt_dist'", "_____no_output_____" ], [ "# Set PBoC plotting format\nccutils.viz.set_plotting_style()\n# Increase dpi\nmpl.rcParams['figure.dpi'] = 110", "_____no_output_____" ] ], [ [ "### $\\LaTeX$ macros\n\n$\\newcommand{kpon}{k^p_{\\text{on}}}$\n$\\newcommand{kpoff}{k^p_{\\text{off}}}$\n$\\newcommand{kron}{k^r_{\\text{on}}}$\n$\\newcommand{kroff}{k^r_{\\text{off}}}$\n$\\newcommand{rm}{r _m}$\n$\\newcommand{rp}{r _p}$\n$\\newcommand{gm}{\\gamma _m}$\n$\\newcommand{gp}{\\gamma _p}$\n$\\newcommand{mm}{\\left\\langle m \\right\\rangle}$\n$\\newcommand{foldchange}{\\text{fold-change}}$\n$\\newcommand{ee}[1]{\\left\\langle #1 \\right\\rangle}$\n$\\newcommand{var}[1]{\\text{Var}\\left( #1 \\right)}$\n$\\newcommand{bb}[1]{\\mathbf{#1}}$\n$\\newcommand{th}[1]{\\text{th}}$", "_____no_output_____" ], [ "## Variability in the kinetic parameters", "_____no_output_____" ], [ "An idea that could explain the systematic variation between our theoretical predictions and the data is the stochasticity that could be associated with random variation of the kinetic parameters. For example, if cells happen to stochastically have different number of ribosomes, how much would that affect the final distribution. Another good example is the variability in repressor copy number, which would affect the $\\kron$ rate.\n\nTo simplify things what we will do is sample random variations to some of the kinetic parameters, run the dynamics with such parameters, and then reconstruct the corresponding MaxEnt distribution. Then we will combine all of these distributions to see how different this is compared to the one with single parameter values.", "_____no_output_____" ], [ "### Unregulated promoter parameter variation", "_____no_output_____" ], [ "Let's begin with the unregulated promoter. The parameters here are $\\kpon, \\kpoff, r_m, \\gm, r_p$, and $\\gp$. The simplest scenario would be to sample variations out of a Gaussian distribution. We will set these distributions to be centered at the current value of the parameter we are using, and allow a variation of some defined percentage.\n\nLet's define a function that given an array of parameters, it samples random variations.", "_____no_output_____" ] ], [ [ "def param_normal_sample(param, n_samples, std=0.2):\n '''\n Function that samples variations to the parameter values out of a normal\n distribution.\n Parameters\n ----------\n param : array-like.\n List of parameters from which the samples will be generated.\n n_samples : int.\n Number or random samples to draw from the distribution\n std : float or array-like.\n Fractional standard deviations for each of the samples to be taken. \n If a single value is given, then all of the distributions will have \n the same standard deviation proportional to the mean.\n \n Returns\n -------\n samples : array-like. Shape = len(param) x n_samples\n Random samples of the parameters.\n '''\n # Initialize array to save output\n samples = np.zeros([n_samples, len(param)])\n \n # Loop through parameters\n for i, par in enumerate(param):\n if len(std) == len(param):\n samples[:, i] = np.random.normal(par, par * std[i], n_samples)\n elif len(std) == 1:\n samples[:, i] = np.random.normal(par, par * std[0], n_samples)\n \n return samples", "_____no_output_____" ] ], [ [ "Let's now load the parameters and generate random samples.", "_____no_output_____" ] ], [ [ "# Load parameter values\npar = ccutils.model.load_constants()\n\n# Define parametesr for unregulated promoter\npar_names = ['kp_on', 'kp_off', 'rm', 'gm', 'rp']\nparam = [par[x] for x in par_names]\n\n# Generate samples of all parameters with a 10% variability\nn_samples = 999 \nstd = [0.15]\nparam_sample = param_normal_sample(param, n_samples, std)\n# Add reference parameters to list\nparam_sample = np.append(np.array([[*param]]), param_sample, axis=0)", "_____no_output_____" ] ], [ [ "Having sampled the parameters let's go ahead and run the dynamics for each of these parameter sets. First we need to load the matrix to compute the moments of the distribution after the cell division as a function of the moments before the cell division.", "_____no_output_____" ] ], [ [ "# Read matrix into memory\nwith open(f'{homedir}/src/theory/pkl_files/binom_coeff_matrix.pkl', \n 'rb') as file:\n unpickler = pickle.Unpickler(file)\n Z_mat = unpickler.load()\n expo_binom = unpickler.load()", "_____no_output_____" ] ], [ [ "Now let's load the matrix to compute the dynamics of the unregualted two-state promoter", "_____no_output_____" ] ], [ [ "with open('../pkl_files/two_state_protein_dynamics_matrix.pkl',\n 'rb') as file:\n A_mat_unreg_lam = cloudpickle.load(file)\n expo_unreg = cloudpickle.load(file)", "_____no_output_____" ] ], [ [ "Next let's define all of the parameters that we will need for the integration.", "_____no_output_____" ] ], [ [ "# Define doubling time\ndoubling_time = 100\n# Define fraction of cell cycle spent with one copy\nt_single_frac = 0.6\n# Define time for single-promoter state\nt_single = 60 * t_single_frac * doubling_time # sec\nt_double = 60 * (1 - t_single_frac) * doubling_time # sec\nn_cycles = 6\n\n# Define names for dataframe columns\nnames = par_names + ['m' + str(m[0]) + 'p' + str(m[1]) for m in expo_unreg]\n\n# Initialize DataFrame to save constraints\ndf_moments = pd.DataFrame([], columns=names)", "_____no_output_____" ] ], [ [ "Now we are ready to run the dynamics in parallel, let's define the function so that we can perform this numerical integration in parallel", "_____no_output_____" ] ], [ [ "compute_dynamics = False\n\nif compute_dynamics:\n # Define function for parallel computation\n def constraints_parallel(par):\n kp_on = par[0]\n kp_off = par[1]\n rm = par[2]\n gm = par[3]\n rp = par[4]\n\n # Single promoter\n gp_init = 1 / (60 * 60)\n rp_init = 500 * gp_init\n\n # Generate matrices for dynamics\n # Single promoter\n par_unreg_s = [kp_on, kp_off, rm, gm, rp, 0]\n # Two promoters\n par_unreg_d = [kp_on, kp_off, 2 * rm, gm, rp, 0]\n\n # Initial conditions\n A_unreg_s_init = A_mat_unreg_lam(\n kp_on, kp_off, rm, gm, rp_init, gp_init\n )\n\n # Define initial conditions\n mom_init = np.zeros(len(expo_unreg) * 2)\n # Set initial condition for zero moment\n # Since this needs to add up to 1\n mom_init[0] = 1\n\n # Define time on which to perform integration\n t = np.linspace(0, 4000 * 60, 10000)\n # Numerically integrate equations\n m_init = sp.integrate.odeint(\n ccutils.model.rhs_dmomdt, mom_init, t, args=(A_unreg_s_init,)\n )\n # Keep last time point as initial condition\n m_init = m_init[-1, :]\n\n # Integrate moment equations\n df = ccutils.model.dmomdt_cycles(\n m_init,\n t_single,\n t_double,\n A_mat_unreg_lam,\n par_unreg_s,\n par_unreg_d,\n expo_unreg,\n n_cycles,\n Z_mat,\n states=[\"A\", \"I\"],\n n_steps=3000,\n )\n\n # Keep only last cycle\n df = df[df[\"cycle\"] == df[\"cycle\"].max()]\n\n # Extract time of last cell cycle\n time = np.sort(df[\"time\"].unique())\n\n # Compute the time differences\n time_diff = np.diff(time)\n # Compute the cumulative time difference\n time_cumsum = np.cumsum(time_diff)\n time_cumsum = time_cumsum / time_cumsum[-1]\n\n # Define array for spacing of cell cycle\n a_array = np.zeros(len(time))\n a_array[1:] = time_cumsum\n\n # Compute probability based on this array\n p_a_array = np.log(2) * 2 ** (1 - a_array)\n\n # Initialize list to append moments\n moms = list()\n # Loop through moments computing the average moment\n for i, mom in enumerate(expo_unreg):\n # Generate string that finds the moment\n mom_name = \"m\" + str(mom[0]) + \"p\" + str(mom[1])\n # List rows with moment\n mom_bool = [x for x in df.columns if mom_name in x]\n # Extract data for this particular moment\n df_mom = df.loc[:, mom_bool].sum(axis=1)\n\n # Average moment and append it to list\n moms.append(sp.integrate.simps(df_mom * p_a_array, a_array))\n\n # Save results into series in order to append it to data frame\n series = pd.Series(list(par) + moms, index=names)\n\n return series\n\n # Run function in parallel\n constraint_series = Parallel(n_jobs=6)(\n delayed(constraints_parallel)(par) for par in param_sample\n )\n\n # Initialize data frame to save list of pareters\n df_moments = pd.DataFrame([], columns=names)\n\n for s in constraint_series:\n df_moments = df_moments.append(s, ignore_index=True)\n df_moments.to_csv(\n f\"{homedir}/data/csv_maxEnt_dist/\" + \"MaxEnt_unreg_random.csv\",\n index=False,\n )\n\ndf_moments = pd.read_csv(\n f\"{homedir}/data/csv_maxEnt_dist/\" + \"MaxEnt_unreg_random.csv\"\n)\ndf_moments.head()", "_____no_output_____" ] ], [ [ "Let's look at the distribution of means and standard deviations in mRNA count for these variations in parameters.", "_____no_output_____" ] ], [ [ "# Compute mRNA standard deviations\nmRNA_std = np.sqrt(df_moments.m2p0 - df_moments.m1p0**2)\n\n# Initialize figure\nfig, ax = plt.subplots(1, 2, figsize=(7, 3))\n\n# Generate ECDF for mean\nx, y = ccutils.stats.ecdf(df_moments.m1p0)\nax[0].plot(x, y, lw=0, marker='.')\n# add reference line\nax[0].axvline(df_moments.m1p0[0], color='black',\n linestyle='--')\n# label axis\nax[0].set_xlabel(r'$\\left\\langle \\right.$mRNA/cell$\\left. \\right\\rangle$')\nax[0].set_ylabel('ECDF')\n\n# Generate ECDF for standard deviation \nx, y = ccutils.stats.ecdf(mRNA_std)\nax[1].plot(x, y, lw=0, marker='.')\n# add reference line\nax[1].axvline(mRNA_std[0], color='black', linestyle='--')\n# label axis\nax[1].set_xlabel('STD(mRNA/cell)')\nax[1].set_ylabel('ECDF');", "_____no_output_____" ] ], [ [ "There is quite a lot of variability compared to the reference value. Let's repeat these plots, but this time for the protein values", "_____no_output_____" ] ], [ [ "# Compute protein standard deviations\nprotein_std = np.sqrt(df_moments.m0p2 - df_moments.m0p1 ** 2)\n\n# Initialize figure\nfig, ax = plt.subplots(1, 2, figsize=(7, 3))\n\n# Generate ECDF for mean\nx, y = ccutils.stats.ecdf(df_moments.m0p1)\nax[0].plot(x, y, lw=0, marker=\".\")\n# add reference line\nax[0].axvline(df_moments.m0p1[0], color=\"black\", linestyle=\"--\")\n# label axis\nax[0].set_xlabel(r\"$\\left\\langle \\right.$protein/cell$\\left. \\right\\rangle$\")\nax[0].set_ylabel(\"ECDF\")\n\n# Generate ECDF for standard deviation\nx, y = ccutils.stats.ecdf(protein_std)\nax[1].plot(x, y, lw=0, marker=\".\")\n# add reference line\nax[1].axvline(protein_std[0], color=\"black\", linestyle=\"--\")\n# label axis\nax[1].set_xlabel(\"STD(protein/cell)\")\nax[1].set_ylabel(\"ECDF\")", "_____no_output_____" ] ], [ [ "### Moments of the conditional distribution", "_____no_output_____" ], [ "Let's now compare the mean, variance and skewness of the resulting distribution. For this all we have to use is the so-called [law of total expectation](https://en.wikipedia.org/wiki/Law_of_total_expectation) that states that\n$$\n\\ee{f(p)} = \\ee{\\ee{f(p) \\mid \\theta}_p}_\\theta,\n$$\ni.e. to compute the expected value of the function $f(p)$ (could be something like $f(p) p^2$) we first compute the average of the function for a parameter set $\\theta$, then we average the expected value of the function over all values of $\\theta$.", "_____no_output_____" ], [ "Let's for example first compare the resulting mean protein copy numbers for the original value and the one that includes the variability", "_____no_output_____" ] ], [ [ "mean_delta = df_moments.m0p1[0]\nmean_sample = df_moments.m0p1.mean()\n\nprint(f'mean delta: {np.round(mean_delta, 0)}')\nprint(f'mean sample: {np.round(mean_sample, 0)}')\nprint(f'fractional change: {(mean_sample - mean_delta) / mean_delta}')", "mean delta: 7733.0\nmean sample: 8075.0\nfractional change: 0.04428981949722924\n" ] ], [ [ "There is an increment of roughly a 4%, so that is pretty small. Let's now look at the variance", "_____no_output_____" ] ], [ [ "var_delta = df_moments.m0p2[0] - df_moments.m0p1[0]**2\nvar_sample = df_moments.m0p2.mean() - df_moments.m0p1.mean()**2\n\nprint(f'variance delta: {np.round(var_delta, 0)}')\nprint(f'variance sample: {np.round(var_sample, 0)}')\nprint(f'fractional change: {(var_sample - var_delta) / var_delta}')", "variance delta: 2607605.0\nvariance sample: 10714739.0\nfractional change: 3.1090341346727075\n" ] ], [ [ "The change in the variance is quite large! Let's see how this reflects to the change in the noise (std/mean).", "_____no_output_____" ] ], [ [ "noise_delta = np.sqrt(var_delta) / mean_delta\nnoise_sample = np.sqrt(var_sample) / mean_sample\n\nprint(f'noise delta: {np.round(noise_delta, 2)}')\nprint(f'noise sample: {np.round(noise_sample, 2)}')", "noise delta: 0.21\nnoise sample: 0.41\n" ] ], [ [ "There is a factor of two of difference when computing the noise. That is quite interesting since that is exactly what we saw the systematic deviation in the data was like. Let's see what the change in the skewness is then.", "_____no_output_____" ] ], [ [ "skew_delta = (\n df_moments.m0p3[0] - 3 * mean_delta * var_delta - mean_delta ** 3\n) / var_delta ** (3 / 2)\n\nskew_sample = (\n df_moments.m0p3.mean() - 3 * mean_sample * var_sample - mean_sample ** 3\n) / var_sample ** (3 / 2)\n\nprint(f\"skewness delta: {np.round(skew_delta, 2)}\")\nprint(f\"skewness sample: {np.round(skew_sample, 2)}\")", "skewness delta: 0.71\nskewness sample: 1.26\n" ] ], [ [ "This is quite suggestive. It seems that the random variability", "_____no_output_____" ] ] ]

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[ [ [ "https://spinningup.openai.com/en/latest/algorithms/ppo.html", "_____no_output_____" ] ], [ [ "%pylab inline\nimport random\nimport time\nimport numpy as np\n\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras.models import Model, clone_model\nfrom tensorflow.keras.optimizers import Adam, SGD\nfrom tensorflow.keras.layers import Input, Dense, Activation, Lambda\n\nimport gym", "Populating the interactive namespace from numpy and matplotlib\n" ], [ "#env = gym.make(\"CartPole-v1\")\nenv = gym.make(\"Lander\")\nenv.observation_space, env.action_space, type(env.action_space)", "_____no_output_____" ] ] ]

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[ [ [ "# Time Series Cross Validation: Holt-Winters Exponential Smoothing with additive errors and seasonality.", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\n\n#forecast error metrics\nfrom forecast_tools.metrics import (mean_absolute_scaled_error, \n root_mean_squared_error,\n symmetric_mean_absolute_percentage_error)\n\nimport statsmodels as sm\nfrom statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing\n\nimport warnings\nwarnings.filterwarnings('ignore')", "_____no_output_____" ], [ "print(sm.__version__)", "0.11.0\n" ], [ "#ensemble learning\nfrom amb_forecast.ensemble import (Ensemble, UnweightedVote)", "_____no_output_____" ] ], [ [ "# Data Input\n\nThe constants `TOP_LEVEL`, `STAGE`, `REGION`,`TRUST` and `METHOD` are used to control data selection and the directory for outputting results. \n\n> Output file is `f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv'.csv`. where metric will be smape, rmse, mase, coverage_80 and coverage_95. Note: `REGION`: is also used to select the correct data from the input dataframe.", "_____no_output_____" ] ], [ [ "TOP_LEVEL = '../../../results/model_selection'\nSTAGE = 'stage1'\nREGION = 'Trust'\nMETHOD = 'hw'\n\nFILE_NAME = 'Daily_Responses_5_Years_2019_full.csv'\n\n#split training and test data.\nTEST_SPLIT_DATE = '2019-01-01'\n\n#second subdivide: train and val\nVAL_SPLIT_DATE = '2017-07-01'\n\n#discard data after 2020 due to coronavirus\n#this is the subject of a seperate study.\nDISCARD_DATE = '2020-01-01'", "_____no_output_____" ], [ "#read in path\npath = f'../../../data/{FILE_NAME}'", "_____no_output_____" ], [ "def pre_process_daily_data(path, index_col, by_col, \n values, dayfirst=False):\n '''\n Daily data is stored in long format. Read in \n and pivot to wide format so that there is a single \n colmumn for each regions time series.\n '''\n df = pd.read_csv(path, index_col=index_col, parse_dates=True, \n dayfirst=dayfirst)\n df.columns = map(str.lower, df.columns)\n df.index.rename(str(df.index.name).lower(), inplace=True)\n \n clean_table = pd.pivot_table(df, values=values.lower(), \n index=[index_col.lower()],\n columns=[by_col.lower()], aggfunc=np.sum)\n \n clean_table.index.freq = 'D'\n \n return clean_table", "_____no_output_____" ], [ "clean = pre_process_daily_data(path, 'Actual_dt', 'ORA', 'Actual_Value', \n dayfirst=False)\nclean.head()", "_____no_output_____" ] ], [ [ "## Train Test Split", "_____no_output_____" ] ], [ [ "def ts_train_test_split(data, split_date):\n '''\n Split time series into training and test data\n \n Parameters:\n -------\n data - pd.DataFrame - time series data. Index expected as datatimeindex\n split_date - the date on which to split the time series\n \n Returns:\n --------\n tuple (len=2) \n 0. pandas.DataFrame - training dataset\n 1. pandas.DataFrame - test dataset\n '''\n train = data.loc[data.index < split_date]\n test = data.loc[data.index >= split_date]\n return train, test", "_____no_output_____" ], [ "train, test = ts_train_test_split(clean, split_date=TEST_SPLIT_DATE)\n\n#exclude data after 2020 due to coronavirus.\ntest, discard = ts_train_test_split(test, split_date=DISCARD_DATE)\n\n#train split into train and validation\ntrain, val = ts_train_test_split(train, split_date=VAL_SPLIT_DATE)", "_____no_output_____" ], [ "train.shape", "_____no_output_____" ], [ "val.shape", "_____no_output_____" ] ], [ [ "# Test fitting and predicting with model.\n\nThe class below is a 'wrapper' class that provides the same interfacew for all methods and works in the time series cross valiation code. ", "_____no_output_____" ] ], [ [ "class ExponentialSmoothingWrapper:\n '''\n Facade for statsmodels exponential smoothing models. This wrapper\n provides a common interface for all models and allow interop with\n the custom time series cross validation code.\n '''\n def __init__(self, trend=False, damped_trend=False, seasonal=None):\n self._trend = trend\n self._seasonal= seasonal\n self._damped_trend = damped_trend\n\n def _get_resids(self):\n return self._fitted.resid\n\n def _get_preds(self):\n return self._fitted.fittedvalues\n\n def fit(self, train):\n '''\n Fit the model\n \n Parameters:\n train: array-like\n time series to fit.\n '''\n self._model = ExponentialSmoothing(endog=train,\n trend=self._trend, \n damped_trend=self._damped_trend,\n seasonal=self._seasonal)\n self._fitted = self._model.fit()\n self._t = len(train)\n \n def predict(self, horizon, return_conf_int=False, alpha=0.2):\n '''\n Forecast the time series from the final point in the fitted series.\n \n Parameters:\n ----------\n \n horizon: int\n steps ahead to forecast \n \n return_conf_int: bool, optional (default=False)\n Return prediction interval? \n \n alpha: float\n Used if return_conf_int=True. 100(1-alpha) interval.\n '''\n \n forecast = self._fitted.get_forecast(horizon)\n \n mean_forecast = forecast.summary_frame()['mean'].to_numpy()\n \n if return_conf_int:\n df = forecast.summary_frame(alpha=alpha)\n pi = df[['mean_ci_lower', 'mean_ci_upper']].to_numpy()\n return mean_forecast, pi \n else:\n return mean_forecast\n\n fittedvalues = property(_get_preds)\n resid = property(_get_resids)", "_____no_output_____" ] ], [ [ "# Example fitting and prediction with comb", "_____no_output_____" ] ], [ [ "model_1 = ExponentialSmoothingWrapper(trend=True, damped_trend=True, \n seasonal=7)", "_____no_output_____" ], [ "estimators = {'shw': model_1}\nens = Ensemble(estimators, UnweightedVote())", "_____no_output_____" ], [ "ens.fit(train[REGION])", "_____no_output_____" ], [ "H = 5\nens_preds = ens.predict(horizon=H)", "_____no_output_____" ], [ "ens_preds, pi = ens.predict(horizon=H, return_conf_int=True)", "_____no_output_____" ], [ "ens_preds", "_____no_output_____" ], [ "pi", "_____no_output_____" ] ], [ [ "## Time Series Cross Validation\n\n`time_series_cv` implements rolling forecast origin cross validation for time series. \nIt does not calculate forecast error, but instead returns the predictions, pred intervals and actuals in an array that can be passed to any forecast error function. (this is for efficiency and allows additional metrics to be calculated if needed).", "_____no_output_____" ] ], [ [ "def time_series_cv(model, train, val, horizons, alpha=0.2, step=1):\n '''\n Time series cross validation across multiple horizons for a single model.\n\n Incrementally adds additional training data to the model and tests\n across a provided list of forecast horizons. Note that function tests a\n model only against complete validation sets. E.g. if horizon = 15 and \n len(val) = 12 then no testing is done. In the case of multiple horizons\n e.g. [7, 14, 28] then the function will use the maximum forecast horizon\n to calculate the number of iterations i.e if len(val) = 365 and step = 1\n then no. iterations = len(val) - max(horizon) = 365 - 28 = 337.\n \n Parameters:\n --------\n model - forecasting model\n\n error_func - function to measure forecast error\n\n train - np.array - vector of training data\n\n val - np.array - vector of validation data\n\n horizon - list of ints, forecast horizon e.g. [7, 14, 28] days\n\n step -- step taken in cross validation \n e.g. 1 in next cross validation training data includes next point \n from the validation set.\n e.g. 7 in the next cross validation training data includes next 7 points\n (default=1)\n \n Returns:\n -------\n np.array - vector of forecast errors from the CVs.\n '''\n cv_preds = [] #mean forecast\n cv_actuals = [] # actuals \n cv_pis = [] #prediction intervals\n split = 0\n\n print('split => ', end=\"\")\n for i in range(0, len(val) - max(horizons) + 1, step):\n split += 1\n print(f'{split}, ', end=\"\")\n \n train_cv = np.concatenate([train, val[:i]], axis=0)\n model.fit(train_cv)\n \n #predict the maximum horizon \n preds, pis = model.predict(horizon=len(val[i:i+max(horizons)]), \n return_conf_int=True,\n alpha=alpha)\n \n cv_h_preds = []\n cv_test = []\n cv_h_pis = []\n \n for h in horizons:\n #store the h-step prediction\n cv_h_preds.append(preds[:h])\n #store the h-step actual value\n cv_test.append(val.iloc[i:i+h]) \n cv_h_pis.append(pis[:h])\n \n cv_preds.append(cv_h_preds)\n cv_actuals.append(cv_test)\n cv_pis.append(cv_h_pis)\n \n print('done.\\n') \n return cv_preds, cv_actuals, cv_pis", "_____no_output_____" ] ], [ [ "## Custom functions for calculating CV scores for point predictions and coverage.\n\nThese functions have been written to work with the output of `time_series_cv`", "_____no_output_____" ] ], [ [ "def split_cv_error(cv_preds, cv_test, error_func):\n '''\n Forecast error in the current split\n \n Params:\n -----\n cv_preds, np.array\n Split predictions\n \n \n cv_test: np.array\n acutal ground truth observations\n \n error_func: object\n function with signature (y_true, y_preds)\n \n Returns:\n -------\n np.ndarray\n cross validation errors for split\n '''\n n_splits = len(cv_preds)\n cv_errors = []\n \n for split in range(n_splits):\n pred_error = error_func(cv_test[split], cv_preds[split])\n cv_errors.append(pred_error)\n \n return np.array(cv_errors)\n\ndef forecast_errors_cv(cv_preds, cv_test, error_func):\n '''\n Forecast errors by forecast horizon\n \n Params:\n ------\n cv_preds: np.ndarray\n Array of arrays. Each array is of size h representing\n the forecast horizon specified.\n \n cv_test: np.ndarray\n Array of arrays. Each array is of size h representing\n the forecast horizon specified.\n \n error_func: object\n function with signature (y_true, y_preds)\n \n Returns:\n -------\n np.ndarray\n \n '''\n cv_test = np.array(cv_test)\n cv_preds = np.array(cv_preds)\n n_horizons = len(cv_test) \n \n horizon_errors = []\n for h in range(n_horizons):\n split_errors = split_cv_error(cv_preds[h], cv_test[h], error_func)\n horizon_errors.append(split_errors)\n\n return np.array(horizon_errors)\n\ndef split_coverage(cv_test, cv_intervals):\n n_splits = len(cv_test)\n cv_errors = []\n \n for split in range(n_splits):\n val = np.asarray(cv_test[split])\n lower = cv_intervals[split].T[0]\n upper = cv_intervals[split].T[1]\n \n coverage = len(np.where((val > lower) & (val < upper))[0])\n coverage = coverage / len(val)\n \n cv_errors.append(coverage)\n \n return np.array(cv_errors)\n \n \ndef prediction_int_coverage_cv(cv_test, cv_intervals):\n cv_test = np.array(cv_test)\n cv_intervals = np.array(cv_intervals)\n n_horizons = len(cv_test) \n \n horizon_coverage = []\n for h in range(n_horizons):\n split_coverages = split_coverage(cv_test[h], cv_intervals[h])\n horizon_coverage.append(split_coverages)\n\n return np.array(horizon_coverage) ", "_____no_output_____" ], [ "def split_cv_error_scaled(cv_preds, cv_test, y_train):\n n_splits = len(cv_preds)\n cv_errors = []\n \n for split in range(n_splits):\n pred_error = mean_absolute_scaled_error(cv_test[split], cv_preds[split], \n y_train, period=7)\n \n cv_errors.append(pred_error)\n \n return np.array(cv_errors)\n\ndef forecast_errors_cv_scaled(cv_preds, cv_test, y_train):\n cv_test = np.array(cv_test)\n cv_preds = np.array(cv_preds)\n n_horizons = len(cv_test) \n \n horizon_errors = []\n for h in range(n_horizons):\n split_errors = split_cv_error_scaled(cv_preds[h], cv_test[h], y_train)\n horizon_errors.append(split_errors)\n \n return np.array(horizon_errors)", "_____no_output_____" ] ], [ [ "### Get model and conduct tscv.", "_____no_output_____" ] ], [ [ "def get_model():\n '''\n Create ensemble model\n '''\n model_1 = ExponentialSmoothingWrapper(trend=True, damped_trend=True, \n seasonal=7)\n estimators = {'hw': model_1}\n return Ensemble(estimators, UnweightedVote())\n ", "_____no_output_____" ], [ "horizons = [7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 365]\nmodel = get_model()\n\nresults = time_series_cv(model, train[REGION], val[REGION], horizons, \n alpha=0.2, step=7)", "split => 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, done.\n\n" ], [ "cv_preds, cv_test, cv_intervals = results\n#CV point predictions smape\ncv_errors = forecast_errors_cv(cv_preds, cv_test, \n symmetric_mean_absolute_percentage_error)\ndf = pd.DataFrame(cv_errors)\ndf.columns = horizons\ndf.describe()", "_____no_output_____" ], [ "#output sMAPE results to file\nmetric = 'smape'\nprint(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')\ndf.to_csv(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')", "../../../results/model_selection/stage1/Trust-hw_smape.csv\n" ], [ "#CV point predictions rmse\ncv_errors = forecast_errors_cv(cv_preds, cv_test, root_mean_squared_error)\ndf = pd.DataFrame(cv_errors)\ndf.columns = horizons\ndf.describe()", "_____no_output_____" ], [ "#output rmse\nmetric = 'rmse'\nprint(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')\ndf.to_csv(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')", "../../../results/model_selection/stage1/Trust-hw_rmse.csv\n" ], [ "#mase\ncv_errors = forecast_errors_cv_scaled(cv_preds, cv_test, train[REGION])\ndf = pd.DataFrame(cv_errors)\ndf.columns = horizons\ndf.describe()", "_____no_output_____" ], [ "#output rmse\nmetric = 'mase'\nprint(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')\ndf.to_csv(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')", "../../../results/model_selection/stage1/Trust-hw_mase.csv\n" ], [ "#80% PIs\ncv_coverage = prediction_int_coverage_cv(cv_test, cv_intervals)\ndf = pd.DataFrame(cv_coverage)\ndf.columns = horizons\ndf.describe()", "_____no_output_____" ], [ "#output 80% PI coverage\nmetric = 'coverage_80'\nprint(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')\ndf.to_csv(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')", "../../../results/model_selection/stage1/Trust-hw_coverage_80.csv\n" ] ], [ [ "### Rerun for 95% PI coverage", "_____no_output_____" ] ], [ [ "horizons = [7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 365]\nmodel = get_model()\n\nresults = time_series_cv(model, train[REGION], val[REGION], horizons, \n alpha=0.05, step=7)", "split => 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, done.\n\n" ], [ "#95% PIs\ncv_preds, cv_test, cv_intervals = results\ncv_coverage = prediction_int_coverage_cv(cv_test, cv_intervals)\ndf = pd.DataFrame(cv_coverage)\ndf.columns = horizons\ndf.describe()", "_____no_output_____" ], [ "#output 95% PI coverage\nmetric = 'coverage_95'\nprint(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')\ndf.to_csv(f'{TOP_LEVEL}/{STAGE}/{REGION}-{METHOD}_{metric}.csv')", "../../../results/model_selection/stage1/Trust-hw_coverage_95.csv\n" ] ], [ [ "# End", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session", "/kaggle/input/human-activity-recognition-with-smartphones/train.csv\n/kaggle/input/human-activity-recognition-with-smartphones/test.csv\n" ] ], [ [ "**Preparing data**", "_____no_output_____" ] ], [ [ "train = pd.read_csv('../input/human-activity-recognition-with-smartphones/train.csv')\ntrain.head()", "_____no_output_____" ], [ "train.shape", "_____no_output_____" ], [ "train.isnull().values.any()", "_____no_output_____" ], [ "test = pd.read_csv('../input/human-activity-recognition-with-smartphones/test.csv')\ntest.head()", "_____no_output_____" ], [ "print(test.shape)\ntest.isnull().values.any()", "(2947, 563)\n" ], [ "X_train = train.iloc[:,:-2]\nY_train = train.iloc[:,-1]\n\nprint(X_train.shape)\nprint(Y_train.shape)", "(7352, 561)\n(7352,)\n" ], [ "X_test = test.iloc[:,:-2]\nY_test = test.iloc[:,-1]\n\nprint(X_test.shape)\nprint(Y_test.shape)", "(2947, 561)\n(2947,)\n" ], [ "Category_counts = np.array(Y_train.value_counts())\nCategory_counts", "_____no_output_____" ] ], [ [ "**There are five different activities i.e 'Standing','Sitting','Laying','Walking','Walking_downstairs','Walking_upstairs'.**", "_____no_output_____" ], [ "**Plotting a count plot of each activity in the training data.**", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport seaborn as sns\n\nplt.figure(figsize=(10,8))\nsns.countplot(train.Activity)\nplt.xticks(rotation=45)\n", "_____no_output_____" ] ], [ [ "**Creating a scatter plot using t-SNE**", "_____no_output_____" ], [ "Using t-SNE data can be visualized from a extremely high dimensional space to a low dimensional space and still it retains lots of actual information. Given training data has 562 unqiue features, using t-SNE let's visualize it to a 2D space.", "_____no_output_____" ] ], [ [ "from sklearn.manifold import TSNE\ntsne = TSNE(random_state = 42, n_components=2, verbose=1, perplexity=50, n_iter=1000).fit_transform(X_train)", "[t-SNE] Computing 151 nearest neighbors...\n[t-SNE] Indexed 7352 samples in 0.518s...\n[t-SNE] Computed neighbors for 7352 samples in 44.519s...\n[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n[t-SNE] Mean sigma: 1.437672\n[t-SNE] KL divergence after 250 iterations with early exaggeration: 74.125961\n[t-SNE] KL divergence after 1000 iterations: 1.282853\n" ], [ "plt.figure(figsize=(12,8))\nsns.scatterplot(x =tsne[:, 0], y = tsne[:, 1],data = train,hue = train[\"Activity\"])\n", "_____no_output_____" ], [ "train['tBodyAcc-mean()-X'].hist()", "_____no_output_____" ], [ "train['tBodyAcc-mean()-Y'].hist()", "_____no_output_____" ], [ "train['tBodyAcc-mean()-Z'].hist()", "_____no_output_____" ], [ "#Y_train = Y_train.reshape((-1,1))\n#Y_test = Y_test.reshape((-1,1))\n\n#print(Y_train.shape)\n#print(Y_test.shape)", "_____no_output_____" ] ], [ [ "**Scaling the data**", "_____no_output_____" ], [ " **Creating labels for different classes**", "_____no_output_____" ] ], [ [ "from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\n\nY_train = le.fit_transform(Y_train)\nY_test = le.transform(Y_test)\n\nle.classes_", "_____no_output_____" ] ], [ [ "**It is necessary to create a one-hot vector for classes to fit the data in the model.**", "_____no_output_____" ] ], [ [ "Y_train = pd.get_dummies(Y_train).values\nY_test = pd.get_dummies(Y_test).values", "_____no_output_____" ], [ "Y_train", "_____no_output_____" ], [ "Y_train.shape\n\n", "_____no_output_____" ] ], [ [ "**Creating our model**", "_____no_output_____" ] ], [ [ "from tensorflow.keras import models\nfrom tensorflow.keras.layers import Dense,Dropout\n\nmodel = models.Sequential()\n\nmodel.add(Dense(64,activation='relu',input_dim=X_train.shape[1]))\nmodel.add(Dropout(0.25))\nmodel.add(Dense(128,activation='relu'))\nmodel.add(Dense(64,activation='relu'))\nmodel.add(Dense(32,activation='relu'))\nmodel.add(Dropout(0.25))\nmodel.add(Dense(10,activation='relu'))\nmodel.add(Dense(6,activation='softmax'))\n\nmodel.summary()\n", "Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ndense (Dense) (None, 64) 35968 \n_________________________________________________________________\ndropout (Dropout) (None, 64) 0 \n_________________________________________________________________\ndense_1 (Dense) (None, 128) 8320 \n_________________________________________________________________\ndense_2 (Dense) (None, 64) 8256 \n_________________________________________________________________\ndense_3 (Dense) (None, 32) 2080 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 32) 0 \n_________________________________________________________________\ndense_4 (Dense) (None, 10) 330 \n_________________________________________________________________\ndense_5 (Dense) (None, 6) 66 \n=================================================================\nTotal params: 55,020\nTrainable params: 55,020\nNon-trainable params: 0\n_________________________________________________________________\n" ] ], [ [ "**Compiling and training the model.**", "_____no_output_____" ] ], [ [ "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])", "_____no_output_____" ], [ "hist = model.fit(X_train,Y_train,epochs=30,batch_size = 128,validation_split=0.3)", "Epoch 1/30\n41/41 [==============================] - 0s 9ms/step - loss: 1.3677 - accuracy: 0.4131 - val_loss: 0.8933 - val_accuracy: 0.6668\nEpoch 2/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.7899 - accuracy: 0.6731 - val_loss: 0.5051 - val_accuracy: 0.8314\nEpoch 3/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.5186 - accuracy: 0.7928 - val_loss: 0.3797 - val_accuracy: 0.8613\nEpoch 4/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.3697 - accuracy: 0.8581 - val_loss: 0.3890 - val_accuracy: 0.8708\nEpoch 5/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.2890 - accuracy: 0.8877 - val_loss: 0.2865 - val_accuracy: 0.9130\nEpoch 6/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.2318 - accuracy: 0.9162 - val_loss: 0.2936 - val_accuracy: 0.8867\nEpoch 7/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.2054 - accuracy: 0.9217 - val_loss: 0.2423 - val_accuracy: 0.9229\nEpoch 8/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1832 - accuracy: 0.9339 - val_loss: 0.2686 - val_accuracy: 0.9180\nEpoch 9/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1609 - accuracy: 0.9403 - val_loss: 0.2350 - val_accuracy: 0.9234\nEpoch 10/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1368 - accuracy: 0.9491 - val_loss: 0.2061 - val_accuracy: 0.9302\nEpoch 11/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1432 - accuracy: 0.9493 - val_loss: 0.2020 - val_accuracy: 0.9311\nEpoch 12/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1283 - accuracy: 0.9532 - val_loss: 0.1948 - val_accuracy: 0.9284\nEpoch 13/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1163 - accuracy: 0.9571 - val_loss: 0.2416 - val_accuracy: 0.9374\nEpoch 14/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1073 - accuracy: 0.9594 - val_loss: 0.2372 - val_accuracy: 0.9329\nEpoch 15/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1043 - accuracy: 0.9637 - val_loss: 0.1879 - val_accuracy: 0.9306\nEpoch 16/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1036 - accuracy: 0.9619 - val_loss: 0.3256 - val_accuracy: 0.9093\nEpoch 17/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1034 - accuracy: 0.9633 - val_loss: 0.2749 - val_accuracy: 0.9248\nEpoch 18/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0881 - accuracy: 0.9689 - val_loss: 0.2850 - val_accuracy: 0.9352\nEpoch 19/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0807 - accuracy: 0.9695 - val_loss: 0.2876 - val_accuracy: 0.9266\nEpoch 20/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.1229 - accuracy: 0.9536 - val_loss: 0.2350 - val_accuracy: 0.9361\nEpoch 21/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0986 - accuracy: 0.9660 - val_loss: 0.2541 - val_accuracy: 0.9306\nEpoch 22/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0909 - accuracy: 0.9666 - val_loss: 0.2134 - val_accuracy: 0.9343\nEpoch 23/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0816 - accuracy: 0.9699 - val_loss: 0.2951 - val_accuracy: 0.9275\nEpoch 24/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0827 - accuracy: 0.9709 - val_loss: 0.1860 - val_accuracy: 0.9406\nEpoch 25/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0900 - accuracy: 0.9648 - val_loss: 0.2276 - val_accuracy: 0.9388\nEpoch 26/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0711 - accuracy: 0.9738 - val_loss: 0.2106 - val_accuracy: 0.9393\nEpoch 27/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0739 - accuracy: 0.9749 - val_loss: 0.2445 - val_accuracy: 0.9329\nEpoch 28/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0848 - accuracy: 0.9675 - val_loss: 0.2974 - val_accuracy: 0.9288\nEpoch 29/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0686 - accuracy: 0.9755 - val_loss: 0.1919 - val_accuracy: 0.9415\nEpoch 30/30\n41/41 [==============================] - 0s 4ms/step - loss: 0.0589 - accuracy: 0.9788 - val_loss: 0.2649 - val_accuracy: 0.9356\n" ] ], [ [ "**Visualising loss and accuracy curve of the model.**", "_____no_output_____" ] ], [ [ "plt.plot(hist.history['loss'],label='train_loss')\nplt.plot(hist.history['val_loss'],label='val_loss')\nplt.xlabel('Epochs',fontsize=18)\nplt.ylabel('Loss',fontsize=18)\nplt.legend()\nplt.title('Loss Curve',fontsize=22)\nplt.show()", "_____no_output_____" ], [ "plt.plot(hist.history['accuracy'],label='train_accuracy')\nplt.plot(hist.history['val_accuracy'],label='val_accuracy')\nplt.xlabel('Epochs',fontsize=18)\nplt.ylabel('Accuracy',fontsize=18)\nplt.legend()\nplt.title('Accuracy Curve',fontsize=22)\nplt.show()", "_____no_output_____" ], [ "model.save('my_model.h5')", "_____no_output_____" ] ], [ [ "**Making predictions on test data**", "_____no_output_____" ] ], [ [ "predict = model.predict(X_test)\n\npredictions = np.argmax(predict,axis=1)\n\n", "_____no_output_____" ], [ "predictions", "_____no_output_____" ], [ "Y_test = np.argmax(Y_test,axis=1)", "_____no_output_____" ] ], [ [ "**Calculating accuracy**", "_____no_output_____" ] ], [ [ "from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix\nfrom mlxtend.plotting import plot_confusion_matrix\n\nconf_matrix = confusion_matrix(Y_test,predictions)\nplot_confusion_matrix(conf_matrix)\n\nprecision = precision_score(Y_test,predictions,average='weighted')\nrecall = recall_score(Y_test, predictions,average='weighted')\naccuracy = accuracy_score(Y_test,predictions)\n\nprint(\"Accuracy = \"+str(accuracy))\nprint(\"Precision = \"+str(precision))\nprint(\"Recall = \"+str(recall))", "Accuracy = 0.9216152019002375\nPrecision = 0.9282570445597496\nRecall = 0.9216152019002375\n" ] ], [ [ "**The model was able to produce 93% accurate results.**", "_____no_output_____" ] ] ]

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[ [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "**Chapter 16 – Natural Language Processing with RNNs and Attention**", "_____no_output_____" ], [ "_This notebook contains all the sample code in chapter 16._", "_____no_output_____" ], [ "<table align=\"left\">\n <td>\n <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n </td>\n</table>", "_____no_output_____" ], [ "# Setup", "_____no_output_____" ], [ "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0.", "_____no_output_____" ] ], [ [ "# Python ≥3.5 is required\nimport sys\nassert sys.version_info >= (3, 5)\n\n# Scikit-Learn ≥0.20 is required\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\ntry:\n # %tensorflow_version only exists in Colab.\n %tensorflow_version 2.x\n !pip install -q -U tensorflow-addons\n IS_COLAB = True\nexcept Exception:\n IS_COLAB = False\n\n# TensorFlow ≥2.0 is required\nimport tensorflow as tf\nfrom tensorflow import keras\nassert tf.__version__ >= \"2.0\"\n\nif not tf.config.list_physical_devices('GPU'):\n print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n if IS_COLAB:\n print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n\n# Common imports\nimport numpy as np\nimport os\n\n# to make this notebook's output stable across runs\nnp.random.seed(42)\ntf.random.set_seed(42)\n\n# To plot pretty figures\n%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\n# Where to save the figures\nPROJECT_ROOT_DIR = \".\"\nCHAPTER_ID = \"nlp\"\nIMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\nos.makedirs(IMAGES_PATH, exist_ok=True)\n\ndef save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n print(\"Saving figure\", fig_id)\n if tight_layout:\n plt.tight_layout()\n plt.savefig(path, format=fig_extension, dpi=resolution)", "No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\n" ] ], [ [ "# Char-RNN", "_____no_output_____" ], [ "## Splitting a sequence into batches of shuffled windows", "_____no_output_____" ], [ "For example, let's split the sequence 0 to 14 into windows of length 5, each shifted by 2 (e.g.,`[0, 1, 2, 3, 4]`, `[2, 3, 4, 5, 6]`, etc.), then shuffle them, and split them into inputs (the first 4 steps) and targets (the last 4 steps) (e.g., `[2, 3, 4, 5, 6]` would be split into `[[2, 3, 4, 5], [3, 4, 5, 6]]`), then create batches of 3 such input/target pairs:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\ntf.random.set_seed(42)\n\nn_steps = 5\ndataset = tf.data.Dataset.from_tensor_slices(tf.range(15))\ndataset = dataset.window(n_steps, shift=2, drop_remainder=True)\ndataset = dataset.flat_map(lambda window: window.batch(n_steps))\ndataset = dataset.shuffle(10).map(lambda window: (window[:-1], window[1:]))\ndataset = dataset.batch(3).prefetch(1)\nfor index, (X_batch, Y_batch) in enumerate(dataset):\n print(\"_\" * 20, \"Batch\", index, \"\\nX_batch\")\n print(X_batch.numpy())\n print(\"=\" * 5, \"\\nY_batch\")\n print(Y_batch.numpy())", "____________________ Batch 0 \nX_batch\n[[6 7 8 9]\n [2 3 4 5]\n [4 5 6 7]]\n===== \nY_batch\n[[ 7 8 9 10]\n [ 3 4 5 6]\n [ 5 6 7 8]]\n____________________ Batch 1 \nX_batch\n[[ 0 1 2 3]\n [ 8 9 10 11]\n [10 11 12 13]]\n===== \nY_batch\n[[ 1 2 3 4]\n [ 9 10 11 12]\n [11 12 13 14]]\n" ] ], [ [ "## Loading the Data and Preparing the Dataset", "_____no_output_____" ] ], [ [ "shakespeare_url = \"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\"\nfilepath = keras.utils.get_file(\"shakespeare.txt\", shakespeare_url)\nwith open(filepath) as f:\n shakespeare_text = f.read()", "Downloading data from https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n1122304/1115394 [==============================] - 0s 0us/step\n" ], [ "print(shakespeare_text[:148])", "First Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citizen:\nYou are all resolved rather to die than to famish?\n\n" ], [ "\"\".join(sorted(set(shakespeare_text.lower())))", "_____no_output_____" ], [ "tokenizer = keras.preprocessing.text.Tokenizer(char_level=True)\ntokenizer.fit_on_texts(shakespeare_text)", "_____no_output_____" ], [ "tokenizer.texts_to_sequences([\"First\"])", "_____no_output_____" ], [ "tokenizer.sequences_to_texts([[20, 6, 9, 8, 3]])", "_____no_output_____" ], [ "max_id = len(tokenizer.word_index) # number of distinct characters\ndataset_size = tokenizer.document_count # total number of characters", "_____no_output_____" ], [ "[encoded] = np.array(tokenizer.texts_to_sequences([shakespeare_text])) - 1\ntrain_size = dataset_size * 90 // 100\ndataset = tf.data.Dataset.from_tensor_slices(encoded[:train_size])", "_____no_output_____" ], [ "n_steps = 100\nwindow_length = n_steps + 1 # target = input shifted 1 character ahead\ndataset = dataset.repeat().window(window_length, shift=1, drop_remainder=True)", "_____no_output_____" ], [ "dataset = dataset.flat_map(lambda window: window.batch(window_length))", "_____no_output_____" ], [ "np.random.seed(42)\ntf.random.set_seed(42)", "_____no_output_____" ], [ "batch_size = 32\ndataset = dataset.shuffle(10000).batch(batch_size)\ndataset = dataset.map(lambda windows: (windows[:, :-1], windows[:, 1:]))", "_____no_output_____" ], [ "dataset = dataset.map(\n lambda X_batch, Y_batch: (tf.one_hot(X_batch, depth=max_id), Y_batch))", "_____no_output_____" ], [ "dataset = dataset.prefetch(1)", "_____no_output_____" ], [ "for X_batch, Y_batch in dataset.take(1):\n print(X_batch.shape, Y_batch.shape)", "(32, 100, 39) (32, 100)\n" ] ], [ [ "## Creating and Training the Model", "_____no_output_____" ] ], [ [ "model = keras.models.Sequential([\n keras.layers.GRU(128, return_sequences=True, input_shape=[None, max_id],\n dropout=0.2, recurrent_dropout=0.2),\n keras.layers.GRU(128, return_sequences=True,\n dropout=0.2, recurrent_dropout=0.2),\n keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n activation=\"softmax\"))\n])\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\nhistory = model.fit(dataset, steps_per_epoch=train_size // batch_size,\n epochs=10)", "Train for 31370 steps\nEpoch 1/10\n31370/31370 [==============================] - 7150s 228ms/step - loss: 1.4671\nEpoch 2/10\n31370/31370 [==============================] - 7094s 226ms/step - loss: 1.3614\nEpoch 3/10\n31370/31370 [==============================] - 7063s 225ms/step - loss: 1.3404\nEpoch 4/10\n31370/31370 [==============================] - 7039s 224ms/step - loss: 1.3311\nEpoch 5/10\n31370/31370 [==============================] - 7056s 225ms/step - loss: 1.3256\nEpoch 6/10\n31370/31370 [==============================] - 7049s 225ms/step - loss: 1.3209\nEpoch 7/10\n31370/31370 [==============================] - 7068s 225ms/step - loss: 1.3166\nEpoch 8/10\n31370/31370 [==============================] - 7030s 224ms/step - loss: 1.3138\nEpoch 9/10\n31370/31370 [==============================] - 7061s 225ms/step - loss: 1.3120\nEpoch 10/10\n31370/31370 [==============================] - 7177s 229ms/step - loss: 1.3105\n" ] ], [ [ "## Using the Model to Generate Text", "_____no_output_____" ] ], [ [ "def preprocess(texts):\n X = np.array(tokenizer.texts_to_sequences(texts)) - 1\n return tf.one_hot(X, max_id)", "_____no_output_____" ], [ "X_new = preprocess([\"How are yo\"])\nY_pred = model.predict_classes(X_new)\ntokenizer.sequences_to_texts(Y_pred + 1)[0][-1] # 1st sentence, last char", "_____no_output_____" ], [ "tf.random.set_seed(42)\n\ntf.random.categorical([[np.log(0.5), np.log(0.4), np.log(0.1)]], num_samples=40).numpy()", "_____no_output_____" ], [ "def next_char(text, temperature=1):\n X_new = preprocess([text])\n y_proba = model.predict(X_new)[0, -1:, :]\n rescaled_logits = tf.math.log(y_proba) / temperature\n char_id = tf.random.categorical(rescaled_logits, num_samples=1) + 1\n return tokenizer.sequences_to_texts(char_id.numpy())[0]", "_____no_output_____" ], [ "tf.random.set_seed(42)\n\nnext_char(\"How are yo\", temperature=1)", "_____no_output_____" ], [ "def complete_text(text, n_chars=50, temperature=1):\n for _ in range(n_chars):\n text += next_char(text, temperature)\n return text", "_____no_output_____" ], [ "tf.random.set_seed(42)\n\nprint(complete_text(\"t\", temperature=0.2))", "WARNING:tensorflow:5 out of the last 6 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:6 out of the last 7 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:7 out of the last 8 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:8 out of the last 9 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:9 out of the last 10 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 12 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44616830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nthe belly the charges of the other words\nand belly \n" ], [ "print(complete_text(\"t\", temperature=1))", "thing! they know't.\n\nbiondello:\nfor you are the own\n" ], [ "print(complete_text(\"t\", temperature=2))", "th no cyty\nuse ffor was firive this toighingaber; b\n" ] ], [ [ "## Stateful RNN", "_____no_output_____" ] ], [ [ "tf.random.set_seed(42)", "_____no_output_____" ], [ "dataset = tf.data.Dataset.from_tensor_slices(encoded[:train_size])\ndataset = dataset.window(window_length, shift=n_steps, drop_remainder=True)\ndataset = dataset.flat_map(lambda window: window.batch(window_length))\ndataset = dataset.repeat().batch(1)\ndataset = dataset.map(lambda windows: (windows[:, :-1], windows[:, 1:]))\ndataset = dataset.map(\n lambda X_batch, Y_batch: (tf.one_hot(X_batch, depth=max_id), Y_batch))\ndataset = dataset.prefetch(1)", "_____no_output_____" ], [ "batch_size = 32\nencoded_parts = np.array_split(encoded[:train_size], batch_size)\ndatasets = []\nfor encoded_part in encoded_parts:\n dataset = tf.data.Dataset.from_tensor_slices(encoded_part)\n dataset = dataset.window(window_length, shift=n_steps, drop_remainder=True)\n dataset = dataset.flat_map(lambda window: window.batch(window_length))\n datasets.append(dataset)\ndataset = tf.data.Dataset.zip(tuple(datasets)).map(lambda *windows: tf.stack(windows))\ndataset = dataset.repeat().map(lambda windows: (windows[:, :-1], windows[:, 1:]))\ndataset = dataset.map(\n lambda X_batch, Y_batch: (tf.one_hot(X_batch, depth=max_id), Y_batch))\ndataset = dataset.prefetch(1)", "_____no_output_____" ], [ "model = keras.models.Sequential([\n keras.layers.GRU(128, return_sequences=True, stateful=True,\n dropout=0.2, recurrent_dropout=0.2,\n batch_input_shape=[batch_size, None, max_id]),\n keras.layers.GRU(128, return_sequences=True, stateful=True,\n dropout=0.2, recurrent_dropout=0.2),\n keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n activation=\"softmax\"))\n])", "_____no_output_____" ], [ "class ResetStatesCallback(keras.callbacks.Callback):\n def on_epoch_begin(self, epoch, logs):\n self.model.reset_states()", "_____no_output_____" ], [ "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\nsteps_per_epoch = train_size // batch_size // n_steps\nhistory = model.fit(dataset, steps_per_epoch=steps_per_epoch, epochs=50,\n callbacks=[ResetStatesCallback()])", "Train for 313 steps\nEpoch 1/50\n313/313 [==============================] - 62s 198ms/step - loss: 2.6189\nEpoch 2/50\n313/313 [==============================] - 58s 187ms/step - loss: 2.2091\nEpoch 3/50\n313/313 [==============================] - 56s 178ms/step - loss: 2.0775\nEpoch 4/50\n313/313 [==============================] - 56s 179ms/step - loss: 2.4689\nEpoch 5/50\n313/313 [==============================] - 56s 179ms/step - loss: 2.3274\nEpoch 6/50\n313/313 [==============================] - 57s 183ms/step - loss: 2.1412\nEpoch 7/50\n313/313 [==============================] - 57s 183ms/step - loss: 2.0748\nEpoch 8/50\n313/313 [==============================] - 56s 179ms/step - loss: 1.9850\nEpoch 9/50\n313/313 [==============================] - 56s 179ms/step - loss: 1.9465\nEpoch 10/50\n313/313 [==============================] - 56s 179ms/step - loss: 1.8995\nEpoch 11/50\n313/313 [==============================] - 57s 182ms/step - loss: 1.8576\nEpoch 12/50\n313/313 [==============================] - 56s 179ms/step - loss: 1.8510\nEpoch 13/50\n313/313 [==============================] - 57s 184ms/step - loss: 1.8038\nEpoch 14/50\n313/313 [==============================] - 56s 178ms/step - loss: 1.7867\nEpoch 15/50\n313/313 [==============================] - 56s 180ms/step - loss: 1.7635\nEpoch 16/50\n313/313 [==============================] - 56s 179ms/step - loss: 1.7270\nEpoch 17/50\n313/313 [==============================] - 58s 184ms/step - loss: 1.7097\n<<31 more lines>>\n313/313 [==============================] - 58s 185ms/step - loss: 1.5998\nEpoch 34/50\n313/313 [==============================] - 58s 184ms/step - loss: 1.5954\nEpoch 35/50\n313/313 [==============================] - 58s 185ms/step - loss: 1.5944\nEpoch 36/50\n313/313 [==============================] - 57s 183ms/step - loss: 1.5902\nEpoch 37/50\n313/313 [==============================] - 57s 183ms/step - loss: 1.5893\nEpoch 38/50\n313/313 [==============================] - 59s 187ms/step - loss: 1.5845\nEpoch 39/50\n313/313 [==============================] - 57s 183ms/step - loss: 1.5821\nEpoch 40/50\n313/313 [==============================] - 59s 187ms/step - loss: 1.5798\nEpoch 41/50\n313/313 [==============================] - 57s 181ms/step - loss: 1.5794\nEpoch 42/50\n313/313 [==============================] - 57s 182ms/step - loss: 1.5774\nEpoch 43/50\n313/313 [==============================] - 57s 182ms/step - loss: 1.5755\nEpoch 44/50\n313/313 [==============================] - 58s 186ms/step - loss: 1.5735\nEpoch 45/50\n313/313 [==============================] - 58s 186ms/step - loss: 1.5714\nEpoch 46/50\n313/313 [==============================] - 57s 181ms/step - loss: 1.5686\nEpoch 47/50\n313/313 [==============================] - 57s 181ms/step - loss: 1.5675\nEpoch 48/50\n313/313 [==============================] - 56s 180ms/step - loss: 1.5657\nEpoch 49/50\n313/313 [==============================] - 58s 185ms/step - loss: 1.5654\nEpoch 50/50\n313/313 [==============================] - 57s 182ms/step - loss: 1.5620\n" ] ], [ [ "To use the model with different batch sizes, we need to create a stateless copy. We can get rid of dropout since it is only used during training:", "_____no_output_____" ] ], [ [ "stateless_model = keras.models.Sequential([\n keras.layers.GRU(128, return_sequences=True, input_shape=[None, max_id]),\n keras.layers.GRU(128, return_sequences=True),\n keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n activation=\"softmax\"))\n])", "_____no_output_____" ] ], [ [ "To set the weights, we first need to build the model (so the weights get created):", "_____no_output_____" ] ], [ [ "stateless_model.build(tf.TensorShape([None, None, max_id]))", "_____no_output_____" ], [ "stateless_model.set_weights(model.get_weights())\nmodel = stateless_model", "_____no_output_____" ], [ "tf.random.set_seed(42)\n\nprint(complete_text(\"t\"))", "WARNING:tensorflow:5 out of the last 5 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:6 out of the last 6 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:7 out of the last 7 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:8 out of the last 8 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:9 out of the last 9 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:10 out of the last 10 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\nWARNING:tensorflow:11 out of the last 11 calls to <function _make_execution_function.<locals>.distributed_function at 0x7f8d44bc53b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\ntor:\nin the negver up how it thou like him;\nwhen it\n" ] ], [ [ "# Sentiment Analysis", "_____no_output_____" ] ], [ [ "tf.random.set_seed(42)", "_____no_output_____" ] ], [ [ "You can load the IMDB dataset easily:", "_____no_output_____" ] ], [ [ "(X_train, y_test), (X_valid, y_test) = keras.datasets.imdb.load_data()", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n17465344/17464789 [==============================] - 0s 0us/step\n" ], [ "X_train[0][:10]", "_____no_output_____" ], [ "word_index = keras.datasets.imdb.get_word_index()\nid_to_word = {id_ + 3: word for word, id_ in word_index.items()}\nfor id_, token in enumerate((\"<pad>\", \"<sos>\", \"<unk>\")):\n id_to_word[id_] = token\n\" \".join([id_to_word[id_] for id_ in X_train[0][:10]])", "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n1646592/1641221 [==============================] - 0s 0us/step\n" ], [ "import tensorflow_datasets as tfds\n\ndatasets, info = tfds.load(\"imdb_reviews\", as_supervised=True, with_info=True)", "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /home/aurelien_geron_kiwisoft_io/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n" ], [ "datasets.keys()", "_____no_output_____" ], [ "train_size = info.splits[\"train\"].num_examples\ntest_size = info.splits[\"test\"].num_examples", "_____no_output_____" ], [ "train_size, test_size", "_____no_output_____" ], [ "for X_batch, y_batch in datasets[\"train\"].batch(2).take(1):\n for review, label in zip(X_batch.numpy(), y_batch.numpy()):\n print(\"Review:\", review.decode(\"utf-8\")[:200], \"...\")\n print(\"Label:\", label, \"= Positive\" if label else \"= Negative\")\n print()", "Review: This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting ...\nLabel: 0 = Negative\n\nReview: I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However ...\nLabel: 0 = Negative\n\n" ], [ "def preprocess(X_batch, y_batch):\n X_batch = tf.strings.substr(X_batch, 0, 300)\n X_batch = tf.strings.regex_replace(X_batch, rb\"<br\\s*/?>\", b\" \")\n X_batch = tf.strings.regex_replace(X_batch, b\"[^a-zA-Z']\", b\" \")\n X_batch = tf.strings.split(X_batch)\n return X_batch.to_tensor(default_value=b\"<pad>\"), y_batch", "_____no_output_____" ], [ "preprocess(X_batch, y_batch)", "_____no_output_____" ], [ "from collections import Counter\n\nvocabulary = Counter()\nfor X_batch, y_batch in datasets[\"train\"].batch(32).map(preprocess):\n for review in X_batch:\n vocabulary.update(list(review.numpy()))", "_____no_output_____" ], [ "vocabulary.most_common()[:3]", "_____no_output_____" ], [ "len(vocabulary)", "_____no_output_____" ], [ "vocab_size = 10000\ntruncated_vocabulary = [\n word for word, count in vocabulary.most_common()[:vocab_size]]", "_____no_output_____" ], [ "word_to_id = {word: index for index, word in enumerate(truncated_vocabulary)}\nfor word in b\"This movie was faaaaaantastic\".split():\n print(word_to_id.get(word) or vocab_size)", "22\n12\n11\n10000\n" ], [ "words = tf.constant(truncated_vocabulary)\nword_ids = tf.range(len(truncated_vocabulary), dtype=tf.int64)\nvocab_init = tf.lookup.KeyValueTensorInitializer(words, word_ids)\nnum_oov_buckets = 1000\ntable = tf.lookup.StaticVocabularyTable(vocab_init, num_oov_buckets)", "_____no_output_____" ], [ "table.lookup(tf.constant([b\"This movie was faaaaaantastic\".split()]))", "_____no_output_____" ], [ "def encode_words(X_batch, y_batch):\n return table.lookup(X_batch), y_batch\n\ntrain_set = datasets[\"train\"].repeat().batch(32).map(preprocess)\ntrain_set = train_set.map(encode_words).prefetch(1)", "_____no_output_____" ], [ "for X_batch, y_batch in train_set.take(1):\n print(X_batch)\n print(y_batch)", "tf.Tensor(\n[[ 22 11 28 ... 0 0 0]\n [ 6 21 70 ... 0 0 0]\n [4099 6881 1 ... 0 0 0]\n ...\n [ 22 12 118 ... 331 1047 0]\n [1757 4101 451 ... 0 0 0]\n [3365 4392 6 ... 0 0 0]], shape=(32, 60), dtype=int64)\ntf.Tensor([0 0 0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 0 0 1 0 0 0], shape=(32,), dtype=int64)\n" ], [ "embed_size = 128\nmodel = keras.models.Sequential([\n keras.layers.Embedding(vocab_size + num_oov_buckets, embed_size,\n mask_zero=True, # not shown in the book\n input_shape=[None]),\n keras.layers.GRU(128, return_sequences=True),\n keras.layers.GRU(128),\n keras.layers.Dense(1, activation=\"sigmoid\")\n])\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nhistory = model.fit(train_set, steps_per_epoch=train_size // 32, epochs=5)", "Train for 781 steps\nEpoch 1/5\n781/781 [==============================] - 118s 152ms/step - loss: 0.5305 - accuracy: 0.7282\nEpoch 2/5\n781/781 [==============================] - 113s 145ms/step - loss: 0.3459 - accuracy: 0.8554\nEpoch 3/5\n781/781 [==============================] - 113s 145ms/step - loss: 0.1913 - accuracy: 0.9319\nEpoch 4/5\n781/781 [==============================] - 114s 146ms/step - loss: 0.1341 - accuracy: 0.9535\nEpoch 5/5\n781/781 [==============================] - 116s 148ms/step - loss: 0.1011 - accuracy: 0.9624\n" ] ], [ [ "Or using manual masking:", "_____no_output_____" ] ], [ [ "K = keras.backend\nembed_size = 128\ninputs = keras.layers.Input(shape=[None])\nmask = keras.layers.Lambda(lambda inputs: K.not_equal(inputs, 0))(inputs)\nz = keras.layers.Embedding(vocab_size + num_oov_buckets, embed_size)(inputs)\nz = keras.layers.GRU(128, return_sequences=True)(z, mask=mask)\nz = keras.layers.GRU(128)(z, mask=mask)\noutputs = keras.layers.Dense(1, activation=\"sigmoid\")(z)\nmodel = keras.models.Model(inputs=[inputs], outputs=[outputs])\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nhistory = model.fit(train_set, steps_per_epoch=train_size // 32, epochs=5)", "Train for 781 steps\nEpoch 1/5\n781/781 [==============================] - 118s 152ms/step - loss: 0.5425 - accuracy: 0.7155\nEpoch 2/5\n781/781 [==============================] - 112s 143ms/step - loss: 0.3479 - accuracy: 0.8558\nEpoch 3/5\n781/781 [==============================] - 112s 144ms/step - loss: 0.1761 - accuracy: 0.9388\nEpoch 4/5\n781/781 [==============================] - 115s 147ms/step - loss: 0.1281 - accuracy: 0.9531\nEpoch 5/5\n781/781 [==============================] - 116s 148ms/step - loss: 0.1088 - accuracy: 0.9603\n" ] ], [ [ "## Reusing Pretrained Embeddings", "_____no_output_____" ] ], [ [ "tf.random.set_seed(42)", "_____no_output_____" ], [ "TFHUB_CACHE_DIR = os.path.join(os.curdir, \"my_tfhub_cache\")\nos.environ[\"TFHUB_CACHE_DIR\"] = TFHUB_CACHE_DIR", "_____no_output_____" ], [ "import tensorflow_hub as hub\n\nmodel = keras.Sequential([\n hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\",\n dtype=tf.string, input_shape=[], output_shape=[50]),\n keras.layers.Dense(128, activation=\"relu\"),\n keras.layers.Dense(1, activation=\"sigmoid\")\n])\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\",\n metrics=[\"accuracy\"])", "_____no_output_____" ], [ "for dirpath, dirnames, filenames in os.walk(TFHUB_CACHE_DIR):\n for filename in filenames:\n print(os.path.join(dirpath, filename))", "./my_tfhub_cache/82c4aaf4250ffb09088bd48368ee7fd00e5464fe.descriptor.txt\n./my_tfhub_cache/82c4aaf4250ffb09088bd48368ee7fd00e5464fe/saved_model.pb\n./my_tfhub_cache/82c4aaf4250ffb09088bd48368ee7fd00e5464fe/variables/variables.data-00000-of-00001\n./my_tfhub_cache/82c4aaf4250ffb09088bd48368ee7fd00e5464fe/variables/variables.index\n./my_tfhub_cache/82c4aaf4250ffb09088bd48368ee7fd00e5464fe/assets/tokens.txt\n" ], [ "import tensorflow_datasets as tfds\n\ndatasets, info = tfds.load(\"imdb_reviews\", as_supervised=True, with_info=True)\ntrain_size = info.splits[\"train\"].num_examples\nbatch_size = 32\ntrain_set = datasets[\"train\"].repeat().batch(batch_size).prefetch(1)\nhistory = model.fit(train_set, steps_per_epoch=train_size // batch_size, epochs=5)", "Train for 781 steps\nEpoch 1/5\n781/781 [==============================] - 128s 164ms/step - loss: 0.5460 - accuracy: 0.7267\nEpoch 2/5\n781/781 [==============================] - 128s 164ms/step - loss: 0.5129 - accuracy: 0.7495\nEpoch 3/5\n781/781 [==============================] - 129s 165ms/step - loss: 0.5082 - accuracy: 0.7530\nEpoch 4/5\n781/781 [==============================] - 128s 164ms/step - loss: 0.5047 - accuracy: 0.7533\nEpoch 5/5\n781/781 [==============================] - 128s 164ms/step - loss: 0.5015 - accuracy: 0.7560\n" ] ], [ [ "## Automatic Translation", "_____no_output_____" ] ], [ [ "tf.random.set_seed(42)", "_____no_output_____" ], [ "vocab_size = 100\nembed_size = 10", "_____no_output_____" ], [ "import tensorflow_addons as tfa\n\nencoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\ndecoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\nsequence_lengths = keras.layers.Input(shape=[], dtype=np.int32)\n\nembeddings = keras.layers.Embedding(vocab_size, embed_size)\nencoder_embeddings = embeddings(encoder_inputs)\ndecoder_embeddings = embeddings(decoder_inputs)\n\nencoder = keras.layers.LSTM(512, return_state=True)\nencoder_outputs, state_h, state_c = encoder(encoder_embeddings)\nencoder_state = [state_h, state_c]\n\nsampler = tfa.seq2seq.sampler.TrainingSampler()\n\ndecoder_cell = keras.layers.LSTMCell(512)\noutput_layer = keras.layers.Dense(vocab_size)\ndecoder = tfa.seq2seq.basic_decoder.BasicDecoder(decoder_cell, sampler,\n output_layer=output_layer)\nfinal_outputs, final_state, final_sequence_lengths = decoder(\n decoder_embeddings, initial_state=encoder_state,\n sequence_length=sequence_lengths)\nY_proba = tf.nn.softmax(final_outputs.rnn_output)\n\nmodel = keras.models.Model(\n inputs=[encoder_inputs, decoder_inputs, sequence_lengths],\n outputs=[Y_proba])", "_____no_output_____" ], [ "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")", "_____no_output_____" ], [ "X = np.random.randint(100, size=10*1000).reshape(1000, 10)\nY = np.random.randint(100, size=15*1000).reshape(1000, 15)\nX_decoder = np.c_[np.zeros((1000, 1)), Y[:, :-1]]\nseq_lengths = np.full([1000], 15)\n\nhistory = model.fit([X, X_decoder, seq_lengths], Y, epochs=2)", "Train on 1000 samples\nEpoch 1/2\n1000/1000 [==============================] - 6s 6ms/sample - loss: 4.6053\nEpoch 2/2\n1000/1000 [==============================] - 3s 3ms/sample - loss: 4.6031\n" ] ], [ [ "### Bidirectional Recurrent Layers", "_____no_output_____" ] ], [ [ "model = keras.models.Sequential([\n keras.layers.GRU(10, return_sequences=True, input_shape=[None, 10]),\n keras.layers.Bidirectional(keras.layers.GRU(10, return_sequences=True))\n])\n\nmodel.summary()", "Model: \"sequential_5\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ngru_10 (GRU) (None, None, 10) 660 \n_________________________________________________________________\nbidirectional (Bidirectional (None, None, 20) 1320 \n=================================================================\nTotal params: 1,980\nTrainable params: 1,980\nNon-trainable params: 0\n_________________________________________________________________\n" ] ], [ [ "### Positional Encoding", "_____no_output_____" ] ], [ [ "class PositionalEncoding(keras.layers.Layer):\n def __init__(self, max_steps, max_dims, dtype=tf.float32, **kwargs):\n super().__init__(dtype=dtype, **kwargs)\n if max_dims % 2 == 1: max_dims += 1 # max_dims must be even\n p, i = np.meshgrid(np.arange(max_steps), np.arange(max_dims // 2))\n pos_emb = np.empty((1, max_steps, max_dims))\n pos_emb[0, :, ::2] = np.sin(p / 10000**(2 * i / max_dims)).T\n pos_emb[0, :, 1::2] = np.cos(p / 10000**(2 * i / max_dims)).T\n self.positional_embedding = tf.constant(pos_emb.astype(self.dtype))\n def call(self, inputs):\n shape = tf.shape(inputs)\n return inputs + self.positional_embedding[:, :shape[-2], :shape[-1]]", "_____no_output_____" ], [ "max_steps = 201\nmax_dims = 512\npos_emb = PositionalEncoding(max_steps, max_dims)\nPE = pos_emb(np.zeros((1, max_steps, max_dims), np.float32))[0].numpy()", "_____no_output_____" ], [ "i1, i2, crop_i = 100, 101, 150\np1, p2, p3 = 22, 60, 35\nfig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(9, 5))\nax1.plot([p1, p1], [-1, 1], \"k--\", label=\"$p = {}$\".format(p1))\nax1.plot([p2, p2], [-1, 1], \"k--\", label=\"$p = {}$\".format(p2), alpha=0.5)\nax1.plot(p3, PE[p3, i1], \"bx\", label=\"$p = {}$\".format(p3))\nax1.plot(PE[:,i1], \"b-\", label=\"$i = {}$\".format(i1))\nax1.plot(PE[:,i2], \"r-\", label=\"$i = {}$\".format(i2))\nax1.plot([p1, p2], [PE[p1, i1], PE[p2, i1]], \"bo\")\nax1.plot([p1, p2], [PE[p1, i2], PE[p2, i2]], \"ro\")\nax1.legend(loc=\"center right\", fontsize=14, framealpha=0.95)\nax1.set_ylabel(\"$P_{(p,i)}$\", rotation=0, fontsize=16)\nax1.grid(True, alpha=0.3)\nax1.hlines(0, 0, max_steps - 1, color=\"k\", linewidth=1, alpha=0.3)\nax1.axis([0, max_steps - 1, -1, 1])\nax2.imshow(PE.T[:crop_i], cmap=\"gray\", interpolation=\"bilinear\", aspect=\"auto\")\nax2.hlines(i1, 0, max_steps - 1, color=\"b\")\ncheat = 2 # need to raise the red line a bit, or else it hides the blue one\nax2.hlines(i2+cheat, 0, max_steps - 1, color=\"r\")\nax2.plot([p1, p1], [0, crop_i], \"k--\")\nax2.plot([p2, p2], [0, crop_i], \"k--\", alpha=0.5)\nax2.plot([p1, p2], [i2+cheat, i2+cheat], \"ro\")\nax2.plot([p1, p2], [i1, i1], \"bo\")\nax2.axis([0, max_steps - 1, 0, crop_i])\nax2.set_xlabel(\"$p$\", fontsize=16)\nax2.set_ylabel(\"$i$\", rotation=0, fontsize=16)\nplt.savefig(\"positional_embedding_plot\")\nplt.show()", "_____no_output_____" ], [ "embed_size = 512; max_steps = 500; vocab_size = 10000\nencoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\ndecoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\nembeddings = keras.layers.Embedding(vocab_size, embed_size)\nencoder_embeddings = embeddings(encoder_inputs)\ndecoder_embeddings = embeddings(decoder_inputs)\npositional_encoding = PositionalEncoding(max_steps, max_dims=embed_size)\nencoder_in = positional_encoding(encoder_embeddings)\ndecoder_in = positional_encoding(decoder_embeddings)", "_____no_output_____" ] ], [ [ "Here is a (very) simplified Transformer (the actual architecture has skip connections, layer norm, dense nets, and most importantly it uses Multi-Head Attention instead of regular Attention):", "_____no_output_____" ] ], [ [ "Z = encoder_in\nfor N in range(6):\n Z = keras.layers.Attention(use_scale=True)([Z, Z])\n\nencoder_outputs = Z\nZ = decoder_in\nfor N in range(6):\n Z = keras.layers.Attention(use_scale=True, causal=True)([Z, Z])\n Z = keras.layers.Attention(use_scale=True)([Z, encoder_outputs])\n\noutputs = keras.layers.TimeDistributed(\n keras.layers.Dense(vocab_size, activation=\"softmax\"))(Z)", "_____no_output_____" ] ], [ [ "Here's a basic implementation of the `MultiHeadAttention` layer. One will likely be added to `keras.layers` in the near future. Note that `Conv1D` layers with `kernel_size=1` (and the default `padding=\"valid\"` and `strides=1`) is equivalent to a `TimeDistributed(Dense(...))` layer.", "_____no_output_____" ] ], [ [ "K = keras.backend\n\nclass MultiHeadAttention(keras.layers.Layer):\n def __init__(self, n_heads, causal=False, use_scale=False, **kwargs):\n self.n_heads = n_heads\n self.causal = causal\n self.use_scale = use_scale\n super().__init__(**kwargs)\n def build(self, batch_input_shape):\n self.dims = batch_input_shape[0][-1]\n self.q_dims, self.v_dims, self.k_dims = [self.dims // self.n_heads] * 3 # could be hyperparameters instead\n self.q_linear = keras.layers.Conv1D(self.n_heads * self.q_dims, kernel_size=1, use_bias=False)\n self.v_linear = keras.layers.Conv1D(self.n_heads * self.v_dims, kernel_size=1, use_bias=False)\n self.k_linear = keras.layers.Conv1D(self.n_heads * self.k_dims, kernel_size=1, use_bias=False)\n self.attention = keras.layers.Attention(causal=self.causal, use_scale=self.use_scale)\n self.out_linear = keras.layers.Conv1D(self.dims, kernel_size=1, use_bias=False)\n super().build(batch_input_shape)\n def _multi_head_linear(self, inputs, linear):\n shape = K.concatenate([K.shape(inputs)[:-1], [self.n_heads, -1]])\n projected = K.reshape(linear(inputs), shape)\n perm = K.permute_dimensions(projected, [0, 2, 1, 3])\n return K.reshape(perm, [shape[0] * self.n_heads, shape[1], -1])\n def call(self, inputs):\n q = inputs[0]\n v = inputs[1]\n k = inputs[2] if len(inputs) > 2 else v\n shape = K.shape(q)\n q_proj = self._multi_head_linear(q, self.q_linear)\n v_proj = self._multi_head_linear(v, self.v_linear)\n k_proj = self._multi_head_linear(k, self.k_linear)\n multi_attended = self.attention([q_proj, v_proj, k_proj])\n shape_attended = K.shape(multi_attended)\n reshaped_attended = K.reshape(multi_attended, [shape[0], self.n_heads, shape_attended[1], shape_attended[2]])\n perm = K.permute_dimensions(reshaped_attended, [0, 2, 1, 3])\n concat = K.reshape(perm, [shape[0], shape_attended[1], -1])\n return self.out_linear(concat)", "_____no_output_____" ], [ "Q = np.random.rand(2, 50, 512)\nV = np.random.rand(2, 80, 512)\nmulti_attn = MultiHeadAttention(8)\nmulti_attn([Q, V]).shape", "WARNING:tensorflow:Layer multi_head_attention is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.\n\nIf you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n\nTo change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n\n" ] ], [ [ "# Exercise solutions", "_____no_output_____" ], [ "## 1. to 7.", "_____no_output_____" ], [ "See Appendix A.", "_____no_output_____" ], [ "## 8.\n_Exercise:_ Embedded Reber grammars _were used by Hochreiter and Schmidhuber in [their paper](https://homl.info/93) about LSTMs. They are artificial grammars that produce strings such as \"BPBTSXXVPSEPE.\" Check out Jenny Orr's [nice introduction](https://homl.info/108) to this topic. Choose a particular embedded Reber grammar (such as the one represented on Jenny Orr's page), then train an RNN to identify whether a string respects that grammar or not. You will first need to write a function capable of generating a training batch containing about 50% strings that respect the grammar, and 50% that don't._", "_____no_output_____" ], [ "First we need to build a function that generates strings based on a grammar. The grammar will be represented as a list of possible transitions for each state. A transition specifies the string to output (or a grammar to generate it) and the next state.", "_____no_output_____" ] ], [ [ "default_reber_grammar = [\n [(\"B\", 1)], # (state 0) =B=>(state 1)\n [(\"T\", 2), (\"P\", 3)], # (state 1) =T=>(state 2) or =P=>(state 3)\n [(\"S\", 2), (\"X\", 4)], # (state 2) =S=>(state 2) or =X=>(state 4)\n [(\"T\", 3), (\"V\", 5)], # and so on...\n [(\"X\", 3), (\"S\", 6)],\n [(\"P\", 4), (\"V\", 6)],\n [(\"E\", None)]] # (state 6) =E=>(terminal state)\n\nembedded_reber_grammar = [\n [(\"B\", 1)],\n [(\"T\", 2), (\"P\", 3)],\n [(default_reber_grammar, 4)],\n [(default_reber_grammar, 5)],\n [(\"T\", 6)],\n [(\"P\", 6)],\n [(\"E\", None)]]\n\ndef generate_string(grammar):\n state = 0\n output = []\n while state is not None:\n index = np.random.randint(len(grammar[state]))\n production, state = grammar[state][index]\n if isinstance(production, list):\n production = generate_string(grammar=production)\n output.append(production)\n return \"\".join(output)", "_____no_output_____" ] ], [ [ "Let's generate a few strings based on the default Reber grammar:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\n\nfor _ in range(25):\n print(generate_string(default_reber_grammar), end=\" \")", "BTXXTTVPXTVPXTTVPSE BPVPSE BTXSE BPVVE BPVVE BTSXSE BPTVPXTTTVVE BPVVE BTXSE BTXXVPSE BPTTTTTTTTVVE BTXSE BPVPSE BTXSE BPTVPSE BTXXTVPSE BPVVE BPVVE BPVVE BPTTVVE BPVVE BPVVE BTXXVVE BTXXVVE BTXXVPXVVE " ] ], [ [ "Looks good. Now let's generate a few strings based on the embedded Reber grammar:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\n\nfor _ in range(25):\n print(generate_string(embedded_reber_grammar), end=\" \")", "BTBPTTTVPXTVPXTTVPSETE BPBPTVPSEPE BPBPVVEPE BPBPVPXVVEPE BPBTXXTTTTVVEPE BPBPVPSEPE BPBTXXVPSEPE BPBTSSSSSSSXSEPE BTBPVVETE BPBTXXVVEPE BPBTXXVPSEPE BTBTXXVVETE BPBPVVEPE BPBPVVEPE BPBTSXSEPE BPBPVVEPE BPBPTVPSEPE BPBTXXVVEPE BTBPTVPXVVETE BTBPVVETE BTBTSSSSSSSXXVVETE BPBTSSSXXTTTTVPSEPE BTBPTTVVETE BPBTXXTVVEPE BTBTXSETE " ] ], [ [ "Okay, now we need a function to generate strings that do not respect the grammar. We could generate a random string, but the task would be a bit too easy, so instead we will generate a string that respects the grammar, and we will corrupt it by changing just one character:", "_____no_output_____" ] ], [ [ "POSSIBLE_CHARS = \"BEPSTVX\"\n\ndef generate_corrupted_string(grammar, chars=POSSIBLE_CHARS):\n good_string = generate_string(grammar)\n index = np.random.randint(len(good_string))\n good_char = good_string[index]\n bad_char = np.random.choice(sorted(set(chars) - set(good_char)))\n return good_string[:index] + bad_char + good_string[index + 1:]", "_____no_output_____" ] ], [ [ "Let's look at a few corrupted strings:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\n\nfor _ in range(25):\n print(generate_corrupted_string(embedded_reber_grammar), end=\" \")", "BTBPTTTPPXTVPXTTVPSETE BPBTXEEPE BPBPTVVVEPE BPBTSSSSXSETE BPTTXSEPE BTBPVPXTTTTTTEVETE BPBTXXSVEPE BSBPTTVPSETE BPBXVVEPE BEBTXSETE BPBPVPSXPE BTBPVVVETE BPBTSXSETE BPBPTTTPTTTTTVPSEPE BTBTXXTTSTVPSETE BBBTXSETE BPBTPXSEPE BPBPVPXTTTTVPXTVPXVPXTTTVVEVE BTBXXXTVPSETE BEBTSSSSSXXVPXTVVETE BTBXTTVVETE BPBTXSTPE BTBTXXTTTVPSBTE BTBTXSETX BTBTSXSSTE " ] ], [ [ "We cannot feed strings directly to an RNN, so we need to encode them somehow. One option would be to one-hot encode each character. Another option is to use embeddings. Let's go for the second option (but since there are just a handful of characters, one-hot encoding would probably be a good option as well). For embeddings to work, we need to convert each string into a sequence of character IDs. Let's write a function for that, using each character's index in the string of possible characters \"BEPSTVX\":", "_____no_output_____" ] ], [ [ "def string_to_ids(s, chars=POSSIBLE_CHARS):\n return [POSSIBLE_CHARS.index(c) for c in s]", "_____no_output_____" ], [ "string_to_ids(\"BTTTXXVVETE\")", "_____no_output_____" ] ], [ [ "We can now generate the dataset, with 50% good strings, and 50% bad strings:", "_____no_output_____" ] ], [ [ "def generate_dataset(size):\n good_strings = [string_to_ids(generate_string(embedded_reber_grammar))\n for _ in range(size // 2)]\n bad_strings = [string_to_ids(generate_corrupted_string(embedded_reber_grammar))\n for _ in range(size - size // 2)]\n all_strings = good_strings + bad_strings\n X = tf.ragged.constant(all_strings, ragged_rank=1)\n y = np.array([[1.] for _ in range(len(good_strings))] +\n [[0.] for _ in range(len(bad_strings))])\n return X, y", "_____no_output_____" ], [ "np.random.seed(42)\n\nX_train, y_train = generate_dataset(10000)\nX_valid, y_valid = generate_dataset(2000)", "_____no_output_____" ] ], [ [ "Let's take a look at the first training sequence:", "_____no_output_____" ] ], [ [ "X_train[0]", "_____no_output_____" ] ], [ [ "What classes does it belong to?", "_____no_output_____" ] ], [ [ "y_train[0]", "_____no_output_____" ] ], [ [ "Perfect! We are ready to create the RNN to identify good strings. We build a simple sequence binary classifier:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\ntf.random.set_seed(42)\n\nembedding_size = 5\n\nmodel = keras.models.Sequential([\n keras.layers.InputLayer(input_shape=[None], dtype=tf.int32, ragged=True),\n keras.layers.Embedding(input_dim=len(POSSIBLE_CHARS), output_dim=embedding_size),\n keras.layers.GRU(30),\n keras.layers.Dense(1, activation=\"sigmoid\")\n])\noptimizer = keras.optimizers.SGD(lr=0.02, momentum = 0.95, nesterov=True)\nmodel.compile(loss=\"binary_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\nhistory = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))", "Train on 10000 samples, validate on 2000 samples\nEpoch 1/20\n" ] ], [ [ "Now let's test our RNN on two tricky strings: the first one is bad while the second one is good. They only differ by the second to last character. If the RNN gets this right, it shows that it managed to notice the pattern that the second letter should always be equal to the second to last letter. That requires a fairly long short-term memory (which is the reason why we used a GRU cell).", "_____no_output_____" ] ], [ [ "test_strings = [\"BPBTSSSSSSSXXTTVPXVPXTTTTTVVETE\",\n \"BPBTSSSSSSSXXTTVPXVPXTTTTTVVEPE\"]\nX_test = tf.ragged.constant([string_to_ids(s) for s in test_strings], ragged_rank=1)\n\ny_proba = model.predict(X_test)\nprint()\nprint(\"Estimated probability that these are Reber strings:\")\nfor index, string in enumerate(test_strings):\n print(\"{}: {:.2f}%\".format(string, 100 * y_proba[index][0]))", "\nEstimated probability that these are Reber strings:\nBPBTSSSSSSSXXTTVPXVPXTTTTTVVETE: 0.40%\nBPBTSSSSSSSXXTTVPXVPXTTTTTVVEPE: 99.96%\n" ] ], [ [ "Ta-da! It worked fine. The RNN found the correct answers with very high confidence. :)", "_____no_output_____" ], [ "## 9.\n_Exercise: Train an Encoder–Decoder model that can convert a date string from one format to another (e.g., from \"April 22, 2019\" to \"2019-04-22\")._", "_____no_output_____" ], [ "Let's start by creating the dataset. We will use random days between 1000-01-01 and 9999-12-31:", "_____no_output_____" ] ], [ [ "from datetime import date\n\n# cannot use strftime()'s %B format since it depends on the locale\nMONTHS = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n\ndef random_dates(n_dates):\n min_date = date(1000, 1, 1).toordinal()\n max_date = date(9999, 12, 31).toordinal()\n\n ordinals = np.random.randint(max_date - min_date, size=n_dates) + min_date\n dates = [date.fromordinal(ordinal) for ordinal in ordinals]\n\n x = [MONTHS[dt.month - 1] + \" \" + dt.strftime(\"%d, %Y\") for dt in dates]\n y = [dt.isoformat() for dt in dates]\n return x, y", "_____no_output_____" ] ], [ [ "Here are a few random dates, displayed in both the input format and the target format:", "_____no_output_____" ] ], [ [ "np.random.seed(42)\n\nn_dates = 3\nx_example, y_example = random_dates(n_dates)\nprint(\"{:25s}{:25s}\".format(\"Input\", \"Target\"))\nprint(\"-\" * 50)\nfor idx in range(n_dates):\n print(\"{:25s}{:25s}\".format(x_example[idx], y_example[idx]))", "Input Target \n--------------------------------------------------\nSeptember 20, 7075 7075-09-20 \nMay 15, 8579 8579-05-15 \nJanuary 11, 7103 7103-01-11 \n" ] ], [ [ "Let's get the list of all possible characters in the inputs:", "_____no_output_____" ] ], [ [ "INPUT_CHARS = \"\".join(sorted(set(\"\".join(MONTHS)))) + \"01234567890, \"\nINPUT_CHARS", "_____no_output_____" ] ], [ [ "And here's the list of possible characters in the outputs:", "_____no_output_____" ] ], [ [ "OUTPUT_CHARS = \"0123456789-\"", "_____no_output_____" ] ], [ [ "Let's write a function to convert a string to a list of character IDs, as we did in the previous exercise:", "_____no_output_____" ] ], [ [ "def date_str_to_ids(date_str, chars=INPUT_CHARS):\n return [chars.index(c) for c in date_str]", "_____no_output_____" ], [ "date_str_to_ids(x_example[0], INPUT_CHARS)", "_____no_output_____" ], [ "date_str_to_ids(y_example[0], OUTPUT_CHARS)", "_____no_output_____" ], [ "def prepare_date_strs(date_strs, chars=INPUT_CHARS):\n X_ids = [date_str_to_ids(dt, chars) for dt in date_strs]\n X = tf.ragged.constant(X_ids, ragged_rank=1)\n return (X + 1).to_tensor() # using 0 as the padding token ID\n\ndef create_dataset(n_dates):\n x, y = random_dates(n_dates)\n return prepare_date_strs(x, INPUT_CHARS), prepare_date_strs(y, OUTPUT_CHARS)", "_____no_output_____" ], [ "np.random.seed(42)\n\nX_train, Y_train = create_dataset(10000)\nX_valid, Y_valid = create_dataset(2000)\nX_test, Y_test = create_dataset(2000)", "_____no_output_____" ], [ "Y_train[0]", "_____no_output_____" ] ], [ [ "### First version: a very basic seq2seq model", "_____no_output_____" ], [ "Let's first try the simplest possible model: we feed in the input sequence, which first goes through the encoder (an embedding layer followed by a single LSTM layer), which outputs a vector, then it goes through a decoder (a single LSTM layer, followed by a dense output layer), which outputs a sequence of vectors, each representing the estimated probabilities for all possible output character.\n\nSince the decoder expects a sequence as input, we repeat the vector (which is output by the decoder) as many times as the longest possible output sequence.", "_____no_output_____" ] ], [ [ "embedding_size = 32\nmax_output_length = Y_train.shape[1]\n\nnp.random.seed(42)\ntf.random.set_seed(42)\n\nencoder = keras.models.Sequential([\n keras.layers.Embedding(input_dim=len(INPUT_CHARS) + 1,\n output_dim=embedding_size,\n input_shape=[None]),\n keras.layers.LSTM(128)\n])\n\ndecoder = keras.models.Sequential([\n keras.layers.LSTM(128, return_sequences=True),\n keras.layers.Dense(len(OUTPUT_CHARS) + 1, activation=\"softmax\")\n])\n\nmodel = keras.models.Sequential([\n encoder,\n keras.layers.RepeatVector(max_output_length),\n decoder\n])\n\noptimizer = keras.optimizers.Nadam()\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\nhistory = model.fit(X_train, Y_train, epochs=20,\n validation_data=(X_valid, Y_valid))", "Epoch 1/20\n313/313 [==============================] - 6s 18ms/step - loss: 1.8111 - accuracy: 0.3533 - val_loss: 1.3581 - val_accuracy: 0.4965\nEpoch 2/20\n313/313 [==============================] - 5s 15ms/step - loss: 1.3518 - accuracy: 0.5103 - val_loss: 1.1915 - val_accuracy: 0.5694\nEpoch 3/20\n313/313 [==============================] - 5s 15ms/step - loss: 1.1706 - accuracy: 0.5908 - val_loss: 0.9983 - val_accuracy: 0.6398\nEpoch 4/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.9158 - accuracy: 0.6686 - val_loss: 0.8012 - val_accuracy: 0.6987\nEpoch 5/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.7058 - accuracy: 0.7308 - val_loss: 0.6224 - val_accuracy: 0.7599\nEpoch 6/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.7756 - accuracy: 0.7203 - val_loss: 0.6541 - val_accuracy: 0.7599\nEpoch 7/20\n313/313 [==============================] - 5s 16ms/step - loss: 0.5379 - accuracy: 0.8034 - val_loss: 0.4174 - val_accuracy: 0.8440\nEpoch 8/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.4867 - accuracy: 0.8262 - val_loss: 0.4188 - val_accuracy: 0.8480\nEpoch 9/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.2979 - accuracy: 0.8951 - val_loss: 0.2549 - val_accuracy: 0.9126\nEpoch 10/20\n313/313 [==============================] - 5s 14ms/step - loss: 0.1785 - accuracy: 0.9479 - val_loss: 0.1461 - val_accuracy: 0.9594\nEpoch 11/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.1830 - accuracy: 0.9557 - val_loss: 0.1644 - val_accuracy: 0.9550\nEpoch 12/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0775 - accuracy: 0.9857 - val_loss: 0.0595 - val_accuracy: 0.9901\nEpoch 13/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0400 - accuracy: 0.9953 - val_loss: 0.0342 - val_accuracy: 0.9957\nEpoch 14/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0248 - accuracy: 0.9979 - val_loss: 0.0231 - val_accuracy: 0.9983\nEpoch 15/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0161 - accuracy: 0.9991 - val_loss: 0.0149 - val_accuracy: 0.9995\nEpoch 16/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0108 - accuracy: 0.9997 - val_loss: 0.0106 - val_accuracy: 0.9996\nEpoch 17/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0074 - accuracy: 0.9999 - val_loss: 0.0077 - val_accuracy: 0.9999\nEpoch 18/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9999\nEpoch 19/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\nEpoch 20/20\n313/313 [==============================] - 5s 15ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0032 - val_accuracy: 1.0000\n" ] ], [ [ "Looks great, we reach 100% validation accuracy! Let's use the model to make some predictions. We will need to be able to convert a sequence of character IDs to a readable string:", "_____no_output_____" ] ], [ [ "def ids_to_date_strs(ids, chars=OUTPUT_CHARS):\n return [\"\".join([(\"?\" + chars)[index] for index in sequence])\n for sequence in ids]", "_____no_output_____" ] ], [ [ "Now we can use the model to convert some dates", "_____no_output_____" ] ], [ [ "X_new = prepare_date_strs([\"September 17, 2009\", \"July 14, 1789\"])", "_____no_output_____" ], [ "ids = model.predict_classes(X_new)\nfor date_str in ids_to_date_strs(ids):\n print(date_str)", "WARNING:tensorflow:From <ipython-input-15-472ea7c41409>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\nInstructions for updating:\nPlease use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).\n2009-09-17\n1789-07-14\n" ] ], [ [ "Perfect! :)", "_____no_output_____" ], [ "However, since the model was only trained on input strings of length 18 (which is the length of the longest date), it does not perform well if we try to use it to make predictions on shorter sequences:", "_____no_output_____" ] ], [ [ "X_new = prepare_date_strs([\"May 02, 2020\", \"July 14, 1789\"])", "_____no_output_____" ], [ "ids = model.predict_classes(X_new)\nfor date_str in ids_to_date_strs(ids):\n print(date_str)", "2020-01-02\n1789-02-14\n" ] ], [ [ "Oops! We need to ensure that we always pass sequences of the same length as during training, using padding if necessary. Let's write a little helper function for that:", "_____no_output_____" ] ], [ [ "max_input_length = X_train.shape[1]\n\ndef prepare_date_strs_padded(date_strs):\n X = prepare_date_strs(date_strs)\n if X.shape[1] < max_input_length:\n X = tf.pad(X, [[0, 0], [0, max_input_length - X.shape[1]]])\n return X\n\ndef convert_date_strs(date_strs):\n X = prepare_date_strs_padded(date_strs)\n ids = model.predict_classes(X)\n return ids_to_date_strs(ids)", "_____no_output_____" ], [ "convert_date_strs([\"May 02, 2020\", \"July 14, 1789\"])", "_____no_output_____" ] ], [ [ "Cool! Granted, there are certainly much easier ways to write a date conversion tool (e.g., using regular expressions or even basic string manipulation), but you have to admit that using neural networks is way cooler. ;-)", "_____no_output_____" ], [ "However, real-life sequence-to-sequence problems will usually be harder, so for the sake of completeness, let's build a more powerful model.", "_____no_output_____" ], [ "### Second version: feeding the shifted targets to the decoder (teacher forcing)", "_____no_output_____" ], [ "Instead of feeding the decoder a simple repetition of the encoder's output vector, we can feed it the target sequence, shifted by one time step to the right. This way, at each time step the decoder will know what the previous target character was. This should help is tackle more complex sequence-to-sequence problems.\n\nSince the first output character of each target sequence has no previous character, we will need a new token to represent the start-of-sequence (sos).\n\nDuring inference, we won't know the target, so what will we feed the decoder? We can just predict one character at a time, starting with an sos token, then feeding the decoder all the characters that were predicted so far (we will look at this in more details later in this notebook).\n\nBut if the decoder's LSTM expects to get the previous target as input at each step, how shall we pass it it the vector output by the encoder? Well, one option is to ignore the output vector, and instead use the encoder's LSTM state as the initial state of the decoder's LSTM (which requires that encoder's LSTM must have the same number of units as the decoder's LSTM).\n\nNow let's create the decoder's inputs (for training, validation and testing). The sos token will be represented using the last possible output character's ID + 1.", "_____no_output_____" ] ], [ [ "sos_id = len(OUTPUT_CHARS) + 1\n\ndef shifted_output_sequences(Y):\n sos_tokens = tf.fill(dims=(len(Y), 1), value=sos_id)\n return tf.concat([sos_tokens, Y[:, :-1]], axis=1)\n\nX_train_decoder = shifted_output_sequences(Y_train)\nX_valid_decoder = shifted_output_sequences(Y_valid)\nX_test_decoder = shifted_output_sequences(Y_test)", "_____no_output_____" ] ], [ [ "Let's take a look at the decoder's training inputs:", "_____no_output_____" ] ], [ [ "X_train_decoder", "_____no_output_____" ] ], [ [ "Now let's build the model. It's not a simple sequential model anymore, so let's use the functional API:", "_____no_output_____" ] ], [ [ "encoder_embedding_size = 32\ndecoder_embedding_size = 32\nlstm_units = 128\n\nnp.random.seed(42)\ntf.random.set_seed(42)\n\nencoder_input = keras.layers.Input(shape=[None], dtype=tf.int32)\nencoder_embedding = keras.layers.Embedding(\n input_dim=len(INPUT_CHARS) + 1,\n output_dim=encoder_embedding_size)(encoder_input)\n_, encoder_state_h, encoder_state_c = keras.layers.LSTM(\n lstm_units, return_state=True)(encoder_embedding)\nencoder_state = [encoder_state_h, encoder_state_c]\n\ndecoder_input = keras.layers.Input(shape=[None], dtype=tf.int32)\ndecoder_embedding = keras.layers.Embedding(\n input_dim=len(OUTPUT_CHARS) + 2,\n output_dim=decoder_embedding_size)(decoder_input)\ndecoder_lstm_output = keras.layers.LSTM(lstm_units, return_sequences=True)(\n decoder_embedding, initial_state=encoder_state)\ndecoder_output = keras.layers.Dense(len(OUTPUT_CHARS) + 1,\n activation=\"softmax\")(decoder_lstm_output)\n\nmodel = keras.models.Model(inputs=[encoder_input, decoder_input],\n outputs=[decoder_output])\n\noptimizer = keras.optimizers.Nadam()\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\nhistory = model.fit([X_train, X_train_decoder], Y_train, epochs=10,\n validation_data=([X_valid, X_valid_decoder], Y_valid))", "Epoch 1/10\n313/313 [==============================] - 5s 17ms/step - loss: 1.6898 - accuracy: 0.3714 - val_loss: 1.4141 - val_accuracy: 0.4603\nEpoch 2/10\n313/313 [==============================] - 5s 15ms/step - loss: 1.2118 - accuracy: 0.5541 - val_loss: 0.9360 - val_accuracy: 0.6653\nEpoch 3/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.6399 - accuracy: 0.7766 - val_loss: 0.4054 - val_accuracy: 0.8631\nEpoch 4/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.2207 - accuracy: 0.9463 - val_loss: 0.1069 - val_accuracy: 0.9869\nEpoch 5/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0805 - accuracy: 0.9910 - val_loss: 0.0445 - val_accuracy: 0.9976\nEpoch 6/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0297 - accuracy: 0.9993 - val_loss: 0.0237 - val_accuracy: 0.9992\nEpoch 7/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0743 - accuracy: 0.9857 - val_loss: 0.0702 - val_accuracy: 0.9889\nEpoch 8/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0187 - accuracy: 0.9995 - val_loss: 0.0112 - val_accuracy: 0.9999\nEpoch 9/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 1.0000\nEpoch 10/10\n313/313 [==============================] - 5s 15ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 1.0000\n" ] ], [ [ "This model also reaches 100% validation accuracy, but it does so even faster.", "_____no_output_____" ], [ "Let's once again use the model to make some predictions. This time we need to predict characters one by one.", "_____no_output_____" ] ], [ [ "sos_id = len(OUTPUT_CHARS) + 1\n\ndef predict_date_strs(date_strs):\n X = prepare_date_strs_padded(date_strs)\n Y_pred = tf.fill(dims=(len(X), 1), value=sos_id)\n for index in range(max_output_length):\n pad_size = max_output_length - Y_pred.shape[1]\n X_decoder = tf.pad(Y_pred, [[0, 0], [0, pad_size]])\n Y_probas_next = model.predict([X, X_decoder])[:, index:index+1]\n Y_pred_next = tf.argmax(Y_probas_next, axis=-1, output_type=tf.int32)\n Y_pred = tf.concat([Y_pred, Y_pred_next], axis=1)\n return ids_to_date_strs(Y_pred[:, 1:])", "_____no_output_____" ], [ "predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "_____no_output_____" ] ], [ [ "Works fine! :)", "_____no_output_____" ], [ "### Third version: using TF-Addons's seq2seq implementation", "_____no_output_____" ], [ "Let's build exactly the same model, but using TF-Addon's seq2seq API. The implementation below is almost very similar to the TFA example higher in this notebook, except without the model input to specify the output sequence length, for simplicity (but you can easily add it back in if you need it for your projects, when the output sequences have very different lengths).", "_____no_output_____" ] ], [ [ "import tensorflow_addons as tfa\n\nnp.random.seed(42)\ntf.random.set_seed(42)\n\nencoder_embedding_size = 32\ndecoder_embedding_size = 32\nunits = 128\n\nencoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\ndecoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\nsequence_lengths = keras.layers.Input(shape=[], dtype=np.int32)\n\nencoder_embeddings = keras.layers.Embedding(\n len(INPUT_CHARS) + 1, encoder_embedding_size)(encoder_inputs)\n\ndecoder_embedding_layer = keras.layers.Embedding(\n len(INPUT_CHARS) + 2, decoder_embedding_size)\ndecoder_embeddings = decoder_embedding_layer(decoder_inputs)\n\nencoder = keras.layers.LSTM(units, return_state=True)\nencoder_outputs, state_h, state_c = encoder(encoder_embeddings)\nencoder_state = [state_h, state_c]\n\nsampler = tfa.seq2seq.sampler.TrainingSampler()\n\ndecoder_cell = keras.layers.LSTMCell(units)\noutput_layer = keras.layers.Dense(len(OUTPUT_CHARS) + 1)\n\ndecoder = tfa.seq2seq.basic_decoder.BasicDecoder(decoder_cell,\n sampler,\n output_layer=output_layer)\nfinal_outputs, final_state, final_sequence_lengths = decoder(\n decoder_embeddings,\n initial_state=encoder_state)\nY_proba = keras.layers.Activation(\"softmax\")(final_outputs.rnn_output)\n\nmodel = keras.models.Model(inputs=[encoder_inputs, decoder_inputs],\n outputs=[Y_proba])\noptimizer = keras.optimizers.Nadam()\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\nhistory = model.fit([X_train, X_train_decoder], Y_train, epochs=15,\n validation_data=([X_valid, X_valid_decoder], Y_valid))", "Epoch 1/15\n313/313 [==============================] - 5s 17ms/step - loss: 1.6757 - accuracy: 0.3683 - val_loss: 1.4602 - val_accuracy: 0.4214\nEpoch 2/15\n313/313 [==============================] - 5s 15ms/step - loss: 1.3873 - accuracy: 0.4566 - val_loss: 1.2904 - val_accuracy: 0.4957\nEpoch 3/15\n313/313 [==============================] - 5s 15ms/step - loss: 1.0471 - accuracy: 0.6109 - val_loss: 0.7737 - val_accuracy: 0.7276\nEpoch 4/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.5056 - accuracy: 0.8296 - val_loss: 0.2695 - val_accuracy: 0.9305\nEpoch 5/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.1677 - accuracy: 0.9657 - val_loss: 0.0870 - val_accuracy: 0.9912\nEpoch 6/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.1007 - accuracy: 0.9850 - val_loss: 0.0492 - val_accuracy: 0.9975\nEpoch 7/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0308 - accuracy: 0.9993 - val_loss: 0.0228 - val_accuracy: 0.9996\nEpoch 8/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0168 - accuracy: 0.9999 - val_loss: 0.0144 - val_accuracy: 0.9999\nEpoch 9/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9999\nEpoch 10/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9999\nEpoch 11/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9999\nEpoch 12/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\nEpoch 13/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000\nEpoch 14/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\nEpoch 15/15\n313/313 [==============================] - 5s 15ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n" ] ], [ [ "And once again, 100% validation accuracy! To use the model, we can just reuse the `predict_date_strs()` function:", "_____no_output_____" ] ], [ [ "predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "_____no_output_____" ] ], [ [ "However, there's a much more efficient way to perform inference. Until now, during inference, we've run the model once for each new character. Instead, we can create a new decoder, based on the previously trained layers, but using a `GreedyEmbeddingSampler` instead of a `TrainingSampler`.\n\nAt each time step, the `GreedyEmbeddingSampler` will compute the argmax of the decoder's outputs, and run the resulting token IDs through the decoder's embedding layer. Then it will feed the resulting embeddings to the decoder's LSTM cell at the next time step. This way, we only need to run the decoder once to get the full prediction.", "_____no_output_____" ] ], [ [ "inference_sampler = tfa.seq2seq.sampler.GreedyEmbeddingSampler(\n embedding_fn=decoder_embedding_layer)\ninference_decoder = tfa.seq2seq.basic_decoder.BasicDecoder(\n decoder_cell, inference_sampler, output_layer=output_layer,\n maximum_iterations=max_output_length)\nbatch_size = tf.shape(encoder_inputs)[:1]\nstart_tokens = tf.fill(dims=batch_size, value=sos_id)\nfinal_outputs, final_state, final_sequence_lengths = inference_decoder(\n start_tokens,\n initial_state=encoder_state,\n start_tokens=start_tokens,\n end_token=0)\n\ninference_model = keras.models.Model(inputs=[encoder_inputs],\n outputs=[final_outputs.sample_id])", "_____no_output_____" ] ], [ [ "A few notes:\n* The `GreedyEmbeddingSampler` needs the `start_tokens` (a vector containing the start-of-sequence ID for each decoder sequence), and the `end_token` (the decoder will stop decoding a sequence once the model outputs this token).\n* We must set `maximum_iterations` when creating the `BasicDecoder`, or else it may run into an infinite loop (if the model never outputs the end token for at least one of the sequences). This would force you would to restart the Jupyter kernel.\n* The decoder inputs are not needed anymore, since all the decoder inputs are generated dynamically based on the outputs from the previous time step.\n* The model's outputs are `final_outputs.sample_id` instead of the softmax of `final_outputs.rnn_outputs`. This allows us to directly get the argmax of the model's outputs. If you prefer to have access to the logits, you can replace `final_outputs.sample_id` with `final_outputs.rnn_outputs`.", "_____no_output_____" ], [ "Now we can write a simple function that uses the model to perform the date format conversion:", "_____no_output_____" ] ], [ [ "def fast_predict_date_strs(date_strs):\n X = prepare_date_strs_padded(date_strs)\n Y_pred = inference_model.predict(X)\n return ids_to_date_strs(Y_pred)", "_____no_output_____" ], [ "fast_predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "_____no_output_____" ] ], [ [ "Let's check that it really is faster:", "_____no_output_____" ] ], [ [ "%timeit predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "199 ms ± 3.94 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ], [ "%timeit fast_predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "18.3 ms ± 366 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] ], [ [ "That's more than a 10x speedup! And it would be even more if we were handling longer sequences.", "_____no_output_____" ], [ "### Fourth version: using TF-Addons's seq2seq implementation with a scheduled sampler", "_____no_output_____" ], [ "**Warning**: due to a TF bug, this version only works using TensorFlow 2.2.", "_____no_output_____" ], [ "When we trained the previous model, at each time step _t_ we gave the model the target token for time step _t_ - 1. However, at inference time, the model did not get the previous target at each time step. Instead, it got the previous prediction. So there is a discrepancy between training and inference, which may lead to disappointing performance. To alleviate this, we can gradually replace the targets with the predictions, during training. For this, we just need to replace the `TrainingSampler` with a `ScheduledEmbeddingTrainingSampler`, and use a Keras callback to gradually increase the `sampling_probability` (i.e., the probability that the decoder will use the prediction from the previous time step rather than the target for the previous time step).", "_____no_output_____" ] ], [ [ "import tensorflow_addons as tfa\n\nnp.random.seed(42)\ntf.random.set_seed(42)\n\nn_epochs = 20\nencoder_embedding_size = 32\ndecoder_embedding_size = 32\nunits = 128\n\nencoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\ndecoder_inputs = keras.layers.Input(shape=[None], dtype=np.int32)\nsequence_lengths = keras.layers.Input(shape=[], dtype=np.int32)\n\nencoder_embeddings = keras.layers.Embedding(\n len(INPUT_CHARS) + 1, encoder_embedding_size)(encoder_inputs)\n\ndecoder_embedding_layer = keras.layers.Embedding(\n len(INPUT_CHARS) + 2, decoder_embedding_size)\ndecoder_embeddings = decoder_embedding_layer(decoder_inputs)\n\nencoder = keras.layers.LSTM(units, return_state=True)\nencoder_outputs, state_h, state_c = encoder(encoder_embeddings)\nencoder_state = [state_h, state_c]\n\nsampler = tfa.seq2seq.sampler.ScheduledEmbeddingTrainingSampler(\n sampling_probability=0.,\n embedding_fn=decoder_embedding_layer)\n# we must set the sampling_probability after creating the sampler\n# (see https://github.com/tensorflow/addons/pull/1714)\nsampler.sampling_probability = tf.Variable(0.)\n\ndecoder_cell = keras.layers.LSTMCell(units)\noutput_layer = keras.layers.Dense(len(OUTPUT_CHARS) + 1)\n\ndecoder = tfa.seq2seq.basic_decoder.BasicDecoder(decoder_cell,\n sampler,\n output_layer=output_layer)\nfinal_outputs, final_state, final_sequence_lengths = decoder(\n decoder_embeddings,\n initial_state=encoder_state)\nY_proba = keras.layers.Activation(\"softmax\")(final_outputs.rnn_output)\n\nmodel = keras.models.Model(inputs=[encoder_inputs, decoder_inputs],\n outputs=[Y_proba])\noptimizer = keras.optimizers.Nadam()\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\n\ndef update_sampling_probability(epoch, logs):\n proba = min(1.0, epoch / (n_epochs - 10))\n sampler.sampling_probability.assign(proba)\n\nsampling_probability_cb = keras.callbacks.LambdaCallback(\n on_epoch_begin=update_sampling_probability)\nhistory = model.fit([X_train, X_train_decoder], Y_train, epochs=n_epochs,\n validation_data=([X_valid, X_valid_decoder], Y_valid),\n callbacks=[sampling_probability_cb])", "Epoch 1/20\n" ] ], [ [ "Not quite 100% validation accuracy, but close enough!", "_____no_output_____" ], [ "For inference, we could do the exact same thing as earlier, using a `GreedyEmbeddingSampler`. However, just for the sake of completeness, let's use a `SampleEmbeddingSampler` instead. It's almost the same thing, except that instead of using the argmax of the model's output to find the token ID, it treats the outputs as logits and uses them to sample a token ID randomly. This can be useful when you want to generate text. The `softmax_temperature` argument serves the \nsame purpose as when we generated Shakespeare-like text (the higher this argument, the more random the generated text will be).", "_____no_output_____" ] ], [ [ "softmax_temperature = tf.Variable(1.)\n\ninference_sampler = tfa.seq2seq.sampler.SampleEmbeddingSampler(\n embedding_fn=decoder_embedding_layer,\n softmax_temperature=softmax_temperature)\ninference_decoder = tfa.seq2seq.basic_decoder.BasicDecoder(\n decoder_cell, inference_sampler, output_layer=output_layer,\n maximum_iterations=max_output_length)\nbatch_size = tf.shape(encoder_inputs)[:1]\nstart_tokens = tf.fill(dims=batch_size, value=sos_id)\nfinal_outputs, final_state, final_sequence_lengths = inference_decoder(\n start_tokens,\n initial_state=encoder_state,\n start_tokens=start_tokens,\n end_token=0)\n\ninference_model = keras.models.Model(inputs=[encoder_inputs],\n outputs=[final_outputs.sample_id])", "_____no_output_____" ], [ "def creative_predict_date_strs(date_strs, temperature=1.0):\n softmax_temperature.assign(temperature)\n X = prepare_date_strs_padded(date_strs)\n Y_pred = inference_model.predict(X)\n return ids_to_date_strs(Y_pred)", "_____no_output_____" ], [ "tf.random.set_seed(42)\n\ncreative_predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])", "_____no_output_____" ] ], [ [ "Dates look good at room temperature. Now let's heat things up a bit:", "_____no_output_____" ] ], [ [ "tf.random.set_seed(42)\n\ncreative_predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"],\n temperature=5.)", "_____no_output_____" ] ], [ [ "Oops, the dates are overcooked, now. Let's call them \"creative\" dates.", "_____no_output_____" ], [ "### Fifth version: using TFA seq2seq, the Keras subclassing API and attention mechanisms", "_____no_output_____" ], [ "The sequences in this problem are pretty short, but if we wanted to tackle longer sequences, we would probably have to use attention mechanisms. While it's possible to code our own implementation, it's simpler and more efficient to use TF-Addons's implementation instead. Let's do that now, this time using Keras' subclassing API.\n\n**Warning**: due to a TensorFlow bug (see [this issue](https://github.com/tensorflow/addons/issues/1153) for details), the `get_initial_state()` method fails in eager mode, so for now we have to use the subclassing API, as Keras automatically calls `tf.function()` on the `call()` method (so it runs in graph mode).", "_____no_output_____" ], [ "In this implementation, we've reverted back to using the `TrainingSampler`, for simplicity (but you can easily tweak it to use a `ScheduledEmbeddingTrainingSampler` instead). We also use a `GreedyEmbeddingSampler` during inference, so this class is pretty easy to use:", "_____no_output_____" ] ], [ [ "class DateTranslation(keras.models.Model):\n def __init__(self, units=128, encoder_embedding_size=32,\n decoder_embedding_size=32, **kwargs):\n super().__init__(**kwargs)\n self.encoder_embedding = keras.layers.Embedding(\n input_dim=len(INPUT_CHARS) + 1,\n output_dim=encoder_embedding_size)\n self.encoder = keras.layers.LSTM(units,\n return_sequences=True,\n return_state=True)\n self.decoder_embedding = keras.layers.Embedding(\n input_dim=len(OUTPUT_CHARS) + 2,\n output_dim=decoder_embedding_size)\n self.attention = tfa.seq2seq.LuongAttention(units)\n decoder_inner_cell = keras.layers.LSTMCell(units)\n self.decoder_cell = tfa.seq2seq.AttentionWrapper(\n cell=decoder_inner_cell,\n attention_mechanism=self.attention)\n output_layer = keras.layers.Dense(len(OUTPUT_CHARS) + 1)\n self.decoder = tfa.seq2seq.BasicDecoder(\n cell=self.decoder_cell,\n sampler=tfa.seq2seq.sampler.TrainingSampler(),\n output_layer=output_layer)\n self.inference_decoder = tfa.seq2seq.BasicDecoder(\n cell=self.decoder_cell,\n sampler=tfa.seq2seq.sampler.GreedyEmbeddingSampler(\n embedding_fn=self.decoder_embedding),\n output_layer=output_layer,\n maximum_iterations=max_output_length)\n\n def call(self, inputs, training=None):\n encoder_input, decoder_input = inputs\n encoder_embeddings = self.encoder_embedding(encoder_input)\n encoder_outputs, encoder_state_h, encoder_state_c = self.encoder(\n encoder_embeddings,\n training=training)\n encoder_state = [encoder_state_h, encoder_state_c]\n\n self.attention(encoder_outputs,\n setup_memory=True)\n \n decoder_embeddings = self.decoder_embedding(decoder_input)\n\n decoder_initial_state = self.decoder_cell.get_initial_state(\n decoder_embeddings)\n decoder_initial_state = decoder_initial_state.clone(\n cell_state=encoder_state)\n \n if training:\n decoder_outputs, _, _ = self.decoder(\n decoder_embeddings,\n initial_state=decoder_initial_state,\n training=training)\n else:\n start_tokens = tf.zeros_like(encoder_input[:, 0]) + sos_id\n decoder_outputs, _, _ = self.inference_decoder(\n decoder_embeddings,\n initial_state=decoder_initial_state,\n start_tokens=start_tokens,\n end_token=0)\n\n return tf.nn.softmax(decoder_outputs.rnn_output)", "_____no_output_____" ], [ "np.random.seed(42)\ntf.random.set_seed(42)\n\nmodel = DateTranslation()\noptimizer = keras.optimizers.Nadam()\nmodel.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n metrics=[\"accuracy\"])\nhistory = model.fit([X_train, X_train_decoder], Y_train, epochs=25,\n validation_data=([X_valid, X_valid_decoder], Y_valid))", "Epoch 1/25\n313/313 [==============================] - 7s 21ms/step - loss: 2.1549 - accuracy: 0.2295 - val_loss: 2.1450 - val_accuracy: 0.2239\nEpoch 2/25\n313/313 [==============================] - 6s 19ms/step - loss: 1.8147 - accuracy: 0.3492 - val_loss: 1.4931 - val_accuracy: 0.4476\nEpoch 3/25\n313/313 [==============================] - 6s 18ms/step - loss: 1.3585 - accuracy: 0.4909 - val_loss: 1.3168 - val_accuracy: 0.5100\nEpoch 4/25\n313/313 [==============================] - 6s 18ms/step - loss: 1.2787 - accuracy: 0.5293 - val_loss: 1.1767 - val_accuracy: 0.5624\nEpoch 5/25\n313/313 [==============================] - 6s 18ms/step - loss: 1.1236 - accuracy: 0.5776 - val_loss: 1.0769 - val_accuracy: 0.5907\nEpoch 6/25\n313/313 [==============================] - 6s 18ms/step - loss: 1.0369 - accuracy: 0.6073 - val_loss: 1.0159 - val_accuracy: 0.6199\nEpoch 7/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.9752 - accuracy: 0.6295 - val_loss: 0.9723 - val_accuracy: 0.6346\nEpoch 8/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.9794 - accuracy: 0.6315 - val_loss: 0.9444 - val_accuracy: 0.6371\nEpoch 9/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.9338 - accuracy: 0.6415 - val_loss: 0.9296 - val_accuracy: 0.6381\nEpoch 10/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.9439 - accuracy: 0.6418 - val_loss: 0.9028 - val_accuracy: 0.6574\nEpoch 11/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.8807 - accuracy: 0.6637 - val_loss: 0.9835 - val_accuracy: 0.6369\nEpoch 12/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.7307 - accuracy: 0.6953 - val_loss: 0.8942 - val_accuracy: 0.6873\nEpoch 13/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.5833 - accuracy: 0.7327 - val_loss: 0.6944 - val_accuracy: 0.7391\nEpoch 14/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.4664 - accuracy: 0.7940 - val_loss: 0.6228 - val_accuracy: 0.7885\nEpoch 15/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.3205 - accuracy: 0.8740 - val_loss: 0.4825 - val_accuracy: 0.8780\nEpoch 16/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.2329 - accuracy: 0.9216 - val_loss: 0.3851 - val_accuracy: 0.9118\nEpoch 17/25\n313/313 [==============================] - 7s 21ms/step - loss: 0.2480 - accuracy: 0.9372 - val_loss: 0.2785 - val_accuracy: 0.9111\nEpoch 18/25\n313/313 [==============================] - 7s 22ms/step - loss: 0.1182 - accuracy: 0.9801 - val_loss: 0.1372 - val_accuracy: 0.9786\nEpoch 19/25\n313/313 [==============================] - 7s 22ms/step - loss: 0.0643 - accuracy: 0.9937 - val_loss: 0.0681 - val_accuracy: 0.9909\nEpoch 20/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.0446 - accuracy: 0.9952 - val_loss: 0.0487 - val_accuracy: 0.9934\nEpoch 21/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.0247 - accuracy: 0.9987 - val_loss: 0.0228 - val_accuracy: 0.9987\nEpoch 22/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.0456 - accuracy: 0.9918 - val_loss: 0.0207 - val_accuracy: 0.9985\nEpoch 23/25\n313/313 [==============================] - 6s 18ms/step - loss: 0.0131 - accuracy: 0.9997 - val_loss: 0.0127 - val_accuracy: 0.9993\nEpoch 24/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.0360 - accuracy: 0.9933 - val_loss: 0.0146 - val_accuracy: 0.9990\nEpoch 25/25\n313/313 [==============================] - 6s 19ms/step - loss: 0.0092 - accuracy: 0.9998 - val_loss: 0.0089 - val_accuracy: 0.9992\n" ] ], [ [ "Not quite 100% validation accuracy, but close. It took a bit longer to converge this time, but there were also more parameters and more computations per iteration. And we did not use a scheduled sampler.\n\nTo use the model, we can write yet another little function:", "_____no_output_____" ] ], [ [ "def fast_predict_date_strs_v2(date_strs):\n X = prepare_date_strs_padded(date_strs)\n X_decoder = tf.zeros(shape=(len(X), max_output_length), dtype=tf.int32)\n Y_probas = model.predict([X, X_decoder])\n Y_pred = tf.argmax(Y_probas, axis=-1)\n return ids_to_date_strs(Y_pred)", "_____no_output_____" ], [ "fast_predict_date_strs_v2([\"July 14, 1789\", \"May 01, 2020\"])", "_____no_output_____" ] ], [ [ "There are still a few interesting features from TF-Addons that you may want to look at:\n* Using a `BeamSearchDecoder` rather than a `BasicDecoder` for inference. Instead of outputing the character with the highest probability, this decoder keeps track of the several candidates, and keeps only the most likely sequences of candidates (see chapter 16 in the book for more details).\n* Setting masks or specifying `sequence_length` if the input or target sequences may have very different lengths.\n* Using a `ScheduledOutputTrainingSampler`, which gives you more flexibility than the `ScheduledEmbeddingTrainingSampler` to decide how to feed the output at time _t_ to the cell at time _t_+1. By default it feeds the outputs directly to cell, without computing the argmax ID and passing it through an embedding layer. Alternatively, you specify a `next_inputs_fn` function that will be used to convert the cell outputs to inputs at the next step.", "_____no_output_____" ], [ "## 10.\n_Exercise: Go through TensorFlow's [Neural Machine Translation with Attention tutorial](https://homl.info/nmttuto)._", "_____no_output_____" ], [ "Simply open the Colab and follow its instructions. Alternatively, if you want a simpler example of using TF-Addons's seq2seq implementation for Neural Machine Translation (NMT), look at the solution to the previous question. The last model implementation will give you a simpler example of using TF-Addons to build an NMT model using attention mechanisms.", "_____no_output_____" ], [ "## 11.\n_Exercise: Use one of the recent language models (e.g., GPT) to generate more convincing Shakespearean text._", "_____no_output_____" ], [ "The simplest way to use recent language models is to use the excellent [transformers library](https://huggingface.co/transformers/), open sourced by Hugging Face. It provides many modern neural net architectures (including BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet and more) for Natural Language Processing (NLP), including many pretrained models. It relies on either TensorFlow or PyTorch. Best of all: it's amazingly simple to use.", "_____no_output_____" ], [ "First, let's load a pretrained model. In this example, we will use OpenAI's GPT model, with an additional Language Model on top (just a linear layer with weights tied to the input embeddings). Let's import it and load the pretrained weights (this will download about 445MB of data to `~/.cache/torch/transformers`):", "_____no_output_____" ] ], [ [ "from transformers import TFOpenAIGPTLMHeadModel\n\nmodel = TFOpenAIGPTLMHeadModel.from_pretrained(\"openai-gpt\")", "_____no_output_____" ] ], [ [ "Next we will need a specialized tokenizer for this model. This one will try to use the [spaCy](https://spacy.io/) and [ftfy](https://pypi.org/project/ftfy/) libraries if they are installed, or else it will fall back to BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most use cases).", "_____no_output_____" ] ], [ [ "from transformers import OpenAIGPTTokenizer\n\ntokenizer = OpenAIGPTTokenizer.from_pretrained(\"openai-gpt\")", "_____no_output_____" ] ], [ [ "Now let's use the tokenizer to tokenize and encode the prompt text:", "_____no_output_____" ] ], [ [ "prompt_text = \"This royal throne of kings, this sceptred isle\"\nencoded_prompt = tokenizer.encode(prompt_text,\n add_special_tokens=False,\n return_tensors=\"tf\")\nencoded_prompt", "_____no_output_____" ] ], [ [ "Easy! Next, let's use the model to generate text after the prompt. We will generate 5 different sentences, each starting with the prompt text, followed by 40 additional tokens. For an explanation of what all the hyperparameters do, make sure to check out this great [blog post](https://huggingface.co/blog/how-to-generate) by Patrick von Platen (from Hugging Face). You can play around with the hyperparameters to try to obtain better results.", "_____no_output_____" ] ], [ [ "num_sequences = 5\nlength = 40\n\ngenerated_sequences = model.generate(\n input_ids=encoded_prompt,\n do_sample=True,\n max_length=length + len(encoded_prompt[0]),\n temperature=1.0,\n top_k=0,\n top_p=0.9,\n repetition_penalty=1.0,\n num_return_sequences=num_sequences,\n)\n\ngenerated_sequences", "_____no_output_____" ] ], [ [ "Now let's decode the generated sequences and print them:", "_____no_output_____" ] ], [ [ "for sequence in generated_sequences:\n text = tokenizer.decode(sequence, clean_up_tokenization_spaces=True)\n print(text)\n print(\"-\" * 80)", "this royal throne of kings, this sceptred isle. even if someone had given them permission, even if it were required, they would never have been allowed to live through the hell they've survived.'\n'they couldn't have known that.\n--------------------------------------------------------------------------------\nthis royal throne of kings, this sceptred isle and these people are royalty.'\n then the mute prince and prince edward broke off and went to their rooms. \n the talk passed again between the princes and the guards and the princess was of great\n--------------------------------------------------------------------------------\nthis royal throne of kings, this sceptred isle has its own highness, an alatte that waits to save you. in this kingdom your people must emulate the kings of the realm. in this kingdom your kin should be saved from this pit and\n--------------------------------------------------------------------------------\nthis royal throne of kings, this sceptred isle belongs to me. \" \n \" the great throne of penvynne? \" \n \" indeed, \" said the king with a nod of his head. \" this world was once composed of a magical\n--------------------------------------------------------------------------------\nthis royal throne of kings, this sceptred isle is empty. this is a modern - day fedaykin court, a place where kings are governed, not emperors and judges. i don't see any sign of life that is not their own\n--------------------------------------------------------------------------------\n" ] ], [ [ "You can try more recent (and larger) models, such as GPT-2, CTRL, Transformer-XL or XLNet, which are all available as pretrained models in the transformers library, including variants with Language Models on top. The preprocessing steps vary slightly between models, so make sure to check out this [generation example](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py) from the transformers documentation (this example uses PyTorch, but it will work with very little tweaks, such as adding `TF` at the beginning of the model class name, removing the `.to()` method calls, and using `return_tensors=\"tf\"` instead of `\"pt\"`.", "_____no_output_____" ], [ "Hope you enjoyed this chapter! :)", "_____no_output_____" ] ] ]

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[ [ [ "<H1> Notebook to verify the calculations of our simulator </H1>", "_____no_output_____" ], [ "## Importing required libraries", "_____no_output_____" ] ], [ [ "# importaing standard libraries\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom scipy.signal import freqs,periodogram,cheby1\nimport numpy as np", "_____no_output_____" ], [ "# import quantum libraries\nimport qutip\nfrom itertools import product\nfrom numpy import array, kron\nfrom qmldataset import pauli_operators, create_custom_simulator, run_experiment", "2021-09-26 16:34:01.309496: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n" ] ], [ [ "## Step 1: Create a simulator\n\nWe supply the parameters and create a simulator. Here we will create a 1-qubit experiment with Control on X-Axis, Type 1 noise on Z-Axis", "_____no_output_____" ] ], [ [ "dimension = 2\nevolution_time = 1\nnum_time_steps = 1024\nomega = 12\ndynamic_operators = [0.5*pauli_operators[1]]\nstatic_operators = [0.5*pauli_operators[3]*omega]\nnoise_operators = [0.5*pauli_operators[3]]\nmeasurement_operators = pauli_operators[1:]\ninitial_states = [\n np.array([[0.5, 0.5], [0.5, 0.5]]), np.array([[0.5, -0.5], [-0.5, 0.5]]),\n np.array([[0.5, -0.5j], [0.5j, 0.5]]), np.array([[0.5, 0.5j], [-0.5j, 0.5]]),\n np.array([[1, 0], [0, 0]]), np.array([[0, 0], [0, 1]])\n]\nnum_realizations = 200\nnum_pulses = 5\nnoise_profile = ['Type 1']\ndistortion = True\n\nsimulator_with_distortion = create_custom_simulator(\n evolution_time=evolution_time,\n num_time_steps=num_time_steps,\n dimension=dimension,\n dynamic_operators=dynamic_operators,\n static_operators=static_operators,\n noise_operators=noise_operators,\n measurement_operators=measurement_operators,\n initial_states=initial_states,\n num_realizations=num_realizations,\n num_pulses=num_pulses,\n noise_profile=noise_profile,\n distortion=distortion,\n pulse_shape=\"Square\"\n)", "2021-09-26 16:34:05.687838: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-26 16:34:05.689143: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n2021-09-26 16:34:05.737996: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.738543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \npciBusID: 0000:0e:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1\ncoreClock: 1.43GHz coreCount: 6 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 104.43GiB/s\n2021-09-26 16:34:05.738610: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n2021-09-26 16:34:05.741933: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n2021-09-26 16:34:05.742095: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n2021-09-26 16:34:05.743695: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n2021-09-26 16:34:05.744247: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n2021-09-26 16:34:05.746528: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n2021-09-26 16:34:05.747524: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n2021-09-26 16:34:05.747858: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n2021-09-26 16:34:05.748020: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.748524: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.748866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n2021-09-26 16:34:05.749988: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-26 16:34:05.750242: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.750682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \npciBusID: 0000:0e:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1\ncoreClock: 1.43GHz coreCount: 6 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 104.43GiB/s\n2021-09-26 16:34:05.750765: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n2021-09-26 16:34:05.750804: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n2021-09-26 16:34:05.750829: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n2021-09-26 16:34:05.750852: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n2021-09-26 16:34:05.750874: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n2021-09-26 16:34:05.750896: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n2021-09-26 16:34:05.750918: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n2021-09-26 16:34:05.750941: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n2021-09-26 16:34:05.751068: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.751499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:05.751834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n2021-09-26 16:34:05.751902: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n2021-09-26 16:34:06.554192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n2021-09-26 16:34:06.554242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 \n2021-09-26 16:34:06.554252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N \n2021-09-26 16:34:06.554522: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:06.555210: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:06.555616: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2021-09-26 16:34:06.555966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3250 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:0e:00.0, compute capability: 6.1)\n" ] ], [ [ "## Now we run a single experiment\n\nThe experiment will produce a result by simulating `num_realizations` number of noise realizations.", "_____no_output_____" ] ], [ [ "experiment_result = run_experiment(simulator=simulator_with_distortion)", "2021-09-26 16:34:09.738690: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n2021-09-26 16:34:09.761987: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3094175000 Hz\n2021-09-26 16:34:13.227801: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n2021-09-26 16:34:13.612214: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n2021-09-26 16:34:13.642600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n2021-09-26 16:34:13.917894: I tensorflow/core/util/cuda_solvers.cc:180] Creating CudaSolver handles for stream 0x5646df642640\n2021-09-26 16:34:13.918105: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n2021-09-26 16:34:14.231919: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n" ] ], [ [ "## Once run, let us read the experiment outcome", "_____no_output_____" ] ], [ [ "# plot the pulse\nplt.figure()\nnum_controls = len(experiment_result[\"sim_parameters\"][\"dynamic_operators\"])\nfor idx in range(num_controls):\n plt.subplot(num_controls , 1, idx+1 )\n plt.plot(experiment_result[\"time_range\"], experiment_result[\"pulses\"][:,0,idx], label=\"undistorted\")\n plt.plot(experiment_result[\"time_range\"], experiment_result[\"distorted_pulses\"][:,0,idx], label=\"distorted\")\n plt.xlabel('t')\n plt.ylabel('f(t)')\n plt.grid()\n plt.legend()\nprint(experiment_result[\"pulse_parameters\"])", "[[-20.345783 0.12233578 0.1 ]\n [ 58.95591 0.27380085 0.1 ]\n [ 38.14025 0.4457677 0.1 ]\n [ 29.669308 0.61551726 0.1 ]\n [-74.14498 0.7660476 0.1 ]]\n" ] ], [ [ "## Display the distortion if exists", "_____no_output_____" ] ], [ [ "if distortion:\n # display distortion filter if exists\n distortion = cheby1(4,0.1,2*np.pi*20, analog=True)\n # evaluate frequency response of the filter\n w, Hw = freqs(distortion[0], distortion[1])\n plt.figure(figsize=[15,4])\n plt.subplot(1,2,1)\n plt.semilogx(w, 20*np.log(np.abs(Hw)))\n plt.xlabel(r'$\\Omega$')\n plt.ylabel(r'$|H(\\Omega)|$')\n plt.grid()\n plt.subplot(1,2,2)\n plt.semilogx(w, np.angle(Hw))\n plt.xlabel(r'$\\Omega$')\n plt.ylabel(r'arg $H(\\Omega)$')\n plt.grid()", "_____no_output_____" ] ], [ [ "## Display the noise", "_____no_output_____" ] ], [ [ "# display noise if exists\nfor idx_profile,profile in enumerate(experiment_result[\"sim_parameters\"][\"noise_profile\"]): \n if profile in ['Type 2','Type 3','Type 4'] or (profile=='Type 6' and p==0): \n # estimate the correlation matrix of the noise\n correlation = 0\n for k in range(experiment_result[\"sim_parameters\"][\"num_realizations\"]):\n correlation = correlation + experiment_result[\"noise\"][:,k:k+1,idx_profile]@experiment_result[\"noise\"][:,k:k+1,idx_profile].T\n correlation = correlation/data[\"sim_parameters\"][\"num_realizations\"]\n # plot correlation matrix\n plt.figure()\n plt.matshow(correlation,0)\n plt.colorbar()\n p = 0\n elif profile in ['Type 1','Type 5']:\n # estimate the PSD of the noise\n psd = 0\n for k in range(experiment_result[\"sim_parameters\"][\"num_realizations\"]):\n f, Pxx = periodogram(experiment_result[\"noise\"][:,k,idx_profile], experiment_result[\"sim_parameters\"][\"num_time_steps\"]/experiment_result[\"sim_parameters\"][\"evolution_time\"]) \n psd = psd + Pxx\n psd = psd/experiment_result[\"sim_parameters\"][\"num_realizations\"]\n plt.figure()\n plt.plot(f[f>0], psd[1:])\n plt.xlabel('f')\n plt.ylabel('psd')\n plt.grid()\n p = 1", "_____no_output_____" ] ], [ [ "## Comparing the output with `qutip`\n\nHint: They should be same !!", "_____no_output_____" ] ], [ [ "# load initial states, measurement operators, and control Hamilotonian\ninitial_states = [qutip.Qobj(state) for state in experiment_result[\"sim_parameters\"][\"initial_states\"] ] \nmeasurements = [qutip.Qobj(op) for op in experiment_result[\"sim_parameters\"][\"measurement_operators\"] ]\n\nH0 = [ [qutip.Qobj(op), np.ones((len(experiment_result[\"sim_parameters\"][\"time_range\"])))] \n for op in experiment_result[\"sim_parameters\"][\"static_operators\"] ] + [\n [qutip.Qobj(op), experiment_result[\"distorted_pulses\"][:,0,idx]] \n for idx, op in enumerate(experiment_result[\"sim_parameters\"][\"dynamic_operators\"])]\n\nexpectations = np.zeros(\n (1,experiment_result[\"sim_parameters\"][\"num_realizations\"], \n len(initial_states)*len(measurements))) \n\nfor idx_K in range(experiment_result[\"sim_parameters\"][\"num_realizations\"]): \n H1 = [ \n [qutip.Qobj(op), experiment_result[\"noise\"][:, idx_K, idx]] \n for idx, op in enumerate(experiment_result[\"sim_parameters\"][\"noise_operators\"]) ]\n results = [ qutip.mesolve(H0 + H1, rho, np.array(experiment_result[\"sim_parameters\"][\"time_range\"]),\n e_ops=measurements).expect for rho in initial_states]\n expectations [0, idx_K, :] = np.concatenate(\n [np.array([results[idx_rho][idx_M][-1] \n for idx_M in range(len(measurements))]) for idx_rho in range(len(initial_states))])\n print(idx_K+1, end=\"\\r\")", "200\r" ], [ "# plot the average expectation over all noise realizations for every observable\nplt.figure()\nplt.plot(np.average(expectations, 1)[0], label=\"qutip\")\nplt.plot(experiment_result[\"average_expectation\"][0], label = \"tf\")\nplt.ylabel(\"Average observable value\")\nplt.xlabel(\"observable Index\")\nplt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\nplt.legend()\nplt.grid()", "_____no_output_____" ], [ "# plot all possible observables for a particular noise realization\nidx_K = 10\nplt.figure()\nplt.plot(expectations[0, idx_K,:], label=\"qutip\")\nplt.plot(experiment_result[\"expectations\"][idx_K,:], label = \"tf\")\nplt.ylabel(\"Observable Value for realization %d\"%idx_K)\nplt.xlabel(\"Observable Index\")\nplt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\nplt.legend()\nplt.grid()", "_____no_output_____" ] ] ]

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[ [ [ "# Continuación clase método de la transformada inversa", "_____no_output_____" ] ], [ [ "# Librería de optimización \nfrom scipy import optimize\nfrom scipy.stats import beta\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n# %matplotlib notebook\n%matplotlib inline", "_____no_output_____" ] ], [ [ "### Función para crear histograma de distribuciones discretas", "_____no_output_____" ] ], [ [ "def Gen_distr_discreta(p_acum: 'P.Acumulada de la distribución a generar',\n indices: 'valores reales a generar aleatoriamente',\n N: 'cantidad de números aleatorios a generar'):\n \n U =np.random.rand(N)\n # Diccionario de valores aleatorios\n rand2reales = {i: idx for i, idx in enumerate(indices)}\n\n # Series de los valores aletorios\n y = pd.Series([sum([1 for p in p_acum if p < ui]) for ui in U]).map(rand2reales)\n\n return y", "_____no_output_____" ], [ "def plot_histogram_discrete(distribucion:'señal de varibles aleatorias de un distribución DISCRETA dada',\n label:'label del legend a aparecer en el gráfica',\n densidad:'por defecto regresa el histograma en densidad'=True):\n # len(set(distribucion)) cuenta la cantidad de elementos distintos de la variable 'distribucion'\n plt.figure(figsize=[10,4])\n y, x = np.histogram(distribucion, bins=len(set(distribucion)), density=densidad) \n plt.bar(x[1:], y, label=label)\n plt.legend()\n plt.show()", "_____no_output_____" ] ], [ [ "### Ejemplo binomial: \nLa distribución binomial modela el número de éxitos de n ensayos independientes donde hay una probabilidad p de éxito en cada ensayo.\n\nGenerar una variable aletoria binomial con parámetros $n=10$ y $p=0.7$. Recordar que\n$$X\\sim binomial(n,p) \\longrightarrow p_i=P(X=i)=\\frac{n!}{i!(n-i)!}p^i(1-p)^{n-i},\\quad i=0,1,\\cdots,n$$\n> ## <font color ='red'>Tarea: Demostrar la validez de la siguiente ecuación\n>$$p_{i+1}=\\frac{n-i}{i+1}\\frac{p}{1-p} p_i \\longrightarrow \\text{Hablar de las ventajas que sea recursiva}$$", "_____no_output_____" ], [ "**El Algoritmo que debemos realizar:**\n 1. Generar $U$.\n 2. Si $U<p_0$, poner $X=0$ y detenerse.\n 3. Si $p_0<U<p_0+p_1$, poner $X=1$ y detenerse.\n $$ \\vdots$$\n 4. Si $p_0+\\cdots+p_{n-1}<U<p_0+\\cdots+p_{n}$, poner $X=n$ y detenerse.", "_____no_output_____" ] ], [ [ "# Función que calcula la probabilidad acumulada optimizada\ndef P_acum_Binomial_o(n,p):\n Pr = np.zeros(n)\n Pr[0] = (1-p)**n\n def pr(i):\n nonlocal Pr\n c = p/(1-p)\n Pr[i+1]=(c*(n-i)/(i+1))*Pr[i]\n \n # Lleno el vector Pr usando compresión de listas\n [pr(i) for i in range(n-1)]\n return np.cumsum(Pr)", "_____no_output_____" ], [ "# def D_binomial_intermedia(n,p,N):\nn = 10; p = 0.7; N = 10**5\n\np_acum = P_acum_Binomial_o(n,p)\n\n# Usando el método de la transformada inversa\nd_binomial = Gen_distr_discreta(p_acum, np.arange(0, n+1), N)\nplot_histogram_discrete(d_binomial, 'función creada con tran. Inversa')\n\n# Usando numpy\nd_bino_numpy = np.random.binomial(n,p,N)\nplot_histogram_discrete(d_bino_numpy, 'función creada con numpy')\n", "_____no_output_____" ] ], [ [ "Explore el funcionamiento del siguiente comando", "_____no_output_____" ] ], [ [ "list(set(d_binomial))", "_____no_output_____" ] ], [ [ "> ## <font color ='red'>Tarea\nSeguir un procedimiento similar al mostrado cuando se generó una distribución binomial, pero en esta caso genere un código que genere variables aletorias Poisson cuya función de distribución de probabilidad esta dada por:\n>$$P(k,\\lambda)=\\frac{e^{-\\lambda}(\\lambda)^k}{k!}$$\n \n> Demuestre matemáticamente que \n> $$P(k+1)=\\frac{\\lambda}{k+1}P(k)$$\n> y a partir de esta relación genere variables aletorias que distribuyen poisson usando el método de la transformada inversa.\n\nEnlace: https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_Poisson", "_____no_output_____" ], [ "$\n\\begin{aligned}\n\\frac{p_{k+1}}{p_k}& = \\frac{e^{-\\lambda}(\\lambda)^k}{k!} \\\\\n& = \\frac{e^{-\\lambda}(\\lambda)^k}{k!}\n\\end{aligned}\n$", "_____no_output_____" ], [ "# Método de aceptación rechazo\n\nEste método surgió debido a que muchas distribuciones continuas, no era factible aplicar el método de transformación inversa porque $x= F^{-1}(U)$ no se puede calcular (o al menos no es computacionalmente eficientemente).Con frecuencia, estos métodos son considerablemente más rápidos que el método de transformación inversa. Ahora ilustramos el **método de aceptación y rechazo** en un ejemplo simple.", "_____no_output_____" ], [ "Suponga que tenemos una función de densidad de probabilidad (PDF) de una distribución beta, la cual viene dada:\n$$f(x)=\\frac{x^{\\alpha_1-1}(1-x)^{\\alpha_2-1}}{B(\\alpha_1,\\alpha_2)} \\quad x\\in[0,1] \\longrightarrow B(\\alpha_1,\\alpha_2)\\equiv \\int_{0}^{1}x^{\\alpha_1-1}(1-x)^{\\alpha_2-1}, \\ \\alpha_1,\\alpha_2>1$$\n\n**Hablar de las desventajas**", "_____no_output_____" ], [ "Ahora definiremos formalmente el método:\n\nNote que $f(x)$ debe ser una **función acotada y con dominio finito** $a\\leq x \\leq b$ como se muestra a continuación:\n\n\nDe acuerdo a esta función $f(x)$ el método propone los siguientes pasos. Asuma que podemos encontrar una función $t(x)$ tal que\n$$t(x)\\geq f(x), \\quad \\forall x$$\nNote que la función $t(x)\\geq 0$ no es una PDF debido a \n$$\\int_{-\\infty}^{\\infty}t(x)dx\\geq \\int_{-\\infty}^{\\infty}f(x)dx =1$$\nTomemos\n$$c=\\int_{-\\infty}^{\\infty}t(x)\\geq 1$$\nDefinamos la función $g(x)=t(x)/c \\rightarrow g(x)$ **es una densidad**. Resultando entonces \n$$\\frac{f(x)}{g(x)}\\leq c,\\quad \\forall x$$\nEl siguiente algoritmo genera una variable aleatoria $X$, distribuida de acuerdo a la densidad $f(x)$\n 1. Generar $R_1$ teniendo densidad $g(x)$ \n 2. Generar $R_2 \\rightarrow U \\sim U(0,1)$ independiente de $R_1$ del paso 1 .\n 3. Evaluar la función de probabilidad en $R_1$.\n 4. Determinar si la siguiente desigualdad se cumple: $$R_2\\leq \\frac{f(R_1)}{t(R_1)}$$\n Si la respuesta es afirmativa se utiliza $X=R_1$, de lo contrario es necesario pasar nuevamente al paso 1, tantas veces como sea necesario.\n\n> Se puede demostrar que la $P(aceptar)=1/c$", "_____no_output_____" ], [ "### Ejemplo 1: Función beta\n\n$$f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}\n (1 - x)^{\\beta - 1}$$", "_____no_output_____" ], [ "### a). Caso particular: $\\alpha=\\beta=3$\nCon estos valores la PDF es \n$$f(x)=30(x^2-2x^3+x^4)$$", "_____no_output_____" ] ], [ [ "# Función de aceptación y rechazo usando for\ndef Acep_rechazo2(R2:'Variables distruidas U~U(0,1)',\n R1:'Variables distribuidas como g(x)',\n f:'función objetivo a generar',\n t:'función que mayora a f'):\n# R1 = np.random.rand(N)\n f_x = f(R1)\n t_x = t(R1)\n condition = R2*t_x <=f_x\n for i in range(len(R1)):\n if condition[i]:\n plt.plot(R1[i],R2[i]*t_x[i],'ob')\n else:\n plt.plot(R1[i],R2[i]*t_x[i],'o')\n plt.show()", "_____no_output_____" ], [ "# Función de aceptación y rechazo usando compresión de listas\ndef Acep_rechazo(R2:'Variables distruidas U~U(0,1)',\n R1:'Variables distribuidas como g(x)',\n f:'función objetivo a generar',\n t:'función que mayora a f'):\n# R1 = np.random.rand(N)\n f_x = f(R1)\n t_x = t(R1)\n condition = R2*t_x <=f_x\n# [plt.plot(R1[i],R2[i]*t_x[i],'ob') if condition[i] else plt.plot(R1[i],R2[i]*t_x[i],'o') \\\n# for i in range(len(R1))] \n# plt.show()\n \n x = [R1[i] for i in range(len(R1)) if condition[i]]\n \n return x", "_____no_output_____" ], [ "# Ilustración del método de aceptación y rechazo cuando se toma t(x) constante\nN = 100\n\n# Función objetivo \nf = lambda x: 30 * (x**2 -2 * x**3 + x**4) \n# Máximo de la función f\nmax_f = f(optimize.fmin(lambda x:-f(x), 0, disp=False))\n# Función t -> Función constante\nt = lambda x: max_f * np.ones([len(x)])\n\n# Rango donde se graficará las funciones\nx = np.arange(0, 1, 0.01)\nprint('El máximo de f es:',max_f)\n\n# Gráficas de las funciones\nplt.plot(x,f(x),label='f(x)')\nplt.plot(x,t(x),label='t(x)')\nplt.legend()\n\n# Validación del método\nN = 20000 # número de puntos a simular\n# Como estoy tomando t(x) constante solo es necesario generar valores aleatorios U~(0,1)\nR2 = np.random.rand(N)\nR1 = np.random.uniform(0, 1, size=N)\n\nx_r = Acep_rechazo(R2, R1, f, t)\n\ny,x_n, _ = plt.hist(x_r, bins=50, density=True)", "El máximo de f es: [1.875]\n" ], [ "np.cumsum(y)[-1]", "_____no_output_____" ] ], [ [ "### b). Caso general: $\\alpha,\\beta>0$ ", "_____no_output_____" ] ], [ [ "# Parámetros de la función beta\na =10; b=3\nN = 500 # número de puntos\n# Función objetivo\nf = lambda x: beta.pdf(x,a,b)\nx = np.arange(0,1,0.01)\nplt.plot(x,f(x),'k')\n# Encuentro el máximo de la función f\nc = float(f(optimize.fmin(lambda x:-f(x),0,disp=False)))\nprint('El máximo de la función es:',c)\n\nt = lambda x: c*np.ones(len(x))\nplt.plot(x,f(x),'k')\nplt.plot(x,t(x),'b')\nR2 = np.random.rand(N)\nR1 = np.random.rand(N)\n\nAcep_rechazo(R2,R1,f,t)\n\nplt.show()", "El máximo de la función es: 3.5848168690361635\n" ] ], [ [ "# Tarea 6\nPartiendo que se desea generar variables aleatorias para la siguiente función de densidad\n$$f(x)=30(x^2-2x^3+x^4)$$\nResponda los siguientes literales:\n1. Usar como función que mayora a $f(x)$ a $t(x)=a \\sin(\\pi x)$ donde a es el máximo de la función $f(x)$ y graficarlas en una misma gráfica, para validar que en realidad si cumple la condición $t(x)\\geq f(x)$.\n2. Encontrar la función de densidad $g(x)$ según lo visto en clase. Reportar todos los cálculos realizados para encontrar dicha función usando Markdown (Latex).\n3. Usar la función encontrada en el punto 2 y utilizar el método de la transformada inversa visto en la clase 9, para generar variables aleatorias que sigan la distribución $g(x)$. **Nota:** Recuerde que el método de la transformada inversa funciona con la distribución de probabilidad acumulada y no con su densidad. Nuevamente similar al punto anterior reportar todos los cálculos usando Markdown (Latex). \n4. Según el punto 3, generar 10000 puntos aleatorios que sigan la distribución $g(x)$ y comparar con su histograma para validar que los puntos generados siguen la distribución deseada. El resultado debe ser como sigue:\n", "_____no_output_____" ], [ "5. Genere 500 puntos aleatorios usando el método de aceptación y rechazo y las funciones $f(x)$ y $t(x)$ para validar que todos los cálculos anteriores están correctamente realizados. El resultado debe de ser como sigue:\n", "_____no_output_____" ], [ "6. Comparar el porcentaje de puntos de aceptación cuando se usa $t(x)$ constante y $t(x)$ un pulso senoidal. Concluir", "_____no_output_____" ], [ "7. Genere una variable aleatoria $X$ a partir de la siguiente PDF\n$$f(x)=20x(1-x)^3$$ \nusando el método de aceptación y rechazo", "_____no_output_____" ], [ "8. Seguir un procedimiento similar al mostrado cuando se generó una distribución binomial, pero en esta caso genere un código que genere variables aletorias Poisson cuya función de distribución de probabilidad esta dada por:\n>$$P(k,\\lambda)=\\frac{e^{-\\lambda}(\\lambda)^k}{k!}$$\n \n> Demuestre matemáticamente que \n> $$P(k+1)=\\frac{\\lambda}{k+1}P(k)$$\n> y a partir de esta relación genere variables aletorias que distribuyen poisson usando el método de la transformada inversa.\n\nEnlace: https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_Poisson", "_____no_output_____" ], [ "## Parámetros de entrega\nVoy a habilitar un link en Canvas donde deben de subir su cuaderno de python con la sulución de los problemas planteados en parejas. La podrán entregar a mas tardar el martes 6 de octubre a las 6pm. Como será en parejas, deben de crear un proyecto conjunto en github y realizar los ejercicios de manera conjunta, de manera similar a como realizaron los ejercicios en la tarea 1. **Deben de poner en la solución de la tarea el enlace de github de el administrador del repositorio**, del cuál me basaré para poner la calificación.", "_____no_output_____" ], [ "# Solución Tarea", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "\n", "_____no_output_____" ] ], [ [ "<script>\n $(document).ready(function(){\n $('div.prompt').hide();\n $('div.back-to-top').hide();\n $('nav#menubar').hide();\n $('.breadcrumb').hide();\n $('.hidden-print').hide();\n });\n</script>\n\n<footer id=\"attribution\" style=\"float:right; color:#808080; background:#fff;\">\nCreated with Jupyter by Oscar David Jaramillo Z.\n</footer>", "_____no_output_____" ] ] ]

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[ [ [ "import pandas as pd\nimport numpy as np\n#import matplotlib.pyplot as plt\n#import seaborn as sns", "_____no_output_____" ], [ "df = pd.read_csv('total_data1.csv')", "_____no_output_____" ] ], [ [ "### xdata로 상관계수가 높은 column을 넣어서 Ridge\n- elasticnet으로 상관계수가 높은 feature를 넣어 모델생성", "_____no_output_____" ] ], [ [ "from sklearn.metrics import mean_squared_error", "_____no_output_____" ], [ "# 필요 패키지 로드\n#from sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import mean_absolute_error", "_____no_output_____" ], [ "# y값인 q1-q5가 결측인 2020년 데이터 제거\na = df[0:-82]\na", "_____no_output_____" ], [ "# 경찰서와 연도 데이터 제거\na.drop(columns = ['jur_stn', 'year'], inplace = True)", "C:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3990: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n return super().drop(\n" ], [ "a_ = a.drop(columns = ['q1', 'q2', 'q3', 'q4', 'q5'])", "_____no_output_____" ], [ "a_", "_____no_output_____" ], [ "a_.columns", "_____no_output_____" ], [ "a_1 = a_", "_____no_output_____" ], [ "a_2 = a_", "_____no_output_____" ], [ "a_3 = a_", "_____no_output_____" ], [ "a_4 = a_", "_____no_output_____" ], [ "a_5 = a_", "_____no_output_____" ], [ "# StandardScaling\nscaler1 = StandardScaler()\nscaler1.fit(a_1) # scaler에 xdata 학습\na_s1 = scaler1.transform(a_1)\na_s1", "_____no_output_____" ], [ "# StandardScaling\nscaler2 = StandardScaler()\nscaler2.fit(a_2) # scaler에 xdata 학습\na_s2 = scaler2.transform(a_2)\na_s2", "_____no_output_____" ], [ "# StandardScaling\nscaler3 = StandardScaler()\nscaler3.fit(a_3) # scaler에 xdata 학습\na_s3 = scaler3.transform(a_3)\na_s3", "_____no_output_____" ], [ "# StandardScaling\nscaler4 = StandardScaler()\nscaler4.fit(a_4) # scaler에 xdata 학습\na_s4 = scaler4.transform(a_4)\na_s4", "_____no_output_____" ], [ "# StandardScaling\nscaler5 = StandardScaler()\nscaler5.fit(a_5) # scaler에 xdata 학습\na_s5 = scaler5.transform(a_5)\na_s5", "_____no_output_____" ], [ "# x데이터 설정 2017, 2018 데이터를 학습용, 2019년 데이터를 검증용 데이터셋으로 설정\n\nxtrain1 = a_s1[:-82]\nxtest1 = a_s1[-82:]\nxtrain2 = a_s2[:-82]\nxtest2 = a_s2[-82:]\nxtrain3 = a_s3[:-82]\nxtest3 = a_s3[-82:]\nxtrain4 = a_s4[:-82]\nxtest4 = a_s4[-82:]\nxtrain5 = a_s5[:-82]\nxtest5 = a_s5[-82:]", "_____no_output_____" ], [ "# y데이터 설정 2017, 2018 데이터를 학습용, 2019년 데이터를 검증용 데이터셋으로 설정\n\ntrain = a[:-82]\ntest = a[-82:]\n\nytrain1 = train['q1']\nytrain2 = train['q2']\nytrain3 = train['q3']\nytrain4 = train['q4']\nytrain5 = train['q5']\n\nytest1 = test['q1']\nytest2 = test['q2']\nytest3 = test['q3']\nytest4 = test['q4']\nytest5 = test['q5']", "_____no_output_____" ], [ "a_1.columns", "_____no_output_____" ], [ "# 그리드 서치 패키지\nfrom sklearn.model_selection import GridSearchCV", "_____no_output_____" ], [ "# 최적 성능을 내는 lasso의 alpha값을 얻기 위해 param_grid 생성\n\nparam_grid = {'alpha' : np.linspace(0.001, 10.0, 10000)}\n\n#그리드 서치 설정\ngrid_search = GridSearchCV(Ridge(), param_grid = param_grid, cv = 10, n_jobs = -1, scoring ='r2')\n#grid_search = GridSearchCV(Ridge(), param_grid = param_grid, cv = 10, n_jobs = -1, scoring ='neg_mean_absolute_error')", "_____no_output_____" ] ], [ [ "#### q1 절도폭력 ", "_____no_output_____" ] ], [ [ "# 그리드 서치 후 최고 성능의 모델을 ridge1에 저장\n\ngrid_search.fit(xtrain1, ytrain1)\nridge1 = grid_search.best_estimator_", "_____no_output_____" ], [ "# MAE 출력\n\ny_pred1 = ridge1.predict(xtest1)\nmean_absolute_error(ytest1, y_pred1)", "_____no_output_____" ], [ "# 결과\n\nprint('alpha =', ridge1.alpha)\nprint(ridge1.coef_) # Ridge 회귀분석으로 나온 weghit값\nprint('가장 강한 양의 상관관계: ',a_1.columns[ridge1.coef_.argmax()], '\\n가장 강한 음의 상관관계: ', a_1.columns[ridge1.coef_.argmin()])", "alpha = 10.0\n[ 0.1858718 0.35944705 -0.18654878 -0.88752003 0.12550527 -0.00879849\n 0.23483285 -0.25640454 0.46469126 0.50380436 0.85031836 -0.29439753\n -0.17669475 0.36080588 0.12274043 -0.78509441 -0.35994638 0.30965558\n 0.10691769 0.51217057 -0.14159903 0.05857899 0.07225416 -0.33213259\n -0.30612817 -0.28521751 -0.02244433 0.04295547 0.23212191 -0.43095751\n 0.99679932 0.67407008 0.04719795 0.02266245 -0.26957157 0.1635311\n 0.27777131 0.18326995 -0.26235237 -0.253049 0.18470932 -0.32227054\n -0.11586935 0.20246951 0.00619761 -0.08666845 -0.39903773 0.31214314\n 0.26027898 -0.34774913 -0.043771 0.18120353 0.03499807 -0.3080387\n -0.20039405 0.26735254 -0.12913279 0.02064122 -0.08359852 0.24854472]\n가장 강한 양의 상관관계: 외국인인구수대비검거수 \n가장 강한 음의 상관관계: vio_cnt\n" ] ], [ [ "#### q2 강도살인", "_____no_output_____" ] ], [ [ "# 그리드 서치 후 최고 성능의 모델을 ela2에 저장\n\ngrid_search.fit(xtrain2, ytrain2)\nridge2 = grid_search.best_estimator_", "_____no_output_____" ], [ "# MAE 출력\n\ny_pred2 = ridge2.predict(xtest2)\nmean_absolute_error(ytest2, y_pred2)", "_____no_output_____" ], [ "# 결과\n\nprint('alpha =', ridge2.alpha)\nprint(ridge2.coef_) # Ridge 회귀분석으로 나온 weghit값\nprint('가장 강한 양의 상관관계: ',a_2.columns[ridge2.coef_.argmax()], '\\n가장 강한 음의 상관관계: ', a_2.columns[ridge2.coef_.argmin()])", "alpha = 4.566000000000001\n[ 0.0860792 0.4554509 -0.42882456 -1.39474717 -0.04082773 0.67245513\n 0.44065367 -0.35757788 0.36512942 1.13101206 1.47174792 -0.50713043\n -0.21119051 0.64726755 0.26600496 -1.10801628 -0.61302412 0.37013122\n -0.29569089 0.61392221 -0.10098044 0.05371732 0.21285102 -0.44627423\n -0.21074038 -0.11088239 -0.02215864 0.05477171 0.20860851 -0.79054475\n 0.7222386 1.1018597 -0.29250057 0.16025184 -0.52723943 0.31317932\n 0.42472581 0.10930088 -0.19559189 -0.25294844 0.02556162 -0.55658869\n -0.10951789 0.24595558 0.02282998 -0.02243589 -0.49783467 0.394488\n 0.30491444 -0.03552448 -0.15389 0.13197577 0.11347984 -0.57586571\n 0.02578398 0.08091628 -0.29398064 0.16571328 -0.32548941 0.66159982]\n가장 강한 양의 상관관계: for_u20 \n가장 강한 음의 상관관계: vio_cnt\n" ] ], [ [ "#### q3 교통안전", "_____no_output_____" ] ], [ [ "# 그리드 서치 후 최고 성능의 모델을 lasso3에 저장\n\ngrid_search.fit(xtrain3, ytrain3)\nridge3 = grid_search.best_estimator_", "_____no_output_____" ], [ "ridge3 = Ridge(alpha = 23)\nridge3.fit(xtrain3, ytrain3)", "_____no_output_____" ], [ "# MAE 출력\n\ny_pred3 = ridge3.predict(xtest3)\nmean_absolute_error(ytest3, y_pred3)", "_____no_output_____" ], [ "# 결과\n\nprint('alpha =', ridge3.alpha)\nprint(ridge3.coef_) # Ridge 회귀분석으로 나온 weghit값\nprint('가장 강한 양의 상관관계: ',a_3.columns[ridge3.coef_.argmax()], '\\n가장 강한 음의 상관관계: ', a_3.columns[ridge3.coef_.argmin()])", "alpha = 23\n[ 0.60462781 -0.01836216 0.33480332 0.07597766 0.05094331 -0.01801049\n 0.12955529 -0.03098228 -0.02389251 0.32231312 0.36155864 -0.06357061\n -0.17338605 0.10758736 -0.13071408 -0.23385295 -0.16651811 0.02234594\n 0.22738254 0.10056028 -0.08911714 -0.08948507 0.10180638 -0.05404081\n 0.32308556 -0.05319068 -0.00210497 0.03723783 0.19267206 0.11423388\n 0.02766457 0.52296002 -0.01345592 0.01568679 0.13478935 -0.21232877\n -0.05575848 0.2075885 -0.04617672 -0.14361172 0.12973644 -0.55315098\n -0.43058753 0.13637724 0.26248718 0.09187102 -0.28458975 0.21837938\n 0.20221084 0.26698087 -0.06346565 -0.14705863 0.1048014 -0.05481377\n -0.00895966 0.02854603 -0.05046488 -0.08276508 0.03386703 0.16616376]\n가장 강한 양의 상관관계: child \n가장 강한 음의 상관관계: ofn_10\n" ] ], [ [ "#### q4 법질서 준수도", "_____no_output_____" ] ], [ [ "# 그리드 서치 후 최고 성능의 모델을 lasso4에 저장\n\ngrid_search.fit(xtrain4, ytrain4)\nridge4 = grid_search.best_estimator_", "_____no_output_____" ], [ "# MAE 출력\n\ny_pred4 = ridge4.predict(xtest4)\nmean_absolute_error(ytest4, y_pred4)", "_____no_output_____" ], [ "# 결과\n\nprint('alpha =', ridge4.alpha)\nprint(ridge4.coef_) # Ridge 회귀분석으로 나온 weghit값\nprint('가장 강한 양의 상관관계: ',a_4.columns[ridge4.coef_.argmax()], '\\n가장 강한 음의 상관관계: ', a_4.columns[ridge4.coef_.argmin()])", "alpha = 10.0\n[ 0.08422014 0.47496439 0.14834264 -0.31512474 0.90924745 0.72152184\n -0.16558919 -0.19975831 -0.11015924 0.08660646 0.65342416 -0.33030785\n -0.23131614 0.15442808 -0.02441021 -0.79282277 -0.16042672 0.17084599\n 0.28093741 0.34898235 -0.48400998 0.13322148 -0.25607675 -0.2081495\n 0.36480096 -0.54529326 -0.28543694 0.0490413 0.16311427 -0.4195897\n 0.58055968 -0.99569428 -0.04666948 0.45819622 -0.02352386 0.81593753\n 0.04091217 0.1235867 0.20385689 -0.3854837 0.20093322 -0.4193185\n -0.48252598 0.45797532 0.03029416 0.08614095 -0.38301169 0.23971484\n 0.48417109 0.48881207 -0.05618829 -0.22196673 0.16491527 -0.3411742\n 0.12608131 -0.01440109 -0.33302747 -0.25247363 0.13361622 0.38994271]\n가장 강한 양의 상관관계: mur_rob_cnt \n가장 강한 음의 상관관계: 인구수대비경찰수\n" ] ], [ [ "#### q5 전반적 안전도", "_____no_output_____" ] ], [ [ "# 그리드 서치 후 최고 성능의 모델을 lasso4에 저장\n\ngrid_search.fit(xtrain5, ytrain5)\nridge5 = grid_search.best_estimator_", "_____no_output_____" ], [ "ridge5 = Ridge(alpha = 6.25)\nridge5.fit(xtrain5, ytrain5)", "_____no_output_____" ], [ "# MAE 출력\n\ny_pred5 = ridge5.predict(xtest5)\nmean_absolute_error(ytest5, y_pred5)", "_____no_output_____" ], [ "# 결과\n\nprint('alpha =', ridge5.alpha)\nprint(ridge5.coef_) # Ridge 회귀분석으로 나온 weghit값\nprint('가장 강한 양의 상관관계: ',a_5.columns[ridge5.coef_.argmax()], '\\n가장 강한 음의 상관관계: ', a_5.columns[ridge5.coef_.argmin()])", "alpha = 6.25\n[ 0.06421938 0.4187727 0.01456397 -0.98143189 0.27599427 0.34433021\n 0.25962214 -0.1884639 0.36799516 0.6268941 0.78630458 -0.33307267\n -0.33674456 0.32468536 -0.07203347 -0.87313847 -0.42267696 0.30238462\n 0.04100536 0.52735428 -0.07934469 -0.02891441 0.096653 -0.20399747\n 0.23988935 -0.51472116 -0.10890164 -0.01168908 0.09871749 -0.28613236\n 0.67575661 0.2864437 -0.0625666 0.13997026 -0.18723721 0.13303888\n 0.25390993 0.1604691 -0.01088797 -0.19593194 0.06767935 -0.38309175\n -0.24771206 0.3046604 0.00549987 0.06127319 -0.37182862 0.27782696\n 0.29352346 -0.01387247 -0.08819971 -0.00617579 0.13260857 -0.30891844\n 0.00401649 0.11547854 -0.32287934 -0.00472153 -0.06880729 0.2824365 ]\n가장 강한 양의 상관관계: for_u20 \n가장 강한 음의 상관관계: vio_cnt\n" ] ] ]

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[ "RSA-MD" ]

[ "RSA-MD" ]

[ "RSA-MD" ]

[ [ [ "# Optional: Dropout\n\n**Note**: This exercise is optional and using dropout is not required to pass beyond the linear regime of the scoring function for your fully connected network.\n\nDropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n\n[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012", "_____no_output_____" ] ], [ [ "# As usual, a bit of setup\n\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom exercise_code.classifiers.fc_net import *\nfrom exercise_code.data_utils import get_CIFAR10_data\nfrom exercise_code.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\nfrom exercise_code.solver import Solver\n\n%matplotlib inline\nplt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\nplt.rcParams['image.interpolation'] = 'nearest'\nplt.rcParams['image.cmap'] = 'gray'\n\n# for auto-reloading external modules\n# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n%load_ext autoreload\n%autoreload 2\n\n# supress cluttering warnings in solutions\nimport warnings\nwarnings.filterwarnings('ignore')\n\ndef rel_error(x, y):\n \"\"\" returns relative error \"\"\"\n return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))", "_____no_output_____" ], [ "# Load the (preprocessed) CIFAR10 data.\n\ndata = get_CIFAR10_data()\nfor k, v in data.items():\n print('%s: ' % k, v.shape)", "_____no_output_____" ] ], [ [ "# Dropout forward pass\nIn the file `exercise_code/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n\nOnce you have done so, run the cell below to test your implementation.", "_____no_output_____" ] ], [ [ "x = np.random.randn(500, 500) + 10\n\nfor p in [0.3, 0.6, 0.75]:\n out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n\n print('Running tests with p = ', p)\n print('Mean of input: ', x.mean())\n print('Mean of train-time output: ', out.mean())\n print('Mean of test-time output: ', out_test.mean())\n print('Fraction of train-time output set to zero: ', (out == 0).mean())\n print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n print()", "_____no_output_____" ] ], [ [ "# Dropout backward pass\nIn the file `exercise_code/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation.", "_____no_output_____" ] ], [ [ "x = np.random.randn(10, 10) + 10\ndout = np.random.randn(*x.shape)\n\ndropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\nout, cache = dropout_forward(x, dropout_param)\ndx = dropout_backward(dout, cache)\ndx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n\nprint('dx relative error: ', rel_error(dx, dx_num))", "_____no_output_____" ] ], [ [ "# Fully-connected nets with Dropout\nIn the file `exercise_code/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation.", "_____no_output_____" ] ], [ [ "N, D, H1, H2, C = 2, 15, 20, 30, 10\nX = np.random.randn(N, D)\ny = np.random.randint(C, size=(N,))\n\nfor dropout in [0, 0.25, 0.5]:\n print('Running check with dropout = ', dropout)\n model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n weight_scale=5e-2, dtype=np.float64,\n dropout=dropout, seed=123)\n\n loss, grads = model.loss(X, y)\n print('Initial loss: ', loss)\n\n for name in sorted(grads):\n f = lambda _: model.loss(X, y)[0]\n grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n print()", "_____no_output_____" ] ], [ [ "# Regularization experiment\nAs an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a dropout probability of 0.75. We will then visualize the training and validation accuracies of the two networks over time.", "_____no_output_____" ] ], [ [ "# Train two identical nets, one with dropout and one without\n\nnum_train = 500\nsmall_data = {\n 'X_train': data['X_train'][:num_train],\n 'y_train': data['y_train'][:num_train],\n 'X_val': data['X_val'],\n 'y_val': data['y_val'],\n}\n\nsolvers = {}\ndropout_choices = [0, 0.75]\nfor dropout in dropout_choices:\n model = FullyConnectedNet([500], dropout=dropout)\n print(\"dropout = \", dropout)\n\n solver = Solver(model, small_data,\n num_epochs=25, batch_size=100,\n update_rule='adam',\n optim_config={\n 'learning_rate': 5e-4,\n },\n verbose=True, print_every=100)\n solver.train()\n solvers[dropout] = solver", "_____no_output_____" ], [ "# Plot train and validation accuracies of the two models\n\ntrain_accs = []\nval_accs = []\nfor dropout in dropout_choices:\n solver = solvers[dropout]\n train_accs.append(solver.train_acc_history[-1])\n val_accs.append(solver.val_acc_history[-1])\n\nplt.subplot(3, 1, 1)\nfor dropout in dropout_choices:\n plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\nplt.title('Train accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(ncol=2, loc='lower right')\n \nplt.subplot(3, 1, 2)\nfor dropout in dropout_choices:\n plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\nplt.title('Val accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend(ncol=2, loc='lower right')\n\nplt.gcf().set_size_inches(15, 15)\nplt.show()", "_____no_output_____" ] ], [ [ "<div class=\"alert alert-info\">\n <h3>Inline Question</h3>\n <p>Describe the results of this experiment and try to reason why you got these results.</p>\n</div>", "_____no_output_____" ] ] ]

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[ [ [ "### QDA", "_____no_output_____" ] ], [ [ "load(\"PCA.rda\")\nload(\"DP.rda\")\nsuppressMessages(library(caret))\nset.seed(201703)", "_____no_output_____" ], [ "options(warn=-1)\n# QDA\npca_qda_s = train(response~., data = pca_train, method = \"qda\", trControl = trainControl(method = \"LOOCV\"))\npca_qda_te = predict(pca_qda_s, data.frame(pca_test_s))\npca_qda_ac = mean(pca_qda_te == golub_test_r)\npca_qda_re = c(LOOCV = pca_qda_s$results$Accuracy, Test = pca_qda_ac)", "_____no_output_____" ], [ "pca_qda_re", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/jtkrohm/jt/blob/master/Udacity%20Course.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ] ], [ [ "!pwd", "/content\n" ] ], [ [ "", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ], [ "print(\"JT\")", "JT\n" ] ] ]

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[ [ [ "## Dependencies", "_____no_output_____" ] ], [ [ "from openvaccine_scripts import *\nimport warnings, json\nfrom sklearn.model_selection import KFold, StratifiedKFold\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras import optimizers, losses, Model\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n\n\nSEED = 0\nseed_everything(SEED)\nwarnings.filterwarnings('ignore')", "_____no_output_____" ] ], [ [ "# Model parameters", "_____no_output_____" ] ], [ [ "config = {\n \"BATCH_SIZE\": 64,\n \"EPOCHS\": 120,\n \"LEARNING_RATE\": 1e-3,\n \"ES_PATIENCE\": 10,\n \"N_FOLDS\": 5,\n \"N_USED_FOLDS\": 5,\n \"PB_SEQ_LEN\": 107,\n \"PV_SEQ_LEN\": 130,\n}\n\nwith open('config.json', 'w') as json_file:\n json.dump(json.loads(json.dumps(config)), json_file)\n \nconfig", "_____no_output_____" ] ], [ [ "# Load data", "_____no_output_____" ] ], [ [ "database_base_path = '/kaggle/input/stanford-covid-vaccine/'\ntrain = pd.read_json(database_base_path + 'train.json', lines=True)\ntest = pd.read_json(database_base_path + 'test.json', lines=True)\n\nprint('Train samples: %d' % len(train))\ndisplay(train.head())\nprint(f'Test samples: {len(test)}')\ndisplay(test.head())", "Train samples: 2400\n" ] ], [ [ "## Auxiliary functions", "_____no_output_____" ] ], [ [ "def get_dataset(x, y=None, sample_weights=None, labeled=True, shuffled=True, batch_size=32, buffer_size=-1, seed=0):\n input_map = {'inputs_seq': x['sequence'], \n 'inputs_struct': x['structure'], \n 'inputs_loop': x['predicted_loop_type'], \n 'inputs_bpps_max': x['bpps_max'], \n 'inputs_bpps_sum': x['bpps_sum'], \n 'inputs_bpps_mean': x['bpps_mean'], \n 'inputs_bpps_scaled': x['bpps_scaled']}\n \n if labeled:\n output_map = {'output_react': y['reactivity'], \n 'output_bg_ph': y['deg_Mg_pH10'], \n 'output_ph': y['deg_pH10'], \n 'output_mg_c': y['deg_Mg_50C'], \n 'output_c': y['deg_50C']}\n if sample_weights is not None:\n dataset = tf.data.Dataset.from_tensor_slices((input_map, output_map, sample_weights))\n else:\n dataset = tf.data.Dataset.from_tensor_slices((input_map, output_map))\n else:\n dataset = tf.data.Dataset.from_tensor_slices((input_map))\n \n if shuffled:\n dataset = dataset.shuffle(2048, seed=seed)\n dataset = dataset.batch(batch_size)\n dataset = dataset.prefetch(buffer_size)\n \n return dataset", "_____no_output_____" ] ], [ [ "# Model", "_____no_output_____" ] ], [ [ "def model_fn(hidden_dim=384, dropout=.5, pred_len=68, n_outputs=5):\n inputs_seq = L.Input(shape=(None, 1), name='inputs_seq') \n inputs_struct = L.Input(shape=(None, 1), name='inputs_struct') \n inputs_loop = L.Input(shape=(None, 1), name='inputs_loop')\n inputs_bpps_max = L.Input(shape=(None, 1), name='inputs_bpps_max')\n inputs_bpps_sum = L.Input(shape=(None, 1), name='inputs_bpps_sum')\n inputs_bpps_mean = L.Input(shape=(None, 1), name='inputs_bpps_mean')\n inputs_bpps_scaled = L.Input(shape=(None, 1), name='inputs_bpps_scaled')\n\n def _one_hot(x, num_classes):\n return K.squeeze(K.one_hot(K.cast(x, 'uint8'), num_classes=num_classes), axis=2)\n\n ohe_seq = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_seq)}, input_shape=(None, 1))(inputs_seq)\n ohe_struct = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_struct)}, input_shape=(None, 1))(inputs_struct)\n ohe_loop = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_loop)}, input_shape=(None, 1))(inputs_loop)\n \n # Conv block\n conv_seq = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(ohe_seq)\n conv_struct = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(ohe_struct)\n conv_loop = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(ohe_loop)\n conv_bpps_max = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(inputs_bpps_max)\n conv_bpps_sum = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(inputs_bpps_sum)\n conv_bpps_mean = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(inputs_bpps_mean)\n conv_bpps_scaled = L.Conv1D(filters=64, \n kernel_size=5, \n strides=1, \n padding='same')(inputs_bpps_scaled)\n \n x_concat = L.concatenate([conv_seq, conv_struct, conv_loop, conv_bpps_max, \n conv_bpps_sum, conv_bpps_mean, conv_bpps_scaled], axis=-1, name='conv_concatenate')\n\n # Recurrent block\n x = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x_concat)\n \n x_rec = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x)\n x = L.Add()([x_rec, x])\n \n x_rec = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x)\n x = L.Add()([x_rec, x])\n \n \n # Since we are only making predictions on the first part of each sequence, we have to truncate it\n x_truncated = x[:, :pred_len]\n \n output_react = L.Dense(1, activation='linear', name='output_react')(x_truncated)\n output_bg_ph = L.Dense(1, activation='linear', name='output_bg_ph')(x_truncated)\n output_ph = L.Dense(1, activation='linear', name='output_ph')(x_truncated)\n output_mg_c = L.Dense(1, activation='linear', name='output_mg_c')(x_truncated)\n output_c = L.Dense(1, activation='linear', name='output_c')(x_truncated)\n \n \n model = Model(inputs=[inputs_seq, inputs_struct, inputs_loop, inputs_bpps_max, \n inputs_bpps_sum, inputs_bpps_mean, inputs_bpps_scaled], \n outputs=[output_react, output_bg_ph, output_ph, output_mg_c, output_c])\n\n opt = optimizers.Adam(learning_rate=config['LEARNING_RATE'])\n model.compile(optimizer=opt, loss={'output_react': MCRMSE, \n 'output_bg_ph': MCRMSE, \n 'output_ph': MCRMSE, \n 'output_mg_c': MCRMSE, \n 'output_c': MCRMSE},\n loss_weights={'output_react': 2., \n 'output_bg_ph': 2., \n 'output_ph': 1., \n 'output_mg_c': 2., \n 'output_c': 1.})\n\n return model\n\nmodel = model_fn()\nmodel.summary()", "Model: \"functional_1\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninputs_seq (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\ninputs_struct (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\ninputs_loop (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\nlambda (Lambda) (None, None, 4) 0 inputs_seq[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, None, 3) 0 inputs_struct[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, None, 7) 0 inputs_loop[0][0] \n__________________________________________________________________________________________________\ninputs_bpps_max (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\ninputs_bpps_sum (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\ninputs_bpps_mean (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\ninputs_bpps_scaled (InputLayer) [(None, None, 1)] 0 \n__________________________________________________________________________________________________\nconv1d (Conv1D) (None, None, 64) 1344 lambda[0][0] \n__________________________________________________________________________________________________\nconv1d_1 (Conv1D) (None, None, 64) 1024 lambda_1[0][0] \n__________________________________________________________________________________________________\nconv1d_2 (Conv1D) (None, None, 64) 2304 lambda_2[0][0] \n__________________________________________________________________________________________________\nconv1d_3 (Conv1D) (None, None, 64) 384 inputs_bpps_max[0][0] \n__________________________________________________________________________________________________\nconv1d_4 (Conv1D) (None, None, 64) 384 inputs_bpps_sum[0][0] \n__________________________________________________________________________________________________\nconv1d_5 (Conv1D) (None, None, 64) 384 inputs_bpps_mean[0][0] \n__________________________________________________________________________________________________\nconv1d_6 (Conv1D) (None, None, 64) 384 inputs_bpps_scaled[0][0] \n__________________________________________________________________________________________________\nconv_concatenate (Concatenate) (None, None, 448) 0 conv1d[0][0] \n conv1d_1[0][0] \n conv1d_2[0][0] \n conv1d_3[0][0] \n conv1d_4[0][0] \n conv1d_5[0][0] \n conv1d_6[0][0] \n__________________________________________________________________________________________________\nbidirectional (Bidirectional) (None, None, 768) 1921536 conv_concatenate[0][0] \n__________________________________________________________________________________________________\nbidirectional_1 (Bidirectional) (None, None, 768) 2658816 bidirectional[0][0] \n__________________________________________________________________________________________________\nadd (Add) (None, None, 768) 0 bidirectional_1[0][0] \n bidirectional[0][0] \n__________________________________________________________________________________________________\nbidirectional_2 (Bidirectional) (None, None, 768) 2658816 add[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, None, 768) 0 bidirectional_2[0][0] \n add[0][0] \n__________________________________________________________________________________________________\ntf_op_layer_strided_slice (Tens [(None, None, 768)] 0 add_1[0][0] \n__________________________________________________________________________________________________\noutput_react (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0] \n__________________________________________________________________________________________________\noutput_bg_ph (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0] \n__________________________________________________________________________________________________\noutput_ph (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0] \n__________________________________________________________________________________________________\noutput_mg_c (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0] \n__________________________________________________________________________________________________\noutput_c (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0] \n==================================================================================================\nTotal params: 7,249,221\nTrainable params: 7,249,221\nNon-trainable params: 0\n__________________________________________________________________________________________________\n" ] ], [ [ "# Pre-process", "_____no_output_____" ] ], [ [ "# Add bpps as features\nbpps_max = []\nbpps_sum = []\nbpps_mean = []\nbpps_scaled = []\nbpps_nb_mean = 0.077522 # mean of bpps_nb across all training data\nbpps_nb_std = 0.08914 # std of bpps_nb across all training data\nfor row in train.itertuples():\n probability = np.load(f'{database_base_path}/bpps/{row.id}.npy')\n bpps_max.append(probability.max(-1).tolist())\n bpps_sum.append((1-probability.sum(-1)).tolist())\n bpps_mean.append((1-probability.mean(-1)).tolist())\n # bpps nb\n bpps_nb = (probability > 0).sum(axis=0) / probability.shape[0]\n bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std\n bpps_scaled.append(bpps_nb)\ntrain = train.assign(bpps_max=bpps_max, bpps_sum=bpps_sum, bpps_mean=bpps_mean, bpps_scaled=bpps_scaled)\n\nbpps_max = []\nbpps_sum = []\nbpps_mean = []\nbpps_scaled = []\nfor row in test.itertuples():\n probability = np.load(f'{database_base_path}/bpps/{row.id}.npy')\n bpps_max.append(probability.max(-1).tolist())\n bpps_sum.append((1-probability.sum(-1)).tolist())\n bpps_mean.append((1-probability.mean(-1)).tolist())\n # bpps nb\n bpps_nb = (probability > 0).sum(axis=0) / probability.shape[0]\n bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std\n bpps_scaled.append(bpps_nb)\ntest = test.assign(bpps_max=bpps_max, bpps_sum=bpps_sum, bpps_mean=bpps_mean, bpps_scaled=bpps_scaled)\n\n\nfeature_cols = ['sequence', 'structure', 'predicted_loop_type', 'bpps_max', 'bpps_sum', 'bpps_mean', 'bpps_scaled']\npred_cols = ['reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C', 'deg_50C']\nencoder_list = [token2int_seq, token2int_struct, token2int_loop, None, None, None, None]\n\npublic_test = test.query(\"seq_length == 107\").copy()\nprivate_test = test.query(\"seq_length == 130\").copy()\n\nx_test_public = get_features_dict(public_test, feature_cols, encoder_list, public_test.index)\nx_test_private = get_features_dict(private_test, feature_cols, encoder_list, private_test.index)\n\n# To use as stratified col\ntrain['signal_to_noise_int'] = train['signal_to_noise'].astype(int)", "_____no_output_____" ] ], [ [ "# Training", "_____no_output_____" ] ], [ [ "AUTO = tf.data.experimental.AUTOTUNE\nskf = KFold(n_splits=config['N_USED_FOLDS'], shuffle=True, random_state=SEED)\nhistory_list = []\n\noof = train[['id', 'SN_filter', 'signal_to_noise'] + pred_cols].copy()\noof_preds = np.zeros((len(train), 68, len(pred_cols)))\ntest_public_preds = np.zeros((len(public_test), config['PB_SEQ_LEN'], len(pred_cols)))\ntest_private_preds = np.zeros((len(private_test), config['PV_SEQ_LEN'], len(pred_cols)))\n\nfor fold,(train_idx, valid_idx) in enumerate(skf.split(train['signal_to_noise_int'])):\n if fold >= config['N_USED_FOLDS']:\n break\n print(f'\\nFOLD: {fold+1}')\n \n ### Create datasets\n x_train = get_features_dict(train, feature_cols, encoder_list, train_idx)\n x_valid = get_features_dict(train, feature_cols, encoder_list, valid_idx)\n y_train = get_targets_dict(train, pred_cols, train_idx)\n y_valid = get_targets_dict(train, pred_cols, valid_idx)\n w_train = np.log(train.iloc[train_idx]['signal_to_noise'].values+1.1)/2\n w_valid = np.log(train.iloc[valid_idx]['signal_to_noise'].values+1.1)/2\n \n \n train_ds = get_dataset(x_train, y_train, w_train, labeled=True, shuffled=True, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)\n valid_ds = get_dataset(x_valid, y_valid, w_valid, labeled=True, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)\n oof_ds = get_dataset(x_valid, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)\n test_public_ds = get_dataset(x_test_public, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)\n test_private_ds = get_dataset(x_test_private, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)\n\n\n ### Model\n K.clear_session()\n model = model_fn()\n\n model_path = f'model_{fold}.h5'\n es = EarlyStopping(monitor='val_loss', mode='min', patience=config['ES_PATIENCE'], restore_best_weights=True, verbose=1)\n rlrp = ReduceLROnPlateau(monitor='val_loss', mode='min', factor=0.1, patience=5, verbose=1)\n \n ### Train\n history = model.fit(train_ds,\n validation_data=valid_ds,\n callbacks=[es, rlrp],\n epochs=config['EPOCHS'],\n batch_size=config['BATCH_SIZE'],\n verbose=2).history\n \n history_list.append(history)\n # Save last model weights\n model.save_weights(model_path)\n\n ### Inference\n oof_ds_preds = np.array(model.predict(oof_ds)).reshape((len(pred_cols), len(valid_idx), 68)).transpose((1, 2, 0))\n oof_preds[valid_idx] = oof_ds_preds\n \n # Short sequence (public test)\n model = model_fn(pred_len=config['PB_SEQ_LEN'])\n model.load_weights(model_path)\n test_public_ds_preds = np.array(model.predict(test_public_ds)).reshape((len(pred_cols), len(public_test), \n config['PB_SEQ_LEN'])).transpose((1, 2, 0))\n test_public_preds += test_public_ds_preds * (1 / config['N_USED_FOLDS'])\n \n # Long sequence (private test)\n model = model_fn(pred_len=config['PV_SEQ_LEN'])\n model.load_weights(model_path)\n test_private_ds_preds = np.array(model.predict(test_private_ds)).reshape((len(pred_cols), len(private_test), \n config['PV_SEQ_LEN'])).transpose((1, 2, 0))\n test_private_preds += test_private_ds_preds * (1 / config['N_USED_FOLDS'])", "\nFOLD: 1\nEpoch 1/120\n30/30 - 7s - loss: 3.5876 - output_react_loss: 0.4014 - output_bg_ph_loss: 0.5248 - output_ph_loss: 0.5226 - output_mg_c_loss: 0.4188 - output_c_loss: 0.3750 - val_loss: 2.3896 - val_output_react_loss: 0.2423 - val_output_bg_ph_loss: 0.3301 - val_output_ph_loss: 0.3582 - val_output_mg_c_loss: 0.3026 - val_output_c_loss: 0.2814\nEpoch 2/120\n30/30 - 4s - loss: 2.3368 - output_react_loss: 0.2458 - output_bg_ph_loss: 0.3183 - output_ph_loss: 0.3391 - output_mg_c_loss: 0.2968 - output_c_loss: 0.2759 - val_loss: 2.1912 - val_output_react_loss: 0.2287 - val_output_bg_ph_loss: 0.2984 - val_output_ph_loss: 0.3103 - val_output_mg_c_loss: 0.2815 - val_output_c_loss: 0.2637\nEpoch 3/120\n30/30 - 4s - loss: 2.1930 - output_react_loss: 0.2331 - output_bg_ph_loss: 0.2995 - output_ph_loss: 0.3077 - output_mg_c_loss: 0.2797 - output_c_loss: 0.2607 - val_loss: 2.1003 - val_output_react_loss: 0.2207 - val_output_bg_ph_loss: 0.2852 - val_output_ph_loss: 0.2954 - val_output_mg_c_loss: 0.2694 - val_output_c_loss: 0.2543\nEpoch 4/120\n30/30 - 4s - loss: 2.1113 - output_react_loss: 0.2256 - output_bg_ph_loss: 0.2875 - output_ph_loss: 0.2958 - output_mg_c_loss: 0.2688 - output_c_loss: 0.2517 - val_loss: 2.0628 - val_output_react_loss: 0.2158 - val_output_bg_ph_loss: 0.2790 - val_output_ph_loss: 0.2873 - val_output_mg_c_loss: 0.2689 - val_output_c_loss: 0.2481\nEpoch 5/120\n30/30 - 4s - loss: 2.0656 - output_react_loss: 0.2222 - output_bg_ph_loss: 0.2797 - output_ph_loss: 0.2893 - output_mg_c_loss: 0.2634 - output_c_loss: 0.2458 - val_loss: 2.0033 - val_output_react_loss: 0.2101 - val_output_bg_ph_loss: 0.2707 - val_output_ph_loss: 0.2806 - val_output_mg_c_loss: 0.2582 - val_output_c_loss: 0.2447\nEpoch 6/120\n30/30 - 4s - loss: 2.0200 - output_react_loss: 0.2169 - output_bg_ph_loss: 0.2731 - output_ph_loss: 0.2834 - output_mg_c_loss: 0.2571 - output_c_loss: 0.2423 - val_loss: 1.9670 - val_output_react_loss: 0.2091 - val_output_bg_ph_loss: 0.2643 - val_output_ph_loss: 0.2761 - val_output_mg_c_loss: 0.2527 - val_output_c_loss: 0.2388\nEpoch 7/120\n30/30 - 4s - loss: 1.9784 - output_react_loss: 0.2111 - output_bg_ph_loss: 0.2672 - output_ph_loss: 0.2794 - output_mg_c_loss: 0.2514 - output_c_loss: 0.2395 - val_loss: 1.9392 - val_output_react_loss: 0.2027 - val_output_bg_ph_loss: 0.2598 - val_output_ph_loss: 0.2728 - val_output_mg_c_loss: 0.2518 - val_output_c_loss: 0.2378\nEpoch 8/120\n30/30 - 4s - loss: 1.9386 - output_react_loss: 0.2071 - output_bg_ph_loss: 0.2625 - output_ph_loss: 0.2737 - output_mg_c_loss: 0.2455 - output_c_loss: 0.2344 - val_loss: 1.8829 - val_output_react_loss: 0.1985 - val_output_bg_ph_loss: 0.2537 - val_output_ph_loss: 0.2657 - val_output_mg_c_loss: 0.2406 - val_output_c_loss: 0.2314\nEpoch 9/120\n30/30 - 4s - loss: 1.8977 - output_react_loss: 0.2042 - output_bg_ph_loss: 0.2566 - output_ph_loss: 0.2687 - output_mg_c_loss: 0.2393 - output_c_loss: 0.2288 - val_loss: 1.8674 - val_output_react_loss: 0.2007 - val_output_bg_ph_loss: 0.2505 - val_output_ph_loss: 0.2620 - val_output_mg_c_loss: 0.2378 - val_output_c_loss: 0.2274\nEpoch 10/120\n30/30 - 4s - loss: 1.8620 - output_react_loss: 0.2009 - output_bg_ph_loss: 0.2526 - output_ph_loss: 0.2634 - output_mg_c_loss: 0.2335 - output_c_loss: 0.2246 - val_loss: 1.7981 - val_output_react_loss: 0.1917 - val_output_bg_ph_loss: 0.2430 - val_output_ph_loss: 0.2556 - val_output_mg_c_loss: 0.2259 - val_output_c_loss: 0.2214\nEpoch 11/120\n30/30 - 4s - loss: 1.8257 - output_react_loss: 0.1971 - output_bg_ph_loss: 0.2474 - output_ph_loss: 0.2592 - output_mg_c_loss: 0.2282 - output_c_loss: 0.2212 - val_loss: 1.7811 - val_output_react_loss: 0.1907 - val_output_bg_ph_loss: 0.2395 - val_output_ph_loss: 0.2524 - val_output_mg_c_loss: 0.2241 - val_output_c_loss: 0.2202\nEpoch 12/120\n30/30 - 4s - loss: 1.7962 - output_react_loss: 0.1954 - output_bg_ph_loss: 0.2432 - output_ph_loss: 0.2553 - output_mg_c_loss: 0.2234 - output_c_loss: 0.2169 - val_loss: 1.7353 - val_output_react_loss: 0.1875 - val_output_bg_ph_loss: 0.2335 - val_output_ph_loss: 0.2477 - val_output_mg_c_loss: 0.2163 - val_output_c_loss: 0.2132\nEpoch 13/120\n30/30 - 4s - loss: 1.7619 - output_react_loss: 0.1927 - output_bg_ph_loss: 0.2374 - output_ph_loss: 0.2505 - output_mg_c_loss: 0.2186 - output_c_loss: 0.2140 - val_loss: 1.6985 - val_output_react_loss: 0.1841 - val_output_bg_ph_loss: 0.2283 - val_output_ph_loss: 0.2429 - val_output_mg_c_loss: 0.2094 - val_output_c_loss: 0.2120\nEpoch 14/120\n30/30 - 4s - loss: 1.7308 - output_react_loss: 0.1895 - output_bg_ph_loss: 0.2336 - output_ph_loss: 0.2465 - output_mg_c_loss: 0.2141 - output_c_loss: 0.2101 - val_loss: 1.6867 - val_output_react_loss: 0.1823 - val_output_bg_ph_loss: 0.2246 - val_output_ph_loss: 0.2381 - val_output_mg_c_loss: 0.2142 - val_output_c_loss: 0.2063\nEpoch 15/120\n30/30 - 4s - loss: 1.7042 - output_react_loss: 0.1884 - output_bg_ph_loss: 0.2298 - output_ph_loss: 0.2409 - output_mg_c_loss: 0.2103 - output_c_loss: 0.2064 - val_loss: 1.6697 - val_output_react_loss: 0.1855 - val_output_bg_ph_loss: 0.2197 - val_output_ph_loss: 0.2369 - val_output_mg_c_loss: 0.2079 - val_output_c_loss: 0.2067\nEpoch 16/120\n30/30 - 4s - loss: 1.6806 - output_react_loss: 0.1866 - output_bg_ph_loss: 0.2256 - output_ph_loss: 0.2385 - output_mg_c_loss: 0.2068 - output_c_loss: 0.2041 - val_loss: 1.6219 - val_output_react_loss: 0.1798 - val_output_bg_ph_loss: 0.2175 - val_output_ph_loss: 0.2300 - val_output_mg_c_loss: 0.1986 - val_output_c_loss: 0.2002\nEpoch 17/120\n30/30 - 4s - loss: 1.6525 - output_react_loss: 0.1837 - output_bg_ph_loss: 0.2234 - output_ph_loss: 0.2343 - output_mg_c_loss: 0.2017 - output_c_loss: 0.2007 - val_loss: 1.6066 - val_output_react_loss: 0.1761 - val_output_bg_ph_loss: 0.2152 - val_output_ph_loss: 0.2272 - val_output_mg_c_loss: 0.1986 - val_output_c_loss: 0.1997\nEpoch 18/120\n30/30 - 4s - loss: 1.6309 - output_react_loss: 0.1818 - output_bg_ph_loss: 0.2197 - output_ph_loss: 0.2312 - output_mg_c_loss: 0.1988 - output_c_loss: 0.1988 - val_loss: 1.5870 - val_output_react_loss: 0.1748 - val_output_bg_ph_loss: 0.2136 - val_output_ph_loss: 0.2283 - val_output_mg_c_loss: 0.1931 - val_output_c_loss: 0.1955\nEpoch 19/120\n30/30 - 4s - loss: 1.6094 - output_react_loss: 0.1798 - output_bg_ph_loss: 0.2166 - output_ph_loss: 0.2287 - output_mg_c_loss: 0.1956 - output_c_loss: 0.1968 - val_loss: 1.5582 - val_output_react_loss: 0.1740 - val_output_bg_ph_loss: 0.2089 - val_output_ph_loss: 0.2212 - val_output_mg_c_loss: 0.1884 - val_output_c_loss: 0.1945\nEpoch 20/120\n30/30 - 4s - loss: 1.5891 - output_react_loss: 0.1787 - output_bg_ph_loss: 0.2138 - output_ph_loss: 0.2258 - output_mg_c_loss: 0.1922 - output_c_loss: 0.1938 - val_loss: 1.5465 - val_output_react_loss: 0.1723 - val_output_bg_ph_loss: 0.2074 - val_output_ph_loss: 0.2199 - val_output_mg_c_loss: 0.1876 - val_output_c_loss: 0.1918\nEpoch 21/120\n30/30 - 4s - loss: 1.5734 - output_react_loss: 0.1770 - output_bg_ph_loss: 0.2115 - output_ph_loss: 0.2227 - output_mg_c_loss: 0.1901 - output_c_loss: 0.1935 - val_loss: 1.5534 - val_output_react_loss: 0.1733 - val_output_bg_ph_loss: 0.2077 - val_output_ph_loss: 0.2189 - val_output_mg_c_loss: 0.1901 - val_output_c_loss: 0.1923\nEpoch 22/120\n30/30 - 4s - loss: 1.5669 - output_react_loss: 0.1770 - output_bg_ph_loss: 0.2107 - output_ph_loss: 0.2226 - output_mg_c_loss: 0.1891 - output_c_loss: 0.1906 - val_loss: 1.5548 - val_output_react_loss: 0.1749 - val_output_bg_ph_loss: 0.2074 - val_output_ph_loss: 0.2198 - val_output_mg_c_loss: 0.1889 - val_output_c_loss: 0.1926\nEpoch 23/120\n30/30 - 4s - loss: 1.5423 - output_react_loss: 0.1747 - output_bg_ph_loss: 0.2067 - output_ph_loss: 0.2198 - output_mg_c_loss: 0.1855 - output_c_loss: 0.1887 - val_loss: 1.5106 - val_output_react_loss: 0.1702 - val_output_bg_ph_loss: 0.2019 - val_output_ph_loss: 0.2144 - val_output_mg_c_loss: 0.1827 - val_output_c_loss: 0.1866\nEpoch 24/120\n30/30 - 4s - loss: 1.5258 - output_react_loss: 0.1735 - output_bg_ph_loss: 0.2046 - output_ph_loss: 0.2168 - output_mg_c_loss: 0.1829 - output_c_loss: 0.1871 - val_loss: 1.5220 - val_output_react_loss: 0.1725 - val_output_bg_ph_loss: 0.2026 - val_output_ph_loss: 0.2141 - val_output_mg_c_loss: 0.1829 - val_output_c_loss: 0.1918\nEpoch 25/120\n30/30 - 4s - loss: 1.5203 - output_react_loss: 0.1732 - output_bg_ph_loss: 0.2036 - output_ph_loss: 0.2154 - output_mg_c_loss: 0.1820 - output_c_loss: 0.1872 - val_loss: 1.5117 - val_output_react_loss: 0.1699 - val_output_bg_ph_loss: 0.2024 - val_output_ph_loss: 0.2127 - val_output_mg_c_loss: 0.1838 - val_output_c_loss: 0.1870\nEpoch 26/120\n30/30 - 4s - loss: 1.5020 - output_react_loss: 0.1709 - output_bg_ph_loss: 0.2012 - output_ph_loss: 0.2140 - output_mg_c_loss: 0.1795 - output_c_loss: 0.1846 - val_loss: 1.4843 - val_output_react_loss: 0.1663 - val_output_bg_ph_loss: 0.1983 - val_output_ph_loss: 0.2096 - val_output_mg_c_loss: 0.1803 - val_output_c_loss: 0.1847\nEpoch 27/120\n30/30 - 4s - loss: 1.4868 - output_react_loss: 0.1695 - output_bg_ph_loss: 0.1987 - output_ph_loss: 0.2121 - output_mg_c_loss: 0.1775 - output_c_loss: 0.1832 - val_loss: 1.4843 - val_output_react_loss: 0.1678 - val_output_bg_ph_loss: 0.1985 - val_output_ph_loss: 0.2095 - val_output_mg_c_loss: 0.1787 - val_output_c_loss: 0.1849\nEpoch 28/120\n30/30 - 4s - loss: 1.4696 - output_react_loss: 0.1672 - output_bg_ph_loss: 0.1963 - output_ph_loss: 0.2095 - output_mg_c_loss: 0.1760 - output_c_loss: 0.1810 - val_loss: 1.4791 - val_output_react_loss: 0.1653 - val_output_bg_ph_loss: 0.1986 - val_output_ph_loss: 0.2102 - val_output_mg_c_loss: 0.1790 - val_output_c_loss: 0.1831\nEpoch 29/120\n30/30 - 4s - loss: 1.4549 - output_react_loss: 0.1656 - output_bg_ph_loss: 0.1946 - output_ph_loss: 0.2077 - output_mg_c_loss: 0.1733 - output_c_loss: 0.1801 - val_loss: 1.4733 - val_output_react_loss: 0.1647 - val_output_bg_ph_loss: 0.1971 - val_output_ph_loss: 0.2090 - val_output_mg_c_loss: 0.1777 - val_output_c_loss: 0.1851\nEpoch 30/120\n30/30 - 4s - loss: 1.4524 - output_react_loss: 0.1648 - output_bg_ph_loss: 0.1944 - output_ph_loss: 0.2081 - output_mg_c_loss: 0.1729 - output_c_loss: 0.1800 - val_loss: 1.4779 - val_output_react_loss: 0.1646 - val_output_bg_ph_loss: 0.1985 - val_output_ph_loss: 0.2102 - val_output_mg_c_loss: 0.1793 - val_output_c_loss: 0.1831\nEpoch 31/120\n30/30 - 4s - loss: 1.4463 - output_react_loss: 0.1650 - output_bg_ph_loss: 0.1932 - output_ph_loss: 0.2056 - output_mg_c_loss: 0.1727 - output_c_loss: 0.1788 - val_loss: 1.4795 - val_output_react_loss: 0.1642 - val_output_bg_ph_loss: 0.1987 - val_output_ph_loss: 0.2091 - val_output_mg_c_loss: 0.1810 - val_output_c_loss: 0.1825\nEpoch 32/120\n30/30 - 4s - loss: 1.4305 - output_react_loss: 0.1628 - output_bg_ph_loss: 0.1910 - output_ph_loss: 0.2042 - output_mg_c_loss: 0.1709 - output_c_loss: 0.1770 - val_loss: 1.4637 - val_output_react_loss: 0.1654 - val_output_bg_ph_loss: 0.1962 - val_output_ph_loss: 0.2053 - val_output_mg_c_loss: 0.1771 - val_output_c_loss: 0.1809\nEpoch 33/120\n30/30 - 4s - loss: 1.4080 - output_react_loss: 0.1612 - output_bg_ph_loss: 0.1875 - output_ph_loss: 0.2017 - output_mg_c_loss: 0.1669 - output_c_loss: 0.1751 - val_loss: 1.4503 - val_output_react_loss: 0.1617 - val_output_bg_ph_loss: 0.1945 - val_output_ph_loss: 0.2064 - val_output_mg_c_loss: 0.1753 - val_output_c_loss: 0.1808\nEpoch 34/120\n30/30 - 4s - loss: 1.3964 - output_react_loss: 0.1589 - output_bg_ph_loss: 0.1861 - output_ph_loss: 0.2005 - output_mg_c_loss: 0.1656 - output_c_loss: 0.1746 - val_loss: 1.4477 - val_output_react_loss: 0.1612 - val_output_bg_ph_loss: 0.1934 - val_output_ph_loss: 0.2053 - val_output_mg_c_loss: 0.1769 - val_output_c_loss: 0.1796\nEpoch 35/120\n30/30 - 4s - loss: 1.3855 - output_react_loss: 0.1582 - output_bg_ph_loss: 0.1841 - output_ph_loss: 0.1993 - output_mg_c_loss: 0.1642 - output_c_loss: 0.1730 - val_loss: 1.4545 - val_output_react_loss: 0.1636 - val_output_bg_ph_loss: 0.1967 - val_output_ph_loss: 0.2045 - val_output_mg_c_loss: 0.1745 - val_output_c_loss: 0.1804\nEpoch 36/120\n30/30 - 4s - loss: 1.3699 - output_react_loss: 0.1563 - output_bg_ph_loss: 0.1831 - output_ph_loss: 0.1966 - output_mg_c_loss: 0.1612 - output_c_loss: 0.1720 - val_loss: 1.4480 - val_output_react_loss: 0.1625 - val_output_bg_ph_loss: 0.1951 - val_output_ph_loss: 0.2040 - val_output_mg_c_loss: 0.1748 - val_output_c_loss: 0.1791\nEpoch 37/120\n30/30 - 4s - loss: 1.3578 - output_react_loss: 0.1550 - output_bg_ph_loss: 0.1809 - output_ph_loss: 0.1955 - output_mg_c_loss: 0.1600 - output_c_loss: 0.1706 - val_loss: 1.4404 - val_output_react_loss: 0.1603 - val_output_bg_ph_loss: 0.1939 - val_output_ph_loss: 0.2032 - val_output_mg_c_loss: 0.1751 - val_output_c_loss: 0.1785\nEpoch 38/120\n30/30 - 4s - loss: 1.3499 - output_react_loss: 0.1540 - output_bg_ph_loss: 0.1800 - output_ph_loss: 0.1946 - output_mg_c_loss: 0.1585 - output_c_loss: 0.1702 - val_loss: 1.4460 - val_output_react_loss: 0.1627 - val_output_bg_ph_loss: 0.1938 - val_output_ph_loss: 0.2036 - val_output_mg_c_loss: 0.1748 - val_output_c_loss: 0.1797\nEpoch 39/120\n30/30 - 4s - loss: 1.3364 - output_react_loss: 0.1533 - output_bg_ph_loss: 0.1777 - output_ph_loss: 0.1922 - output_mg_c_loss: 0.1568 - output_c_loss: 0.1686 - val_loss: 1.4498 - val_output_react_loss: 0.1634 - val_output_bg_ph_loss: 0.1934 - val_output_ph_loss: 0.2049 - val_output_mg_c_loss: 0.1760 - val_output_c_loss: 0.1794\nEpoch 40/120\n30/30 - 4s - loss: 1.3255 - output_react_loss: 0.1515 - output_bg_ph_loss: 0.1755 - output_ph_loss: 0.1918 - output_mg_c_loss: 0.1559 - output_c_loss: 0.1679 - val_loss: 1.4271 - val_output_react_loss: 0.1605 - val_output_bg_ph_loss: 0.1918 - val_output_ph_loss: 0.2000 - val_output_mg_c_loss: 0.1724 - val_output_c_loss: 0.1777\nEpoch 41/120\n30/30 - 4s - loss: 1.3098 - output_react_loss: 0.1499 - output_bg_ph_loss: 0.1738 - output_ph_loss: 0.1895 - output_mg_c_loss: 0.1530 - output_c_loss: 0.1670 - val_loss: 1.4364 - val_output_react_loss: 0.1606 - val_output_bg_ph_loss: 0.1939 - val_output_ph_loss: 0.2019 - val_output_mg_c_loss: 0.1734 - val_output_c_loss: 0.1787\nEpoch 42/120\n30/30 - 4s - loss: 1.2943 - output_react_loss: 0.1479 - output_bg_ph_loss: 0.1708 - output_ph_loss: 0.1874 - output_mg_c_loss: 0.1519 - output_c_loss: 0.1658 - val_loss: 1.4241 - val_output_react_loss: 0.1592 - val_output_bg_ph_loss: 0.1919 - val_output_ph_loss: 0.1998 - val_output_mg_c_loss: 0.1728 - val_output_c_loss: 0.1764\nEpoch 43/120\n30/30 - 4s - loss: 1.2860 - output_react_loss: 0.1469 - output_bg_ph_loss: 0.1700 - output_ph_loss: 0.1867 - output_mg_c_loss: 0.1503 - output_c_loss: 0.1649 - val_loss: 1.4232 - val_output_react_loss: 0.1588 - val_output_bg_ph_loss: 0.1908 - val_output_ph_loss: 0.2020 - val_output_mg_c_loss: 0.1719 - val_output_c_loss: 0.1782\nEpoch 44/120\n30/30 - 4s - loss: 1.2816 - output_react_loss: 0.1464 - output_bg_ph_loss: 0.1691 - output_ph_loss: 0.1864 - output_mg_c_loss: 0.1499 - output_c_loss: 0.1644 - val_loss: 1.4265 - val_output_react_loss: 0.1592 - val_output_bg_ph_loss: 0.1931 - val_output_ph_loss: 0.2009 - val_output_mg_c_loss: 0.1721 - val_output_c_loss: 0.1766\nEpoch 45/120\n30/30 - 4s - loss: 1.2646 - output_react_loss: 0.1461 - output_bg_ph_loss: 0.1662 - output_ph_loss: 0.1838 - output_mg_c_loss: 0.1468 - output_c_loss: 0.1625 - val_loss: 1.4248 - val_output_react_loss: 0.1617 - val_output_bg_ph_loss: 0.1898 - val_output_ph_loss: 0.2020 - val_output_mg_c_loss: 0.1713 - val_output_c_loss: 0.1771\nEpoch 46/120\n30/30 - 4s - loss: 1.2535 - output_react_loss: 0.1443 - output_bg_ph_loss: 0.1645 - output_ph_loss: 0.1822 - output_mg_c_loss: 0.1457 - output_c_loss: 0.1623 - val_loss: 1.4157 - val_output_react_loss: 0.1588 - val_output_bg_ph_loss: 0.1907 - val_output_ph_loss: 0.1993 - val_output_mg_c_loss: 0.1713 - val_output_c_loss: 0.1747\nEpoch 47/120\n30/30 - 4s - loss: 1.2434 - output_react_loss: 0.1428 - output_bg_ph_loss: 0.1631 - output_ph_loss: 0.1813 - output_mg_c_loss: 0.1444 - output_c_loss: 0.1613 - val_loss: 1.4238 - val_output_react_loss: 0.1591 - val_output_bg_ph_loss: 0.1909 - val_output_ph_loss: 0.1999 - val_output_mg_c_loss: 0.1731 - val_output_c_loss: 0.1779\nEpoch 48/120\n30/30 - 4s - loss: 1.2313 - output_react_loss: 0.1413 - output_bg_ph_loss: 0.1612 - output_ph_loss: 0.1800 - output_mg_c_loss: 0.1430 - output_c_loss: 0.1604 - val_loss: 1.4135 - val_output_react_loss: 0.1573 - val_output_bg_ph_loss: 0.1904 - val_output_ph_loss: 0.1980 - val_output_mg_c_loss: 0.1719 - val_output_c_loss: 0.1762\nEpoch 49/120\n30/30 - 4s - loss: 1.2177 - output_react_loss: 0.1402 - output_bg_ph_loss: 0.1592 - output_ph_loss: 0.1783 - output_mg_c_loss: 0.1408 - output_c_loss: 0.1592 - val_loss: 1.4164 - val_output_react_loss: 0.1576 - val_output_bg_ph_loss: 0.1905 - val_output_ph_loss: 0.2003 - val_output_mg_c_loss: 0.1717 - val_output_c_loss: 0.1764\nEpoch 50/120\n30/30 - 4s - loss: 1.2093 - output_react_loss: 0.1388 - output_bg_ph_loss: 0.1581 - output_ph_loss: 0.1777 - output_mg_c_loss: 0.1397 - output_c_loss: 0.1583 - val_loss: 1.4258 - val_output_react_loss: 0.1585 - val_output_bg_ph_loss: 0.1924 - val_output_ph_loss: 0.2019 - val_output_mg_c_loss: 0.1731 - val_output_c_loss: 0.1760\nEpoch 51/120\n30/30 - 4s - loss: 1.1991 - output_react_loss: 0.1378 - output_bg_ph_loss: 0.1561 - output_ph_loss: 0.1764 - output_mg_c_loss: 0.1387 - output_c_loss: 0.1578 - val_loss: 1.4040 - val_output_react_loss: 0.1570 - val_output_bg_ph_loss: 0.1893 - val_output_ph_loss: 0.1959 - val_output_mg_c_loss: 0.1699 - val_output_c_loss: 0.1756\nEpoch 52/120\n30/30 - 4s - loss: 1.1923 - output_react_loss: 0.1369 - output_bg_ph_loss: 0.1546 - output_ph_loss: 0.1757 - output_mg_c_loss: 0.1380 - output_c_loss: 0.1574 - val_loss: 1.4119 - val_output_react_loss: 0.1568 - val_output_bg_ph_loss: 0.1897 - val_output_ph_loss: 0.1990 - val_output_mg_c_loss: 0.1722 - val_output_c_loss: 0.1756\nEpoch 53/120\n30/30 - 4s - loss: 1.1826 - output_react_loss: 0.1350 - output_bg_ph_loss: 0.1534 - output_ph_loss: 0.1749 - output_mg_c_loss: 0.1371 - output_c_loss: 0.1566 - val_loss: 1.3991 - val_output_react_loss: 0.1555 - val_output_bg_ph_loss: 0.1890 - val_output_ph_loss: 0.1962 - val_output_mg_c_loss: 0.1698 - val_output_c_loss: 0.1744\nEpoch 54/120\n30/30 - 4s - loss: 1.1733 - output_react_loss: 0.1351 - output_bg_ph_loss: 0.1520 - output_ph_loss: 0.1738 - output_mg_c_loss: 0.1352 - output_c_loss: 0.1550 - val_loss: 1.3966 - val_output_react_loss: 0.1555 - val_output_bg_ph_loss: 0.1883 - val_output_ph_loss: 0.1963 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1737\nEpoch 55/120\n30/30 - 4s - loss: 1.1656 - output_react_loss: 0.1341 - output_bg_ph_loss: 0.1510 - output_ph_loss: 0.1722 - output_mg_c_loss: 0.1341 - output_c_loss: 0.1552 - val_loss: 1.4028 - val_output_react_loss: 0.1571 - val_output_bg_ph_loss: 0.1893 - val_output_ph_loss: 0.1979 - val_output_mg_c_loss: 0.1684 - val_output_c_loss: 0.1754\nEpoch 56/120\n30/30 - 4s - loss: 1.1554 - output_react_loss: 0.1320 - output_bg_ph_loss: 0.1492 - output_ph_loss: 0.1718 - output_mg_c_loss: 0.1333 - output_c_loss: 0.1546 - val_loss: 1.4014 - val_output_react_loss: 0.1566 - val_output_bg_ph_loss: 0.1881 - val_output_ph_loss: 0.1978 - val_output_mg_c_loss: 0.1700 - val_output_c_loss: 0.1743\nEpoch 57/120\n30/30 - 4s - loss: 1.1445 - output_react_loss: 0.1312 - output_bg_ph_loss: 0.1472 - output_ph_loss: 0.1705 - output_mg_c_loss: 0.1321 - output_c_loss: 0.1531 - val_loss: 1.3987 - val_output_react_loss: 0.1584 - val_output_bg_ph_loss: 0.1880 - val_output_ph_loss: 0.1964 - val_output_mg_c_loss: 0.1679 - val_output_c_loss: 0.1737\nEpoch 58/120\n30/30 - 4s - loss: 1.1385 - output_react_loss: 0.1304 - output_bg_ph_loss: 0.1464 - output_ph_loss: 0.1695 - output_mg_c_loss: 0.1314 - output_c_loss: 0.1527 - val_loss: 1.3989 - val_output_react_loss: 0.1554 - val_output_bg_ph_loss: 0.1891 - val_output_ph_loss: 0.1963 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1748\nEpoch 59/120\n\nEpoch 00059: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n30/30 - 4s - loss: 1.1291 - output_react_loss: 0.1290 - output_bg_ph_loss: 0.1454 - output_ph_loss: 0.1685 - output_mg_c_loss: 0.1298 - output_c_loss: 0.1523 - val_loss: 1.4020 - val_output_react_loss: 0.1566 - val_output_bg_ph_loss: 0.1908 - val_output_ph_loss: 0.1954 - val_output_mg_c_loss: 0.1697 - val_output_c_loss: 0.1724\nEpoch 60/120\n30/30 - 4s - loss: 1.0961 - output_react_loss: 0.1250 - output_bg_ph_loss: 0.1409 - output_ph_loss: 0.1642 - output_mg_c_loss: 0.1256 - output_c_loss: 0.1490 - val_loss: 1.3785 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1865 - val_output_ph_loss: 0.1942 - val_output_mg_c_loss: 0.1664 - val_output_c_loss: 0.1715\nEpoch 61/120\n30/30 - 4s - loss: 1.0803 - output_react_loss: 0.1238 - output_bg_ph_loss: 0.1385 - output_ph_loss: 0.1622 - output_mg_c_loss: 0.1229 - output_c_loss: 0.1477 - val_loss: 1.3770 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1859 - val_output_ph_loss: 0.1939 - val_output_mg_c_loss: 0.1665 - val_output_c_loss: 0.1710\nEpoch 62/120\n30/30 - 4s - loss: 1.0757 - output_react_loss: 0.1226 - output_bg_ph_loss: 0.1379 - output_ph_loss: 0.1620 - output_mg_c_loss: 0.1226 - output_c_loss: 0.1474 - val_loss: 1.3769 - val_output_react_loss: 0.1540 - val_output_bg_ph_loss: 0.1861 - val_output_ph_loss: 0.1931 - val_output_mg_c_loss: 0.1663 - val_output_c_loss: 0.1710\nEpoch 63/120\n30/30 - 4s - loss: 1.0726 - output_react_loss: 0.1226 - output_bg_ph_loss: 0.1371 - output_ph_loss: 0.1613 - output_mg_c_loss: 0.1224 - output_c_loss: 0.1471 - val_loss: 1.3742 - val_output_react_loss: 0.1535 - val_output_bg_ph_loss: 0.1855 - val_output_ph_loss: 0.1931 - val_output_mg_c_loss: 0.1660 - val_output_c_loss: 0.1710\nEpoch 64/120\n30/30 - 4s - loss: 1.0701 - output_react_loss: 0.1222 - output_bg_ph_loss: 0.1370 - output_ph_loss: 0.1612 - output_mg_c_loss: 0.1219 - output_c_loss: 0.1469 - val_loss: 1.3771 - val_output_react_loss: 0.1539 - val_output_bg_ph_loss: 0.1862 - val_output_ph_loss: 0.1932 - val_output_mg_c_loss: 0.1664 - val_output_c_loss: 0.1710\nEpoch 65/120\n30/30 - 4s - loss: 1.0683 - output_react_loss: 0.1217 - output_bg_ph_loss: 0.1365 - output_ph_loss: 0.1614 - output_mg_c_loss: 0.1218 - output_c_loss: 0.1469 - val_loss: 1.3770 - val_output_react_loss: 0.1540 - val_output_bg_ph_loss: 0.1861 - val_output_ph_loss: 0.1932 - val_output_mg_c_loss: 0.1663 - val_output_c_loss: 0.1708\nEpoch 66/120\n30/30 - 4s - loss: 1.0662 - output_react_loss: 0.1215 - output_bg_ph_loss: 0.1363 - output_ph_loss: 0.1608 - output_mg_c_loss: 0.1215 - output_c_loss: 0.1467 - val_loss: 1.3724 - val_output_react_loss: 0.1534 - val_output_bg_ph_loss: 0.1858 - val_output_ph_loss: 0.1926 - val_output_mg_c_loss: 0.1655 - val_output_c_loss: 0.1705\nEpoch 67/120\n30/30 - 4s - loss: 1.0620 - output_react_loss: 0.1211 - output_bg_ph_loss: 0.1360 - output_ph_loss: 0.1600 - output_mg_c_loss: 0.1208 - output_c_loss: 0.1462 - val_loss: 1.3752 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1860 - val_output_ph_loss: 0.1928 - val_output_mg_c_loss: 0.1661 - val_output_c_loss: 0.1707\nEpoch 68/120\n30/30 - 4s - loss: 1.0630 - output_react_loss: 0.1213 - output_bg_ph_loss: 0.1362 - output_ph_loss: 0.1605 - output_mg_c_loss: 0.1208 - output_c_loss: 0.1459 - val_loss: 1.3735 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1854 - val_output_ph_loss: 0.1928 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1707\nEpoch 69/120\n30/30 - 4s - loss: 1.0598 - output_react_loss: 0.1211 - output_bg_ph_loss: 0.1353 - output_ph_loss: 0.1602 - output_mg_c_loss: 0.1203 - output_c_loss: 0.1461 - val_loss: 1.3737 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1856 - val_output_ph_loss: 0.1925 - val_output_mg_c_loss: 0.1661 - val_output_c_loss: 0.1706\nEpoch 70/120\n30/30 - 4s - loss: 1.0572 - output_react_loss: 0.1203 - output_bg_ph_loss: 0.1349 - output_ph_loss: 0.1597 - output_mg_c_loss: 0.1204 - output_c_loss: 0.1462 - val_loss: 1.3752 - val_output_react_loss: 0.1540 - val_output_bg_ph_loss: 0.1860 - val_output_ph_loss: 0.1928 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1709\nEpoch 71/120\n\nEpoch 00071: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n30/30 - 4s - loss: 1.0552 - output_react_loss: 0.1202 - output_bg_ph_loss: 0.1348 - output_ph_loss: 0.1597 - output_mg_c_loss: 0.1198 - output_c_loss: 0.1457 - val_loss: 1.3739 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1856 - val_output_ph_loss: 0.1925 - val_output_mg_c_loss: 0.1660 - val_output_c_loss: 0.1707\nEpoch 72/120\n30/30 - 4s - loss: 1.0525 - output_react_loss: 0.1202 - output_bg_ph_loss: 0.1344 - output_ph_loss: 0.1588 - output_mg_c_loss: 0.1196 - output_c_loss: 0.1453 - val_loss: 1.3732 - val_output_react_loss: 0.1535 - val_output_bg_ph_loss: 0.1856 - val_output_ph_loss: 0.1926 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1707\nEpoch 73/120\n30/30 - 4s - loss: 1.0532 - output_react_loss: 0.1202 - output_bg_ph_loss: 0.1344 - output_ph_loss: 0.1589 - output_mg_c_loss: 0.1198 - output_c_loss: 0.1455 - val_loss: 1.3745 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1859 - val_output_ph_loss: 0.1927 - val_output_mg_c_loss: 0.1659 - val_output_c_loss: 0.1707\nEpoch 74/120\n30/30 - 4s - loss: 1.0519 - output_react_loss: 0.1197 - output_bg_ph_loss: 0.1340 - output_ph_loss: 0.1591 - output_mg_c_loss: 0.1198 - output_c_loss: 0.1458 - val_loss: 1.3735 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1857 - val_output_ph_loss: 0.1927 - val_output_mg_c_loss: 0.1657 - val_output_c_loss: 0.1706\nEpoch 75/120\n30/30 - 4s - loss: 1.0519 - output_react_loss: 0.1201 - output_bg_ph_loss: 0.1342 - output_ph_loss: 0.1590 - output_mg_c_loss: 0.1194 - output_c_loss: 0.1454 - val_loss: 1.3752 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1861 - val_output_ph_loss: 0.1929 - val_output_mg_c_loss: 0.1660 - val_output_c_loss: 0.1706\nEpoch 76/120\nRestoring model weights from the end of the best epoch.\n\nEpoch 00076: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n30/30 - 4s - loss: 1.0535 - output_react_loss: 0.1202 - output_bg_ph_loss: 0.1344 - output_ph_loss: 0.1595 - output_mg_c_loss: 0.1198 - output_c_loss: 0.1453 - val_loss: 1.3733 - val_output_react_loss: 0.1535 - val_output_bg_ph_loss: 0.1857 - val_output_ph_loss: 0.1926 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1706\nEpoch 00076: early stopping\n\nFOLD: 2\nEpoch 1/120\n30/30 - 6s - loss: 3.6145 - output_react_loss: 0.3859 - output_bg_ph_loss: 0.5921 - output_ph_loss: 0.4493 - output_mg_c_loss: 0.4325 - output_c_loss: 0.3441 - val_loss: 2.3399 - val_output_react_loss: 0.2505 - val_output_bg_ph_loss: 0.3187 - val_output_ph_loss: 0.3453 - val_output_mg_c_loss: 0.2940 - val_output_c_loss: 0.2685\nEpoch 2/120\n30/30 - 4s - loss: 2.3576 - output_react_loss: 0.2459 - output_bg_ph_loss: 0.3224 - output_ph_loss: 0.3355 - output_mg_c_loss: 0.3020 - output_c_loss: 0.2814 - val_loss: 2.2172 - val_output_react_loss: 0.2371 - val_output_bg_ph_loss: 0.3000 - val_output_ph_loss: 0.3191 - val_output_mg_c_loss: 0.2854 - val_output_c_loss: 0.2530\nEpoch 3/120\n30/30 - 4s - loss: 2.2149 - output_react_loss: 0.2330 - output_bg_ph_loss: 0.3027 - output_ph_loss: 0.3102 - output_mg_c_loss: 0.2846 - output_c_loss: 0.2642 - val_loss: 2.0746 - val_output_react_loss: 0.2288 - val_output_bg_ph_loss: 0.2810 - val_output_ph_loss: 0.2896 - val_output_mg_c_loss: 0.2628 - val_output_c_loss: 0.2399\nEpoch 4/120\n30/30 - 4s - loss: 2.1275 - output_react_loss: 0.2246 - output_bg_ph_loss: 0.2904 - output_ph_loss: 0.2974 - output_mg_c_loss: 0.2725 - output_c_loss: 0.2550 - val_loss: 2.0056 - val_output_react_loss: 0.2222 - val_output_bg_ph_loss: 0.2705 - val_output_ph_loss: 0.2807 - val_output_mg_c_loss: 0.2531 - val_output_c_loss: 0.2333\nEpoch 5/120\n30/30 - 4s - loss: 2.0735 - output_react_loss: 0.2192 - output_bg_ph_loss: 0.2818 - output_ph_loss: 0.2905 - output_mg_c_loss: 0.2655 - output_c_loss: 0.2498 - val_loss: 1.9704 - val_output_react_loss: 0.2194 - val_output_bg_ph_loss: 0.2644 - val_output_ph_loss: 0.2763 - val_output_mg_c_loss: 0.2475 - val_output_c_loss: 0.2314\nEpoch 6/120\n30/30 - 4s - loss: 2.0289 - output_react_loss: 0.2151 - output_bg_ph_loss: 0.2754 - output_ph_loss: 0.2853 - output_mg_c_loss: 0.2589 - output_c_loss: 0.2449 - val_loss: 1.9270 - val_output_react_loss: 0.2155 - val_output_bg_ph_loss: 0.2589 - val_output_ph_loss: 0.2696 - val_output_mg_c_loss: 0.2413 - val_output_c_loss: 0.2260\nEpoch 7/120\n30/30 - 4s - loss: 1.9888 - output_react_loss: 0.2102 - output_bg_ph_loss: 0.2702 - output_ph_loss: 0.2806 - output_mg_c_loss: 0.2530 - output_c_loss: 0.2415 - val_loss: 1.8879 - val_output_react_loss: 0.2115 - val_output_bg_ph_loss: 0.2536 - val_output_ph_loss: 0.2647 - val_output_mg_c_loss: 0.2361 - val_output_c_loss: 0.2208\nEpoch 8/120\n30/30 - 4s - loss: 1.9514 - output_react_loss: 0.2075 - output_bg_ph_loss: 0.2642 - output_ph_loss: 0.2749 - output_mg_c_loss: 0.2480 - output_c_loss: 0.2371 - val_loss: 1.8471 - val_output_react_loss: 0.2068 - val_output_bg_ph_loss: 0.2480 - val_output_ph_loss: 0.2605 - val_output_mg_c_loss: 0.2298 - val_output_c_loss: 0.2174\nEpoch 9/120\n30/30 - 4s - loss: 1.9122 - output_react_loss: 0.2040 - output_bg_ph_loss: 0.2591 - output_ph_loss: 0.2702 - output_mg_c_loss: 0.2419 - output_c_loss: 0.2320 - val_loss: 1.8044 - val_output_react_loss: 0.2034 - val_output_bg_ph_loss: 0.2428 - val_output_ph_loss: 0.2542 - val_output_mg_c_loss: 0.2228 - val_output_c_loss: 0.2123\nEpoch 10/120\n30/30 - 4s - loss: 1.8711 - output_react_loss: 0.2001 - output_bg_ph_loss: 0.2537 - output_ph_loss: 0.2650 - output_mg_c_loss: 0.2357 - output_c_loss: 0.2270 - val_loss: 1.7675 - val_output_react_loss: 0.1992 - val_output_bg_ph_loss: 0.2380 - val_output_ph_loss: 0.2501 - val_output_mg_c_loss: 0.2171 - val_output_c_loss: 0.2087\nEpoch 11/120\n30/30 - 4s - loss: 1.8335 - output_react_loss: 0.1971 - output_bg_ph_loss: 0.2484 - output_ph_loss: 0.2608 - output_mg_c_loss: 0.2294 - output_c_loss: 0.2228 - val_loss: 1.7336 - val_output_react_loss: 0.1977 - val_output_bg_ph_loss: 0.2323 - val_output_ph_loss: 0.2453 - val_output_mg_c_loss: 0.2118 - val_output_c_loss: 0.2047\nEpoch 12/120\n30/30 - 4s - loss: 1.8107 - output_react_loss: 0.1965 - output_bg_ph_loss: 0.2439 - output_ph_loss: 0.2586 - output_mg_c_loss: 0.2258 - output_c_loss: 0.2198 - val_loss: 1.7074 - val_output_react_loss: 0.1958 - val_output_bg_ph_loss: 0.2284 - val_output_ph_loss: 0.2408 - val_output_mg_c_loss: 0.2082 - val_output_c_loss: 0.2018\nEpoch 13/120\n30/30 - 4s - loss: 1.7743 - output_react_loss: 0.1928 - output_bg_ph_loss: 0.2393 - output_ph_loss: 0.2523 - output_mg_c_loss: 0.2205 - output_c_loss: 0.2166 - val_loss: 1.6761 - val_output_react_loss: 0.1924 - val_output_bg_ph_loss: 0.2229 - val_output_ph_loss: 0.2397 - val_output_mg_c_loss: 0.2029 - val_output_c_loss: 0.1999\nEpoch 14/120\n30/30 - 4s - loss: 1.7513 - output_react_loss: 0.1912 - output_bg_ph_loss: 0.2354 - output_ph_loss: 0.2497 - output_mg_c_loss: 0.2166 - output_c_loss: 0.2151 - val_loss: 1.6811 - val_output_react_loss: 0.1942 - val_output_bg_ph_loss: 0.2250 - val_output_ph_loss: 0.2379 - val_output_mg_c_loss: 0.2030 - val_output_c_loss: 0.1988\nEpoch 15/120\n30/30 - 4s - loss: 1.7119 - output_react_loss: 0.1879 - output_bg_ph_loss: 0.2308 - output_ph_loss: 0.2437 - output_mg_c_loss: 0.2109 - output_c_loss: 0.2090 - val_loss: 1.6297 - val_output_react_loss: 0.1884 - val_output_bg_ph_loss: 0.2167 - val_output_ph_loss: 0.2328 - val_output_mg_c_loss: 0.1967 - val_output_c_loss: 0.1935\nEpoch 16/120\n30/30 - 4s - loss: 1.6810 - output_react_loss: 0.1860 - output_bg_ph_loss: 0.2267 - output_ph_loss: 0.2385 - output_mg_c_loss: 0.2058 - output_c_loss: 0.2055 - val_loss: 1.6170 - val_output_react_loss: 0.1875 - val_output_bg_ph_loss: 0.2155 - val_output_ph_loss: 0.2305 - val_output_mg_c_loss: 0.1937 - val_output_c_loss: 0.1930\nEpoch 17/120\n30/30 - 4s - loss: 1.6523 - output_react_loss: 0.1832 - output_bg_ph_loss: 0.2224 - output_ph_loss: 0.2354 - output_mg_c_loss: 0.2018 - output_c_loss: 0.2021 - val_loss: 1.5968 - val_output_react_loss: 0.1862 - val_output_bg_ph_loss: 0.2149 - val_output_ph_loss: 0.2259 - val_output_mg_c_loss: 0.1904 - val_output_c_loss: 0.1879\nEpoch 18/120\n30/30 - 4s - loss: 1.6380 - output_react_loss: 0.1828 - output_bg_ph_loss: 0.2208 - output_ph_loss: 0.2325 - output_mg_c_loss: 0.1994 - output_c_loss: 0.1995 - val_loss: 1.5862 - val_output_react_loss: 0.1854 - val_output_bg_ph_loss: 0.2106 - val_output_ph_loss: 0.2241 - val_output_mg_c_loss: 0.1908 - val_output_c_loss: 0.1885\nEpoch 19/120\n30/30 - 4s - loss: 1.6157 - output_react_loss: 0.1806 - output_bg_ph_loss: 0.2169 - output_ph_loss: 0.2290 - output_mg_c_loss: 0.1973 - output_c_loss: 0.1972 - val_loss: 1.5765 - val_output_react_loss: 0.1847 - val_output_bg_ph_loss: 0.2082 - val_output_ph_loss: 0.2216 - val_output_mg_c_loss: 0.1914 - val_output_c_loss: 0.1865\nEpoch 20/120\n30/30 - 4s - loss: 1.6029 - output_react_loss: 0.1791 - output_bg_ph_loss: 0.2152 - output_ph_loss: 0.2268 - output_mg_c_loss: 0.1956 - output_c_loss: 0.1964 - val_loss: 1.5573 - val_output_react_loss: 0.1836 - val_output_bg_ph_loss: 0.2079 - val_output_ph_loss: 0.2193 - val_output_mg_c_loss: 0.1852 - val_output_c_loss: 0.1847\nEpoch 21/120\n30/30 - 4s - loss: 1.5803 - output_react_loss: 0.1772 - output_bg_ph_loss: 0.2124 - output_ph_loss: 0.2248 - output_mg_c_loss: 0.1916 - output_c_loss: 0.1931 - val_loss: 1.5394 - val_output_react_loss: 0.1816 - val_output_bg_ph_loss: 0.2045 - val_output_ph_loss: 0.2176 - val_output_mg_c_loss: 0.1823 - val_output_c_loss: 0.1849\nEpoch 22/120\n30/30 - 4s - loss: 1.5563 - output_react_loss: 0.1744 - output_bg_ph_loss: 0.2090 - output_ph_loss: 0.2214 - output_mg_c_loss: 0.1885 - output_c_loss: 0.1912 - val_loss: 1.5260 - val_output_react_loss: 0.1777 - val_output_bg_ph_loss: 0.2033 - val_output_ph_loss: 0.2172 - val_output_mg_c_loss: 0.1819 - val_output_c_loss: 0.1830\nEpoch 23/120\n30/30 - 4s - loss: 1.5434 - output_react_loss: 0.1736 - output_bg_ph_loss: 0.2071 - output_ph_loss: 0.2185 - output_mg_c_loss: 0.1869 - output_c_loss: 0.1897 - val_loss: 1.5234 - val_output_react_loss: 0.1809 - val_output_bg_ph_loss: 0.2025 - val_output_ph_loss: 0.2137 - val_output_mg_c_loss: 0.1805 - val_output_c_loss: 0.1819\nEpoch 24/120\n30/30 - 4s - loss: 1.5343 - output_react_loss: 0.1722 - output_bg_ph_loss: 0.2054 - output_ph_loss: 0.2185 - output_mg_c_loss: 0.1856 - output_c_loss: 0.1893 - val_loss: 1.5283 - val_output_react_loss: 0.1780 - val_output_bg_ph_loss: 0.2032 - val_output_ph_loss: 0.2157 - val_output_mg_c_loss: 0.1841 - val_output_c_loss: 0.1821\nEpoch 25/120\n30/30 - 4s - loss: 1.5174 - output_react_loss: 0.1716 - output_bg_ph_loss: 0.2034 - output_ph_loss: 0.2156 - output_mg_c_loss: 0.1826 - output_c_loss: 0.1867 - val_loss: 1.5109 - val_output_react_loss: 0.1774 - val_output_bg_ph_loss: 0.2005 - val_output_ph_loss: 0.2153 - val_output_mg_c_loss: 0.1783 - val_output_c_loss: 0.1831\nEpoch 26/120\n30/30 - 4s - loss: 1.5015 - output_react_loss: 0.1694 - output_bg_ph_loss: 0.2014 - output_ph_loss: 0.2131 - output_mg_c_loss: 0.1808 - output_c_loss: 0.1853 - val_loss: 1.5142 - val_output_react_loss: 0.1784 - val_output_bg_ph_loss: 0.2008 - val_output_ph_loss: 0.2152 - val_output_mg_c_loss: 0.1790 - val_output_c_loss: 0.1829\nEpoch 27/120\n30/30 - 4s - loss: 1.4996 - output_react_loss: 0.1689 - output_bg_ph_loss: 0.2007 - output_ph_loss: 0.2131 - output_mg_c_loss: 0.1809 - output_c_loss: 0.1855 - val_loss: 1.4946 - val_output_react_loss: 0.1759 - val_output_bg_ph_loss: 0.1988 - val_output_ph_loss: 0.2121 - val_output_mg_c_loss: 0.1765 - val_output_c_loss: 0.1802\nEpoch 28/120\n30/30 - 4s - loss: 1.4816 - output_react_loss: 0.1671 - output_bg_ph_loss: 0.1989 - output_ph_loss: 0.2108 - output_mg_c_loss: 0.1780 - output_c_loss: 0.1829 - val_loss: 1.4931 - val_output_react_loss: 0.1736 - val_output_bg_ph_loss: 0.1997 - val_output_ph_loss: 0.2120 - val_output_mg_c_loss: 0.1778 - val_output_c_loss: 0.1788\nEpoch 29/120\n30/30 - 4s - loss: 1.4568 - output_react_loss: 0.1649 - output_bg_ph_loss: 0.1953 - output_ph_loss: 0.2080 - output_mg_c_loss: 0.1738 - output_c_loss: 0.1806 - val_loss: 1.4792 - val_output_react_loss: 0.1718 - val_output_bg_ph_loss: 0.1966 - val_output_ph_loss: 0.2116 - val_output_mg_c_loss: 0.1762 - val_output_c_loss: 0.1785\nEpoch 30/120\n30/30 - 4s - loss: 1.4500 - output_react_loss: 0.1651 - output_bg_ph_loss: 0.1939 - output_ph_loss: 0.2071 - output_mg_c_loss: 0.1727 - output_c_loss: 0.1796 - val_loss: 1.4692 - val_output_react_loss: 0.1729 - val_output_bg_ph_loss: 0.1954 - val_output_ph_loss: 0.2076 - val_output_mg_c_loss: 0.1738 - val_output_c_loss: 0.1774\nEpoch 31/120\n30/30 - 4s - loss: 1.4315 - output_react_loss: 0.1628 - output_bg_ph_loss: 0.1913 - output_ph_loss: 0.2046 - output_mg_c_loss: 0.1702 - output_c_loss: 0.1783 - val_loss: 1.4725 - val_output_react_loss: 0.1725 - val_output_bg_ph_loss: 0.1965 - val_output_ph_loss: 0.2093 - val_output_mg_c_loss: 0.1739 - val_output_c_loss: 0.1775\nEpoch 32/120\n30/30 - 4s - loss: 1.4301 - output_react_loss: 0.1631 - output_bg_ph_loss: 0.1913 - output_ph_loss: 0.2036 - output_mg_c_loss: 0.1701 - output_c_loss: 0.1775 - val_loss: 1.4742 - val_output_react_loss: 0.1708 - val_output_bg_ph_loss: 0.1988 - val_output_ph_loss: 0.2076 - val_output_mg_c_loss: 0.1744 - val_output_c_loss: 0.1785\nEpoch 33/120\n30/30 - 4s - loss: 1.4128 - output_react_loss: 0.1607 - output_bg_ph_loss: 0.1888 - output_ph_loss: 0.2019 - output_mg_c_loss: 0.1677 - output_c_loss: 0.1768 - val_loss: 1.4512 - val_output_react_loss: 0.1699 - val_output_bg_ph_loss: 0.1930 - val_output_ph_loss: 0.2068 - val_output_mg_c_loss: 0.1718 - val_output_c_loss: 0.1749\nEpoch 34/120\n30/30 - 4s - loss: 1.4018 - output_react_loss: 0.1591 - output_bg_ph_loss: 0.1874 - output_ph_loss: 0.1999 - output_mg_c_loss: 0.1671 - output_c_loss: 0.1747 - val_loss: 1.4551 - val_output_react_loss: 0.1690 - val_output_bg_ph_loss: 0.1959 - val_output_ph_loss: 0.2054 - val_output_mg_c_loss: 0.1725 - val_output_c_loss: 0.1748\nEpoch 35/120\n30/30 - 4s - loss: 1.3803 - output_react_loss: 0.1570 - output_bg_ph_loss: 0.1846 - output_ph_loss: 0.1977 - output_mg_c_loss: 0.1634 - output_c_loss: 0.1726 - val_loss: 1.4474 - val_output_react_loss: 0.1691 - val_output_bg_ph_loss: 0.1930 - val_output_ph_loss: 0.2054 - val_output_mg_c_loss: 0.1712 - val_output_c_loss: 0.1755\nEpoch 36/120\n30/30 - 4s - loss: 1.3753 - output_react_loss: 0.1562 - output_bg_ph_loss: 0.1836 - output_ph_loss: 0.1969 - output_mg_c_loss: 0.1628 - output_c_loss: 0.1732 - val_loss: 1.4415 - val_output_react_loss: 0.1677 - val_output_bg_ph_loss: 0.1933 - val_output_ph_loss: 0.2050 - val_output_mg_c_loss: 0.1693 - val_output_c_loss: 0.1759\nEpoch 37/120\n30/30 - 4s - loss: 1.3664 - output_react_loss: 0.1550 - output_bg_ph_loss: 0.1825 - output_ph_loss: 0.1963 - output_mg_c_loss: 0.1618 - output_c_loss: 0.1714 - val_loss: 1.4405 - val_output_react_loss: 0.1660 - val_output_bg_ph_loss: 0.1931 - val_output_ph_loss: 0.2038 - val_output_mg_c_loss: 0.1717 - val_output_c_loss: 0.1752\nEpoch 38/120\n30/30 - 4s - loss: 1.3563 - output_react_loss: 0.1542 - output_bg_ph_loss: 0.1803 - output_ph_loss: 0.1948 - output_mg_c_loss: 0.1611 - output_c_loss: 0.1705 - val_loss: 1.4525 - val_output_react_loss: 0.1674 - val_output_bg_ph_loss: 0.1941 - val_output_ph_loss: 0.2042 - val_output_mg_c_loss: 0.1751 - val_output_c_loss: 0.1751\nEpoch 39/120\n30/30 - 4s - loss: 1.3425 - output_react_loss: 0.1530 - output_bg_ph_loss: 0.1785 - output_ph_loss: 0.1926 - output_mg_c_loss: 0.1587 - output_c_loss: 0.1695 - val_loss: 1.4482 - val_output_react_loss: 0.1660 - val_output_bg_ph_loss: 0.1944 - val_output_ph_loss: 0.2048 - val_output_mg_c_loss: 0.1745 - val_output_c_loss: 0.1739\nEpoch 40/120\n30/30 - 4s - loss: 1.3267 - output_react_loss: 0.1514 - output_bg_ph_loss: 0.1765 - output_ph_loss: 0.1906 - output_mg_c_loss: 0.1561 - output_c_loss: 0.1680 - val_loss: 1.4371 - val_output_react_loss: 0.1674 - val_output_bg_ph_loss: 0.1924 - val_output_ph_loss: 0.2034 - val_output_mg_c_loss: 0.1701 - val_output_c_loss: 0.1740\nEpoch 41/120\n30/30 - 4s - loss: 1.3195 - output_react_loss: 0.1513 - output_bg_ph_loss: 0.1748 - output_ph_loss: 0.1901 - output_mg_c_loss: 0.1548 - output_c_loss: 0.1675 - val_loss: 1.4320 - val_output_react_loss: 0.1677 - val_output_bg_ph_loss: 0.1907 - val_output_ph_loss: 0.2030 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1732\nEpoch 42/120\n30/30 - 4s - loss: 1.3130 - output_react_loss: 0.1507 - output_bg_ph_loss: 0.1737 - output_ph_loss: 0.1888 - output_mg_c_loss: 0.1543 - output_c_loss: 0.1668 - val_loss: 1.4384 - val_output_react_loss: 0.1667 - val_output_bg_ph_loss: 0.1920 - val_output_ph_loss: 0.2024 - val_output_mg_c_loss: 0.1724 - val_output_c_loss: 0.1740\nEpoch 43/120\n30/30 - 4s - loss: 1.2921 - output_react_loss: 0.1475 - output_bg_ph_loss: 0.1710 - output_ph_loss: 0.1871 - output_mg_c_loss: 0.1514 - output_c_loss: 0.1653 - val_loss: 1.4282 - val_output_react_loss: 0.1652 - val_output_bg_ph_loss: 0.1922 - val_output_ph_loss: 0.2021 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1723\nEpoch 44/120\n30/30 - 4s - loss: 1.2815 - output_react_loss: 0.1466 - output_bg_ph_loss: 0.1692 - output_ph_loss: 0.1856 - output_mg_c_loss: 0.1500 - output_c_loss: 0.1642 - val_loss: 1.4339 - val_output_react_loss: 0.1656 - val_output_bg_ph_loss: 0.1940 - val_output_ph_loss: 0.2022 - val_output_mg_c_loss: 0.1696 - val_output_c_loss: 0.1732\nEpoch 45/120\n30/30 - 4s - loss: 1.2732 - output_react_loss: 0.1459 - output_bg_ph_loss: 0.1680 - output_ph_loss: 0.1845 - output_mg_c_loss: 0.1482 - output_c_loss: 0.1642 - val_loss: 1.4221 - val_output_react_loss: 0.1641 - val_output_bg_ph_loss: 0.1924 - val_output_ph_loss: 0.2002 - val_output_mg_c_loss: 0.1686 - val_output_c_loss: 0.1718\nEpoch 46/120\n30/30 - 4s - loss: 1.2570 - output_react_loss: 0.1437 - output_bg_ph_loss: 0.1657 - output_ph_loss: 0.1829 - output_mg_c_loss: 0.1467 - output_c_loss: 0.1619 - val_loss: 1.4303 - val_output_react_loss: 0.1656 - val_output_bg_ph_loss: 0.1914 - val_output_ph_loss: 0.2012 - val_output_mg_c_loss: 0.1709 - val_output_c_loss: 0.1733\nEpoch 47/120\n30/30 - 4s - loss: 1.2501 - output_react_loss: 0.1438 - output_bg_ph_loss: 0.1645 - output_ph_loss: 0.1816 - output_mg_c_loss: 0.1453 - output_c_loss: 0.1614 - val_loss: 1.4307 - val_output_react_loss: 0.1647 - val_output_bg_ph_loss: 0.1925 - val_output_ph_loss: 0.2022 - val_output_mg_c_loss: 0.1704 - val_output_c_loss: 0.1734\nEpoch 48/120\n30/30 - 4s - loss: 1.2440 - output_react_loss: 0.1427 - output_bg_ph_loss: 0.1635 - output_ph_loss: 0.1804 - output_mg_c_loss: 0.1451 - output_c_loss: 0.1610 - val_loss: 1.4270 - val_output_react_loss: 0.1643 - val_output_bg_ph_loss: 0.1927 - val_output_ph_loss: 0.2016 - val_output_mg_c_loss: 0.1691 - val_output_c_loss: 0.1731\nEpoch 49/120\n30/30 - 4s - loss: 1.2308 - output_react_loss: 0.1412 - output_bg_ph_loss: 0.1621 - output_ph_loss: 0.1796 - output_mg_c_loss: 0.1426 - output_c_loss: 0.1595 - val_loss: 1.4181 - val_output_react_loss: 0.1639 - val_output_bg_ph_loss: 0.1909 - val_output_ph_loss: 0.2019 - val_output_mg_c_loss: 0.1673 - val_output_c_loss: 0.1721\nEpoch 50/120\n30/30 - 4s - loss: 1.2201 - output_react_loss: 0.1394 - output_bg_ph_loss: 0.1601 - output_ph_loss: 0.1780 - output_mg_c_loss: 0.1418 - output_c_loss: 0.1596 - val_loss: 1.4202 - val_output_react_loss: 0.1648 - val_output_bg_ph_loss: 0.1908 - val_output_ph_loss: 0.1992 - val_output_mg_c_loss: 0.1688 - val_output_c_loss: 0.1722\nEpoch 51/120\n30/30 - 4s - loss: 1.2128 - output_react_loss: 0.1396 - output_bg_ph_loss: 0.1582 - output_ph_loss: 0.1767 - output_mg_c_loss: 0.1410 - output_c_loss: 0.1586 - val_loss: 1.4129 - val_output_react_loss: 0.1630 - val_output_bg_ph_loss: 0.1901 - val_output_ph_loss: 0.1986 - val_output_mg_c_loss: 0.1676 - val_output_c_loss: 0.1728\nEpoch 52/120\n30/30 - 4s - loss: 1.1992 - output_react_loss: 0.1369 - output_bg_ph_loss: 0.1572 - output_ph_loss: 0.1754 - output_mg_c_loss: 0.1391 - output_c_loss: 0.1575 - val_loss: 1.4104 - val_output_react_loss: 0.1624 - val_output_bg_ph_loss: 0.1908 - val_output_ph_loss: 0.1987 - val_output_mg_c_loss: 0.1671 - val_output_c_loss: 0.1710\nEpoch 53/120\n30/30 - 4s - loss: 1.1897 - output_react_loss: 0.1354 - output_bg_ph_loss: 0.1559 - output_ph_loss: 0.1739 - output_mg_c_loss: 0.1382 - output_c_loss: 0.1568 - val_loss: 1.4089 - val_output_react_loss: 0.1623 - val_output_bg_ph_loss: 0.1895 - val_output_ph_loss: 0.1988 - val_output_mg_c_loss: 0.1672 - val_output_c_loss: 0.1720\nEpoch 54/120\n30/30 - 4s - loss: 1.1787 - output_react_loss: 0.1347 - output_bg_ph_loss: 0.1537 - output_ph_loss: 0.1731 - output_mg_c_loss: 0.1364 - output_c_loss: 0.1561 - val_loss: 1.4114 - val_output_react_loss: 0.1631 - val_output_bg_ph_loss: 0.1891 - val_output_ph_loss: 0.1992 - val_output_mg_c_loss: 0.1684 - val_output_c_loss: 0.1711\nEpoch 55/120\n30/30 - 4s - loss: 1.1707 - output_react_loss: 0.1338 - output_bg_ph_loss: 0.1526 - output_ph_loss: 0.1727 - output_mg_c_loss: 0.1350 - output_c_loss: 0.1553 - val_loss: 1.4131 - val_output_react_loss: 0.1642 - val_output_bg_ph_loss: 0.1899 - val_output_ph_loss: 0.1999 - val_output_mg_c_loss: 0.1669 - val_output_c_loss: 0.1713\nEpoch 56/120\n30/30 - 4s - loss: 1.1640 - output_react_loss: 0.1334 - output_bg_ph_loss: 0.1515 - output_ph_loss: 0.1714 - output_mg_c_loss: 0.1341 - output_c_loss: 0.1547 - val_loss: 1.4110 - val_output_react_loss: 0.1626 - val_output_bg_ph_loss: 0.1892 - val_output_ph_loss: 0.2002 - val_output_mg_c_loss: 0.1675 - val_output_c_loss: 0.1722\nEpoch 57/120\n30/30 - 4s - loss: 1.1600 - output_react_loss: 0.1331 - output_bg_ph_loss: 0.1498 - output_ph_loss: 0.1712 - output_mg_c_loss: 0.1343 - output_c_loss: 0.1544 - val_loss: 1.4093 - val_output_react_loss: 0.1622 - val_output_bg_ph_loss: 0.1895 - val_output_ph_loss: 0.2006 - val_output_mg_c_loss: 0.1668 - val_output_c_loss: 0.1716\nEpoch 58/120\n\nEpoch 00058: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n30/30 - 4s - loss: 1.1474 - output_react_loss: 0.1314 - output_bg_ph_loss: 0.1481 - output_ph_loss: 0.1701 - output_mg_c_loss: 0.1322 - output_c_loss: 0.1538 - val_loss: 1.4093 - val_output_react_loss: 0.1614 - val_output_bg_ph_loss: 0.1886 - val_output_ph_loss: 0.1999 - val_output_mg_c_loss: 0.1685 - val_output_c_loss: 0.1724\nEpoch 59/120\n30/30 - 4s - loss: 1.1176 - output_react_loss: 0.1275 - output_bg_ph_loss: 0.1443 - output_ph_loss: 0.1662 - output_mg_c_loss: 0.1284 - output_c_loss: 0.1510 - val_loss: 1.3902 - val_output_react_loss: 0.1602 - val_output_bg_ph_loss: 0.1875 - val_output_ph_loss: 0.1962 - val_output_mg_c_loss: 0.1648 - val_output_c_loss: 0.1690\nEpoch 60/120\n30/30 - 4s - loss: 1.1001 - output_react_loss: 0.1252 - output_bg_ph_loss: 0.1424 - output_ph_loss: 0.1636 - output_mg_c_loss: 0.1260 - output_c_loss: 0.1493 - val_loss: 1.3827 - val_output_react_loss: 0.1596 - val_output_bg_ph_loss: 0.1860 - val_output_ph_loss: 0.1955 - val_output_mg_c_loss: 0.1637 - val_output_c_loss: 0.1687\nEpoch 61/120\n30/30 - 4s - loss: 1.0945 - output_react_loss: 0.1250 - output_bg_ph_loss: 0.1411 - output_ph_loss: 0.1633 - output_mg_c_loss: 0.1252 - output_c_loss: 0.1487 - val_loss: 1.3811 - val_output_react_loss: 0.1592 - val_output_bg_ph_loss: 0.1861 - val_output_ph_loss: 0.1953 - val_output_mg_c_loss: 0.1634 - val_output_c_loss: 0.1685\nEpoch 62/120\n30/30 - 4s - loss: 1.0906 - output_react_loss: 0.1246 - output_bg_ph_loss: 0.1407 - output_ph_loss: 0.1625 - output_mg_c_loss: 0.1246 - output_c_loss: 0.1482 - val_loss: 1.3849 - val_output_react_loss: 0.1597 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1955 - val_output_mg_c_loss: 0.1638 - val_output_c_loss: 0.1688\nEpoch 63/120\n30/30 - 4s - loss: 1.0896 - output_react_loss: 0.1246 - output_bg_ph_loss: 0.1402 - output_ph_loss: 0.1625 - output_mg_c_loss: 0.1246 - output_c_loss: 0.1482 - val_loss: 1.3850 - val_output_react_loss: 0.1597 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1955 - val_output_mg_c_loss: 0.1639 - val_output_c_loss: 0.1689\nEpoch 64/120\n30/30 - 4s - loss: 1.0856 - output_react_loss: 0.1240 - output_bg_ph_loss: 0.1397 - output_ph_loss: 0.1618 - output_mg_c_loss: 0.1241 - output_c_loss: 0.1481 - val_loss: 1.3833 - val_output_react_loss: 0.1593 - val_output_bg_ph_loss: 0.1866 - val_output_ph_loss: 0.1956 - val_output_mg_c_loss: 0.1636 - val_output_c_loss: 0.1688\nEpoch 65/120\n30/30 - 4s - loss: 1.0841 - output_react_loss: 0.1239 - output_bg_ph_loss: 0.1396 - output_ph_loss: 0.1618 - output_mg_c_loss: 0.1237 - output_c_loss: 0.1477 - val_loss: 1.3844 - val_output_react_loss: 0.1594 - val_output_bg_ph_loss: 0.1868 - val_output_ph_loss: 0.1957 - val_output_mg_c_loss: 0.1638 - val_output_c_loss: 0.1688\nEpoch 66/120\n\nEpoch 00066: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n30/30 - 4s - loss: 1.0825 - output_react_loss: 0.1236 - output_bg_ph_loss: 0.1392 - output_ph_loss: 0.1619 - output_mg_c_loss: 0.1238 - output_c_loss: 0.1474 - val_loss: 1.3842 - val_output_react_loss: 0.1594 - val_output_bg_ph_loss: 0.1869 - val_output_ph_loss: 0.1953 - val_output_mg_c_loss: 0.1638 - val_output_c_loss: 0.1688\nEpoch 67/120\n30/30 - 4s - loss: 1.0786 - output_react_loss: 0.1230 - output_bg_ph_loss: 0.1389 - output_ph_loss: 0.1613 - output_mg_c_loss: 0.1230 - output_c_loss: 0.1475 - val_loss: 1.3829 - val_output_react_loss: 0.1594 - val_output_bg_ph_loss: 0.1866 - val_output_ph_loss: 0.1953 - val_output_mg_c_loss: 0.1635 - val_output_c_loss: 0.1686\nEpoch 68/120\n30/30 - 4s - loss: 1.0785 - output_react_loss: 0.1231 - output_bg_ph_loss: 0.1389 - output_ph_loss: 0.1613 - output_mg_c_loss: 0.1231 - output_c_loss: 0.1472 - val_loss: 1.3836 - val_output_react_loss: 0.1594 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1954 - val_output_mg_c_loss: 0.1637 - val_output_c_loss: 0.1686\nEpoch 69/120\n30/30 - 4s - loss: 1.0787 - output_react_loss: 0.1231 - output_bg_ph_loss: 0.1390 - output_ph_loss: 0.1613 - output_mg_c_loss: 0.1230 - output_c_loss: 0.1471 - val_loss: 1.3834 - val_output_react_loss: 0.1594 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1954 - val_output_mg_c_loss: 0.1636 - val_output_c_loss: 0.1686\nEpoch 70/120\n30/30 - 4s - loss: 1.0777 - output_react_loss: 0.1229 - output_bg_ph_loss: 0.1388 - output_ph_loss: 0.1613 - output_mg_c_loss: 0.1229 - output_c_loss: 0.1471 - val_loss: 1.3826 - val_output_react_loss: 0.1593 - val_output_bg_ph_loss: 0.1865 - val_output_ph_loss: 0.1952 - val_output_mg_c_loss: 0.1635 - val_output_c_loss: 0.1686\nEpoch 71/120\nRestoring model weights from the end of the best epoch.\n\nEpoch 00071: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n30/30 - 4s - loss: 1.0761 - output_react_loss: 0.1230 - output_bg_ph_loss: 0.1383 - output_ph_loss: 0.1610 - output_mg_c_loss: 0.1226 - output_c_loss: 0.1472 - val_loss: 1.3837 - val_output_react_loss: 0.1595 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1953 - val_output_mg_c_loss: 0.1636 - val_output_c_loss: 0.1686\nEpoch 00071: early stopping\n\nFOLD: 3\nEpoch 1/120\n30/30 - 6s - loss: 3.6279 - output_react_loss: 0.4708 - output_bg_ph_loss: 0.4401 - output_ph_loss: 0.5027 - output_mg_c_loss: 0.4080 - output_c_loss: 0.4872 - val_loss: 2.3493 - val_output_react_loss: 0.2399 - val_output_bg_ph_loss: 0.3151 - val_output_ph_loss: 0.3559 - val_output_mg_c_loss: 0.2959 - val_output_c_loss: 0.2916\nEpoch 2/120\n30/30 - 4s - loss: 2.3306 - output_react_loss: 0.2492 - output_bg_ph_loss: 0.3149 - output_ph_loss: 0.3309 - output_mg_c_loss: 0.2956 - output_c_loss: 0.2803 - val_loss: 2.1497 - val_output_react_loss: 0.2243 - val_output_bg_ph_loss: 0.2951 - val_output_ph_loss: 0.3007 - val_output_mg_c_loss: 0.2761 - val_output_c_loss: 0.2581\nEpoch 3/120\n30/30 - 4s - loss: 2.1989 - output_react_loss: 0.2361 - output_bg_ph_loss: 0.2977 - output_ph_loss: 0.3080 - output_mg_c_loss: 0.2802 - output_c_loss: 0.2626 - val_loss: 2.0834 - val_output_react_loss: 0.2165 - val_output_bg_ph_loss: 0.2847 - val_output_ph_loss: 0.2912 - val_output_mg_c_loss: 0.2693 - val_output_c_loss: 0.2512\nEpoch 4/120\n30/30 - 4s - loss: 2.1209 - output_react_loss: 0.2280 - output_bg_ph_loss: 0.2863 - output_ph_loss: 0.2969 - output_mg_c_loss: 0.2705 - output_c_loss: 0.2542 - val_loss: 2.0157 - val_output_react_loss: 0.2112 - val_output_bg_ph_loss: 0.2745 - val_output_ph_loss: 0.2839 - val_output_mg_c_loss: 0.2586 - val_output_c_loss: 0.2433\nEpoch 5/120\n30/30 - 4s - loss: 2.0648 - output_react_loss: 0.2220 - output_bg_ph_loss: 0.2786 - output_ph_loss: 0.2894 - output_mg_c_loss: 0.2631 - output_c_loss: 0.2480 - val_loss: 1.9764 - val_output_react_loss: 0.2073 - val_output_bg_ph_loss: 0.2704 - val_output_ph_loss: 0.2772 - val_output_mg_c_loss: 0.2526 - val_output_c_loss: 0.2388\nEpoch 6/120\n30/30 - 4s - loss: 2.0171 - output_react_loss: 0.2174 - output_bg_ph_loss: 0.2718 - output_ph_loss: 0.2831 - output_mg_c_loss: 0.2561 - output_c_loss: 0.2433 - val_loss: 1.9254 - val_output_react_loss: 0.2017 - val_output_bg_ph_loss: 0.2617 - val_output_ph_loss: 0.2729 - val_output_mg_c_loss: 0.2456 - val_output_c_loss: 0.2346\nEpoch 7/120\n30/30 - 4s - loss: 1.9804 - output_react_loss: 0.2134 - output_bg_ph_loss: 0.2666 - output_ph_loss: 0.2790 - output_mg_c_loss: 0.2510 - output_c_loss: 0.2393 - val_loss: 1.8904 - val_output_react_loss: 0.1986 - val_output_bg_ph_loss: 0.2558 - val_output_ph_loss: 0.2685 - val_output_mg_c_loss: 0.2407 - val_output_c_loss: 0.2317\nEpoch 8/120\n30/30 - 4s - loss: 1.9470 - output_react_loss: 0.2095 - output_bg_ph_loss: 0.2620 - output_ph_loss: 0.2744 - output_mg_c_loss: 0.2469 - output_c_loss: 0.2359 - val_loss: 1.8707 - val_output_react_loss: 0.1958 - val_output_bg_ph_loss: 0.2540 - val_output_ph_loss: 0.2636 - val_output_mg_c_loss: 0.2396 - val_output_c_loss: 0.2282\nEpoch 9/120\n30/30 - 4s - loss: 1.9133 - output_react_loss: 0.2058 - output_bg_ph_loss: 0.2575 - output_ph_loss: 0.2693 - output_mg_c_loss: 0.2427 - output_c_loss: 0.2320 - val_loss: 1.8330 - val_output_react_loss: 0.1929 - val_output_bg_ph_loss: 0.2480 - val_output_ph_loss: 0.2612 - val_output_mg_c_loss: 0.2325 - val_output_c_loss: 0.2251\nEpoch 10/120\n30/30 - 4s - loss: 1.8769 - output_react_loss: 0.2033 - output_bg_ph_loss: 0.2529 - output_ph_loss: 0.2644 - output_mg_c_loss: 0.2360 - output_c_loss: 0.2282 - val_loss: 1.7937 - val_output_react_loss: 0.1910 - val_output_bg_ph_loss: 0.2442 - val_output_ph_loss: 0.2535 - val_output_mg_c_loss: 0.2252 - val_output_c_loss: 0.2194\nEpoch 11/120\n30/30 - 4s - loss: 1.8367 - output_react_loss: 0.1997 - output_bg_ph_loss: 0.2481 - output_ph_loss: 0.2597 - output_mg_c_loss: 0.2289 - output_c_loss: 0.2235 - val_loss: 1.7648 - val_output_react_loss: 0.1890 - val_output_bg_ph_loss: 0.2392 - val_output_ph_loss: 0.2512 - val_output_mg_c_loss: 0.2202 - val_output_c_loss: 0.2167\nEpoch 12/120\n30/30 - 4s - loss: 1.8048 - output_react_loss: 0.1977 - output_bg_ph_loss: 0.2433 - output_ph_loss: 0.2558 - output_mg_c_loss: 0.2239 - output_c_loss: 0.2192 - val_loss: 1.7376 - val_output_react_loss: 0.1860 - val_output_bg_ph_loss: 0.2374 - val_output_ph_loss: 0.2471 - val_output_mg_c_loss: 0.2156 - val_output_c_loss: 0.2126\nEpoch 13/120\n30/30 - 4s - loss: 1.7767 - output_react_loss: 0.1948 - output_bg_ph_loss: 0.2394 - output_ph_loss: 0.2518 - output_mg_c_loss: 0.2202 - output_c_loss: 0.2162 - val_loss: 1.7016 - val_output_react_loss: 0.1839 - val_output_bg_ph_loss: 0.2292 - val_output_ph_loss: 0.2427 - val_output_mg_c_loss: 0.2118 - val_output_c_loss: 0.2093\nEpoch 14/120\n30/30 - 4s - loss: 1.7474 - output_react_loss: 0.1928 - output_bg_ph_loss: 0.2342 - output_ph_loss: 0.2482 - output_mg_c_loss: 0.2160 - output_c_loss: 0.2130 - val_loss: 1.6829 - val_output_react_loss: 0.1834 - val_output_bg_ph_loss: 0.2253 - val_output_ph_loss: 0.2390 - val_output_mg_c_loss: 0.2099 - val_output_c_loss: 0.2067\nEpoch 15/120\n30/30 - 4s - loss: 1.7325 - output_react_loss: 0.1918 - output_bg_ph_loss: 0.2336 - output_ph_loss: 0.2449 - output_mg_c_loss: 0.2133 - output_c_loss: 0.2102 - val_loss: 1.6513 - val_output_react_loss: 0.1831 - val_output_bg_ph_loss: 0.2217 - val_output_ph_loss: 0.2346 - val_output_mg_c_loss: 0.2026 - val_output_c_loss: 0.2020\nEpoch 16/120\n30/30 - 4s - loss: 1.6941 - output_react_loss: 0.1881 - output_bg_ph_loss: 0.2277 - output_ph_loss: 0.2409 - output_mg_c_loss: 0.2077 - output_c_loss: 0.2060 - val_loss: 1.6195 - val_output_react_loss: 0.1785 - val_output_bg_ph_loss: 0.2184 - val_output_ph_loss: 0.2301 - val_output_mg_c_loss: 0.1983 - val_output_c_loss: 0.1989\nEpoch 17/120\n30/30 - 4s - loss: 1.6725 - output_react_loss: 0.1860 - output_bg_ph_loss: 0.2254 - output_ph_loss: 0.2373 - output_mg_c_loss: 0.2044 - output_c_loss: 0.2035 - val_loss: 1.6132 - val_output_react_loss: 0.1794 - val_output_bg_ph_loss: 0.2200 - val_output_ph_loss: 0.2279 - val_output_mg_c_loss: 0.1947 - val_output_c_loss: 0.1969\nEpoch 18/120\n30/30 - 4s - loss: 1.6494 - output_react_loss: 0.1849 - output_bg_ph_loss: 0.2210 - output_ph_loss: 0.2333 - output_mg_c_loss: 0.2015 - output_c_loss: 0.2011 - val_loss: 1.5786 - val_output_react_loss: 0.1762 - val_output_bg_ph_loss: 0.2117 - val_output_ph_loss: 0.2239 - val_output_mg_c_loss: 0.1929 - val_output_c_loss: 0.1933\nEpoch 19/120\n30/30 - 4s - loss: 1.6229 - output_react_loss: 0.1826 - output_bg_ph_loss: 0.2178 - output_ph_loss: 0.2306 - output_mg_c_loss: 0.1967 - output_c_loss: 0.1980 - val_loss: 1.5810 - val_output_react_loss: 0.1745 - val_output_bg_ph_loss: 0.2117 - val_output_ph_loss: 0.2241 - val_output_mg_c_loss: 0.1959 - val_output_c_loss: 0.1928\nEpoch 20/120\n30/30 - 4s - loss: 1.6070 - output_react_loss: 0.1805 - output_bg_ph_loss: 0.2163 - output_ph_loss: 0.2276 - output_mg_c_loss: 0.1949 - output_c_loss: 0.1959 - val_loss: 1.5546 - val_output_react_loss: 0.1742 - val_output_bg_ph_loss: 0.2108 - val_output_ph_loss: 0.2193 - val_output_mg_c_loss: 0.1880 - val_output_c_loss: 0.1892\nEpoch 21/120\n30/30 - 4s - loss: 1.5869 - output_react_loss: 0.1796 - output_bg_ph_loss: 0.2125 - output_ph_loss: 0.2249 - output_mg_c_loss: 0.1919 - output_c_loss: 0.1940 - val_loss: 1.5329 - val_output_react_loss: 0.1726 - val_output_bg_ph_loss: 0.2073 - val_output_ph_loss: 0.2173 - val_output_mg_c_loss: 0.1846 - val_output_c_loss: 0.1866\nEpoch 22/120\n30/30 - 4s - loss: 1.5733 - output_react_loss: 0.1779 - output_bg_ph_loss: 0.2106 - output_ph_loss: 0.2236 - output_mg_c_loss: 0.1904 - output_c_loss: 0.1919 - val_loss: 1.5222 - val_output_react_loss: 0.1710 - val_output_bg_ph_loss: 0.2052 - val_output_ph_loss: 0.2158 - val_output_mg_c_loss: 0.1834 - val_output_c_loss: 0.1872\nEpoch 23/120\n30/30 - 4s - loss: 1.5579 - output_react_loss: 0.1772 - output_bg_ph_loss: 0.2084 - output_ph_loss: 0.2210 - output_mg_c_loss: 0.1876 - output_c_loss: 0.1904 - val_loss: 1.5246 - val_output_react_loss: 0.1722 - val_output_bg_ph_loss: 0.2057 - val_output_ph_loss: 0.2136 - val_output_mg_c_loss: 0.1846 - val_output_c_loss: 0.1860\nEpoch 24/120\n30/30 - 4s - loss: 1.5364 - output_react_loss: 0.1741 - output_bg_ph_loss: 0.2062 - output_ph_loss: 0.2176 - output_mg_c_loss: 0.1850 - output_c_loss: 0.1881 - val_loss: 1.5003 - val_output_react_loss: 0.1682 - val_output_bg_ph_loss: 0.2027 - val_output_ph_loss: 0.2137 - val_output_mg_c_loss: 0.1802 - val_output_c_loss: 0.1843\nEpoch 25/120\n30/30 - 4s - loss: 1.5273 - output_react_loss: 0.1735 - output_bg_ph_loss: 0.2043 - output_ph_loss: 0.2179 - output_mg_c_loss: 0.1834 - output_c_loss: 0.1871 - val_loss: 1.4845 - val_output_react_loss: 0.1662 - val_output_bg_ph_loss: 0.1994 - val_output_ph_loss: 0.2108 - val_output_mg_c_loss: 0.1792 - val_output_c_loss: 0.1840\nEpoch 26/120\n30/30 - 4s - loss: 1.5129 - output_react_loss: 0.1714 - output_bg_ph_loss: 0.2027 - output_ph_loss: 0.2159 - output_mg_c_loss: 0.1817 - output_c_loss: 0.1855 - val_loss: 1.4768 - val_output_react_loss: 0.1652 - val_output_bg_ph_loss: 0.1994 - val_output_ph_loss: 0.2103 - val_output_mg_c_loss: 0.1780 - val_output_c_loss: 0.1812\nEpoch 27/120\n30/30 - 4s - loss: 1.4976 - output_react_loss: 0.1711 - output_bg_ph_loss: 0.2004 - output_ph_loss: 0.2128 - output_mg_c_loss: 0.1791 - output_c_loss: 0.1837 - val_loss: 1.4699 - val_output_react_loss: 0.1654 - val_output_bg_ph_loss: 0.1987 - val_output_ph_loss: 0.2090 - val_output_mg_c_loss: 0.1759 - val_output_c_loss: 0.1810\nEpoch 28/120\n30/30 - 4s - loss: 1.4888 - output_react_loss: 0.1696 - output_bg_ph_loss: 0.1992 - output_ph_loss: 0.2127 - output_mg_c_loss: 0.1777 - output_c_loss: 0.1830 - val_loss: 1.4813 - val_output_react_loss: 0.1656 - val_output_bg_ph_loss: 0.2007 - val_output_ph_loss: 0.2127 - val_output_mg_c_loss: 0.1770 - val_output_c_loss: 0.1819\nEpoch 29/120\n30/30 - 4s - loss: 1.4731 - output_react_loss: 0.1675 - output_bg_ph_loss: 0.1969 - output_ph_loss: 0.2094 - output_mg_c_loss: 0.1768 - output_c_loss: 0.1813 - val_loss: 1.4557 - val_output_react_loss: 0.1649 - val_output_bg_ph_loss: 0.1959 - val_output_ph_loss: 0.2065 - val_output_mg_c_loss: 0.1743 - val_output_c_loss: 0.1790\nEpoch 30/120\n30/30 - 4s - loss: 1.4539 - output_react_loss: 0.1659 - output_bg_ph_loss: 0.1936 - output_ph_loss: 0.2075 - output_mg_c_loss: 0.1740 - output_c_loss: 0.1794 - val_loss: 1.4551 - val_output_react_loss: 0.1638 - val_output_bg_ph_loss: 0.1959 - val_output_ph_loss: 0.2085 - val_output_mg_c_loss: 0.1741 - val_output_c_loss: 0.1788\nEpoch 31/120\n30/30 - 4s - loss: 1.4469 - output_react_loss: 0.1637 - output_bg_ph_loss: 0.1931 - output_ph_loss: 0.2079 - output_mg_c_loss: 0.1732 - output_c_loss: 0.1790 - val_loss: 1.4536 - val_output_react_loss: 0.1646 - val_output_bg_ph_loss: 0.1944 - val_output_ph_loss: 0.2072 - val_output_mg_c_loss: 0.1746 - val_output_c_loss: 0.1792\nEpoch 32/120\n30/30 - 4s - loss: 1.4325 - output_react_loss: 0.1638 - output_bg_ph_loss: 0.1906 - output_ph_loss: 0.2051 - output_mg_c_loss: 0.1706 - output_c_loss: 0.1775 - val_loss: 1.4452 - val_output_react_loss: 0.1623 - val_output_bg_ph_loss: 0.1946 - val_output_ph_loss: 0.2037 - val_output_mg_c_loss: 0.1747 - val_output_c_loss: 0.1784\nEpoch 33/120\n30/30 - 4s - loss: 1.4220 - output_react_loss: 0.1622 - output_bg_ph_loss: 0.1893 - output_ph_loss: 0.2040 - output_mg_c_loss: 0.1692 - output_c_loss: 0.1767 - val_loss: 1.4594 - val_output_react_loss: 0.1629 - val_output_bg_ph_loss: 0.1978 - val_output_ph_loss: 0.2033 - val_output_mg_c_loss: 0.1769 - val_output_c_loss: 0.1812\nEpoch 34/120\n30/30 - 4s - loss: 1.4184 - output_react_loss: 0.1625 - output_bg_ph_loss: 0.1887 - output_ph_loss: 0.2031 - output_mg_c_loss: 0.1680 - output_c_loss: 0.1769 - val_loss: 1.4377 - val_output_react_loss: 0.1619 - val_output_bg_ph_loss: 0.1934 - val_output_ph_loss: 0.2034 - val_output_mg_c_loss: 0.1733 - val_output_c_loss: 0.1773\nEpoch 35/120\n30/30 - 4s - loss: 1.4003 - output_react_loss: 0.1603 - output_bg_ph_loss: 0.1857 - output_ph_loss: 0.2006 - output_mg_c_loss: 0.1664 - output_c_loss: 0.1748 - val_loss: 1.4235 - val_output_react_loss: 0.1610 - val_output_bg_ph_loss: 0.1923 - val_output_ph_loss: 0.2016 - val_output_mg_c_loss: 0.1700 - val_output_c_loss: 0.1751\nEpoch 36/120\n30/30 - 4s - loss: 1.3808 - output_react_loss: 0.1586 - output_bg_ph_loss: 0.1832 - output_ph_loss: 0.1980 - output_mg_c_loss: 0.1633 - output_c_loss: 0.1724 - val_loss: 1.4229 - val_output_react_loss: 0.1599 - val_output_bg_ph_loss: 0.1926 - val_output_ph_loss: 0.2031 - val_output_mg_c_loss: 0.1697 - val_output_c_loss: 0.1755\nEpoch 37/120\n30/30 - 4s - loss: 1.3685 - output_react_loss: 0.1564 - output_bg_ph_loss: 0.1817 - output_ph_loss: 0.1970 - output_mg_c_loss: 0.1615 - output_c_loss: 0.1722 - val_loss: 1.4198 - val_output_react_loss: 0.1599 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.2013 - val_output_mg_c_loss: 0.1707 - val_output_c_loss: 0.1743\nEpoch 38/120\n30/30 - 4s - loss: 1.3600 - output_react_loss: 0.1562 - output_bg_ph_loss: 0.1804 - output_ph_loss: 0.1961 - output_mg_c_loss: 0.1599 - output_c_loss: 0.1710 - val_loss: 1.4149 - val_output_react_loss: 0.1596 - val_output_bg_ph_loss: 0.1908 - val_output_ph_loss: 0.1992 - val_output_mg_c_loss: 0.1703 - val_output_c_loss: 0.1743\nEpoch 39/120\n30/30 - 4s - loss: 1.3497 - output_react_loss: 0.1552 - output_bg_ph_loss: 0.1786 - output_ph_loss: 0.1941 - output_mg_c_loss: 0.1589 - output_c_loss: 0.1702 - val_loss: 1.4254 - val_output_react_loss: 0.1590 - val_output_bg_ph_loss: 0.1934 - val_output_ph_loss: 0.2004 - val_output_mg_c_loss: 0.1725 - val_output_c_loss: 0.1750\nEpoch 40/120\n30/30 - 4s - loss: 1.3433 - output_react_loss: 0.1548 - output_bg_ph_loss: 0.1776 - output_ph_loss: 0.1930 - output_mg_c_loss: 0.1579 - output_c_loss: 0.1695 - val_loss: 1.4085 - val_output_react_loss: 0.1595 - val_output_bg_ph_loss: 0.1896 - val_output_ph_loss: 0.1986 - val_output_mg_c_loss: 0.1694 - val_output_c_loss: 0.1730\nEpoch 41/120\n30/30 - 4s - loss: 1.3233 - output_react_loss: 0.1523 - output_bg_ph_loss: 0.1749 - output_ph_loss: 0.1909 - output_mg_c_loss: 0.1550 - output_c_loss: 0.1679 - val_loss: 1.4082 - val_output_react_loss: 0.1584 - val_output_bg_ph_loss: 0.1920 - val_output_ph_loss: 0.1984 - val_output_mg_c_loss: 0.1675 - val_output_c_loss: 0.1739\nEpoch 42/120\n30/30 - 4s - loss: 1.3142 - output_react_loss: 0.1512 - output_bg_ph_loss: 0.1739 - output_ph_loss: 0.1907 - output_mg_c_loss: 0.1533 - output_c_loss: 0.1668 - val_loss: 1.4005 - val_output_react_loss: 0.1576 - val_output_bg_ph_loss: 0.1896 - val_output_ph_loss: 0.1986 - val_output_mg_c_loss: 0.1675 - val_output_c_loss: 0.1725\nEpoch 43/120\n30/30 - 4s - loss: 1.3001 - output_react_loss: 0.1491 - output_bg_ph_loss: 0.1718 - output_ph_loss: 0.1889 - output_mg_c_loss: 0.1516 - output_c_loss: 0.1661 - val_loss: 1.3872 - val_output_react_loss: 0.1562 - val_output_bg_ph_loss: 0.1884 - val_output_ph_loss: 0.1952 - val_output_mg_c_loss: 0.1655 - val_output_c_loss: 0.1716\nEpoch 44/120\n30/30 - 4s - loss: 1.2919 - output_react_loss: 0.1484 - output_bg_ph_loss: 0.1699 - output_ph_loss: 0.1882 - output_mg_c_loss: 0.1510 - output_c_loss: 0.1651 - val_loss: 1.3897 - val_output_react_loss: 0.1564 - val_output_bg_ph_loss: 0.1880 - val_output_ph_loss: 0.1962 - val_output_mg_c_loss: 0.1666 - val_output_c_loss: 0.1716\nEpoch 45/120\n30/30 - 4s - loss: 1.2778 - output_react_loss: 0.1472 - output_bg_ph_loss: 0.1683 - output_ph_loss: 0.1851 - output_mg_c_loss: 0.1490 - output_c_loss: 0.1635 - val_loss: 1.3925 - val_output_react_loss: 0.1570 - val_output_bg_ph_loss: 0.1884 - val_output_ph_loss: 0.1960 - val_output_mg_c_loss: 0.1669 - val_output_c_loss: 0.1719\nEpoch 46/120\n30/30 - 4s - loss: 1.2677 - output_react_loss: 0.1463 - output_bg_ph_loss: 0.1662 - output_ph_loss: 0.1843 - output_mg_c_loss: 0.1479 - output_c_loss: 0.1627 - val_loss: 1.3833 - val_output_react_loss: 0.1568 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1960 - val_output_mg_c_loss: 0.1646 - val_output_c_loss: 0.1712\nEpoch 47/120\n30/30 - 4s - loss: 1.2556 - output_react_loss: 0.1445 - output_bg_ph_loss: 0.1647 - output_ph_loss: 0.1827 - output_mg_c_loss: 0.1461 - output_c_loss: 0.1622 - val_loss: 1.4004 - val_output_react_loss: 0.1552 - val_output_bg_ph_loss: 0.1895 - val_output_ph_loss: 0.1976 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1743\nEpoch 48/120\n30/30 - 4s - loss: 1.2482 - output_react_loss: 0.1434 - output_bg_ph_loss: 0.1634 - output_ph_loss: 0.1821 - output_mg_c_loss: 0.1456 - output_c_loss: 0.1614 - val_loss: 1.3820 - val_output_react_loss: 0.1557 - val_output_bg_ph_loss: 0.1876 - val_output_ph_loss: 0.1940 - val_output_mg_c_loss: 0.1656 - val_output_c_loss: 0.1703\nEpoch 49/120\n30/30 - 4s - loss: 1.2309 - output_react_loss: 0.1416 - output_bg_ph_loss: 0.1612 - output_ph_loss: 0.1799 - output_mg_c_loss: 0.1428 - output_c_loss: 0.1598 - val_loss: 1.3827 - val_output_react_loss: 0.1567 - val_output_bg_ph_loss: 0.1871 - val_output_ph_loss: 0.1941 - val_output_mg_c_loss: 0.1650 - val_output_c_loss: 0.1711\nEpoch 50/120\n30/30 - 4s - loss: 1.2231 - output_react_loss: 0.1412 - output_bg_ph_loss: 0.1596 - output_ph_loss: 0.1790 - output_mg_c_loss: 0.1418 - output_c_loss: 0.1589 - val_loss: 1.3795 - val_output_react_loss: 0.1544 - val_output_bg_ph_loss: 0.1885 - val_output_ph_loss: 0.1933 - val_output_mg_c_loss: 0.1649 - val_output_c_loss: 0.1706\nEpoch 51/120\n30/30 - 4s - loss: 1.2174 - output_react_loss: 0.1405 - output_bg_ph_loss: 0.1583 - output_ph_loss: 0.1784 - output_mg_c_loss: 0.1411 - output_c_loss: 0.1593 - val_loss: 1.3675 - val_output_react_loss: 0.1533 - val_output_bg_ph_loss: 0.1867 - val_output_ph_loss: 0.1930 - val_output_mg_c_loss: 0.1627 - val_output_c_loss: 0.1690\nEpoch 52/120\n30/30 - 4s - loss: 1.2036 - output_react_loss: 0.1388 - output_bg_ph_loss: 0.1565 - output_ph_loss: 0.1764 - output_mg_c_loss: 0.1395 - output_c_loss: 0.1576 - val_loss: 1.3853 - val_output_react_loss: 0.1563 - val_output_bg_ph_loss: 0.1880 - val_output_ph_loss: 0.1951 - val_output_mg_c_loss: 0.1656 - val_output_c_loss: 0.1704\nEpoch 53/120\n30/30 - 4s - loss: 1.1960 - output_react_loss: 0.1374 - output_bg_ph_loss: 0.1554 - output_ph_loss: 0.1759 - output_mg_c_loss: 0.1384 - output_c_loss: 0.1575 - val_loss: 1.3918 - val_output_react_loss: 0.1545 - val_output_bg_ph_loss: 0.1888 - val_output_ph_loss: 0.1959 - val_output_mg_c_loss: 0.1683 - val_output_c_loss: 0.1728\nEpoch 54/120\n30/30 - 4s - loss: 1.1874 - output_react_loss: 0.1364 - output_bg_ph_loss: 0.1540 - output_ph_loss: 0.1747 - output_mg_c_loss: 0.1374 - output_c_loss: 0.1571 - val_loss: 1.3927 - val_output_react_loss: 0.1559 - val_output_bg_ph_loss: 0.1898 - val_output_ph_loss: 0.1962 - val_output_mg_c_loss: 0.1667 - val_output_c_loss: 0.1717\nEpoch 55/120\n30/30 - 4s - loss: 1.1849 - output_react_loss: 0.1362 - output_bg_ph_loss: 0.1539 - output_ph_loss: 0.1743 - output_mg_c_loss: 0.1369 - output_c_loss: 0.1565 - val_loss: 1.3862 - val_output_react_loss: 0.1550 - val_output_bg_ph_loss: 0.1889 - val_output_ph_loss: 0.1968 - val_output_mg_c_loss: 0.1655 - val_output_c_loss: 0.1707\nEpoch 56/120\n30/30 - 4s - loss: 1.1682 - output_react_loss: 0.1345 - output_bg_ph_loss: 0.1513 - output_ph_loss: 0.1728 - output_mg_c_loss: 0.1344 - output_c_loss: 0.1550 - val_loss: 1.3599 - val_output_react_loss: 0.1527 - val_output_bg_ph_loss: 0.1842 - val_output_ph_loss: 0.1922 - val_output_mg_c_loss: 0.1627 - val_output_c_loss: 0.1685\nEpoch 57/120\n30/30 - 4s - loss: 1.1616 - output_react_loss: 0.1333 - output_bg_ph_loss: 0.1503 - output_ph_loss: 0.1717 - output_mg_c_loss: 0.1338 - output_c_loss: 0.1550 - val_loss: 1.3782 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1884 - val_output_ph_loss: 0.1950 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1698\nEpoch 58/120\n30/30 - 4s - loss: 1.1523 - output_react_loss: 0.1327 - output_bg_ph_loss: 0.1490 - output_ph_loss: 0.1706 - output_mg_c_loss: 0.1324 - output_c_loss: 0.1536 - val_loss: 1.3716 - val_output_react_loss: 0.1548 - val_output_bg_ph_loss: 0.1865 - val_output_ph_loss: 0.1927 - val_output_mg_c_loss: 0.1632 - val_output_c_loss: 0.1698\nEpoch 59/120\n30/30 - 4s - loss: 1.1425 - output_react_loss: 0.1312 - output_bg_ph_loss: 0.1474 - output_ph_loss: 0.1693 - output_mg_c_loss: 0.1314 - output_c_loss: 0.1531 - val_loss: 1.3839 - val_output_react_loss: 0.1552 - val_output_bg_ph_loss: 0.1883 - val_output_ph_loss: 0.1946 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1708\nEpoch 60/120\n30/30 - 4s - loss: 1.1355 - output_react_loss: 0.1303 - output_bg_ph_loss: 0.1460 - output_ph_loss: 0.1680 - output_mg_c_loss: 0.1314 - output_c_loss: 0.1522 - val_loss: 1.3736 - val_output_react_loss: 0.1523 - val_output_bg_ph_loss: 0.1877 - val_output_ph_loss: 0.1945 - val_output_mg_c_loss: 0.1644 - val_output_c_loss: 0.1702\nEpoch 61/120\n\nEpoch 00061: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n30/30 - 4s - loss: 1.1305 - output_react_loss: 0.1289 - output_bg_ph_loss: 0.1459 - output_ph_loss: 0.1683 - output_mg_c_loss: 0.1298 - output_c_loss: 0.1527 - val_loss: 1.3654 - val_output_react_loss: 0.1529 - val_output_bg_ph_loss: 0.1861 - val_output_ph_loss: 0.1922 - val_output_mg_c_loss: 0.1635 - val_output_c_loss: 0.1682\nEpoch 62/120\n30/30 - 4s - loss: 1.0979 - output_react_loss: 0.1258 - output_bg_ph_loss: 0.1411 - output_ph_loss: 0.1641 - output_mg_c_loss: 0.1255 - output_c_loss: 0.1490 - val_loss: 1.3404 - val_output_react_loss: 0.1499 - val_output_bg_ph_loss: 0.1825 - val_output_ph_loss: 0.1894 - val_output_mg_c_loss: 0.1597 - val_output_c_loss: 0.1668\nEpoch 63/120\n30/30 - 4s - loss: 1.0789 - output_react_loss: 0.1238 - output_bg_ph_loss: 0.1381 - output_ph_loss: 0.1619 - output_mg_c_loss: 0.1229 - output_c_loss: 0.1476 - val_loss: 1.3418 - val_output_react_loss: 0.1497 - val_output_bg_ph_loss: 0.1827 - val_output_ph_loss: 0.1899 - val_output_mg_c_loss: 0.1600 - val_output_c_loss: 0.1671\nEpoch 64/120\n30/30 - 4s - loss: 1.0765 - output_react_loss: 0.1233 - output_bg_ph_loss: 0.1380 - output_ph_loss: 0.1616 - output_mg_c_loss: 0.1225 - output_c_loss: 0.1472 - val_loss: 1.3376 - val_output_react_loss: 0.1495 - val_output_bg_ph_loss: 0.1822 - val_output_ph_loss: 0.1888 - val_output_mg_c_loss: 0.1595 - val_output_c_loss: 0.1664\nEpoch 65/120\n30/30 - 4s - loss: 1.0710 - output_react_loss: 0.1227 - output_bg_ph_loss: 0.1369 - output_ph_loss: 0.1608 - output_mg_c_loss: 0.1220 - output_c_loss: 0.1470 - val_loss: 1.3407 - val_output_react_loss: 0.1497 - val_output_bg_ph_loss: 0.1825 - val_output_ph_loss: 0.1895 - val_output_mg_c_loss: 0.1601 - val_output_c_loss: 0.1666\nEpoch 66/120\n30/30 - 4s - loss: 1.0704 - output_react_loss: 0.1227 - output_bg_ph_loss: 0.1370 - output_ph_loss: 0.1607 - output_mg_c_loss: 0.1218 - output_c_loss: 0.1467 - val_loss: 1.3403 - val_output_react_loss: 0.1492 - val_output_bg_ph_loss: 0.1826 - val_output_ph_loss: 0.1891 - val_output_mg_c_loss: 0.1604 - val_output_c_loss: 0.1669\nEpoch 67/120\n30/30 - 4s - loss: 1.0669 - output_react_loss: 0.1224 - output_bg_ph_loss: 0.1365 - output_ph_loss: 0.1602 - output_mg_c_loss: 0.1214 - output_c_loss: 0.1462 - val_loss: 1.3418 - val_output_react_loss: 0.1498 - val_output_bg_ph_loss: 0.1827 - val_output_ph_loss: 0.1895 - val_output_mg_c_loss: 0.1603 - val_output_c_loss: 0.1667\nEpoch 68/120\n30/30 - 4s - loss: 1.0631 - output_react_loss: 0.1218 - output_bg_ph_loss: 0.1359 - output_ph_loss: 0.1593 - output_mg_c_loss: 0.1212 - output_c_loss: 0.1460 - val_loss: 1.3403 - val_output_react_loss: 0.1498 - val_output_bg_ph_loss: 0.1825 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1600 - val_output_c_loss: 0.1666\nEpoch 69/120\n\nEpoch 00069: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n30/30 - 4s - loss: 1.0639 - output_react_loss: 0.1221 - output_bg_ph_loss: 0.1357 - output_ph_loss: 0.1603 - output_mg_c_loss: 0.1210 - output_c_loss: 0.1461 - val_loss: 1.3406 - val_output_react_loss: 0.1498 - val_output_bg_ph_loss: 0.1826 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1600 - val_output_c_loss: 0.1666\nEpoch 70/120\n30/30 - 4s - loss: 1.0592 - output_react_loss: 0.1215 - output_bg_ph_loss: 0.1353 - output_ph_loss: 0.1594 - output_mg_c_loss: 0.1203 - output_c_loss: 0.1456 - val_loss: 1.3397 - val_output_react_loss: 0.1497 - val_output_bg_ph_loss: 0.1824 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1599 - val_output_c_loss: 0.1666\nEpoch 71/120\n30/30 - 4s - loss: 1.0588 - output_react_loss: 0.1216 - output_bg_ph_loss: 0.1351 - output_ph_loss: 0.1593 - output_mg_c_loss: 0.1203 - output_c_loss: 0.1453 - val_loss: 1.3392 - val_output_react_loss: 0.1496 - val_output_bg_ph_loss: 0.1824 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1599 - val_output_c_loss: 0.1666\nEpoch 72/120\n30/30 - 4s - loss: 1.0593 - output_react_loss: 0.1213 - output_bg_ph_loss: 0.1353 - output_ph_loss: 0.1589 - output_mg_c_loss: 0.1208 - output_c_loss: 0.1456 - val_loss: 1.3390 - val_output_react_loss: 0.1496 - val_output_bg_ph_loss: 0.1823 - val_output_ph_loss: 0.1889 - val_output_mg_c_loss: 0.1599 - val_output_c_loss: 0.1666\nEpoch 73/120\n30/30 - 4s - loss: 1.0578 - output_react_loss: 0.1212 - output_bg_ph_loss: 0.1352 - output_ph_loss: 0.1589 - output_mg_c_loss: 0.1201 - output_c_loss: 0.1458 - val_loss: 1.3388 - val_output_react_loss: 0.1496 - val_output_bg_ph_loss: 0.1823 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1598 - val_output_c_loss: 0.1665\nEpoch 74/120\nRestoring model weights from the end of the best epoch.\n\nEpoch 00074: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n30/30 - 4s - loss: 1.0578 - output_react_loss: 0.1212 - output_bg_ph_loss: 0.1350 - output_ph_loss: 0.1591 - output_mg_c_loss: 0.1203 - output_c_loss: 0.1457 - val_loss: 1.3391 - val_output_react_loss: 0.1496 - val_output_bg_ph_loss: 0.1824 - val_output_ph_loss: 0.1890 - val_output_mg_c_loss: 0.1598 - val_output_c_loss: 0.1666\nEpoch 00074: early stopping\n\nFOLD: 4\nEpoch 1/120\n30/30 - 6s - loss: 3.5823 - output_react_loss: 0.4021 - output_bg_ph_loss: 0.4325 - output_ph_loss: 0.4183 - output_mg_c_loss: 0.5652 - output_c_loss: 0.3643 - val_loss: 2.3387 - val_output_react_loss: 0.2429 - val_output_bg_ph_loss: 0.3170 - val_output_ph_loss: 0.3297 - val_output_mg_c_loss: 0.3079 - val_output_c_loss: 0.2732\nEpoch 2/120\n30/30 - 4s - loss: 2.3416 - output_react_loss: 0.2468 - output_bg_ph_loss: 0.3150 - output_ph_loss: 0.3305 - output_mg_c_loss: 0.3056 - output_c_loss: 0.2763 - val_loss: 2.1665 - val_output_react_loss: 0.2286 - val_output_bg_ph_loss: 0.2981 - val_output_ph_loss: 0.3018 - val_output_mg_c_loss: 0.2791 - val_output_c_loss: 0.2531\nEpoch 3/120\n30/30 - 4s - loss: 2.2095 - output_react_loss: 0.2356 - output_bg_ph_loss: 0.2987 - output_ph_loss: 0.3096 - output_mg_c_loss: 0.2841 - output_c_loss: 0.2631 - val_loss: 2.0789 - val_output_react_loss: 0.2214 - val_output_bg_ph_loss: 0.2849 - val_output_ph_loss: 0.2868 - val_output_mg_c_loss: 0.2673 - val_output_c_loss: 0.2448\nEpoch 4/120\n30/30 - 4s - loss: 2.1361 - output_react_loss: 0.2284 - output_bg_ph_loss: 0.2880 - output_ph_loss: 0.3003 - output_mg_c_loss: 0.2736 - output_c_loss: 0.2558 - val_loss: 2.0266 - val_output_react_loss: 0.2151 - val_output_bg_ph_loss: 0.2761 - val_output_ph_loss: 0.2838 - val_output_mg_c_loss: 0.2601 - val_output_c_loss: 0.2403\nEpoch 5/120\n30/30 - 4s - loss: 2.0682 - output_react_loss: 0.2218 - output_bg_ph_loss: 0.2780 - output_ph_loss: 0.2908 - output_mg_c_loss: 0.2647 - output_c_loss: 0.2484 - val_loss: 1.9716 - val_output_react_loss: 0.2101 - val_output_bg_ph_loss: 0.2693 - val_output_ph_loss: 0.2742 - val_output_mg_c_loss: 0.2521 - val_output_c_loss: 0.2345\nEpoch 6/120\n30/30 - 4s - loss: 2.0186 - output_react_loss: 0.2167 - output_bg_ph_loss: 0.2715 - output_ph_loss: 0.2837 - output_mg_c_loss: 0.2577 - output_c_loss: 0.2432 - val_loss: 1.9286 - val_output_react_loss: 0.2043 - val_output_bg_ph_loss: 0.2636 - val_output_ph_loss: 0.2702 - val_output_mg_c_loss: 0.2461 - val_output_c_loss: 0.2306\nEpoch 7/120\n30/30 - 4s - loss: 1.9794 - output_react_loss: 0.2131 - output_bg_ph_loss: 0.2661 - output_ph_loss: 0.2788 - output_mg_c_loss: 0.2519 - output_c_loss: 0.2383 - val_loss: 1.8964 - val_output_react_loss: 0.2028 - val_output_bg_ph_loss: 0.2595 - val_output_ph_loss: 0.2648 - val_output_mg_c_loss: 0.2403 - val_output_c_loss: 0.2263\nEpoch 8/120\n30/30 - 4s - loss: 1.9333 - output_react_loss: 0.2085 - output_bg_ph_loss: 0.2602 - output_ph_loss: 0.2725 - output_mg_c_loss: 0.2448 - output_c_loss: 0.2339 - val_loss: 1.8370 - val_output_react_loss: 0.1947 - val_output_bg_ph_loss: 0.2520 - val_output_ph_loss: 0.2572 - val_output_mg_c_loss: 0.2333 - val_output_c_loss: 0.2198\nEpoch 9/120\n30/30 - 4s - loss: 1.8834 - output_react_loss: 0.2045 - output_bg_ph_loss: 0.2542 - output_ph_loss: 0.2659 - output_mg_c_loss: 0.2361 - output_c_loss: 0.2281 - val_loss: 1.8088 - val_output_react_loss: 0.1929 - val_output_bg_ph_loss: 0.2510 - val_output_ph_loss: 0.2530 - val_output_mg_c_loss: 0.2263 - val_output_c_loss: 0.2153\nEpoch 10/120\n30/30 - 4s - loss: 1.8425 - output_react_loss: 0.2013 - output_bg_ph_loss: 0.2478 - output_ph_loss: 0.2609 - output_mg_c_loss: 0.2304 - output_c_loss: 0.2228 - val_loss: 1.7647 - val_output_react_loss: 0.1912 - val_output_bg_ph_loss: 0.2408 - val_output_ph_loss: 0.2485 - val_output_mg_c_loss: 0.2200 - val_output_c_loss: 0.2122\nEpoch 11/120\n30/30 - 4s - loss: 1.8167 - output_react_loss: 0.1992 - output_bg_ph_loss: 0.2440 - output_ph_loss: 0.2577 - output_mg_c_loss: 0.2260 - output_c_loss: 0.2206 - val_loss: 1.7501 - val_output_react_loss: 0.1888 - val_output_bg_ph_loss: 0.2405 - val_output_ph_loss: 0.2455 - val_output_mg_c_loss: 0.2174 - val_output_c_loss: 0.2113\nEpoch 12/120\n30/30 - 4s - loss: 1.7783 - output_react_loss: 0.1968 - output_bg_ph_loss: 0.2379 - output_ph_loss: 0.2527 - output_mg_c_loss: 0.2198 - output_c_loss: 0.2167 - val_loss: 1.7254 - val_output_react_loss: 0.1857 - val_output_bg_ph_loss: 0.2370 - val_output_ph_loss: 0.2433 - val_output_mg_c_loss: 0.2153 - val_output_c_loss: 0.2064\nEpoch 13/120\n30/30 - 4s - loss: 1.7545 - output_react_loss: 0.1948 - output_bg_ph_loss: 0.2343 - output_ph_loss: 0.2489 - output_mg_c_loss: 0.2165 - output_c_loss: 0.2144 - val_loss: 1.6945 - val_output_react_loss: 0.1844 - val_output_bg_ph_loss: 0.2308 - val_output_ph_loss: 0.2409 - val_output_mg_c_loss: 0.2091 - val_output_c_loss: 0.2049\nEpoch 14/120\n30/30 - 4s - loss: 1.7199 - output_react_loss: 0.1912 - output_bg_ph_loss: 0.2298 - output_ph_loss: 0.2453 - output_mg_c_loss: 0.2111 - output_c_loss: 0.2104 - val_loss: 1.6693 - val_output_react_loss: 0.1828 - val_output_bg_ph_loss: 0.2273 - val_output_ph_loss: 0.2347 - val_output_mg_c_loss: 0.2062 - val_output_c_loss: 0.2020\nEpoch 15/120\n30/30 - 4s - loss: 1.7001 - output_react_loss: 0.1908 - output_bg_ph_loss: 0.2265 - output_ph_loss: 0.2412 - output_mg_c_loss: 0.2083 - output_c_loss: 0.2076 - val_loss: 1.6376 - val_output_react_loss: 0.1790 - val_output_bg_ph_loss: 0.2223 - val_output_ph_loss: 0.2338 - val_output_mg_c_loss: 0.2018 - val_output_c_loss: 0.1975\nEpoch 16/120\n30/30 - 4s - loss: 1.6763 - output_react_loss: 0.1888 - output_bg_ph_loss: 0.2231 - output_ph_loss: 0.2393 - output_mg_c_loss: 0.2040 - output_c_loss: 0.2052 - val_loss: 1.6350 - val_output_react_loss: 0.1826 - val_output_bg_ph_loss: 0.2237 - val_output_ph_loss: 0.2268 - val_output_mg_c_loss: 0.1991 - val_output_c_loss: 0.1974\nEpoch 17/120\n30/30 - 4s - loss: 1.6524 - output_react_loss: 0.1862 - output_bg_ph_loss: 0.2214 - output_ph_loss: 0.2341 - output_mg_c_loss: 0.2005 - output_c_loss: 0.2021 - val_loss: 1.5882 - val_output_react_loss: 0.1756 - val_output_bg_ph_loss: 0.2165 - val_output_ph_loss: 0.2255 - val_output_mg_c_loss: 0.1940 - val_output_c_loss: 0.1905\nEpoch 18/120\n30/30 - 4s - loss: 1.6277 - output_react_loss: 0.1833 - output_bg_ph_loss: 0.2173 - output_ph_loss: 0.2318 - output_mg_c_loss: 0.1976 - output_c_loss: 0.1996 - val_loss: 1.5811 - val_output_react_loss: 0.1760 - val_output_bg_ph_loss: 0.2158 - val_output_ph_loss: 0.2202 - val_output_mg_c_loss: 0.1939 - val_output_c_loss: 0.1894\nEpoch 19/120\n30/30 - 4s - loss: 1.6048 - output_react_loss: 0.1822 - output_bg_ph_loss: 0.2142 - output_ph_loss: 0.2282 - output_mg_c_loss: 0.1936 - output_c_loss: 0.1967 - val_loss: 1.5823 - val_output_react_loss: 0.1775 - val_output_bg_ph_loss: 0.2169 - val_output_ph_loss: 0.2204 - val_output_mg_c_loss: 0.1927 - val_output_c_loss: 0.1878\nEpoch 20/120\n30/30 - 4s - loss: 1.5862 - output_react_loss: 0.1800 - output_bg_ph_loss: 0.2113 - output_ph_loss: 0.2259 - output_mg_c_loss: 0.1914 - output_c_loss: 0.1948 - val_loss: 1.5650 - val_output_react_loss: 0.1768 - val_output_bg_ph_loss: 0.2115 - val_output_ph_loss: 0.2230 - val_output_mg_c_loss: 0.1889 - val_output_c_loss: 0.1875\nEpoch 21/120\n30/30 - 4s - loss: 1.5777 - output_react_loss: 0.1791 - output_bg_ph_loss: 0.2095 - output_ph_loss: 0.2254 - output_mg_c_loss: 0.1905 - output_c_loss: 0.1941 - val_loss: 1.5429 - val_output_react_loss: 0.1721 - val_output_bg_ph_loss: 0.2116 - val_output_ph_loss: 0.2152 - val_output_mg_c_loss: 0.1877 - val_output_c_loss: 0.1849\nEpoch 22/120\n30/30 - 4s - loss: 1.5588 - output_react_loss: 0.1763 - output_bg_ph_loss: 0.2085 - output_ph_loss: 0.2219 - output_mg_c_loss: 0.1877 - output_c_loss: 0.1920 - val_loss: 1.5415 - val_output_react_loss: 0.1723 - val_output_bg_ph_loss: 0.2125 - val_output_ph_loss: 0.2138 - val_output_mg_c_loss: 0.1864 - val_output_c_loss: 0.1854\nEpoch 23/120\n30/30 - 4s - loss: 1.5387 - output_react_loss: 0.1756 - output_bg_ph_loss: 0.2049 - output_ph_loss: 0.2187 - output_mg_c_loss: 0.1848 - output_c_loss: 0.1894 - val_loss: 1.5317 - val_output_react_loss: 0.1734 - val_output_bg_ph_loss: 0.2069 - val_output_ph_loss: 0.2141 - val_output_mg_c_loss: 0.1860 - val_output_c_loss: 0.1850\nEpoch 24/120\n30/30 - 4s - loss: 1.5313 - output_react_loss: 0.1749 - output_bg_ph_loss: 0.2038 - output_ph_loss: 0.2174 - output_mg_c_loss: 0.1841 - output_c_loss: 0.1884 - val_loss: 1.5225 - val_output_react_loss: 0.1708 - val_output_bg_ph_loss: 0.2102 - val_output_ph_loss: 0.2109 - val_output_mg_c_loss: 0.1831 - val_output_c_loss: 0.1832\nEpoch 25/120\n30/30 - 4s - loss: 1.5198 - output_react_loss: 0.1732 - output_bg_ph_loss: 0.2032 - output_ph_loss: 0.2165 - output_mg_c_loss: 0.1820 - output_c_loss: 0.1865 - val_loss: 1.5092 - val_output_react_loss: 0.1707 - val_output_bg_ph_loss: 0.2060 - val_output_ph_loss: 0.2100 - val_output_mg_c_loss: 0.1819 - val_output_c_loss: 0.1819\nEpoch 26/120\n30/30 - 4s - loss: 1.4993 - output_react_loss: 0.1707 - output_bg_ph_loss: 0.1994 - output_ph_loss: 0.2137 - output_mg_c_loss: 0.1800 - output_c_loss: 0.1853 - val_loss: 1.5011 - val_output_react_loss: 0.1676 - val_output_bg_ph_loss: 0.2052 - val_output_ph_loss: 0.2101 - val_output_mg_c_loss: 0.1827 - val_output_c_loss: 0.1802\nEpoch 27/120\n30/30 - 4s - loss: 1.4854 - output_react_loss: 0.1697 - output_bg_ph_loss: 0.1975 - output_ph_loss: 0.2121 - output_mg_c_loss: 0.1775 - output_c_loss: 0.1838 - val_loss: 1.4949 - val_output_react_loss: 0.1661 - val_output_bg_ph_loss: 0.2053 - val_output_ph_loss: 0.2086 - val_output_mg_c_loss: 0.1820 - val_output_c_loss: 0.1796\nEpoch 28/120\n30/30 - 4s - loss: 1.4652 - output_react_loss: 0.1671 - output_bg_ph_loss: 0.1949 - output_ph_loss: 0.2095 - output_mg_c_loss: 0.1749 - output_c_loss: 0.1820 - val_loss: 1.4817 - val_output_react_loss: 0.1649 - val_output_bg_ph_loss: 0.2028 - val_output_ph_loss: 0.2067 - val_output_mg_c_loss: 0.1799 - val_output_c_loss: 0.1797\nEpoch 29/120\n30/30 - 4s - loss: 1.4570 - output_react_loss: 0.1660 - output_bg_ph_loss: 0.1937 - output_ph_loss: 0.2082 - output_mg_c_loss: 0.1741 - output_c_loss: 0.1812 - val_loss: 1.4784 - val_output_react_loss: 0.1650 - val_output_bg_ph_loss: 0.2023 - val_output_ph_loss: 0.2065 - val_output_mg_c_loss: 0.1798 - val_output_c_loss: 0.1777\nEpoch 30/120\n30/30 - 4s - loss: 1.4460 - output_react_loss: 0.1661 - output_bg_ph_loss: 0.1917 - output_ph_loss: 0.2062 - output_mg_c_loss: 0.1721 - output_c_loss: 0.1802 - val_loss: 1.4758 - val_output_react_loss: 0.1670 - val_output_bg_ph_loss: 0.2029 - val_output_ph_loss: 0.2043 - val_output_mg_c_loss: 0.1774 - val_output_c_loss: 0.1770\nEpoch 31/120\n30/30 - 4s - loss: 1.4358 - output_react_loss: 0.1645 - output_bg_ph_loss: 0.1911 - output_ph_loss: 0.2049 - output_mg_c_loss: 0.1706 - output_c_loss: 0.1785 - val_loss: 1.4832 - val_output_react_loss: 0.1686 - val_output_bg_ph_loss: 0.2021 - val_output_ph_loss: 0.2041 - val_output_mg_c_loss: 0.1796 - val_output_c_loss: 0.1785\nEpoch 32/120\n30/30 - 4s - loss: 1.4263 - output_react_loss: 0.1634 - output_bg_ph_loss: 0.1897 - output_ph_loss: 0.2040 - output_mg_c_loss: 0.1691 - output_c_loss: 0.1779 - val_loss: 1.4731 - val_output_react_loss: 0.1646 - val_output_bg_ph_loss: 0.2030 - val_output_ph_loss: 0.2041 - val_output_mg_c_loss: 0.1781 - val_output_c_loss: 0.1774\nEpoch 33/120\n30/30 - 4s - loss: 1.4067 - output_react_loss: 0.1617 - output_bg_ph_loss: 0.1863 - output_ph_loss: 0.2020 - output_mg_c_loss: 0.1664 - output_c_loss: 0.1758 - val_loss: 1.4670 - val_output_react_loss: 0.1635 - val_output_bg_ph_loss: 0.2002 - val_output_ph_loss: 0.2062 - val_output_mg_c_loss: 0.1782 - val_output_c_loss: 0.1769\nEpoch 34/120\n30/30 - 4s - loss: 1.3967 - output_react_loss: 0.1602 - output_bg_ph_loss: 0.1851 - output_ph_loss: 0.2008 - output_mg_c_loss: 0.1653 - output_c_loss: 0.1748 - val_loss: 1.4740 - val_output_react_loss: 0.1648 - val_output_bg_ph_loss: 0.2007 - val_output_ph_loss: 0.2066 - val_output_mg_c_loss: 0.1789 - val_output_c_loss: 0.1787\nEpoch 35/120\n30/30 - 4s - loss: 1.3834 - output_react_loss: 0.1589 - output_bg_ph_loss: 0.1827 - output_ph_loss: 0.1988 - output_mg_c_loss: 0.1639 - output_c_loss: 0.1735 - val_loss: 1.4549 - val_output_react_loss: 0.1615 - val_output_bg_ph_loss: 0.2006 - val_output_ph_loss: 0.2023 - val_output_mg_c_loss: 0.1769 - val_output_c_loss: 0.1745\nEpoch 36/120\n30/30 - 4s - loss: 1.3745 - output_react_loss: 0.1570 - output_bg_ph_loss: 0.1814 - output_ph_loss: 0.1975 - output_mg_c_loss: 0.1635 - output_c_loss: 0.1733 - val_loss: 1.4527 - val_output_react_loss: 0.1617 - val_output_bg_ph_loss: 0.1999 - val_output_ph_loss: 0.2023 - val_output_mg_c_loss: 0.1758 - val_output_c_loss: 0.1757\nEpoch 37/120\n30/30 - 4s - loss: 1.3622 - output_react_loss: 0.1562 - output_bg_ph_loss: 0.1804 - output_ph_loss: 0.1964 - output_mg_c_loss: 0.1608 - output_c_loss: 0.1711 - val_loss: 1.4521 - val_output_react_loss: 0.1622 - val_output_bg_ph_loss: 0.1991 - val_output_ph_loss: 0.2038 - val_output_mg_c_loss: 0.1743 - val_output_c_loss: 0.1769\nEpoch 38/120\n30/30 - 4s - loss: 1.3482 - output_react_loss: 0.1548 - output_bg_ph_loss: 0.1783 - output_ph_loss: 0.1951 - output_mg_c_loss: 0.1582 - output_c_loss: 0.1707 - val_loss: 1.4610 - val_output_react_loss: 0.1620 - val_output_bg_ph_loss: 0.2001 - val_output_ph_loss: 0.2033 - val_output_mg_c_loss: 0.1781 - val_output_c_loss: 0.1773\nEpoch 39/120\n30/30 - 4s - loss: 1.3435 - output_react_loss: 0.1556 - output_bg_ph_loss: 0.1775 - output_ph_loss: 0.1932 - output_mg_c_loss: 0.1570 - output_c_loss: 0.1701 - val_loss: 1.4591 - val_output_react_loss: 0.1601 - val_output_bg_ph_loss: 0.1998 - val_output_ph_loss: 0.2049 - val_output_mg_c_loss: 0.1774 - val_output_c_loss: 0.1796\nEpoch 40/120\n30/30 - 4s - loss: 1.3337 - output_react_loss: 0.1529 - output_bg_ph_loss: 0.1768 - output_ph_loss: 0.1918 - output_mg_c_loss: 0.1566 - output_c_loss: 0.1694 - val_loss: 1.4426 - val_output_react_loss: 0.1603 - val_output_bg_ph_loss: 0.1975 - val_output_ph_loss: 0.2000 - val_output_mg_c_loss: 0.1764 - val_output_c_loss: 0.1741\nEpoch 41/120\n30/30 - 4s - loss: 1.3097 - output_react_loss: 0.1507 - output_bg_ph_loss: 0.1727 - output_ph_loss: 0.1900 - output_mg_c_loss: 0.1529 - output_c_loss: 0.1671 - val_loss: 1.4403 - val_output_react_loss: 0.1614 - val_output_bg_ph_loss: 0.1966 - val_output_ph_loss: 0.2001 - val_output_mg_c_loss: 0.1746 - val_output_c_loss: 0.1748\nEpoch 42/120\n30/30 - 4s - loss: 1.2990 - output_react_loss: 0.1496 - output_bg_ph_loss: 0.1711 - output_ph_loss: 0.1881 - output_mg_c_loss: 0.1516 - output_c_loss: 0.1663 - val_loss: 1.4326 - val_output_react_loss: 0.1595 - val_output_bg_ph_loss: 0.1977 - val_output_ph_loss: 0.1992 - val_output_mg_c_loss: 0.1733 - val_output_c_loss: 0.1722\nEpoch 43/120\n30/30 - 4s - loss: 1.2873 - output_react_loss: 0.1487 - output_bg_ph_loss: 0.1690 - output_ph_loss: 0.1869 - output_mg_c_loss: 0.1500 - output_c_loss: 0.1650 - val_loss: 1.4424 - val_output_react_loss: 0.1615 - val_output_bg_ph_loss: 0.1981 - val_output_ph_loss: 0.2005 - val_output_mg_c_loss: 0.1741 - val_output_c_loss: 0.1744\nEpoch 44/120\n30/30 - 4s - loss: 1.2752 - output_react_loss: 0.1478 - output_bg_ph_loss: 0.1677 - output_ph_loss: 0.1849 - output_mg_c_loss: 0.1478 - output_c_loss: 0.1637 - val_loss: 1.4444 - val_output_react_loss: 0.1597 - val_output_bg_ph_loss: 0.1990 - val_output_ph_loss: 0.2009 - val_output_mg_c_loss: 0.1753 - val_output_c_loss: 0.1754\nEpoch 45/120\n30/30 - 4s - loss: 1.2712 - output_react_loss: 0.1466 - output_bg_ph_loss: 0.1671 - output_ph_loss: 0.1844 - output_mg_c_loss: 0.1477 - output_c_loss: 0.1639 - val_loss: 1.4365 - val_output_react_loss: 0.1613 - val_output_bg_ph_loss: 0.1957 - val_output_ph_loss: 0.1989 - val_output_mg_c_loss: 0.1747 - val_output_c_loss: 0.1741\nEpoch 46/120\n30/30 - 4s - loss: 1.2567 - output_react_loss: 0.1453 - output_bg_ph_loss: 0.1646 - output_ph_loss: 0.1829 - output_mg_c_loss: 0.1460 - output_c_loss: 0.1621 - val_loss: 1.4306 - val_output_react_loss: 0.1597 - val_output_bg_ph_loss: 0.1965 - val_output_ph_loss: 0.1983 - val_output_mg_c_loss: 0.1734 - val_output_c_loss: 0.1732\nEpoch 47/120\n30/30 - 4s - loss: 1.2402 - output_react_loss: 0.1428 - output_bg_ph_loss: 0.1625 - output_ph_loss: 0.1811 - output_mg_c_loss: 0.1436 - output_c_loss: 0.1612 - val_loss: 1.4266 - val_output_react_loss: 0.1595 - val_output_bg_ph_loss: 0.1955 - val_output_ph_loss: 0.1983 - val_output_mg_c_loss: 0.1724 - val_output_c_loss: 0.1735\nEpoch 48/120\n30/30 - 4s - loss: 1.2358 - output_react_loss: 0.1429 - output_bg_ph_loss: 0.1610 - output_ph_loss: 0.1803 - output_mg_c_loss: 0.1435 - output_c_loss: 0.1607 - val_loss: 1.4273 - val_output_react_loss: 0.1578 - val_output_bg_ph_loss: 0.1974 - val_output_ph_loss: 0.1983 - val_output_mg_c_loss: 0.1730 - val_output_c_loss: 0.1726\nEpoch 49/120\n30/30 - 4s - loss: 1.2235 - output_react_loss: 0.1419 - output_bg_ph_loss: 0.1597 - output_ph_loss: 0.1784 - output_mg_c_loss: 0.1414 - output_c_loss: 0.1592 - val_loss: 1.4392 - val_output_react_loss: 0.1610 - val_output_bg_ph_loss: 0.1981 - val_output_ph_loss: 0.1990 - val_output_mg_c_loss: 0.1740 - val_output_c_loss: 0.1740\nEpoch 50/120\n30/30 - 4s - loss: 1.2135 - output_react_loss: 0.1402 - output_bg_ph_loss: 0.1583 - output_ph_loss: 0.1773 - output_mg_c_loss: 0.1404 - output_c_loss: 0.1585 - val_loss: 1.4262 - val_output_react_loss: 0.1578 - val_output_bg_ph_loss: 0.1959 - val_output_ph_loss: 0.1997 - val_output_mg_c_loss: 0.1728 - val_output_c_loss: 0.1736\nEpoch 51/120\n30/30 - 4s - loss: 1.2031 - output_react_loss: 0.1393 - output_bg_ph_loss: 0.1566 - output_ph_loss: 0.1761 - output_mg_c_loss: 0.1388 - output_c_loss: 0.1576 - val_loss: 1.4383 - val_output_react_loss: 0.1601 - val_output_bg_ph_loss: 0.1985 - val_output_ph_loss: 0.1987 - val_output_mg_c_loss: 0.1738 - val_output_c_loss: 0.1747\nEpoch 52/120\n30/30 - 4s - loss: 1.1939 - output_react_loss: 0.1373 - output_bg_ph_loss: 0.1555 - output_ph_loss: 0.1759 - output_mg_c_loss: 0.1377 - output_c_loss: 0.1572 - val_loss: 1.4197 - val_output_react_loss: 0.1575 - val_output_bg_ph_loss: 0.1939 - val_output_ph_loss: 0.1999 - val_output_mg_c_loss: 0.1721 - val_output_c_loss: 0.1728\nEpoch 53/120\n30/30 - 4s - loss: 1.1848 - output_react_loss: 0.1372 - output_bg_ph_loss: 0.1532 - output_ph_loss: 0.1748 - output_mg_c_loss: 0.1361 - output_c_loss: 0.1570 - val_loss: 1.4191 - val_output_react_loss: 0.1578 - val_output_bg_ph_loss: 0.1945 - val_output_ph_loss: 0.1976 - val_output_mg_c_loss: 0.1723 - val_output_c_loss: 0.1721\nEpoch 54/120\n30/30 - 4s - loss: 1.1797 - output_react_loss: 0.1367 - output_bg_ph_loss: 0.1527 - output_ph_loss: 0.1738 - output_mg_c_loss: 0.1354 - output_c_loss: 0.1562 - val_loss: 1.4224 - val_output_react_loss: 0.1571 - val_output_bg_ph_loss: 0.1954 - val_output_ph_loss: 0.2002 - val_output_mg_c_loss: 0.1720 - val_output_c_loss: 0.1730\nEpoch 55/120\n30/30 - 4s - loss: 1.1625 - output_react_loss: 0.1344 - output_bg_ph_loss: 0.1498 - output_ph_loss: 0.1721 - output_mg_c_loss: 0.1335 - output_c_loss: 0.1550 - val_loss: 1.4158 - val_output_react_loss: 0.1557 - val_output_bg_ph_loss: 0.1957 - val_output_ph_loss: 0.1979 - val_output_mg_c_loss: 0.1713 - val_output_c_loss: 0.1724\nEpoch 56/120\n30/30 - 4s - loss: 1.1535 - output_react_loss: 0.1331 - output_bg_ph_loss: 0.1486 - output_ph_loss: 0.1706 - output_mg_c_loss: 0.1326 - output_c_loss: 0.1542 - val_loss: 1.4149 - val_output_react_loss: 0.1578 - val_output_bg_ph_loss: 0.1943 - val_output_ph_loss: 0.1963 - val_output_mg_c_loss: 0.1714 - val_output_c_loss: 0.1717\nEpoch 57/120\n30/30 - 4s - loss: 1.1467 - output_react_loss: 0.1326 - output_bg_ph_loss: 0.1476 - output_ph_loss: 0.1699 - output_mg_c_loss: 0.1313 - output_c_loss: 0.1537 - val_loss: 1.4238 - val_output_react_loss: 0.1579 - val_output_bg_ph_loss: 0.1965 - val_output_ph_loss: 0.1971 - val_output_mg_c_loss: 0.1729 - val_output_c_loss: 0.1721\nEpoch 58/120\n30/30 - 4s - loss: 1.1412 - output_react_loss: 0.1322 - output_bg_ph_loss: 0.1468 - output_ph_loss: 0.1691 - output_mg_c_loss: 0.1307 - output_c_loss: 0.1528 - val_loss: 1.4103 - val_output_react_loss: 0.1573 - val_output_bg_ph_loss: 0.1933 - val_output_ph_loss: 0.1966 - val_output_mg_c_loss: 0.1702 - val_output_c_loss: 0.1722\nEpoch 59/120\n30/30 - 4s - loss: 1.1307 - output_react_loss: 0.1303 - output_bg_ph_loss: 0.1454 - output_ph_loss: 0.1680 - output_mg_c_loss: 0.1297 - output_c_loss: 0.1521 - val_loss: 1.4202 - val_output_react_loss: 0.1564 - val_output_bg_ph_loss: 0.1952 - val_output_ph_loss: 0.1973 - val_output_mg_c_loss: 0.1730 - val_output_c_loss: 0.1736\nEpoch 60/120\n30/30 - 4s - loss: 1.1223 - output_react_loss: 0.1289 - output_bg_ph_loss: 0.1443 - output_ph_loss: 0.1668 - output_mg_c_loss: 0.1285 - output_c_loss: 0.1520 - val_loss: 1.4188 - val_output_react_loss: 0.1562 - val_output_bg_ph_loss: 0.1951 - val_output_ph_loss: 0.1973 - val_output_mg_c_loss: 0.1725 - val_output_c_loss: 0.1740\nEpoch 61/120\n30/30 - 4s - loss: 1.1185 - output_react_loss: 0.1289 - output_bg_ph_loss: 0.1435 - output_ph_loss: 0.1667 - output_mg_c_loss: 0.1277 - output_c_loss: 0.1516 - val_loss: 1.4116 - val_output_react_loss: 0.1573 - val_output_bg_ph_loss: 0.1939 - val_output_ph_loss: 0.1964 - val_output_mg_c_loss: 0.1706 - val_output_c_loss: 0.1716\nEpoch 62/120\n30/30 - 4s - loss: 1.1087 - output_react_loss: 0.1275 - output_bg_ph_loss: 0.1422 - output_ph_loss: 0.1657 - output_mg_c_loss: 0.1266 - output_c_loss: 0.1504 - val_loss: 1.4122 - val_output_react_loss: 0.1556 - val_output_bg_ph_loss: 0.1954 - val_output_ph_loss: 0.1955 - val_output_mg_c_loss: 0.1718 - val_output_c_loss: 0.1710\nEpoch 63/120\n\nEpoch 00063: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n30/30 - 4s - loss: 1.1008 - output_react_loss: 0.1265 - output_bg_ph_loss: 0.1410 - output_ph_loss: 0.1645 - output_mg_c_loss: 0.1257 - output_c_loss: 0.1499 - val_loss: 1.4111 - val_output_react_loss: 0.1567 - val_output_bg_ph_loss: 0.1946 - val_output_ph_loss: 0.1957 - val_output_mg_c_loss: 0.1706 - val_output_c_loss: 0.1715\nEpoch 64/120\n30/30 - 4s - loss: 1.0687 - output_react_loss: 0.1228 - output_bg_ph_loss: 0.1365 - output_ph_loss: 0.1606 - output_mg_c_loss: 0.1214 - output_c_loss: 0.1468 - val_loss: 1.3944 - val_output_react_loss: 0.1544 - val_output_bg_ph_loss: 0.1922 - val_output_ph_loss: 0.1940 - val_output_mg_c_loss: 0.1689 - val_output_c_loss: 0.1695\nEpoch 65/120\n30/30 - 4s - loss: 1.0562 - output_react_loss: 0.1207 - output_bg_ph_loss: 0.1348 - output_ph_loss: 0.1592 - output_mg_c_loss: 0.1202 - output_c_loss: 0.1455 - val_loss: 1.3915 - val_output_react_loss: 0.1541 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.1938 - val_output_mg_c_loss: 0.1686 - val_output_c_loss: 0.1694\nEpoch 66/120\n30/30 - 4s - loss: 1.0498 - output_react_loss: 0.1208 - output_bg_ph_loss: 0.1336 - output_ph_loss: 0.1583 - output_mg_c_loss: 0.1187 - output_c_loss: 0.1452 - val_loss: 1.3897 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.1934 - val_output_mg_c_loss: 0.1682 - val_output_c_loss: 0.1693\nEpoch 67/120\n30/30 - 4s - loss: 1.0466 - output_react_loss: 0.1199 - output_bg_ph_loss: 0.1331 - output_ph_loss: 0.1581 - output_mg_c_loss: 0.1187 - output_c_loss: 0.1452 - val_loss: 1.3895 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1916 - val_output_ph_loss: 0.1930 - val_output_mg_c_loss: 0.1682 - val_output_c_loss: 0.1694\nEpoch 68/120\n30/30 - 4s - loss: 1.0450 - output_react_loss: 0.1201 - output_bg_ph_loss: 0.1331 - output_ph_loss: 0.1574 - output_mg_c_loss: 0.1183 - output_c_loss: 0.1448 - val_loss: 1.3906 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1917 - val_output_ph_loss: 0.1935 - val_output_mg_c_loss: 0.1683 - val_output_c_loss: 0.1696\nEpoch 69/120\n30/30 - 4s - loss: 1.0420 - output_react_loss: 0.1199 - output_bg_ph_loss: 0.1325 - output_ph_loss: 0.1573 - output_mg_c_loss: 0.1176 - output_c_loss: 0.1446 - val_loss: 1.3891 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1914 - val_output_ph_loss: 0.1934 - val_output_mg_c_loss: 0.1679 - val_output_c_loss: 0.1694\nEpoch 70/120\n30/30 - 4s - loss: 1.0388 - output_react_loss: 0.1191 - output_bg_ph_loss: 0.1323 - output_ph_loss: 0.1571 - output_mg_c_loss: 0.1173 - output_c_loss: 0.1443 - val_loss: 1.3896 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.1931 - val_output_mg_c_loss: 0.1683 - val_output_c_loss: 0.1695\nEpoch 71/120\n30/30 - 4s - loss: 1.0374 - output_react_loss: 0.1192 - output_bg_ph_loss: 0.1318 - output_ph_loss: 0.1571 - output_mg_c_loss: 0.1171 - output_c_loss: 0.1441 - val_loss: 1.3843 - val_output_react_loss: 0.1532 - val_output_bg_ph_loss: 0.1906 - val_output_ph_loss: 0.1926 - val_output_mg_c_loss: 0.1674 - val_output_c_loss: 0.1692\nEpoch 72/120\n30/30 - 4s - loss: 1.0367 - output_react_loss: 0.1191 - output_bg_ph_loss: 0.1315 - output_ph_loss: 0.1569 - output_mg_c_loss: 0.1171 - output_c_loss: 0.1444 - val_loss: 1.3886 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1913 - val_output_ph_loss: 0.1931 - val_output_mg_c_loss: 0.1679 - val_output_c_loss: 0.1694\nEpoch 73/120\n30/30 - 4s - loss: 1.0347 - output_react_loss: 0.1186 - output_bg_ph_loss: 0.1316 - output_ph_loss: 0.1566 - output_mg_c_loss: 0.1169 - output_c_loss: 0.1438 - val_loss: 1.3895 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.1933 - val_output_mg_c_loss: 0.1680 - val_output_c_loss: 0.1696\nEpoch 74/120\n30/30 - 4s - loss: 1.0339 - output_react_loss: 0.1185 - output_bg_ph_loss: 0.1315 - output_ph_loss: 0.1565 - output_mg_c_loss: 0.1167 - output_c_loss: 0.1441 - val_loss: 1.3882 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1915 - val_output_ph_loss: 0.1930 - val_output_mg_c_loss: 0.1678 - val_output_c_loss: 0.1692\nEpoch 75/120\n30/30 - 4s - loss: 1.0325 - output_react_loss: 0.1184 - output_bg_ph_loss: 0.1313 - output_ph_loss: 0.1565 - output_mg_c_loss: 0.1164 - output_c_loss: 0.1438 - val_loss: 1.3885 - val_output_react_loss: 0.1540 - val_output_bg_ph_loss: 0.1912 - val_output_ph_loss: 0.1935 - val_output_mg_c_loss: 0.1677 - val_output_c_loss: 0.1694\nEpoch 76/120\n\nEpoch 00076: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n30/30 - 4s - loss: 1.0312 - output_react_loss: 0.1178 - output_bg_ph_loss: 0.1312 - output_ph_loss: 0.1562 - output_mg_c_loss: 0.1166 - output_c_loss: 0.1436 - val_loss: 1.3896 - val_output_react_loss: 0.1540 - val_output_bg_ph_loss: 0.1918 - val_output_ph_loss: 0.1930 - val_output_mg_c_loss: 0.1679 - val_output_c_loss: 0.1693\nEpoch 77/120\n30/30 - 4s - loss: 1.0297 - output_react_loss: 0.1183 - output_bg_ph_loss: 0.1305 - output_ph_loss: 0.1558 - output_mg_c_loss: 0.1164 - output_c_loss: 0.1435 - val_loss: 1.3870 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1912 - val_output_ph_loss: 0.1928 - val_output_mg_c_loss: 0.1676 - val_output_c_loss: 0.1691\nEpoch 78/120\n30/30 - 4s - loss: 1.0273 - output_react_loss: 0.1175 - output_bg_ph_loss: 0.1306 - output_ph_loss: 0.1558 - output_mg_c_loss: 0.1160 - output_c_loss: 0.1434 - val_loss: 1.3881 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1914 - val_output_ph_loss: 0.1929 - val_output_mg_c_loss: 0.1678 - val_output_c_loss: 0.1693\nEpoch 79/120\n30/30 - 4s - loss: 1.0267 - output_react_loss: 0.1175 - output_bg_ph_loss: 0.1307 - output_ph_loss: 0.1554 - output_mg_c_loss: 0.1159 - output_c_loss: 0.1432 - val_loss: 1.3860 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1910 - val_output_ph_loss: 0.1927 - val_output_mg_c_loss: 0.1675 - val_output_c_loss: 0.1690\nEpoch 80/120\n30/30 - 4s - loss: 1.0253 - output_react_loss: 0.1177 - output_bg_ph_loss: 0.1301 - output_ph_loss: 0.1553 - output_mg_c_loss: 0.1157 - output_c_loss: 0.1430 - val_loss: 1.3869 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1911 - val_output_ph_loss: 0.1928 - val_output_mg_c_loss: 0.1677 - val_output_c_loss: 0.1691\nEpoch 81/120\nRestoring model weights from the end of the best epoch.\n\nEpoch 00081: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n30/30 - 4s - loss: 1.0248 - output_react_loss: 0.1175 - output_bg_ph_loss: 0.1302 - output_ph_loss: 0.1550 - output_mg_c_loss: 0.1157 - output_c_loss: 0.1430 - val_loss: 1.3866 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1911 - val_output_ph_loss: 0.1927 - val_output_mg_c_loss: 0.1676 - val_output_c_loss: 0.1691\nEpoch 00081: early stopping\n\nFOLD: 5\nEpoch 1/120\n30/30 - 7s - loss: 3.4458 - output_react_loss: 0.3593 - output_bg_ph_loss: 0.4657 - output_ph_loss: 0.4703 - output_mg_c_loss: 0.4055 - output_c_loss: 0.5145 - val_loss: 2.3242 - val_output_react_loss: 0.2370 - val_output_bg_ph_loss: 0.3207 - val_output_ph_loss: 0.3390 - val_output_mg_c_loss: 0.2940 - val_output_c_loss: 0.2819\nEpoch 2/120\n30/30 - 4s - loss: 2.3412 - output_react_loss: 0.2486 - output_bg_ph_loss: 0.3172 - output_ph_loss: 0.3346 - output_mg_c_loss: 0.2977 - output_c_loss: 0.2796 - val_loss: 2.1392 - val_output_react_loss: 0.2231 - val_output_bg_ph_loss: 0.2903 - val_output_ph_loss: 0.3000 - val_output_mg_c_loss: 0.2757 - val_output_c_loss: 0.2611\nEpoch 3/120\n30/30 - 4s - loss: 2.1906 - output_react_loss: 0.2336 - output_bg_ph_loss: 0.2973 - output_ph_loss: 0.3080 - output_mg_c_loss: 0.2799 - output_c_loss: 0.2610 - val_loss: 2.0496 - val_output_react_loss: 0.2155 - val_output_bg_ph_loss: 0.2775 - val_output_ph_loss: 0.2863 - val_output_mg_c_loss: 0.2639 - val_output_c_loss: 0.2495\nEpoch 4/120\n30/30 - 4s - loss: 2.1081 - output_react_loss: 0.2254 - output_bg_ph_loss: 0.2860 - output_ph_loss: 0.2960 - output_mg_c_loss: 0.2687 - output_c_loss: 0.2519 - val_loss: 1.9834 - val_output_react_loss: 0.2082 - val_output_bg_ph_loss: 0.2697 - val_output_ph_loss: 0.2792 - val_output_mg_c_loss: 0.2532 - val_output_c_loss: 0.2422\nEpoch 5/120\n30/30 - 4s - loss: 2.0613 - output_react_loss: 0.2210 - output_bg_ph_loss: 0.2797 - output_ph_loss: 0.2892 - output_mg_c_loss: 0.2618 - output_c_loss: 0.2471 - val_loss: 1.9542 - val_output_react_loss: 0.2049 - val_output_bg_ph_loss: 0.2635 - val_output_ph_loss: 0.2762 - val_output_mg_c_loss: 0.2509 - val_output_c_loss: 0.2396\nEpoch 6/120\n30/30 - 4s - loss: 2.0134 - output_react_loss: 0.2154 - output_bg_ph_loss: 0.2728 - output_ph_loss: 0.2841 - output_mg_c_loss: 0.2554 - output_c_loss: 0.2420 - val_loss: 1.9134 - val_output_react_loss: 0.2021 - val_output_bg_ph_loss: 0.2574 - val_output_ph_loss: 0.2724 - val_output_mg_c_loss: 0.2428 - val_output_c_loss: 0.2362\nEpoch 7/120\n30/30 - 4s - loss: 1.9820 - output_react_loss: 0.2123 - output_bg_ph_loss: 0.2671 - output_ph_loss: 0.2796 - output_mg_c_loss: 0.2520 - output_c_loss: 0.2396 - val_loss: 1.8820 - val_output_react_loss: 0.1984 - val_output_bg_ph_loss: 0.2530 - val_output_ph_loss: 0.2684 - val_output_mg_c_loss: 0.2393 - val_output_c_loss: 0.2321\nEpoch 8/120\n30/30 - 4s - loss: 1.9454 - output_react_loss: 0.2090 - output_bg_ph_loss: 0.2620 - output_ph_loss: 0.2747 - output_mg_c_loss: 0.2467 - output_c_loss: 0.2352 - val_loss: 1.8977 - val_output_react_loss: 0.2002 - val_output_bg_ph_loss: 0.2519 - val_output_ph_loss: 0.2695 - val_output_mg_c_loss: 0.2451 - val_output_c_loss: 0.2338\nEpoch 9/120\n30/30 - 4s - loss: 1.9095 - output_react_loss: 0.2074 - output_bg_ph_loss: 0.2577 - output_ph_loss: 0.2687 - output_mg_c_loss: 0.2401 - output_c_loss: 0.2304 - val_loss: 1.8009 - val_output_react_loss: 0.1922 - val_output_bg_ph_loss: 0.2415 - val_output_ph_loss: 0.2581 - val_output_mg_c_loss: 0.2264 - val_output_c_loss: 0.2226\nEpoch 10/120\n30/30 - 4s - loss: 1.8745 - output_react_loss: 0.2030 - output_bg_ph_loss: 0.2535 - output_ph_loss: 0.2633 - output_mg_c_loss: 0.2359 - output_c_loss: 0.2263 - val_loss: 1.7623 - val_output_react_loss: 0.1896 - val_output_bg_ph_loss: 0.2361 - val_output_ph_loss: 0.2520 - val_output_mg_c_loss: 0.2206 - val_output_c_loss: 0.2178\nEpoch 11/120\n30/30 - 4s - loss: 1.8198 - output_react_loss: 0.1991 - output_bg_ph_loss: 0.2455 - output_ph_loss: 0.2571 - output_mg_c_loss: 0.2266 - output_c_loss: 0.2203 - val_loss: 1.7228 - val_output_react_loss: 0.1851 - val_output_bg_ph_loss: 0.2301 - val_output_ph_loss: 0.2478 - val_output_mg_c_loss: 0.2159 - val_output_c_loss: 0.2128\nEpoch 12/120\n30/30 - 4s - loss: 1.7833 - output_react_loss: 0.1955 - output_bg_ph_loss: 0.2410 - output_ph_loss: 0.2523 - output_mg_c_loss: 0.2210 - output_c_loss: 0.2160 - val_loss: 1.7021 - val_output_react_loss: 0.1849 - val_output_bg_ph_loss: 0.2294 - val_output_ph_loss: 0.2438 - val_output_mg_c_loss: 0.2097 - val_output_c_loss: 0.2104\nEpoch 13/120\n30/30 - 4s - loss: 1.7582 - output_react_loss: 0.1937 - output_bg_ph_loss: 0.2375 - output_ph_loss: 0.2488 - output_mg_c_loss: 0.2169 - output_c_loss: 0.2132 - val_loss: 1.6732 - val_output_react_loss: 0.1831 - val_output_bg_ph_loss: 0.2242 - val_output_ph_loss: 0.2397 - val_output_mg_c_loss: 0.2050 - val_output_c_loss: 0.2091\nEpoch 14/120\n30/30 - 4s - loss: 1.7339 - output_react_loss: 0.1927 - output_bg_ph_loss: 0.2336 - output_ph_loss: 0.2440 - output_mg_c_loss: 0.2130 - output_c_loss: 0.2112 - val_loss: 1.6394 - val_output_react_loss: 0.1817 - val_output_bg_ph_loss: 0.2180 - val_output_ph_loss: 0.2364 - val_output_mg_c_loss: 0.1997 - val_output_c_loss: 0.2042\nEpoch 15/120\n30/30 - 4s - loss: 1.7064 - output_react_loss: 0.1901 - output_bg_ph_loss: 0.2288 - output_ph_loss: 0.2406 - output_mg_c_loss: 0.2105 - output_c_loss: 0.2070 - val_loss: 1.6321 - val_output_react_loss: 0.1809 - val_output_bg_ph_loss: 0.2161 - val_output_ph_loss: 0.2334 - val_output_mg_c_loss: 0.2008 - val_output_c_loss: 0.2030\nEpoch 16/120\n30/30 - 4s - loss: 1.6720 - output_react_loss: 0.1868 - output_bg_ph_loss: 0.2251 - output_ph_loss: 0.2358 - output_mg_c_loss: 0.2047 - output_c_loss: 0.2028 - val_loss: 1.5972 - val_output_react_loss: 0.1764 - val_output_bg_ph_loss: 0.2135 - val_output_ph_loss: 0.2312 - val_output_mg_c_loss: 0.1941 - val_output_c_loss: 0.1979\nEpoch 17/120\n30/30 - 4s - loss: 1.6563 - output_react_loss: 0.1854 - output_bg_ph_loss: 0.2229 - output_ph_loss: 0.2326 - output_mg_c_loss: 0.2035 - output_c_loss: 0.2002 - val_loss: 1.5762 - val_output_react_loss: 0.1759 - val_output_bg_ph_loss: 0.2097 - val_output_ph_loss: 0.2266 - val_output_mg_c_loss: 0.1917 - val_output_c_loss: 0.1948\nEpoch 18/120\n30/30 - 4s - loss: 1.6257 - output_react_loss: 0.1831 - output_bg_ph_loss: 0.2185 - output_ph_loss: 0.2303 - output_mg_c_loss: 0.1976 - output_c_loss: 0.1968 - val_loss: 1.5659 - val_output_react_loss: 0.1721 - val_output_bg_ph_loss: 0.2082 - val_output_ph_loss: 0.2277 - val_output_mg_c_loss: 0.1911 - val_output_c_loss: 0.1953\nEpoch 19/120\n30/30 - 4s - loss: 1.6121 - output_react_loss: 0.1813 - output_bg_ph_loss: 0.2168 - output_ph_loss: 0.2274 - output_mg_c_loss: 0.1960 - output_c_loss: 0.1966 - val_loss: 1.5488 - val_output_react_loss: 0.1719 - val_output_bg_ph_loss: 0.2071 - val_output_ph_loss: 0.2247 - val_output_mg_c_loss: 0.1867 - val_output_c_loss: 0.1927\nEpoch 20/120\n30/30 - 4s - loss: 1.5916 - output_react_loss: 0.1797 - output_bg_ph_loss: 0.2138 - output_ph_loss: 0.2248 - output_mg_c_loss: 0.1932 - output_c_loss: 0.1934 - val_loss: 1.5293 - val_output_react_loss: 0.1700 - val_output_bg_ph_loss: 0.2039 - val_output_ph_loss: 0.2205 - val_output_mg_c_loss: 0.1851 - val_output_c_loss: 0.1908\nEpoch 21/120\n30/30 - 4s - loss: 1.5736 - output_react_loss: 0.1787 - output_bg_ph_loss: 0.2109 - output_ph_loss: 0.2210 - output_mg_c_loss: 0.1913 - output_c_loss: 0.1907 - val_loss: 1.5306 - val_output_react_loss: 0.1701 - val_output_bg_ph_loss: 0.2029 - val_output_ph_loss: 0.2201 - val_output_mg_c_loss: 0.1869 - val_output_c_loss: 0.1908\nEpoch 22/120\n30/30 - 4s - loss: 1.5604 - output_react_loss: 0.1762 - output_bg_ph_loss: 0.2097 - output_ph_loss: 0.2204 - output_mg_c_loss: 0.1891 - output_c_loss: 0.1900 - val_loss: 1.5215 - val_output_react_loss: 0.1683 - val_output_bg_ph_loss: 0.2035 - val_output_ph_loss: 0.2204 - val_output_mg_c_loss: 0.1841 - val_output_c_loss: 0.1895\nEpoch 23/120\n30/30 - 4s - loss: 1.5393 - output_react_loss: 0.1746 - output_bg_ph_loss: 0.2069 - output_ph_loss: 0.2178 - output_mg_c_loss: 0.1855 - output_c_loss: 0.1875 - val_loss: 1.5298 - val_output_react_loss: 0.1689 - val_output_bg_ph_loss: 0.2016 - val_output_ph_loss: 0.2216 - val_output_mg_c_loss: 0.1889 - val_output_c_loss: 0.1893\nEpoch 24/120\n30/30 - 4s - loss: 1.5338 - output_react_loss: 0.1746 - output_bg_ph_loss: 0.2053 - output_ph_loss: 0.2184 - output_mg_c_loss: 0.1847 - output_c_loss: 0.1863 - val_loss: 1.4955 - val_output_react_loss: 0.1678 - val_output_bg_ph_loss: 0.1998 - val_output_ph_loss: 0.2164 - val_output_mg_c_loss: 0.1794 - val_output_c_loss: 0.1852\nEpoch 25/120\n30/30 - 4s - loss: 1.5171 - output_react_loss: 0.1725 - output_bg_ph_loss: 0.2039 - output_ph_loss: 0.2148 - output_mg_c_loss: 0.1823 - output_c_loss: 0.1851 - val_loss: 1.4934 - val_output_react_loss: 0.1669 - val_output_bg_ph_loss: 0.2001 - val_output_ph_loss: 0.2154 - val_output_mg_c_loss: 0.1795 - val_output_c_loss: 0.1852\nEpoch 26/120\n30/30 - 4s - loss: 1.5046 - output_react_loss: 0.1709 - output_bg_ph_loss: 0.2015 - output_ph_loss: 0.2142 - output_mg_c_loss: 0.1805 - output_c_loss: 0.1846 - val_loss: 1.5036 - val_output_react_loss: 0.1680 - val_output_bg_ph_loss: 0.1997 - val_output_ph_loss: 0.2164 - val_output_mg_c_loss: 0.1818 - val_output_c_loss: 0.1882\nEpoch 27/120\n30/30 - 4s - loss: 1.4908 - output_react_loss: 0.1699 - output_bg_ph_loss: 0.1989 - output_ph_loss: 0.2112 - output_mg_c_loss: 0.1796 - output_c_loss: 0.1827 - val_loss: 1.4716 - val_output_react_loss: 0.1648 - val_output_bg_ph_loss: 0.1954 - val_output_ph_loss: 0.2138 - val_output_mg_c_loss: 0.1767 - val_output_c_loss: 0.1838\nEpoch 28/120\n30/30 - 4s - loss: 1.4667 - output_react_loss: 0.1673 - output_bg_ph_loss: 0.1962 - output_ph_loss: 0.2087 - output_mg_c_loss: 0.1754 - output_c_loss: 0.1803 - val_loss: 1.4610 - val_output_react_loss: 0.1630 - val_output_bg_ph_loss: 0.1939 - val_output_ph_loss: 0.2120 - val_output_mg_c_loss: 0.1761 - val_output_c_loss: 0.1832\nEpoch 29/120\n30/30 - 4s - loss: 1.4574 - output_react_loss: 0.1658 - output_bg_ph_loss: 0.1956 - output_ph_loss: 0.2075 - output_mg_c_loss: 0.1738 - output_c_loss: 0.1796 - val_loss: 1.4602 - val_output_react_loss: 0.1624 - val_output_bg_ph_loss: 0.1954 - val_output_ph_loss: 0.2102 - val_output_mg_c_loss: 0.1756 - val_output_c_loss: 0.1832\nEpoch 30/120\n30/30 - 4s - loss: 1.4417 - output_react_loss: 0.1647 - output_bg_ph_loss: 0.1932 - output_ph_loss: 0.2046 - output_mg_c_loss: 0.1720 - output_c_loss: 0.1773 - val_loss: 1.4464 - val_output_react_loss: 0.1611 - val_output_bg_ph_loss: 0.1924 - val_output_ph_loss: 0.2095 - val_output_mg_c_loss: 0.1743 - val_output_c_loss: 0.1814\nEpoch 31/120\n30/30 - 4s - loss: 1.4285 - output_react_loss: 0.1642 - output_bg_ph_loss: 0.1907 - output_ph_loss: 0.2035 - output_mg_c_loss: 0.1696 - output_c_loss: 0.1760 - val_loss: 1.4446 - val_output_react_loss: 0.1618 - val_output_bg_ph_loss: 0.1919 - val_output_ph_loss: 0.2100 - val_output_mg_c_loss: 0.1734 - val_output_c_loss: 0.1803\nEpoch 32/120\n30/30 - 4s - loss: 1.4184 - output_react_loss: 0.1622 - output_bg_ph_loss: 0.1895 - output_ph_loss: 0.2019 - output_mg_c_loss: 0.1689 - output_c_loss: 0.1754 - val_loss: 1.4534 - val_output_react_loss: 0.1609 - val_output_bg_ph_loss: 0.1953 - val_output_ph_loss: 0.2112 - val_output_mg_c_loss: 0.1741 - val_output_c_loss: 0.1815\nEpoch 33/120\n30/30 - 4s - loss: 1.4091 - output_react_loss: 0.1610 - output_bg_ph_loss: 0.1895 - output_ph_loss: 0.2008 - output_mg_c_loss: 0.1665 - output_c_loss: 0.1745 - val_loss: 1.4502 - val_output_react_loss: 0.1623 - val_output_bg_ph_loss: 0.1925 - val_output_ph_loss: 0.2104 - val_output_mg_c_loss: 0.1750 - val_output_c_loss: 0.1802\nEpoch 34/120\n30/30 - 4s - loss: 1.3896 - output_react_loss: 0.1586 - output_bg_ph_loss: 0.1855 - output_ph_loss: 0.1991 - output_mg_c_loss: 0.1648 - output_c_loss: 0.1725 - val_loss: 1.4296 - val_output_react_loss: 0.1595 - val_output_bg_ph_loss: 0.1910 - val_output_ph_loss: 0.2064 - val_output_mg_c_loss: 0.1716 - val_output_c_loss: 0.1789\nEpoch 35/120\n30/30 - 4s - loss: 1.3768 - output_react_loss: 0.1578 - output_bg_ph_loss: 0.1833 - output_ph_loss: 0.1967 - output_mg_c_loss: 0.1630 - output_c_loss: 0.1719 - val_loss: 1.4474 - val_output_react_loss: 0.1614 - val_output_bg_ph_loss: 0.1937 - val_output_ph_loss: 0.2085 - val_output_mg_c_loss: 0.1749 - val_output_c_loss: 0.1790\nEpoch 36/120\n30/30 - 4s - loss: 1.3706 - output_react_loss: 0.1572 - output_bg_ph_loss: 0.1834 - output_ph_loss: 0.1951 - output_mg_c_loss: 0.1617 - output_c_loss: 0.1709 - val_loss: 1.4299 - val_output_react_loss: 0.1599 - val_output_bg_ph_loss: 0.1896 - val_output_ph_loss: 0.2079 - val_output_mg_c_loss: 0.1724 - val_output_c_loss: 0.1781\nEpoch 37/120\n30/30 - 4s - loss: 1.3548 - output_react_loss: 0.1558 - output_bg_ph_loss: 0.1795 - output_ph_loss: 0.1946 - output_mg_c_loss: 0.1597 - output_c_loss: 0.1702 - val_loss: 1.4276 - val_output_react_loss: 0.1587 - val_output_bg_ph_loss: 0.1909 - val_output_ph_loss: 0.2072 - val_output_mg_c_loss: 0.1711 - val_output_c_loss: 0.1792\nEpoch 38/120\n30/30 - 4s - loss: 1.3451 - output_react_loss: 0.1547 - output_bg_ph_loss: 0.1786 - output_ph_loss: 0.1928 - output_mg_c_loss: 0.1586 - output_c_loss: 0.1685 - val_loss: 1.4262 - val_output_react_loss: 0.1580 - val_output_bg_ph_loss: 0.1890 - val_output_ph_loss: 0.2086 - val_output_mg_c_loss: 0.1729 - val_output_c_loss: 0.1779\nEpoch 39/120\n30/30 - 4s - loss: 1.3280 - output_react_loss: 0.1532 - output_bg_ph_loss: 0.1765 - output_ph_loss: 0.1897 - output_mg_c_loss: 0.1559 - output_c_loss: 0.1670 - val_loss: 1.4240 - val_output_react_loss: 0.1572 - val_output_bg_ph_loss: 0.1906 - val_output_ph_loss: 0.2071 - val_output_mg_c_loss: 0.1718 - val_output_c_loss: 0.1777\nEpoch 40/120\n30/30 - 4s - loss: 1.3176 - output_react_loss: 0.1509 - output_bg_ph_loss: 0.1751 - output_ph_loss: 0.1895 - output_mg_c_loss: 0.1548 - output_c_loss: 0.1665 - val_loss: 1.4150 - val_output_react_loss: 0.1572 - val_output_bg_ph_loss: 0.1895 - val_output_ph_loss: 0.2054 - val_output_mg_c_loss: 0.1696 - val_output_c_loss: 0.1771\nEpoch 41/120\n30/30 - 4s - loss: 1.2996 - output_react_loss: 0.1494 - output_bg_ph_loss: 0.1720 - output_ph_loss: 0.1880 - output_mg_c_loss: 0.1517 - output_c_loss: 0.1655 - val_loss: 1.4102 - val_output_react_loss: 0.1566 - val_output_bg_ph_loss: 0.1882 - val_output_ph_loss: 0.2034 - val_output_mg_c_loss: 0.1698 - val_output_c_loss: 0.1775\nEpoch 42/120\n30/30 - 4s - loss: 1.2954 - output_react_loss: 0.1483 - output_bg_ph_loss: 0.1717 - output_ph_loss: 0.1868 - output_mg_c_loss: 0.1519 - output_c_loss: 0.1649 - val_loss: 1.4034 - val_output_react_loss: 0.1561 - val_output_bg_ph_loss: 0.1871 - val_output_ph_loss: 0.2030 - val_output_mg_c_loss: 0.1694 - val_output_c_loss: 0.1751\nEpoch 43/120\n30/30 - 4s - loss: 1.2840 - output_react_loss: 0.1478 - output_bg_ph_loss: 0.1697 - output_ph_loss: 0.1851 - output_mg_c_loss: 0.1500 - output_c_loss: 0.1639 - val_loss: 1.4042 - val_output_react_loss: 0.1552 - val_output_bg_ph_loss: 0.1871 - val_output_ph_loss: 0.2044 - val_output_mg_c_loss: 0.1698 - val_output_c_loss: 0.1757\nEpoch 44/120\n30/30 - 4s - loss: 1.2650 - output_react_loss: 0.1461 - output_bg_ph_loss: 0.1671 - output_ph_loss: 0.1829 - output_mg_c_loss: 0.1472 - output_c_loss: 0.1614 - val_loss: 1.4036 - val_output_react_loss: 0.1555 - val_output_bg_ph_loss: 0.1871 - val_output_ph_loss: 0.2035 - val_output_mg_c_loss: 0.1695 - val_output_c_loss: 0.1760\nEpoch 45/120\n30/30 - 4s - loss: 1.2563 - output_react_loss: 0.1449 - output_bg_ph_loss: 0.1655 - output_ph_loss: 0.1821 - output_mg_c_loss: 0.1463 - output_c_loss: 0.1609 - val_loss: 1.4015 - val_output_react_loss: 0.1549 - val_output_bg_ph_loss: 0.1872 - val_output_ph_loss: 0.2050 - val_output_mg_c_loss: 0.1680 - val_output_c_loss: 0.1765\nEpoch 46/120\n30/30 - 4s - loss: 1.2468 - output_react_loss: 0.1436 - output_bg_ph_loss: 0.1643 - output_ph_loss: 0.1808 - output_mg_c_loss: 0.1448 - output_c_loss: 0.1605 - val_loss: 1.4169 - val_output_react_loss: 0.1565 - val_output_bg_ph_loss: 0.1893 - val_output_ph_loss: 0.2080 - val_output_mg_c_loss: 0.1704 - val_output_c_loss: 0.1765\nEpoch 47/120\n30/30 - 4s - loss: 1.2330 - output_react_loss: 0.1423 - output_bg_ph_loss: 0.1624 - output_ph_loss: 0.1792 - output_mg_c_loss: 0.1425 - output_c_loss: 0.1592 - val_loss: 1.4140 - val_output_react_loss: 0.1580 - val_output_bg_ph_loss: 0.1892 - val_output_ph_loss: 0.2049 - val_output_mg_c_loss: 0.1693 - val_output_c_loss: 0.1761\nEpoch 48/120\n30/30 - 4s - loss: 1.2217 - output_react_loss: 0.1412 - output_bg_ph_loss: 0.1608 - output_ph_loss: 0.1776 - output_mg_c_loss: 0.1410 - output_c_loss: 0.1582 - val_loss: 1.4001 - val_output_react_loss: 0.1567 - val_output_bg_ph_loss: 0.1860 - val_output_ph_loss: 0.2021 - val_output_mg_c_loss: 0.1687 - val_output_c_loss: 0.1752\nEpoch 49/120\n30/30 - 4s - loss: 1.2181 - output_react_loss: 0.1397 - output_bg_ph_loss: 0.1599 - output_ph_loss: 0.1769 - output_mg_c_loss: 0.1415 - output_c_loss: 0.1589 - val_loss: 1.4031 - val_output_react_loss: 0.1582 - val_output_bg_ph_loss: 0.1863 - val_output_ph_loss: 0.2025 - val_output_mg_c_loss: 0.1677 - val_output_c_loss: 0.1762\nEpoch 50/120\n30/30 - 4s - loss: 1.2070 - output_react_loss: 0.1389 - output_bg_ph_loss: 0.1581 - output_ph_loss: 0.1765 - output_mg_c_loss: 0.1397 - output_c_loss: 0.1572 - val_loss: 1.4027 - val_output_react_loss: 0.1563 - val_output_bg_ph_loss: 0.1855 - val_output_ph_loss: 0.2055 - val_output_mg_c_loss: 0.1693 - val_output_c_loss: 0.1751\nEpoch 51/120\n30/30 - 4s - loss: 1.1981 - output_react_loss: 0.1384 - output_bg_ph_loss: 0.1564 - output_ph_loss: 0.1750 - output_mg_c_loss: 0.1385 - output_c_loss: 0.1565 - val_loss: 1.3961 - val_output_react_loss: 0.1568 - val_output_bg_ph_loss: 0.1855 - val_output_ph_loss: 0.2011 - val_output_mg_c_loss: 0.1673 - val_output_c_loss: 0.1756\nEpoch 52/120\n30/30 - 4s - loss: 1.1854 - output_react_loss: 0.1367 - output_bg_ph_loss: 0.1551 - output_ph_loss: 0.1732 - output_mg_c_loss: 0.1366 - output_c_loss: 0.1556 - val_loss: 1.3965 - val_output_react_loss: 0.1543 - val_output_bg_ph_loss: 0.1869 - val_output_ph_loss: 0.2033 - val_output_mg_c_loss: 0.1681 - val_output_c_loss: 0.1746\nEpoch 53/120\n30/30 - 4s - loss: 1.1750 - output_react_loss: 0.1346 - output_bg_ph_loss: 0.1535 - output_ph_loss: 0.1729 - output_mg_c_loss: 0.1355 - output_c_loss: 0.1548 - val_loss: 1.3956 - val_output_react_loss: 0.1550 - val_output_bg_ph_loss: 0.1876 - val_output_ph_loss: 0.2023 - val_output_mg_c_loss: 0.1670 - val_output_c_loss: 0.1740\nEpoch 54/120\n30/30 - 4s - loss: 1.1608 - output_react_loss: 0.1338 - output_bg_ph_loss: 0.1511 - output_ph_loss: 0.1706 - output_mg_c_loss: 0.1334 - output_c_loss: 0.1535 - val_loss: 1.3888 - val_output_react_loss: 0.1533 - val_output_bg_ph_loss: 0.1854 - val_output_ph_loss: 0.2016 - val_output_mg_c_loss: 0.1672 - val_output_c_loss: 0.1752\nEpoch 55/120\n30/30 - 4s - loss: 1.1588 - output_react_loss: 0.1331 - output_bg_ph_loss: 0.1512 - output_ph_loss: 0.1709 - output_mg_c_loss: 0.1330 - output_c_loss: 0.1533 - val_loss: 1.3910 - val_output_react_loss: 0.1560 - val_output_bg_ph_loss: 0.1858 - val_output_ph_loss: 0.2017 - val_output_mg_c_loss: 0.1661 - val_output_c_loss: 0.1734\nEpoch 56/120\n30/30 - 4s - loss: 1.1483 - output_react_loss: 0.1316 - output_bg_ph_loss: 0.1495 - output_ph_loss: 0.1692 - output_mg_c_loss: 0.1323 - output_c_loss: 0.1525 - val_loss: 1.3862 - val_output_react_loss: 0.1553 - val_output_bg_ph_loss: 0.1846 - val_output_ph_loss: 0.2018 - val_output_mg_c_loss: 0.1654 - val_output_c_loss: 0.1739\nEpoch 57/120\n30/30 - 4s - loss: 1.1401 - output_react_loss: 0.1306 - output_bg_ph_loss: 0.1481 - output_ph_loss: 0.1686 - output_mg_c_loss: 0.1311 - output_c_loss: 0.1519 - val_loss: 1.3812 - val_output_react_loss: 0.1548 - val_output_bg_ph_loss: 0.1853 - val_output_ph_loss: 0.1993 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1728\nEpoch 58/120\n30/30 - 4s - loss: 1.1335 - output_react_loss: 0.1299 - output_bg_ph_loss: 0.1469 - output_ph_loss: 0.1677 - output_mg_c_loss: 0.1300 - output_c_loss: 0.1522 - val_loss: 1.3878 - val_output_react_loss: 0.1552 - val_output_bg_ph_loss: 0.1857 - val_output_ph_loss: 0.2006 - val_output_mg_c_loss: 0.1659 - val_output_c_loss: 0.1736\nEpoch 59/120\n30/30 - 4s - loss: 1.1235 - output_react_loss: 0.1286 - output_bg_ph_loss: 0.1459 - output_ph_loss: 0.1659 - output_mg_c_loss: 0.1292 - output_c_loss: 0.1503 - val_loss: 1.3851 - val_output_react_loss: 0.1539 - val_output_bg_ph_loss: 0.1864 - val_output_ph_loss: 0.2004 - val_output_mg_c_loss: 0.1656 - val_output_c_loss: 0.1731\nEpoch 60/120\n30/30 - 4s - loss: 1.1176 - output_react_loss: 0.1273 - output_bg_ph_loss: 0.1444 - output_ph_loss: 0.1662 - output_mg_c_loss: 0.1286 - output_c_loss: 0.1509 - val_loss: 1.3796 - val_output_react_loss: 0.1534 - val_output_bg_ph_loss: 0.1850 - val_output_ph_loss: 0.1988 - val_output_mg_c_loss: 0.1654 - val_output_c_loss: 0.1733\nEpoch 61/120\n30/30 - 4s - loss: 1.1088 - output_react_loss: 0.1266 - output_bg_ph_loss: 0.1433 - output_ph_loss: 0.1646 - output_mg_c_loss: 0.1273 - output_c_loss: 0.1497 - val_loss: 1.3802 - val_output_react_loss: 0.1539 - val_output_bg_ph_loss: 0.1847 - val_output_ph_loss: 0.1986 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1728\nEpoch 62/120\n30/30 - 4s - loss: 1.0994 - output_react_loss: 0.1260 - output_bg_ph_loss: 0.1420 - output_ph_loss: 0.1630 - output_mg_c_loss: 0.1258 - output_c_loss: 0.1487 - val_loss: 1.3924 - val_output_react_loss: 0.1541 - val_output_bg_ph_loss: 0.1875 - val_output_ph_loss: 0.1997 - val_output_mg_c_loss: 0.1679 - val_output_c_loss: 0.1738\nEpoch 63/120\n30/30 - 4s - loss: 1.0950 - output_react_loss: 0.1249 - output_bg_ph_loss: 0.1416 - output_ph_loss: 0.1630 - output_mg_c_loss: 0.1253 - output_c_loss: 0.1485 - val_loss: 1.3826 - val_output_react_loss: 0.1545 - val_output_bg_ph_loss: 0.1859 - val_output_ph_loss: 0.1999 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1728\nEpoch 64/120\n30/30 - 4s - loss: 1.0861 - output_react_loss: 0.1235 - output_bg_ph_loss: 0.1400 - output_ph_loss: 0.1621 - output_mg_c_loss: 0.1244 - output_c_loss: 0.1480 - val_loss: 1.3814 - val_output_react_loss: 0.1538 - val_output_bg_ph_loss: 0.1852 - val_output_ph_loss: 0.1987 - val_output_mg_c_loss: 0.1660 - val_output_c_loss: 0.1728\nEpoch 65/120\n30/30 - 4s - loss: 1.0824 - output_react_loss: 0.1231 - output_bg_ph_loss: 0.1395 - output_ph_loss: 0.1610 - output_mg_c_loss: 0.1243 - output_c_loss: 0.1475 - val_loss: 1.3740 - val_output_react_loss: 0.1532 - val_output_bg_ph_loss: 0.1842 - val_output_ph_loss: 0.1975 - val_output_mg_c_loss: 0.1643 - val_output_c_loss: 0.1731\nEpoch 66/120\n30/30 - 4s - loss: 1.0727 - output_react_loss: 0.1223 - output_bg_ph_loss: 0.1380 - output_ph_loss: 0.1603 - output_mg_c_loss: 0.1224 - output_c_loss: 0.1472 - val_loss: 1.3818 - val_output_react_loss: 0.1545 - val_output_bg_ph_loss: 0.1845 - val_output_ph_loss: 0.1992 - val_output_mg_c_loss: 0.1656 - val_output_c_loss: 0.1732\nEpoch 67/120\n30/30 - 4s - loss: 1.0669 - output_react_loss: 0.1214 - output_bg_ph_loss: 0.1374 - output_ph_loss: 0.1591 - output_mg_c_loss: 0.1220 - output_c_loss: 0.1462 - val_loss: 1.3793 - val_output_react_loss: 0.1535 - val_output_bg_ph_loss: 0.1842 - val_output_ph_loss: 0.1990 - val_output_mg_c_loss: 0.1658 - val_output_c_loss: 0.1735\nEpoch 68/120\n30/30 - 4s - loss: 1.0648 - output_react_loss: 0.1207 - output_bg_ph_loss: 0.1369 - output_ph_loss: 0.1597 - output_mg_c_loss: 0.1217 - output_c_loss: 0.1466 - val_loss: 1.3731 - val_output_react_loss: 0.1524 - val_output_bg_ph_loss: 0.1842 - val_output_ph_loss: 0.1980 - val_output_mg_c_loss: 0.1649 - val_output_c_loss: 0.1720\nEpoch 69/120\n30/30 - 4s - loss: 1.0554 - output_react_loss: 0.1203 - output_bg_ph_loss: 0.1356 - output_ph_loss: 0.1577 - output_mg_c_loss: 0.1203 - output_c_loss: 0.1453 - val_loss: 1.3766 - val_output_react_loss: 0.1543 - val_output_bg_ph_loss: 0.1839 - val_output_ph_loss: 0.1981 - val_output_mg_c_loss: 0.1648 - val_output_c_loss: 0.1726\nEpoch 70/120\n30/30 - 4s - loss: 1.0462 - output_react_loss: 0.1189 - output_bg_ph_loss: 0.1337 - output_ph_loss: 0.1576 - output_mg_c_loss: 0.1193 - output_c_loss: 0.1447 - val_loss: 1.3739 - val_output_react_loss: 0.1541 - val_output_bg_ph_loss: 0.1839 - val_output_ph_loss: 0.1983 - val_output_mg_c_loss: 0.1638 - val_output_c_loss: 0.1721\nEpoch 71/120\n30/30 - 4s - loss: 1.0415 - output_react_loss: 0.1181 - output_bg_ph_loss: 0.1331 - output_ph_loss: 0.1571 - output_mg_c_loss: 0.1190 - output_c_loss: 0.1442 - val_loss: 1.3765 - val_output_react_loss: 0.1533 - val_output_bg_ph_loss: 0.1847 - val_output_ph_loss: 0.1975 - val_output_mg_c_loss: 0.1651 - val_output_c_loss: 0.1727\nEpoch 72/120\n30/30 - 4s - loss: 1.0342 - output_react_loss: 0.1173 - output_bg_ph_loss: 0.1322 - output_ph_loss: 0.1552 - output_mg_c_loss: 0.1180 - output_c_loss: 0.1442 - val_loss: 1.3744 - val_output_react_loss: 0.1531 - val_output_bg_ph_loss: 0.1840 - val_output_ph_loss: 0.1988 - val_output_mg_c_loss: 0.1643 - val_output_c_loss: 0.1729\nEpoch 73/120\n30/30 - 4s - loss: 1.0332 - output_react_loss: 0.1169 - output_bg_ph_loss: 0.1318 - output_ph_loss: 0.1559 - output_mg_c_loss: 0.1183 - output_c_loss: 0.1433 - val_loss: 1.3703 - val_output_react_loss: 0.1526 - val_output_bg_ph_loss: 0.1837 - val_output_ph_loss: 0.1982 - val_output_mg_c_loss: 0.1635 - val_output_c_loss: 0.1725\nEpoch 74/120\n30/30 - 4s - loss: 1.0244 - output_react_loss: 0.1163 - output_bg_ph_loss: 0.1304 - output_ph_loss: 0.1541 - output_mg_c_loss: 0.1171 - output_c_loss: 0.1429 - val_loss: 1.3742 - val_output_react_loss: 0.1532 - val_output_bg_ph_loss: 0.1841 - val_output_ph_loss: 0.1984 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1723\nEpoch 75/120\n30/30 - 4s - loss: 1.0191 - output_react_loss: 0.1154 - output_bg_ph_loss: 0.1299 - output_ph_loss: 0.1534 - output_mg_c_loss: 0.1162 - output_c_loss: 0.1426 - val_loss: 1.3764 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1844 - val_output_ph_loss: 0.1981 - val_output_mg_c_loss: 0.1650 - val_output_c_loss: 0.1723\nEpoch 76/120\n30/30 - 4s - loss: 1.0117 - output_react_loss: 0.1140 - output_bg_ph_loss: 0.1289 - output_ph_loss: 0.1533 - output_mg_c_loss: 0.1153 - output_c_loss: 0.1419 - val_loss: 1.3760 - val_output_react_loss: 0.1528 - val_output_bg_ph_loss: 0.1840 - val_output_ph_loss: 0.1975 - val_output_mg_c_loss: 0.1663 - val_output_c_loss: 0.1724\nEpoch 77/120\n30/30 - 4s - loss: 1.0066 - output_react_loss: 0.1138 - output_bg_ph_loss: 0.1276 - output_ph_loss: 0.1524 - output_mg_c_loss: 0.1151 - output_c_loss: 0.1414 - val_loss: 1.3641 - val_output_react_loss: 0.1525 - val_output_bg_ph_loss: 0.1827 - val_output_ph_loss: 0.1961 - val_output_mg_c_loss: 0.1630 - val_output_c_loss: 0.1715\nEpoch 78/120\n30/30 - 4s - loss: 1.0015 - output_react_loss: 0.1136 - output_bg_ph_loss: 0.1269 - output_ph_loss: 0.1516 - output_mg_c_loss: 0.1137 - output_c_loss: 0.1415 - val_loss: 1.3709 - val_output_react_loss: 0.1526 - val_output_bg_ph_loss: 0.1836 - val_output_ph_loss: 0.1965 - val_output_mg_c_loss: 0.1649 - val_output_c_loss: 0.1722\nEpoch 79/120\n30/30 - 4s - loss: 1.0020 - output_react_loss: 0.1129 - output_bg_ph_loss: 0.1273 - output_ph_loss: 0.1513 - output_mg_c_loss: 0.1145 - output_c_loss: 0.1413 - val_loss: 1.3770 - val_output_react_loss: 0.1553 - val_output_bg_ph_loss: 0.1840 - val_output_ph_loss: 0.1985 - val_output_mg_c_loss: 0.1636 - val_output_c_loss: 0.1727\nEpoch 80/120\n30/30 - 4s - loss: 0.9949 - output_react_loss: 0.1124 - output_bg_ph_loss: 0.1257 - output_ph_loss: 0.1514 - output_mg_c_loss: 0.1134 - output_c_loss: 0.1405 - val_loss: 1.3778 - val_output_react_loss: 0.1536 - val_output_bg_ph_loss: 0.1844 - val_output_ph_loss: 0.1979 - val_output_mg_c_loss: 0.1655 - val_output_c_loss: 0.1729\nEpoch 81/120\n30/30 - 4s - loss: 0.9892 - output_react_loss: 0.1110 - output_bg_ph_loss: 0.1255 - output_ph_loss: 0.1500 - output_mg_c_loss: 0.1128 - output_c_loss: 0.1405 - val_loss: 1.3727 - val_output_react_loss: 0.1537 - val_output_bg_ph_loss: 0.1836 - val_output_ph_loss: 0.1973 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1719\nEpoch 82/120\n\nEpoch 00082: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n30/30 - 4s - loss: 0.9845 - output_react_loss: 0.1109 - output_bg_ph_loss: 0.1248 - output_ph_loss: 0.1497 - output_mg_c_loss: 0.1119 - output_c_loss: 0.1398 - val_loss: 1.3714 - val_output_react_loss: 0.1526 - val_output_bg_ph_loss: 0.1843 - val_output_ph_loss: 0.1971 - val_output_mg_c_loss: 0.1645 - val_output_c_loss: 0.1716\nEpoch 83/120\n30/30 - 4s - loss: 0.9578 - output_react_loss: 0.1073 - output_bg_ph_loss: 0.1210 - output_ph_loss: 0.1464 - output_mg_c_loss: 0.1087 - output_c_loss: 0.1373 - val_loss: 1.3552 - val_output_react_loss: 0.1510 - val_output_bg_ph_loss: 0.1816 - val_output_ph_loss: 0.1953 - val_output_mg_c_loss: 0.1623 - val_output_c_loss: 0.1701\nEpoch 84/120\n30/30 - 4s - loss: 0.9430 - output_react_loss: 0.1058 - output_bg_ph_loss: 0.1188 - output_ph_loss: 0.1444 - output_mg_c_loss: 0.1066 - output_c_loss: 0.1360 - val_loss: 1.3524 - val_output_react_loss: 0.1510 - val_output_bg_ph_loss: 0.1808 - val_output_ph_loss: 0.1954 - val_output_mg_c_loss: 0.1617 - val_output_c_loss: 0.1699\nEpoch 85/120\n30/30 - 4s - loss: 0.9379 - output_react_loss: 0.1051 - output_bg_ph_loss: 0.1180 - output_ph_loss: 0.1442 - output_mg_c_loss: 0.1060 - output_c_loss: 0.1355 - val_loss: 1.3515 - val_output_react_loss: 0.1510 - val_output_bg_ph_loss: 0.1808 - val_output_ph_loss: 0.1951 - val_output_mg_c_loss: 0.1615 - val_output_c_loss: 0.1697\nEpoch 86/120\n30/30 - 4s - loss: 0.9328 - output_react_loss: 0.1049 - output_bg_ph_loss: 0.1170 - output_ph_loss: 0.1433 - output_mg_c_loss: 0.1053 - output_c_loss: 0.1352 - val_loss: 1.3519 - val_output_react_loss: 0.1511 - val_output_bg_ph_loss: 0.1806 - val_output_ph_loss: 0.1954 - val_output_mg_c_loss: 0.1617 - val_output_c_loss: 0.1698\nEpoch 87/120\n30/30 - 4s - loss: 0.9294 - output_react_loss: 0.1041 - output_bg_ph_loss: 0.1172 - output_ph_loss: 0.1429 - output_mg_c_loss: 0.1045 - output_c_loss: 0.1348 - val_loss: 1.3500 - val_output_react_loss: 0.1509 - val_output_bg_ph_loss: 0.1803 - val_output_ph_loss: 0.1952 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1695\nEpoch 88/120\n30/30 - 4s - loss: 0.9272 - output_react_loss: 0.1036 - output_bg_ph_loss: 0.1168 - output_ph_loss: 0.1426 - output_mg_c_loss: 0.1046 - output_c_loss: 0.1347 - val_loss: 1.3502 - val_output_react_loss: 0.1509 - val_output_bg_ph_loss: 0.1807 - val_output_ph_loss: 0.1948 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1696\nEpoch 89/120\n30/30 - 4s - loss: 0.9260 - output_react_loss: 0.1035 - output_bg_ph_loss: 0.1166 - output_ph_loss: 0.1427 - output_mg_c_loss: 0.1044 - output_c_loss: 0.1343 - val_loss: 1.3490 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1802 - val_output_ph_loss: 0.1949 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1697\nEpoch 90/120\n30/30 - 4s - loss: 0.9232 - output_react_loss: 0.1035 - output_bg_ph_loss: 0.1159 - output_ph_loss: 0.1420 - output_mg_c_loss: 0.1041 - output_c_loss: 0.1342 - val_loss: 1.3500 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1807 - val_output_ph_loss: 0.1948 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1696\nEpoch 91/120\n30/30 - 4s - loss: 0.9227 - output_react_loss: 0.1034 - output_bg_ph_loss: 0.1157 - output_ph_loss: 0.1421 - output_mg_c_loss: 0.1040 - output_c_loss: 0.1344 - val_loss: 1.3476 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1802 - val_output_ph_loss: 0.1944 - val_output_mg_c_loss: 0.1611 - val_output_c_loss: 0.1694\nEpoch 92/120\n30/30 - 4s - loss: 0.9214 - output_react_loss: 0.1029 - output_bg_ph_loss: 0.1158 - output_ph_loss: 0.1416 - output_mg_c_loss: 0.1042 - output_c_loss: 0.1341 - val_loss: 1.3497 - val_output_react_loss: 0.1510 - val_output_bg_ph_loss: 0.1804 - val_output_ph_loss: 0.1947 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1696\nEpoch 93/120\n30/30 - 4s - loss: 0.9200 - output_react_loss: 0.1031 - output_bg_ph_loss: 0.1155 - output_ph_loss: 0.1419 - output_mg_c_loss: 0.1036 - output_c_loss: 0.1338 - val_loss: 1.3500 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1807 - val_output_ph_loss: 0.1948 - val_output_mg_c_loss: 0.1615 - val_output_c_loss: 0.1696\nEpoch 94/120\n30/30 - 4s - loss: 0.9215 - output_react_loss: 0.1032 - output_bg_ph_loss: 0.1155 - output_ph_loss: 0.1419 - output_mg_c_loss: 0.1039 - output_c_loss: 0.1342 - val_loss: 1.3506 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1808 - val_output_ph_loss: 0.1950 - val_output_mg_c_loss: 0.1615 - val_output_c_loss: 0.1697\nEpoch 95/120\n30/30 - 4s - loss: 0.9169 - output_react_loss: 0.1029 - output_bg_ph_loss: 0.1148 - output_ph_loss: 0.1411 - output_mg_c_loss: 0.1032 - output_c_loss: 0.1339 - val_loss: 1.3491 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1804 - val_output_ph_loss: 0.1946 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1697\nEpoch 96/120\n\nEpoch 00096: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n30/30 - 4s - loss: 0.9155 - output_react_loss: 0.1027 - output_bg_ph_loss: 0.1143 - output_ph_loss: 0.1413 - output_mg_c_loss: 0.1032 - output_c_loss: 0.1337 - val_loss: 1.3490 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1805 - val_output_ph_loss: 0.1945 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1697\nEpoch 97/120\n30/30 - 4s - loss: 0.9136 - output_react_loss: 0.1022 - output_bg_ph_loss: 0.1145 - output_ph_loss: 0.1411 - output_mg_c_loss: 0.1028 - output_c_loss: 0.1336 - val_loss: 1.3496 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1806 - val_output_ph_loss: 0.1946 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1697\nEpoch 98/120\n30/30 - 4s - loss: 0.9124 - output_react_loss: 0.1021 - output_bg_ph_loss: 0.1139 - output_ph_loss: 0.1412 - output_mg_c_loss: 0.1027 - output_c_loss: 0.1338 - val_loss: 1.3495 - val_output_react_loss: 0.1507 - val_output_bg_ph_loss: 0.1805 - val_output_ph_loss: 0.1946 - val_output_mg_c_loss: 0.1614 - val_output_c_loss: 0.1697\nEpoch 99/120\n30/30 - 4s - loss: 0.9112 - output_react_loss: 0.1018 - output_bg_ph_loss: 0.1140 - output_ph_loss: 0.1411 - output_mg_c_loss: 0.1025 - output_c_loss: 0.1334 - val_loss: 1.3486 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1804 - val_output_ph_loss: 0.1945 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1696\nEpoch 100/120\n30/30 - 4s - loss: 0.9126 - output_react_loss: 0.1022 - output_bg_ph_loss: 0.1142 - output_ph_loss: 0.1408 - output_mg_c_loss: 0.1027 - output_c_loss: 0.1336 - val_loss: 1.3490 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1805 - val_output_ph_loss: 0.1945 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1696\nEpoch 101/120\nRestoring model weights from the end of the best epoch.\n\nEpoch 00101: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n30/30 - 4s - loss: 0.9104 - output_react_loss: 0.1018 - output_bg_ph_loss: 0.1138 - output_ph_loss: 0.1407 - output_mg_c_loss: 0.1026 - output_c_loss: 0.1335 - val_loss: 1.3486 - val_output_react_loss: 0.1506 - val_output_bg_ph_loss: 0.1804 - val_output_ph_loss: 0.1945 - val_output_mg_c_loss: 0.1613 - val_output_c_loss: 0.1696\nEpoch 00101: early stopping\n" ] ], [ [ "## Model loss graph", "_____no_output_____" ] ], [ [ "for fold, history in enumerate(history_list):\n print(f'\\nFOLD: {fold+1}')\n print(f\"Train {np.array(history['loss']).min():.5f} Validation {np.array(history['val_loss']).min():.5f}\")\n\nplot_metrics_agg(history_list)", "\nFOLD: 1\nTrain 1.05189 Validation 1.37240\n\nFOLD: 2\nTrain 1.07609 Validation 1.38107\n\nFOLD: 3\nTrain 1.05777 Validation 1.33757\n\nFOLD: 4\nTrain 1.02478 Validation 1.38429\n\nFOLD: 5\nTrain 0.91044 Validation 1.34764\n" ] ], [ [ "# Post-processing", "_____no_output_____" ] ], [ [ "# Assign preds to OOF set\nfor idx, col in enumerate(pred_cols):\n val = oof_preds[:, :, idx]\n oof = oof.assign(**{f'{col}_pred': list(val)})\n \noof.to_csv('oof.csv', index=False)\n\noof_preds_dict = {}\nfor col in pred_cols:\n oof_preds_dict[col] = oof_preds[:, :, idx]\n\n# Assign values to test set\npreds_ls = []\n\nfor df, preds in [(public_test, test_public_preds), (private_test, test_private_preds)]:\n for i, uid in enumerate(df.id):\n single_pred = preds[i]\n\n single_df = pd.DataFrame(single_pred, columns=pred_cols)\n single_df['id_seqpos'] = [f'{uid}_{x}' for x in range(single_df.shape[0])]\n\n preds_ls.append(single_df)\n\npreds_df = pd.concat(preds_ls)", "_____no_output_____" ] ], [ [ "# Model evaluation", "_____no_output_____" ] ], [ [ "y_true_dict = get_targets_dict(train, pred_cols, train.index)\ny_true = np.array([y_true_dict[col] for col in pred_cols]).transpose((1, 2, 0, 3)).reshape(oof_preds.shape)\n\ndisplay(evaluate_model(train, y_true, oof_preds, pred_cols))", "_____no_output_____" ] ], [ [ "# Visualize test predictions", "_____no_output_____" ] ], [ [ "submission = pd.read_csv(database_base_path + 'sample_submission.csv')\nsubmission = submission[['id_seqpos']].merge(preds_df, on=['id_seqpos'])", "_____no_output_____" ] ], [ [ "# Test set predictions", "_____no_output_____" ] ], [ [ "display(submission.head(10))\ndisplay(submission.describe())\n\nsubmission.to_csv('submission.csv', index=False)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import matplotlib.pyplot as plt\nimport pickle\nimport numpy as np\nimport glob\nimport os\nfrom PIL import Image\nimport pandas as pd\nimport copy\nfrom PIL import Image, ImageOps", "_____no_output_____" ], [ "sample_num = 0\n\nroot = '/home/electron/Desktop/rovit dataset/amme dataset/'\npath = os.path.join(root,'annotated_frames')\nlpath = os.path.join(root,'projections_2d')\nbpath = os.path.join(root,'bounding_boxes')\npath_save = root", "_____no_output_____" ], [ "sample_num = 0\nfor folder in sorted(glob.glob(os.path.join(path,'data_'+'[1-2]'))):\n sample_num += len(glob.glob(os.path.join(folder,'*.jpg')))\n\nimages = []*sample_num\nprint(sample_num)\n\nfor foldername in sorted(glob.glob(os.path.join(path,'data_'+'[1-2]'))):\n print(foldername)\n for i,filename in enumerate(sorted(glob.glob(os.path.join(foldername,'*.jpg')))):\n if i%250 == 0:\n print(i)\n #image = np.array(Image.open(filename))\n image = Image.open(filename)\n images.append(copy.copy(image))\n image.close()\n \n\nprint(np.shape(images[0]))\nplt.imshow(images[0])", "5636\n/home/electron/Desktop/rovit dataset/amme dataset/annotated_frames/data_1\n0\n500\n1000\n1500\n2000\n2500\n/home/electron/Desktop/rovit dataset/amme dataset/annotated_frames/data_2\n0\n500\n1000\n1500\n2000\n2500\n(480, 640, 3)\n" ], [ "sample_num = 0\nfor folder in sorted(glob.glob(os.path.join(lpath,'data_'+'[1-2]'))):\n sample_num += len(glob.glob(os.path.join(folder,'*.txt')))\n\nlabels = []*sample_num\nprint(sample_num)\n\nfor foldername in sorted(glob.glob(os.path.join(lpath,'data_'+'[1-2]'))):\n print(foldername)\n for i,filename in enumerate(sorted(glob.glob(os.path.join(foldername,'*.txt')))):\n if i%500 == 0:\n print(i) \n label=pd.read_csv(filename,delimiter =' ', header = None, usecols = [1,2]).values\n labels.append(label)", "5636\n/home/electron/Desktop/rovit dataset/amme dataset/projections_2d/data_1\n0\n500\n1000\n1500\n2000\n2500\n/home/electron/Desktop/rovit dataset/amme dataset/projections_2d/data_2\n0\n500\n1000\n1500\n2000\n2500\n" ], [ "sample_num = 0\nfor folder in sorted(glob.glob(os.path.join(bpath,'data_'+'[1-2]'))):\n sample_num += len(glob.glob(os.path.join(folder,'*.txt')))\n\nblabels = []*sample_num\nprint(sample_num)\n\nfor foldername in sorted(glob.glob(os.path.join(bpath,'data_'+'[1-2]'))):\n print(foldername)\n for i,filename in enumerate(sorted(glob.glob(os.path.join(foldername,'*.txt')))):\n if i%500 == 0:\n print(i) \n blabel=pd.read_csv(filename,delimiter =' ' , header = None, usecols = [1]).values\n blabels.append(blabel)", "5636\n/home/electron/Desktop/rovit dataset/amme dataset/bounding_boxes/data_1\n0\n500\n1000\n1500\n2000\n2500\n/home/electron/Desktop/rovit dataset/amme dataset/bounding_boxes/data_2\n0\n500\n1000\n1500\n2000\n2500\n" ], [ "from matplotlib.patches import Circle\nimport random\ni = random.randint(1,8000)\ni = 0\nprint(i)\nfig,ax = plt.subplots(1)\nax.set_aspect('equal')\nax.imshow(images[i])\n\nfor xx,yy in labels[i]:\n circ = Circle((xx,yy),3, color = 'red')\n ax.add_patch(circ)", "0\n" ], [ "print(len(images))\nprint(len(labels))\nprint(len(blabels))", "5636\n5636\n5636\n" ], [ "myrandom=random.randint(1,30)\nfor i,_ in enumerate(images):\n t, l, b, r = blabels[i]\n t,l,b,r = int(t),int(l),int(b),int(r)\n plt.imshow(images[i].crop((l,t,r,b)))\n \n if(i==myrandom):\n break\n \n \n ", "_____no_output_____" ], [ "mh,mw = 0,0\nfor i,_ in enumerate(blabels):\n t, l, b, r = blabels[i]\n t,l,b,r = int(t),int(l),int(b),int(r)\n \n if(b-t>mh):\n mh=b-t\n if(r-l>mw):\n mw=r-l\n \nprint(mh,mw)", "282 334\n" ], [ "cropped = sample_num * []\n\n\nfor i,_ in enumerate(images):\n t, l, b, r = blabels[i]\n t,l,b,r = int(t),int(l),int(b),int(r)\n cropped.append(images[i].crop((l,t,r,b)))\n ", "_____no_output_____" ], [ "plt.imshow(cropped[0])", "_____no_output_____" ], [ "images = None\npadded = sample_num * []\n\n\nfor i,item in enumerate(cropped):\n t, l, b, r = blabels[i]\n t,l,b,r = int(t),int(l),int(b),int(r)\n right_pad = mw-item.size[0]\n bottom_pad = mh-item.size[1]\n padded.append(ImageOps.expand(cropped[i], (0,0,right_pad,bottom_pad)))", "_____no_output_____" ], [ "plt.imshow(padded[100])", "_____no_output_____" ], [ "labels = np.array(labels)", "_____no_output_____" ], [ "for i,item in enumerate(labels):\n t, l, b, r = blabels[i]\n t,l,b,r = int(t),int(l),int(b),int(r)\n labels[i,:,0] = labels[i,:,0] - l \n labels[i,:,1] = labels[i,:,1] - t\n ", "_____no_output_____" ], [ "np.amin(np.array(labels)[:,:,1])", "_____no_output_____" ], [ "print(np.shape(labels))\nprint(np.shape(blabels))", "(5636, 21, 2)\n(5636, 4, 1)\n" ], [ "jo = random.randint(1,8000)\nfig,ax = plt.subplots(1)\nax.set_aspect('equal')\n\nax.imshow(padded[jo])\n\nfor xx,yy in labels[jo]:\n circ = Circle((xx,yy),2, color = 'red')\n ax.add_patch(circ)", "_____no_output_____" ], [ "cropped = None\npadded_pickle = sample_num * []\nlabels_pickle = sample_num * []\nblabels_pickle = sample_num * []\n\n\nfor i,item in enumerate(padded):\n padded_pickle.append(np.array(padded[i]))\npadded_pickle=np.array(padded_pickle) \n\nfor i,item in enumerate(labels):\n labels_pickle.append(np.array(labels[i]))\nlabels_pickle=np.array(labels_pickle) \n\n\nfor i,item in enumerate(blabels):\n blabels_pickle.append(np.array(blabels[i]))\nblabels_pickle=np.array(blabels_pickle) ", "_____no_output_____" ], [ "print(padded_pickle.shape)\nprint(labels_pickle.shape)\nprint(blabels_pickle.shape)\n\ndel padded,labels, blabels", "(5636, 282, 334, 3)\n(5636, 21, 2)\n(5636, 4, 1)\n" ], [ "my_dict = {'padded':padded_pickle,'labels':labels_pickle,'blabels':blabels_pickle}\n\nfor key in my_dict.keys():\n print(my_dict[key].shape)\n", "(5636, 282, 334, 3)\n(5636, 21, 2)\n(5636, 4, 1)\n" ], [ "with open('data_hand_pose.pickle','wb') as file_to_dump:\n pickle.dump(my_dict,file_to_dump)\n\n ", "_____no_output_____" ], [ "# file_to_dump=open('data_hand_pose3.pickle','wb')\n# pickle.dump(my_dict,file_to_dump)\n\n# file_to_dump.close()", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nfrom collections import Counter\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier", "_____no_output_____" ], [ "df_test = pd.read_csv('test.csv')\ndf_train = pd.read_csv('train.csv')\n\n#Counter(df_train.IN_TREINEIRO)\n\nID = 'NU_INSCRICAO'\ntarget = 'IN_TREINEIRO'\ndf = df_train[list(df_test.columns)].fillna(0)\ndf = df.drop(columns=[ID])\ntrain_features = pd.get_dummies(df)\n\n\n# Labels are the values we want to predict\ntrain_labels = np.array(df_train[target].to_list())\n# Saving feature names for later use\nfeature_list = list(train_features.columns)\n# Convert to numpy array\nfeatures_nparray = np.array(train_features)\n\nprint('Training Features Shape:', train_features.shape)\nprint('Training Labels Shape:', train_labels.shape)\n\n\n# Instantiate model with 1000 decision trees\nrf = RandomForestClassifier(n_estimators = 200, random_state = 42, n_jobs = -1, verbose = 1)\n# Train the model on training data\nrf.fit(train_features, train_labels);\n\n#predictions = rf.predict(train_features)\n\n#sum(predictions-train_labels)\n\ndf_answer = pd.DataFrame()\n\ndf_answer[ID] = df_test[ID]\ndf_test = df_test.drop(columns=[ID]).fillna(0)\ntest_features = pd.get_dummies(df_test)\npredictions = rf.predict(test_features)\n\ndf_answer[target] = list(predictions)\n#df_answer.head()\n\ndf_answer.to_csv('answer.csv', index=False)", "Training Features Shape: (13730, 123)\nTraining Labels Shape: (13730,)\n" ] ] ]

[ "code" ]

[ [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import torch\nimport torchvision\n\nfrom models import LFADS, LadderLFADS\nfrom utils import read_data, load_parameters, save_parameters, batchify_random_sample\n\nnp = torch._np\nimport matplotlib.pyplot as plt\nimport yaml\nimport os", "_____no_output_____" ], [ "device = 'cuda' if torch.cuda.is_available() else 'cpu'; print('Using device: %s'%device)", "Using device: cuda\n" ], [ "seed = 700\nif os.path.exists('./synth_data/lorenz_%s'%seed):\n data_dict = read_data('./synth_data/lorenz_%s'%seed)\nelse:\n from synthetic_data import generate_lorenz_data\n data_dict = generate_lorenz_data(N_cells=30, N_inits=65, N_trials=20, N_steps=200, N_stepsinbin=2, dt_lorenz=0.015, dt_spike = 1./20, base_firing_rate= 1.0, save=True, seed=250)\n\n# For spike data\ntrain_data = torch.Tensor(data_dict['train_fluor']).to(device)\nvalid_data = torch.Tensor(data_dict['valid_fluor']).to(device)\n\ntrain_truth = {'spikes' : data_dict['train_spikes'],\n 'rates' : data_dict['train_rates'],\n 'latent' : data_dict['train_latent']}\n\nvalid_truth = {'spikes' : data_dict['valid_spikes'],\n 'rates' : data_dict['valid_rates'],\n 'latent' : data_dict['valid_latent']}\n\ntrain_ds = torch.utils.data.TensorDataset(train_data)\nvalid_ds = torch.utils.data.TensorDataset(valid_data)\n\nnum_trials, num_steps, num_cells = train_data.shape;\nprint(train_data.shape);\nprint('Number of datapoints = %s'%train_data.numel())", "torch.Size([1040, 100, 30])\nNumber of datapoints = 3120000\n" ], [ "hyperparams = load_parameters('./parameters/parameters_lorenz_spikes.yaml')\nhyperparams['run_name'] += '_localtest'\nsave_parameters(hyperparams, path=None)\n\nhyperparams", "_____no_output_____" ], [ "model = LFADS(inputs_dim = num_cells, T = num_steps, dt = float(data_dict['dt']), device=device,\n model_hyperparams=hyperparams).to(device)", "Random seed: 7629\n" ], [ "total_params = 0\nfor ix, (name, param) in enumerate(model.named_parameters()):\n print(ix, name, list(param.shape), param.numel(), param.requires_grad)\n total_params += param.numel()\n \nprint('Total parameters: %i'%total_params)", "0 efgen_g0_init [64] 64 True\n1 ebgen_g0_init [64] 64 True\n2 g0_prior_mu [64] 64 True\n3 gru_Egen_g0.weight_ih_l0 [192, 30] 5760 True\n4 gru_Egen_g0.weight_hh_l0 [192, 64] 12288 True\n5 gru_Egen_g0.bias_ih_l0 [192] 192 True\n6 gru_Egen_g0.bias_hh_l0 [192] 192 True\n7 gru_Egen_g0.weight_ih_l0_reverse [192, 30] 5760 True\n8 gru_Egen_g0.weight_hh_l0_reverse [192, 64] 12288 True\n9 gru_Egen_g0.bias_ih_l0_reverse [192] 192 True\n10 gru_Egen_g0.bias_hh_l0_reverse [192] 192 True\n11 fc_g0mean.weight [64, 128] 8192 True\n12 fc_g0mean.bias [64] 64 True\n13 fc_g0logvar.weight [64, 128] 8192 True\n14 fc_g0logvar.bias [64] 64 True\n15 gru_generator.fc_h_ru.weight [128, 64] 8192 True\n16 gru_generator.fc_h_ru.bias [128] 128 True\n17 gru_generator.fc_rh_c.weight [64, 64] 4096 True\n18 gru_generator.fc_rh_c.bias [64] 64 True\n19 fc_factors.weight [3, 64] 192 True\n20 fc_logrates.weight [30, 3] 90 True\n21 fc_logrates.bias [30] 30 True\nTotal parameters: 66360\n" ], [ "model.load_checkpoint('recent')", "_____no_output_____" ], [ "model.fit(train_ds, valid_ds, train_truth=train_truth, valid_truth=valid_truth,\n max_epochs=2000, batch_size=65, use_tensorboard=False, health_check=False)", "Beginning training...\nEpoch: 1, Step: 16, Losses [Train, Valid]: Total [1223.56, 1429.92], Recon [1222.28, 1211.00], KL [242.74, 214.96], L2 3.96, Runtime: 3.3538 secs\nEpoch: 2, Step: 32, Losses [Train, Valid]: Total [1208.90, 1341.27], Recon [1206.05, 1199.39], KL [186.39, 137.93], L2 3.95, Runtime: 4.0260 secs\nEpoch: 3, Step: 48, Losses [Train, Valid]: Total [1200.50, 1233.25], Recon [1198.18, 1195.49], KL [91.16, 33.82], L2 3.94, Runtime: 3.2413 secs\nEpoch: 4, Step: 64, Losses [Train, Valid]: Total [1196.92, 1209.23], Recon [1196.01, 1194.70], KL [22.53, 10.60], L2 3.93, Runtime: 3.2689 secs\nEpoch: 5, Step: 80, Losses [Train, Valid]: Total [1196.49, 1209.41], Recon [1195.55, 1194.39], KL [16.84, 11.12], L2 3.91, Runtime: 3.2406 secs\nEpoch: 6, Step: 96, Losses [Train, Valid]: Total [1196.17, 1209.18], Recon [1195.08, 1193.67], KL [15.82, 11.61], L2 3.90, Runtime: 3.2648 secs\nEpoch: 7, Step: 112, Losses [Train, Valid]: Total [1195.84, 1209.29], Recon [1194.53, 1193.10], KL [16.05, 12.31], L2 3.88, Runtime: 3.2407 secs\nEpoch: 8, Step: 128, Losses [Train, Valid]: Total [1195.19, 1210.67], Recon [1193.63, 1191.98], KL [16.90, 14.81], L2 3.87, Runtime: 4.9034 secs\nEpoch: 9, Step: 144, Losses [Train, Valid]: Total [1194.16, 1212.68], Recon [1192.13, 1190.18], KL [19.87, 18.65], L2 3.85, Runtime: 3.3241 secs\nEpoch: 10, Step: 160, Losses [Train, Valid]: Total [1191.64, 1208.77], Recon [1189.08, 1184.34], KL [22.98, 20.60], L2 3.83, Runtime: 3.2432 secs\nEpoch: 11, Step: 176, Losses [Train, Valid]: Total [1188.27, 1214.20], Recon [1185.22, 1182.64], KL [25.12, 27.74], L2 3.81, Runtime: 3.3212 secs\nEpoch: 12, Step: 192, Losses [Train, Valid]: Total [1185.66, 1205.84], Recon [1181.82, 1175.49], KL [29.51, 26.51], L2 3.84, Runtime: 6.4502 secs\nEpoch: 13, Step: 208, Losses [Train, Valid]: Total [1181.76, 1202.69], Recon [1177.74, 1175.28], KL [28.25, 23.56], L2 3.85, Runtime: 3.2918 secs\nEpoch: 14, Step: 224, Losses [Train, Valid]: Total [1178.20, 1195.75], Recon [1174.41, 1168.98], KL [24.11, 22.90], L2 3.88, Runtime: 4.1914 secs\nEpoch: 15, Step: 240, Losses [Train, Valid]: Total [1174.72, 1195.80], Recon [1170.39, 1166.75], KL [25.86, 25.14], L2 3.92, Runtime: 4.0741 secs\nEpoch: 16, Step: 256, Losses [Train, Valid]: Total [1174.95, 1198.30], Recon [1169.91, 1165.38], KL [28.48, 28.98], L2 3.93, Runtime: 4.0882 secs\nEpoch: 17, Step: 272, Losses [Train, Valid]: Total [1170.92, 1191.69], Recon [1165.25, 1159.09], KL [30.38, 28.63], L2 3.97, Runtime: 5.8759 secs\nEpoch: 18, Step: 288, Losses [Train, Valid]: Total [1165.61, 1187.43], Recon [1159.73, 1154.96], KL [29.57, 28.47], L2 4.01, Runtime: 3.3429 secs\nEpoch: 19, Step: 304, Losses [Train, Valid]: Total [1161.83, 1182.92], Recon [1155.55, 1150.21], KL [29.85, 28.65], L2 4.05, Runtime: 4.2123 secs\nEpoch: 20, Step: 320, Losses [Train, Valid]: Total [1160.93, 1185.23], Recon [1153.50, 1149.66], KL [33.93, 31.51], L2 4.07, Runtime: 4.1309 secs\nEpoch: 21, Step: 336, Losses [Train, Valid]: Total [1158.46, 1187.02], Recon [1151.09, 1149.74], KL [31.76, 33.17], L2 4.12, Runtime: 3.4619 secs\nEpoch: 22, Step: 352, Losses [Train, Valid]: Total [1156.51, 1179.28], Recon [1148.17, 1143.13], KL [34.62, 31.99], L2 4.15, Runtime: 3.2580 secs\nEpoch: 23, Step: 368, Losses [Train, Valid]: Total [1152.17, 1186.00], Recon [1143.84, 1147.34], KL [32.77, 34.48], L2 4.18, Runtime: 3.2593 secs\nEpoch: 24, Step: 384, Losses [Train, Valid]: Total [1154.21, 1173.13], Recon [1145.72, 1140.99], KL [31.92, 27.94], L2 4.20, Runtime: 3.2593 secs\nEpoch: 25, Step: 400, Losses [Train, Valid]: Total [1148.81, 1170.54], Recon [1140.34, 1137.06], KL [30.34, 29.26], L2 4.22, Runtime: 3.2382 secs\nEpoch: 26, Step: 416, Losses [Train, Valid]: Total [1147.33, 1175.81], Recon [1138.69, 1139.24], KL [29.61, 32.33], L2 4.24, Runtime: 5.7616 secs\nEpoch: 27, Step: 432, Losses [Train, Valid]: Total [1148.16, 1171.31], Recon [1138.70, 1135.65], KL [31.39, 31.40], L2 4.26, Runtime: 3.2619 secs\nEpoch: 28, Step: 448, Losses [Train, Valid]: Total [1146.03, 1168.33], Recon [1136.19, 1135.31], KL [31.47, 28.72], L2 4.30, Runtime: 3.3002 secs\nEpoch: 29, Step: 464, Losses [Train, Valid]: Total [1146.17, 1169.16], Recon [1136.71, 1136.01], KL [28.85, 28.82], L2 4.33, Runtime: 3.2639 secs\nEpoch: 30, Step: 480, Losses [Train, Valid]: Total [1146.24, 1167.56], Recon [1135.92, 1133.74], KL [30.60, 29.48], L2 4.35, Runtime: 4.0749 secs\nEpoch: 31, Step: 496, Losses [Train, Valid]: Total [1143.69, 1158.40], Recon [1133.77, 1127.10], KL [28.10, 26.93], L2 4.38, Runtime: 3.2785 secs\nEpoch: 32, Step: 512, Losses [Train, Valid]: Total [1141.11, 1156.92], Recon [1130.80, 1125.71], KL [28.28, 26.81], L2 4.41, Runtime: 4.9470 secs\nEpoch: 33, Step: 528, Losses [Train, Valid]: Total [1140.28, 1158.23], Recon [1130.45, 1129.67], KL [25.82, 24.12], L2 4.43, Runtime: 4.0175 secs\nEpoch: 34, Step: 544, Losses [Train, Valid]: Total [1142.77, 1159.81], Recon [1132.54, 1129.55], KL [26.08, 25.81], L2 4.45, Runtime: 3.2377 secs\nEpoch: 35, Step: 560, Losses [Train, Valid]: Total [1141.07, 1158.03], Recon [1130.36, 1127.37], KL [26.55, 26.18], L2 4.47, Runtime: 3.9893 secs\nEpoch: 36, Step: 576, Losses [Train, Valid]: Total [1139.38, 1153.71], Recon [1128.72, 1123.54], KL [25.53, 25.67], L2 4.51, Runtime: 3.1807 secs\nEpoch: 37, Step: 592, Losses [Train, Valid]: Total [1138.49, 1151.20], Recon [1127.59, 1122.08], KL [25.36, 24.61], L2 4.51, Runtime: 4.7887 secs\nEpoch: 38, Step: 608, Losses [Train, Valid]: Total [1141.19, 1163.81], Recon [1129.46, 1134.86], KL [26.73, 24.41], L2 4.54, Runtime: 3.2046 secs\nEpoch: 39, Step: 624, Losses [Train, Valid]: Total [1140.81, 1153.15], Recon [1129.02, 1122.18], KL [26.05, 26.40], L2 4.57, Runtime: 3.2497 secs\nEpoch: 40, Step: 640, Losses [Train, Valid]: Total [1140.31, 1159.85], Recon [1128.08, 1130.73], KL [26.36, 24.52], L2 4.59, Runtime: 3.2250 secs\nEpoch: 41, Step: 656, Losses [Train, Valid]: Total [1139.98, 1156.80], Recon [1127.57, 1127.72], KL [26.02, 24.46], L2 4.62, Runtime: 3.3206 secs\nEpoch: 42, Step: 672, Losses [Train, Valid]: Total [1142.88, 1156.18], Recon [1130.95, 1127.73], KL [24.12, 23.84], L2 4.61, Runtime: 3.2998 secs\nLearning rate decreased to 0.009500\nEpoch: 43, Step: 688, Losses [Train, Valid]: Total [1141.09, 1154.91], Recon [1128.83, 1127.54], KL [24.21, 22.72], L2 4.64, Runtime: 4.0649 secs\nEpoch: 44, Step: 704, Losses [Train, Valid]: Total [1138.97, 1156.69], Recon [1126.12, 1129.13], KL [24.88, 22.91], L2 4.65, Runtime: 3.2075 secs\nEpoch: 45, Step: 720, Losses [Train, Valid]: Total [1141.58, 1150.89], Recon [1128.99, 1122.46], KL [23.62, 23.79], L2 4.64, Runtime: 3.3005 secs\nEpoch: 46, Step: 736, Losses [Train, Valid]: Total [1137.10, 1152.35], Recon [1124.28, 1125.68], KL [23.53, 22.02], L2 4.66, Runtime: 5.6883 secs\nEpoch: 47, Step: 752, Losses [Train, Valid]: Total [1137.74, 1151.94], Recon [1125.46, 1125.00], KL [21.74, 22.28], L2 4.66, Runtime: 4.0759 secs\nEpoch: 48, Step: 768, Losses [Train, Valid]: Total [1138.97, 1150.46], Recon [1125.33, 1122.28], KL [24.02, 23.49], L2 4.68, Runtime: 4.0757 secs\nEpoch: 49, Step: 784, Losses [Train, Valid]: Total [1138.71, 1149.83], Recon [1125.19, 1124.35], KL [23.18, 20.79], L2 4.69, Runtime: 3.2572 secs\nEpoch: 50, Step: 800, Losses [Train, Valid]: Total [1136.08, 1146.38], Recon [1123.26, 1119.53], KL [21.19, 22.14], L2 4.71, Runtime: 3.3566 secs\nEpoch: 51, Step: 816, Losses [Train, Valid]: Total [1134.70, 1144.84], Recon [1121.38, 1120.51], KL [21.65, 19.61], L2 4.72, Runtime: 4.1092 secs\nEpoch: 52, Step: 832, Losses [Train, Valid]: Total [1135.87, 1144.78], Recon [1122.39, 1120.09], KL [21.42, 19.96], L2 4.73, Runtime: 3.2566 secs\nEpoch: 53, Step: 848, Losses [Train, Valid]: Total [1133.87, 1146.80], Recon [1120.84, 1122.91], KL [20.08, 19.15], L2 4.75, Runtime: 4.1511 secs\nEpoch: 54, Step: 864, Losses [Train, Valid]: Total [1136.18, 1148.14], Recon [1122.32, 1122.60], KL [21.12, 20.77], L2 4.77, Runtime: 4.2592 secs\nEpoch: 55, Step: 880, Losses [Train, Valid]: Total [1135.13, 1145.06], Recon [1121.39, 1121.20], KL [20.41, 19.08], L2 4.78, Runtime: 4.1454 secs\nEpoch: 56, Step: 896, Losses [Train, Valid]: Total [1135.71, 1144.15], Recon [1122.07, 1119.84], KL [19.79, 19.53], L2 4.78, Runtime: 4.7441 secs\nEpoch: 57, Step: 912, Losses [Train, Valid]: Total [1133.51, 1141.99], Recon [1119.58, 1117.79], KL [19.85, 19.41], L2 4.80, Runtime: 6.6054 secs\nEpoch: 58, Step: 928, Losses [Train, Valid]: Total [1136.61, 1141.60], Recon [1122.55, 1118.04], KL [19.64, 18.77], L2 4.80, Runtime: 3.2627 secs\nLearning rate decreased to 0.009025\nEpoch: 59, Step: 944, Losses [Train, Valid]: Total [1134.37, 1141.22], Recon [1120.16, 1117.40], KL [19.48, 19.00], L2 4.81, Runtime: 3.2770 secs\nEpoch: 60, Step: 960, Losses [Train, Valid]: Total [1135.98, 1143.95], Recon [1121.13, 1120.59], KL [20.13, 18.54], L2 4.82, Runtime: 3.2584 secs\nEpoch: 61, Step: 976, Losses [Train, Valid]: Total [1136.12, 1143.58], Recon [1121.17, 1119.38], KL [19.87, 19.36], L2 4.84, Runtime: 4.0300 secs\nEpoch: 62, Step: 992, Losses [Train, Valid]: Total [1133.64, 1138.54], Recon [1118.71, 1116.19], KL [19.42, 17.48], L2 4.87, Runtime: 3.2744 secs\nEpoch: 63, Step: 1008, Losses [Train, Valid]: Total [1134.92, 1142.03], Recon [1120.06, 1117.47], KL [18.90, 19.69], L2 4.88, Runtime: 5.0180 secs\nEpoch: 64, Step: 1024, Losses [Train, Valid]: Total [1136.03, 1142.93], Recon [1120.54, 1118.74], KL [19.51, 19.31], L2 4.88, Runtime: 7.4739 secs\nEpoch: 65, Step: 1040, Losses [Train, Valid]: Total [1133.65, 1139.81], Recon [1118.25, 1117.89], KL [18.99, 17.04], L2 4.88, Runtime: 4.8514 secs\nEpoch: 66, Step: 1056, Losses [Train, Valid]: Total [1132.42, 1138.22], Recon [1117.70, 1115.85], KL [17.58, 17.49], L2 4.89, Runtime: 4.9008 secs\nEpoch: 67, Step: 1072, Losses [Train, Valid]: Total [1133.02, 1141.19], Recon [1118.00, 1119.49], KL [17.68, 16.82], L2 4.89, Runtime: 4.1459 secs\nEpoch: 68, Step: 1088, Losses [Train, Valid]: Total [1133.83, 1139.92], Recon [1119.09, 1117.70], KL [16.93, 17.32], L2 4.90, Runtime: 5.8687 secs\nEpoch: 69, Step: 1104, Losses [Train, Valid]: Total [1135.62, 1143.28], Recon [1119.90, 1120.82], KL [18.04, 17.56], L2 4.90, Runtime: 4.1210 secs\nEpoch: 70, Step: 1120, Losses [Train, Valid]: Total [1133.20, 1138.72], Recon [1117.57, 1117.48], KL [17.56, 16.34], L2 4.90, Runtime: 5.7058 secs\nEpoch: 71, Step: 1136, Losses [Train, Valid]: Total [1132.51, 1136.60], Recon [1117.03, 1114.66], KL [17.04, 17.03], L2 4.91, Runtime: 4.2924 secs\nEpoch: 72, Step: 1152, Losses [Train, Valid]: Total [1132.82, 1141.01], Recon [1117.27, 1119.84], KL [16.83, 16.26], L2 4.91, Runtime: 4.6844 secs\nEpoch: 73, Step: 1168, Losses [Train, Valid]: Total [1134.18, 1142.42], Recon [1117.49, 1120.67], KL [18.09, 16.82], L2 4.92, Runtime: 5.3389 secs\nEpoch: 74, Step: 1184, Losses [Train, Valid]: Total [1134.73, 1138.50], Recon [1118.71, 1116.75], KL [16.87, 16.84], L2 4.92, Runtime: 5.7647 secs\nEpoch: 75, Step: 1200, Losses [Train, Valid]: Total [1135.46, 1138.98], Recon [1119.17, 1117.67], KL [16.93, 16.36], L2 4.95, Runtime: 4.5398 secs\nEpoch: 76, Step: 1216, Losses [Train, Valid]: Total [1137.68, 1138.91], Recon [1120.35, 1116.39], KL [17.99, 17.59], L2 4.94, Runtime: 4.1875 secs\nLearning rate decreased to 0.008574\nEpoch: 77, Step: 1232, Losses [Train, Valid]: Total [1135.21, 1137.07], Recon [1118.54, 1116.34], KL [16.85, 15.79], L2 4.94, Runtime: 3.2931 secs\nEpoch: 78, Step: 1248, Losses [Train, Valid]: Total [1133.14, 1138.99], Recon [1116.90, 1118.86], KL [16.01, 15.17], L2 4.96, Runtime: 7.0967 secs\nEpoch: 79, Step: 1264, Losses [Train, Valid]: Total [1132.75, 1135.72], Recon [1116.53, 1115.33], KL [15.70, 15.42], L2 4.97, Runtime: 4.0180 secs\nEpoch: 80, Step: 1280, Losses [Train, Valid]: Total [1134.55, 1135.89], Recon [1117.47, 1114.58], KL [16.51, 16.34], L2 4.97, Runtime: 3.2372 secs\nEpoch: 81, Step: 1296, Losses [Train, Valid]: Total [1134.34, 1138.12], Recon [1117.02, 1117.76], KL [16.53, 15.39], L2 4.97, Runtime: 4.0390 secs\nEpoch: 82, Step: 1312, Losses [Train, Valid]: Total [1132.69, 1134.93], Recon [1115.98, 1115.28], KL [15.52, 14.66], L2 4.99, Runtime: 7.3063 secs\nEpoch: 83, Step: 1328, Losses [Train, Valid]: Total [1133.12, 1136.29], Recon [1116.31, 1116.02], KL [15.37, 15.27], L2 4.99, Runtime: 4.0403 secs\nEpoch: 84, Step: 1344, Losses [Train, Valid]: Total [1133.72, 1137.16], Recon [1116.08, 1117.15], KL [16.13, 15.02], L2 4.98, Runtime: 4.0337 secs\nEpoch: 85, Step: 1360, Losses [Train, Valid]: Total [1132.65, 1133.04], Recon [1115.57, 1113.82], KL [15.22, 14.22], L2 5.00, Runtime: 3.8663 secs\nEpoch: 86, Step: 1376, Losses [Train, Valid]: Total [1132.49, 1133.94], Recon [1115.48, 1114.78], KL [14.89, 14.18], L2 4.99, Runtime: 3.5105 secs\nEpoch: 87, Step: 1392, Losses [Train, Valid]: Total [1131.25, 1132.99], Recon [1113.98, 1113.93], KL [14.98, 14.08], L2 4.98, Runtime: 3.2784 secs\nEpoch: 88, Step: 1408, Losses [Train, Valid]: Total [1132.19, 1134.75], Recon [1115.16, 1115.53], KL [14.49, 14.25], L2 4.97, Runtime: 4.1551 secs\nEpoch: 89, Step: 1424, Losses [Train, Valid]: Total [1133.63, 1137.60], Recon [1115.69, 1118.53], KL [15.29, 14.11], L2 4.96, Runtime: 3.3313 secs\nEpoch: 90, Step: 1440, Losses [Train, Valid]: Total [1134.76, 1135.91], Recon [1117.34, 1116.18], KL [14.49, 14.77], L2 4.96, Runtime: 4.9523 secs\nLearning rate decreased to 0.008145\nEpoch: 91, Step: 1456, Losses [Train, Valid]: Total [1134.12, 1134.44], Recon [1116.26, 1115.04], KL [14.78, 14.45], L2 4.95, Runtime: 4.1994 secs\nEpoch: 92, Step: 1472, Losses [Train, Valid]: Total [1134.65, 1134.52], Recon [1116.39, 1115.27], KL [14.99, 14.29], L2 4.96, Runtime: 4.9495 secs\nEpoch: 93, Step: 1488, Losses [Train, Valid]: Total [1135.53, 1137.67], Recon [1117.07, 1118.46], KL [14.98, 14.23], L2 4.97, Runtime: 6.5290 secs\nEpoch: 94, Step: 1504, Losses [Train, Valid]: Total [1136.74, 1137.71], Recon [1118.15, 1117.94], KL [14.90, 14.80], L2 4.98, Runtime: 4.1493 secs\nEpoch: 95, Step: 1520, Losses [Train, Valid]: Total [1135.88, 1135.23], Recon [1117.38, 1116.09], KL [14.60, 14.17], L2 4.98, Runtime: 3.3552 secs\nEpoch: 96, Step: 1536, Losses [Train, Valid]: Total [1135.25, 1133.37], Recon [1116.24, 1114.34], KL [14.92, 14.04], L2 4.99, Runtime: 4.1896 secs\nEpoch: 97, Step: 1552, Losses [Train, Valid]: Total [1133.80, 1136.38], Recon [1115.18, 1117.59], KL [14.29, 13.79], L2 5.00, Runtime: 5.8199 secs\nEpoch: 98, Step: 1568, Losses [Train, Valid]: Total [1136.22, 1135.96], Recon [1116.70, 1116.19], KL [15.01, 14.79], L2 4.99, Runtime: 4.0600 secs\nEpoch: 99, Step: 1584, Losses [Train, Valid]: Total [1136.62, 1136.65], Recon [1116.89, 1117.55], KL [15.04, 14.11], L2 4.99, Runtime: 4.0681 secs\n" ], [ "model.load_checkpoint('best')", "_____no_output_____" ], [ "fig_dict = model.plot_summary(data=valid_data, truth=valid_truth)", "_____no_output_____" ], [ "results_dict = model.plot_recon_rsquared(valid_data, valid_truth, train_data, train_truth)", "_____no_output_____" ], [ "fig, axs = plt.subplots(figsize=(8,8), nrows=3, ncols=1, sharex=True, sharey=True)\nfor ix in range(3):\n plt.sca(axs[ix])\n plt.plot(data_dict['valid_latent'][0, :100, ix])\n plt.plot(results_dict['factors']['aligned'][:100, ix])\n \nfig.suptitle('Aligned factors')\nfig.legend(['data', 'reconstruction'])", "_____no_output_____" ] ], [ [ "## Inspecting trained model ", "_____no_output_____" ] ], [ [ "seed = 600\nsystem = 'chaotic-rnn'\nif os.path.exists('./synth_data/%s_%s'%(system, seed)):\n data_dict = read_data('./synth_data/%s_%s'%(system, seed))\nelse:\n from synthetic_data import generate_chaotic_rnn_data\n param_dict = yaml.load(open('./synth_data/%s_params.yaml'%system, 'r'), Loader=yaml.FullLoader)\n data_dict = generate_chaotic_rnn_data(Ncells=param_dict['cells'],\n Ninits=param_dict['inits'],\n Ntrial=param_dict['trials'],\n Nsteps=param_dict['steps'],\n# Nstepsinbin=param_dict['steps_in_bin'],\n dt_rnn=param_dict['dt_sys'],\n dt_spike = param_dict['dt_spike'],\n maxRate= param_dict['rate_scale'],\n save=False,\n seed=seed)\n\n \n \n# For spike data\ntrain_data = torch.Tensor(data_dict['train_spikes']).to(device)\nvalid_data = torch.Tensor(data_dict['valid_spikes']).to(device)\n\ntrain_truth = {'rates' : data_dict['train_rates']}\n\nvalid_truth = {'rates' : data_dict['valid_rates']}\n\ntrain_ds = torch.utils.data.TensorDataset(train_data)\nvalid_ds = torch.utils.data.TensorDataset(valid_data)\n\nnum_trials, num_steps, num_cells = train_data.shape;\nprint(train_data.shape);\nprint('Number of datapoints = %s'%train_data.numel())", "torch.Size([3200, 100, 50])\nNumber of datapoints = 16000000\n" ], [ "hyperparams = load_parameters('parameters/parameters_%s_spikes.yaml'%system)\nhyperparams['run_name'] = 'poisson_%s%i_f20_g1200_eg1128_u1_c1128_ec1128_191125_localtest'%(system, seed)", "_____no_output_____" ], [ "model = LFADS(inputs_dim = num_cells, T = num_steps, dt = float(data_dict['dt']), device=device,\n model_hyperparams=hyperparams).to(device)\n# model.load_checkpoint('best')\n# model.epochs", "Random seed: 392\n" ], [ "model.gru_generator.fc_h_ru.weight.std()", "_____no_output_____" ], [ "1/(np.sqrt(400))", "_____no_output_____" ], [ "total_params = 0\nfor ix, (name, param) in enumerate(model.named_parameters()):\n print(ix, name, list(param.shape), param.numel(), param.requires_grad)\n total_params += param.numel()\n \nprint('Total parameters: %i'%total_params)", "0 efgen_g0_init [128] 128 True\n1 ebgen_g0_init [128] 128 True\n2 efcon_c_init [128] 128 True\n3 ebcon_c_init [128] 128 True\n4 c_init [128] 128 True\n5 g0_prior_mu [64] 64 True\n6 u_prior_logtau [1] 1 True\n7 u_prior_logkappa [1] 1 True\n8 gru_Egen_g0.weight_ih_l0 [384, 50] 19200 True\n9 gru_Egen_g0.weight_hh_l0 [384, 128] 49152 True\n10 gru_Egen_g0.bias_ih_l0 [384] 384 True\n11 gru_Egen_g0.bias_hh_l0 [384] 384 True\n12 gru_Egen_g0.weight_ih_l0_reverse [384, 50] 19200 True\n13 gru_Egen_g0.weight_hh_l0_reverse [384, 128] 49152 True\n14 gru_Egen_g0.bias_ih_l0_reverse [384] 384 True\n15 gru_Egen_g0.bias_hh_l0_reverse [384] 384 True\n16 gru_Econ_c.weight_ih_l0 [384, 50] 19200 True\n17 gru_Econ_c.weight_hh_l0 [384, 128] 49152 True\n18 gru_Econ_c.bias_ih_l0 [384] 384 True\n19 gru_Econ_c.bias_hh_l0 [384] 384 True\n20 gru_Econ_c.weight_ih_l0_reverse [384, 50] 19200 True\n21 gru_Econ_c.weight_hh_l0_reverse [384, 128] 49152 True\n22 gru_Econ_c.bias_ih_l0_reverse [384] 384 True\n23 gru_Econ_c.bias_hh_l0_reverse [384] 384 True\n24 fc_g0mean.weight [64, 256] 16384 True\n25 fc_g0mean.bias [64] 64 True\n26 fc_g0logvar.weight [64, 256] 16384 True\n27 fc_g0logvar.bias [64] 64 True\n28 gru_controller_c.fc_x_ru.weight [256, 276] 70656 True\n29 gru_controller_c.fc_x_c.weight [128, 276] 35328 True\n30 gru_controller_c.fc_h_ru.weight [256, 128] 32768 True\n31 gru_controller_c.fc_h_ru.bias [256] 256 True\n32 gru_controller_c.fc_rh_c.weight [128, 128] 16384 True\n33 gru_controller_c.fc_rh_c.bias [128] 128 True\n34 fc_umean.weight [1, 128] 128 True\n35 fc_umean.bias [1] 1 True\n36 fc_ulogvar.weight [1, 128] 128 True\n37 fc_ulogvar.bias [1] 1 True\n38 fc_icgen.weight [200, 64] 12800 True\n39 fc_icgen.bias [200] 200 True\n40 gru_generator.fc_x_ru.weight [400, 1] 400 True\n41 gru_generator.fc_x_c.weight [200, 1] 200 True\n42 gru_generator.fc_h_ru.weight [400, 200] 80000 True\n43 gru_generator.fc_h_ru.bias [400] 400 True\n44 gru_generator.fc_rh_c.weight [200, 200] 40000 True\n45 gru_generator.fc_rh_c.bias [200] 200 True\n46 fc_factors.weight [20, 200] 4000 True\n47 fc_logrates.weight [50, 20] 1000 True\n48 fc_logrates.bias [50] 50 True\nTotal parameters: 605110\n" ], [ "model.fit(train_ds, valid_ds, train_truth=train_truth, valid_truth=valid_truth,\n max_epochs=2000, batch_size=128, use_tensorboard=True, health_check=True)", "Beginning training...\nEpoch: 1, Step: 25, training loss: 2557.217, validation loss: 2610.053, Runtime: 17.2304 secs\nEpoch: 2, Step: 50, training loss: 2208.789, validation loss: 2479.728, Runtime: 17.0755 secs\nEpoch: 3, Step: 75, training loss: 2140.418, validation loss: 2327.982, Runtime: 17.3312 secs\nEpoch: 4, Step: 100, training loss: 2118.082, validation loss: 2299.716, Runtime: 18.5046 secs\nEpoch: 5, Step: 125, training loss: 2106.918, validation loss: 2264.256, Runtime: 17.5414 secs\nEpoch: 6, Step: 150, training loss: 2101.052, validation loss: 2238.768, Runtime: 17.4161 secs\nEpoch: 7, Step: 175, training loss: 2094.568, validation loss: 2230.944, Runtime: 17.3690 secs\nEpoch: 8, Step: 200, training loss: 2086.985, validation loss: 2213.271, Runtime: 16.8770 secs\nEpoch: 9, Step: 225, training loss: 2083.442, validation loss: 2219.212, Runtime: 16.9674 secs\nEpoch: 10, Step: 250, training loss: 2083.308, validation loss: 2194.288, Runtime: 17.6247 secs\nEpoch: 11, Step: 275, training loss: 2077.380, validation loss: 2191.504, Runtime: 17.2168 secs\nEpoch: 12, Step: 300, training loss: 2073.728, validation loss: 2186.133, Runtime: 17.1301 secs\nEpoch: 13, Step: 325, training loss: 2071.191, validation loss: 2174.103, Runtime: 19.4337 secs\nEpoch: 14, Step: 350, training loss: 2067.840, validation loss: 2169.330, Runtime: 17.3926 secs\nEpoch: 15, Step: 375, training loss: 2071.379, validation loss: 2168.049, Runtime: 17.1836 secs\nEpoch: 16, Step: 400, training loss: 2067.823, validation loss: 2174.122, Runtime: 17.1792 secs\nEpoch: 17, Step: 425, training loss: 2067.137, validation loss: 2164.121, Runtime: 17.0217 secs\nEpoch: 18, Step: 450, training loss: 2065.873, validation loss: 2158.567, Runtime: 17.2118 secs\nEpoch: 19, Step: 475, training loss: 2065.072, validation loss: 2155.914, Runtime: 17.0723 secs\nEpoch: 20, Step: 500, training loss: 2064.417, validation loss: 2149.900, Runtime: 17.0730 secs\nEpoch: 21, Step: 525, training loss: 2065.707, validation loss: 2149.442, Runtime: 17.1612 secs\nEpoch: 22, Step: 550, training loss: 2066.400, validation loss: 2142.000, Runtime: 17.0136 secs\nEpoch: 23, Step: 575, training loss: 2066.134, validation loss: 2143.854, Runtime: 16.9664 secs\nEpoch: 24, Step: 600, training loss: 2066.398, validation loss: 2140.450, Runtime: 16.9461 secs\nEpoch: 25, Step: 625, training loss: 2067.836, validation loss: 2133.832, Runtime: 17.0684 secs\nLearning rate decreased to 0.009500\nEpoch: 26, Step: 650, training loss: 2068.568, validation loss: 2135.610, Runtime: 17.1019 secs\nEpoch: 27, Step: 675, training loss: 2069.200, validation loss: 2137.359, Runtime: 16.9644 secs\nEpoch: 28, Step: 700, training loss: 2070.060, validation loss: 2131.551, Runtime: 16.9092 secs\nEpoch: 29, Step: 725, training loss: 2069.614, validation loss: 2123.320, Runtime: 16.7581 secs\nEpoch: 30, Step: 750, training loss: 2070.496, validation loss: 2127.988, Runtime: 16.9421 secs\nEpoch: 31, Step: 775, training loss: 2070.842, validation loss: 2129.290, Runtime: 16.9420 secs\nLearning rate decreased to 0.009025\nEpoch: 32, Step: 800, training loss: 2071.292, validation loss: 2122.950, Runtime: 16.9435 secs\nEpoch: 33, Step: 825, training loss: 2071.658, validation loss: 2116.812, Runtime: 16.8053 secs\nEpoch: 34, Step: 850, training loss: 2072.536, validation loss: 2119.495, Runtime: 17.3354 secs\nEpoch: 35, Step: 875, training loss: 2073.963, validation loss: 2120.222, Runtime: 16.9053 secs\nEpoch: 36, Step: 900, training loss: 2073.641, validation loss: 2115.243, Runtime: 17.0454 secs\nEpoch: 37, Step: 925, training loss: 2074.630, validation loss: 2114.853, Runtime: 16.9358 secs\nLearning rate decreased to 0.008574\nEpoch: 38, Step: 950, training loss: 2074.569, validation loss: 2111.541, Runtime: 16.8864 secs\nEpoch: 39, Step: 975, training loss: 2074.638, validation loss: 2110.571, Runtime: 17.0627 secs\nEpoch: 40, Step: 1000, training loss: 2075.544, validation loss: 2107.673, Runtime: 17.2504 secs\nEpoch: 41, Step: 1025, training loss: 2076.550, validation loss: 2106.699, Runtime: 17.0304 secs\nEpoch: 42, Step: 1050, training loss: 2079.190, validation loss: 2109.195, Runtime: 17.0670 secs\nEpoch: 43, Step: 1075, training loss: 2078.244, validation loss: 2107.376, Runtime: 17.0391 secs\nEpoch: 44, Step: 1100, training loss: 2078.474, validation loss: 2105.810, Runtime: 16.8592 secs\nEpoch: 45, Step: 1125, training loss: 2078.417, validation loss: 2105.702, Runtime: 17.0339 secs\nEpoch: 46, Step: 1150, training loss: 2079.082, validation loss: 2104.099, Runtime: 17.0431 secs\nEpoch: 47, Step: 1175, training loss: 2081.112, validation loss: 2103.929, Runtime: 17.7244 secs\nLearning rate decreased to 0.008145\nEpoch: 48, Step: 1200, training loss: 2080.164, validation loss: 2102.307, Runtime: 17.7893 secs\nEpoch: 49, Step: 1225, training loss: 2080.680, validation loss: 2100.925, Runtime: 18.4226 secs\nEpoch: 50, Step: 1250, training loss: 2083.177, validation loss: 2103.238, Runtime: 18.3050 secs\nEpoch: 51, Step: 1275, training loss: 2082.651, validation loss: 2103.636, Runtime: 17.5654 secs\nEpoch: 52, Step: 1300, training loss: 2083.261, validation loss: 2099.311, Runtime: 17.0221 secs\nEpoch: 53, Step: 1325, training loss: 2084.113, validation loss: 2100.873, Runtime: 16.9593 secs\nLearning rate decreased to 0.007738\nEpoch: 54, Step: 1350, training loss: 2083.778, validation loss: 2099.234, Runtime: 16.9570 secs\nEpoch: 55, Step: 1375, training loss: 2083.756, validation loss: 2098.243, Runtime: 16.7348 secs\nEpoch: 56, Step: 1400, training loss: 2084.866, validation loss: 2096.655, Runtime: 16.8157 secs\nEpoch: 57, Step: 1425, training loss: 2084.759, validation loss: 2095.695, Runtime: 16.6578 secs\nEpoch: 58, Step: 1450, training loss: 2084.947, validation loss: 2096.219, Runtime: 18.2158 secs\nEpoch: 59, Step: 1475, training loss: 2086.287, validation loss: 2096.567, Runtime: 17.3117 secs\nLearning rate decreased to 0.007351\nEpoch: 60, Step: 1500, training loss: 2086.724, validation loss: 2095.279, Runtime: 17.7393 secs\nEpoch: 61, Step: 1525, training loss: 2087.409, validation loss: 2095.054, Runtime: 17.0783 secs\nEpoch: 62, Step: 1550, training loss: 2086.950, validation loss: 2095.240, Runtime: 16.8750 secs\nEpoch: 63, Step: 1575, training loss: 2087.802, validation loss: 2094.493, Runtime: 16.8748 secs\nEpoch: 64, Step: 1600, training loss: 2088.250, validation loss: 2095.209, Runtime: 16.8424 secs\nEpoch: 65, Step: 1625, training loss: 2092.006, validation loss: 2094.851, Runtime: 16.8836 secs\nLearning rate decreased to 0.006983\nEpoch: 66, Step: 1650, training loss: 2089.890, validation loss: 2094.803, Runtime: 16.9112 secs\nEpoch: 67, Step: 1675, training loss: 2091.322, validation loss: 2096.738, Runtime: 16.7898 secs\nEpoch: 68, Step: 1700, training loss: 2091.398, validation loss: 2093.709, Runtime: 16.7823 secs\nEpoch: 69, Step: 1725, training loss: 2090.719, validation loss: 2093.071, Runtime: 16.8302 secs\nEpoch: 70, Step: 1750, training loss: 2090.991, validation loss: 2092.824, Runtime: 16.7782 secs\nEpoch: 71, Step: 1775, training loss: 2091.716, validation loss: 2093.909, Runtime: 18.4659 secs\nEpoch: 72, Step: 1800, training loss: 2092.196, validation loss: 2094.285, Runtime: 18.0281 secs\nLearning rate decreased to 0.006634\nEpoch: 73, Step: 1825, training loss: 2092.286, validation loss: 2092.443, Runtime: 18.9930 secs\nEpoch: 74, Step: 1850, training loss: 2092.779, validation loss: 2094.164, Runtime: 17.1668 secs\nEpoch: 75, Step: 1875, training loss: 2093.282, validation loss: 2093.334, Runtime: 17.1332 secs\nEpoch: 76, Step: 1900, training loss: 2095.317, validation loss: 2092.685, Runtime: 17.1204 secs\nEpoch: 77, Step: 1925, training loss: 2093.348, validation loss: 2091.717, Runtime: 17.3353 secs\nEpoch: 78, Step: 1950, training loss: 2093.715, validation loss: 2091.104, Runtime: 17.0682 secs\nEpoch: 79, Step: 1975, training loss: 2094.113, validation loss: 2091.806, Runtime: 16.9667 secs\nEpoch: 80, Step: 2000, training loss: 2095.139, validation loss: 2093.281, Runtime: 16.9700 secs\nSaving checkpoint: 0.0114 s taken\nEpoch: 81, Step: 2025, training loss: 2094.763, validation loss: 2092.162, Runtime: 18.3518 secs\nSaving checkpoint: 0.0123 s taken\nEpoch: 82, Step: 2050, training loss: 2094.215, validation loss: 2090.937, Runtime: 18.8124 secs\nSaving checkpoint: 0.0123 s taken\nEpoch: 83, Step: 2075, training loss: 2094.516, validation loss: 2091.899, Runtime: 18.3377 secs\nEpoch: 84, Step: 2100, training loss: 2094.275, validation loss: 2093.546, Runtime: 17.6807 secs\nEpoch: 85, Step: 2125, training loss: 2094.466, validation loss: 2091.580, Runtime: 17.5125 secs\nEpoch: 86, Step: 2150, training loss: 2093.487, validation loss: 2092.417, Runtime: 17.2390 secs\nEpoch: 87, Step: 2175, training loss: 2096.345, validation loss: 2094.923, Runtime: 17.7086 secs\nLearning rate decreased to 0.006302\nEpoch: 88, Step: 2200, training loss: 2094.774, validation loss: 2090.439, Runtime: 17.5883 secs\nSaving checkpoint: 0.0122 s taken\nEpoch: 89, Step: 2225, training loss: 2093.257, validation loss: 2090.267, Runtime: 17.2987 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 90, Step: 2250, training loss: 2094.950, validation loss: 2091.810, Runtime: 18.8502 secs\nEpoch: 91, Step: 2275, training loss: 2093.756, validation loss: 2089.801, Runtime: 17.4153 secs\nSaving checkpoint: 0.0118 s taken\nEpoch: 92, Step: 2300, training loss: 2093.240, validation loss: 2090.921, Runtime: 18.5797 secs\nEpoch: 93, Step: 2325, training loss: 2094.063, validation loss: 2092.502, Runtime: 18.6665 secs\nEpoch: 94, Step: 2350, training loss: 2093.097, validation loss: 2091.004, Runtime: 18.6146 secs\nEpoch: 95, Step: 2375, training loss: 2092.921, validation loss: 2090.645, Runtime: 17.7936 secs\nEpoch: 96, Step: 2400, training loss: 2092.869, validation loss: 2090.207, Runtime: 17.4161 secs\nEpoch: 97, Step: 2425, training loss: 2092.778, validation loss: 2090.204, Runtime: 17.8503 secs\nEpoch: 98, Step: 2450, training loss: 2092.721, validation loss: 2089.999, Runtime: 18.5511 secs\nEpoch: 99, Step: 2475, training loss: 2092.169, validation loss: 2090.886, Runtime: 17.5230 secs\nEpoch: 100, Step: 2500, training loss: 2092.364, validation loss: 2089.582, Runtime: 17.5571 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 101, Step: 2525, training loss: 2092.410, validation loss: 2093.627, Runtime: 19.4166 secs\nEpoch: 102, Step: 2550, training loss: 2094.083, validation loss: 2090.050, Runtime: 17.6604 secs\nLearning rate decreased to 0.005987\nEpoch: 103, Step: 2575, training loss: 2092.059, validation loss: 2088.891, Runtime: 18.6726 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 104, Step: 2600, training loss: 2091.418, validation loss: 2089.193, Runtime: 19.1972 secs\nEpoch: 105, Step: 2625, training loss: 2091.610, validation loss: 2090.213, Runtime: 18.8939 secs\nEpoch: 106, Step: 2650, training loss: 2091.648, validation loss: 2090.257, Runtime: 18.0404 secs\nEpoch: 107, Step: 2675, training loss: 2091.624, validation loss: 2088.615, Runtime: 17.9147 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 108, Step: 2700, training loss: 2091.148, validation loss: 2090.734, Runtime: 18.1776 secs\nEpoch: 109, Step: 2725, training loss: 2093.523, validation loss: 2090.241, Runtime: 17.7027 secs\nLearning rate decreased to 0.005688\nEpoch: 110, Step: 2750, training loss: 2091.603, validation loss: 2088.875, Runtime: 17.7850 secs\nEpoch: 111, Step: 2775, training loss: 2090.695, validation loss: 2089.929, Runtime: 17.6940 secs\nEpoch: 112, Step: 2800, training loss: 2091.062, validation loss: 2088.886, Runtime: 17.6880 secs\nEpoch: 113, Step: 2825, training loss: 2090.546, validation loss: 2089.800, Runtime: 17.7293 secs\nEpoch: 114, Step: 2850, training loss: 2092.872, validation loss: 2089.582, Runtime: 17.6147 secs\nEpoch: 115, Step: 2875, training loss: 2091.223, validation loss: 2088.700, Runtime: 17.6011 secs\nEpoch: 116, Step: 2900, training loss: 2091.355, validation loss: 2092.352, Runtime: 17.5403 secs\nEpoch: 117, Step: 2925, training loss: 2091.161, validation loss: 2088.697, Runtime: 17.5880 secs\nEpoch: 118, Step: 2950, training loss: 2090.829, validation loss: 2089.151, Runtime: 17.1864 secs\nEpoch: 119, Step: 2975, training loss: 2091.971, validation loss: 2090.777, Runtime: 17.2890 secs\nEpoch: 120, Step: 3000, training loss: 2090.681, validation loss: 2089.204, Runtime: 17.2815 secs\nEpoch: 121, Step: 3025, training loss: 2090.094, validation loss: 2089.403, Runtime: 17.2736 secs\nEpoch: 122, Step: 3050, training loss: 2090.743, validation loss: 2088.776, Runtime: 17.1933 secs\nEpoch: 123, Step: 3075, training loss: 2090.178, validation loss: 2088.152, Runtime: 17.1438 secs\nSaving checkpoint: 0.0120 s taken\nEpoch: 124, Step: 3100, training loss: 2090.269, validation loss: 2090.168, Runtime: 17.2830 secs\nEpoch: 125, Step: 3125, training loss: 2090.022, validation loss: 2088.736, Runtime: 17.2406 secs\nEpoch: 126, Step: 3150, training loss: 2089.945, validation loss: 2088.403, Runtime: 17.2731 secs\nEpoch: 127, Step: 3175, training loss: 2090.107, validation loss: 2088.738, Runtime: 17.3145 secs\nEpoch: 128, Step: 3200, training loss: 2089.872, validation loss: 2088.277, Runtime: 17.2772 secs\nEpoch: 129, Step: 3225, training loss: 2089.760, validation loss: 2091.068, Runtime: 17.0515 secs\nEpoch: 130, Step: 3250, training loss: 2090.951, validation loss: 2087.813, Runtime: 17.2143 secs\nLearning rate decreased to 0.005404\nSaving checkpoint: 0.0122 s taken\nEpoch: 131, Step: 3275, training loss: 2089.330, validation loss: 2088.217, Runtime: 17.3209 secs\nEpoch: 132, Step: 3300, training loss: 2089.707, validation loss: 2088.515, Runtime: 17.2765 secs\nEpoch: 133, Step: 3325, training loss: 2089.926, validation loss: 2088.532, Runtime: 17.2861 secs\nEpoch: 134, Step: 3350, training loss: 2089.162, validation loss: 2088.852, Runtime: 17.1830 secs\nEpoch: 135, Step: 3375, training loss: 2089.409, validation loss: 2087.770, Runtime: 17.4957 secs\nSaving checkpoint: 0.0126 s taken\nEpoch: 136, Step: 3400, training loss: 2088.823, validation loss: 2089.170, Runtime: 17.2138 secs\nEpoch: 137, Step: 3425, training loss: 2089.726, validation loss: 2088.773, Runtime: 17.2521 secs\nEpoch: 138, Step: 3450, training loss: 2089.493, validation loss: 2087.706, Runtime: 17.2729 secs\nSaving checkpoint: 0.0120 s taken\nEpoch: 139, Step: 3475, training loss: 2089.084, validation loss: 2088.140, Runtime: 17.2735 secs\nEpoch: 140, Step: 3500, training loss: 2089.281, validation loss: 2088.184, Runtime: 17.5229 secs\nEpoch: 141, Step: 3525, training loss: 2089.173, validation loss: 2088.621, Runtime: 17.2021 secs\nEpoch: 142, Step: 3550, training loss: 2089.331, validation loss: 2088.189, Runtime: 17.2656 secs\nEpoch: 143, Step: 3575, training loss: 2089.893, validation loss: 2090.813, Runtime: 17.2831 secs\nLearning rate decreased to 0.005133\nEpoch: 144, Step: 3600, training loss: 2089.574, validation loss: 2088.197, Runtime: 17.2496 secs\nEpoch: 145, Step: 3625, training loss: 2088.185, validation loss: 2087.163, Runtime: 17.2435 secs\nSaving checkpoint: 0.0119 s taken\nEpoch: 146, Step: 3650, training loss: 2088.306, validation loss: 2087.844, Runtime: 17.2380 secs\nEpoch: 147, Step: 3675, training loss: 2088.323, validation loss: 2087.980, Runtime: 17.2756 secs\nEpoch: 148, Step: 3700, training loss: 2088.221, validation loss: 2087.567, Runtime: 17.1709 secs\nEpoch: 149, Step: 3725, training loss: 2089.204, validation loss: 2088.667, Runtime: 17.3133 secs\nEpoch: 150, Step: 3750, training loss: 2088.540, validation loss: 2087.231, Runtime: 17.2668 secs\nEpoch: 151, Step: 3775, training loss: 2089.352, validation loss: 2088.374, Runtime: 17.1433 secs\nLearning rate decreased to 0.004877\nEpoch: 152, Step: 3800, training loss: 2088.277, validation loss: 2087.791, Runtime: 17.1756 secs\nEpoch: 153, Step: 3825, training loss: 2088.238, validation loss: 2087.448, Runtime: 17.1852 secs\nEpoch: 154, Step: 3850, training loss: 2088.124, validation loss: 2087.645, Runtime: 17.6555 secs\nEpoch: 155, Step: 3875, training loss: 2088.001, validation loss: 2086.973, Runtime: 17.2430 secs\nSaving checkpoint: 0.0124 s taken\nEpoch: 156, Step: 3900, training loss: 2087.852, validation loss: 2086.883, Runtime: 17.2762 secs\nSaving checkpoint: 0.0119 s taken\nEpoch: 157, Step: 3925, training loss: 2087.797, validation loss: 2087.191, Runtime: 17.2783 secs\nEpoch: 158, Step: 3950, training loss: 2088.032, validation loss: 2087.229, Runtime: 17.1849 secs\nEpoch: 159, Step: 3975, training loss: 2087.671, validation loss: 2087.547, Runtime: 17.5071 secs\nEpoch: 160, Step: 4000, training loss: 2088.077, validation loss: 2087.109, Runtime: 17.1807 secs\nEpoch: 161, Step: 4025, training loss: 2087.662, validation loss: 2086.691, Runtime: 17.3615 secs\nSaving checkpoint: 0.0126 s taken\nEpoch: 162, Step: 4050, training loss: 2087.688, validation loss: 2086.468, Runtime: 17.4640 secs\nSaving checkpoint: 0.0122 s taken\nEpoch: 163, Step: 4075, training loss: 2087.851, validation loss: 2087.604, Runtime: 17.4069 secs\nEpoch: 164, Step: 4100, training loss: 2087.475, validation loss: 2086.868, Runtime: 17.3153 secs\nEpoch: 165, Step: 4125, training loss: 2087.576, validation loss: 2087.004, Runtime: 17.1360 secs\nEpoch: 166, Step: 4150, training loss: 2087.731, validation loss: 2087.134, Runtime: 17.2306 secs\nEpoch: 167, Step: 4175, training loss: 2087.316, validation loss: 2086.410, Runtime: 17.3174 secs\nSaving checkpoint: 0.0124 s taken\nEpoch: 168, Step: 4200, training loss: 2087.553, validation loss: 2087.152, Runtime: 17.2569 secs\nEpoch: 169, Step: 4225, training loss: 2087.156, validation loss: 2086.566, Runtime: 17.2512 secs\nEpoch: 170, Step: 4250, training loss: 2087.121, validation loss: 2086.875, Runtime: 17.0890 secs\nEpoch: 171, Step: 4275, training loss: 2087.990, validation loss: 2089.144, Runtime: 17.1245 secs\nLearning rate decreased to 0.004633\nEpoch: 172, Step: 4300, training loss: 2087.625, validation loss: 2087.084, Runtime: 17.3916 secs\nEpoch: 173, Step: 4325, training loss: 2087.594, validation loss: 2088.207, Runtime: 17.3392 secs\nEpoch: 174, Step: 4350, training loss: 2087.408, validation loss: 2086.192, Runtime: 17.2900 secs\nSaving checkpoint: 0.0123 s taken\nEpoch: 175, Step: 4375, training loss: 2086.760, validation loss: 2086.352, Runtime: 17.3438 secs\nEpoch: 176, Step: 4400, training loss: 2086.773, validation loss: 2086.994, Runtime: 17.3175 secs\nEpoch: 177, Step: 4425, training loss: 2087.526, validation loss: 2086.160, Runtime: 17.2179 secs\nSaving checkpoint: 0.0124 s taken\nEpoch: 178, Step: 4450, training loss: 2086.867, validation loss: 2087.184, Runtime: 17.3915 secs\nEpoch: 179, Step: 4475, training loss: 2087.004, validation loss: 2087.292, Runtime: 17.2580 secs\nEpoch: 180, Step: 4500, training loss: 2086.840, validation loss: 2087.277, Runtime: 17.4833 secs\nEpoch: 181, Step: 4525, training loss: 2086.998, validation loss: 2086.753, Runtime: 17.3611 secs\nEpoch: 182, Step: 4550, training loss: 2086.882, validation loss: 2087.314, Runtime: 17.3764 secs\nEpoch: 183, Step: 4575, training loss: 2086.773, validation loss: 2085.969, Runtime: 17.3662 secs\nSaving checkpoint: 0.0119 s taken\nEpoch: 184, Step: 4600, training loss: 2086.482, validation loss: 2087.134, Runtime: 17.1217 secs\nEpoch: 185, Step: 4625, training loss: 2086.625, validation loss: 2087.222, Runtime: 17.0685 secs\nEpoch: 186, Step: 4650, training loss: 2086.786, validation loss: 2086.500, Runtime: 17.3040 secs\nEpoch: 187, Step: 4675, training loss: 2086.871, validation loss: 2086.711, Runtime: 17.3649 secs\nEpoch: 188, Step: 4700, training loss: 2087.088, validation loss: 2087.094, Runtime: 17.3635 secs\nLearning rate decreased to 0.004401\nEpoch: 189, Step: 4725, training loss: 2086.966, validation loss: 2088.581, Runtime: 17.2786 secs\nEpoch: 190, Step: 4750, training loss: 2086.689, validation loss: 2087.201, Runtime: 17.2264 secs\nEpoch: 191, Step: 4775, training loss: 2086.466, validation loss: 2086.854, Runtime: 17.1603 secs\nEpoch: 192, Step: 4800, training loss: 2086.654, validation loss: 2086.980, Runtime: 17.1964 secs\nEpoch: 193, Step: 4825, training loss: 2086.562, validation loss: 2087.721, Runtime: 17.0258 secs\nEpoch: 194, Step: 4850, training loss: 2086.908, validation loss: 2086.041, Runtime: 17.3135 secs\nEpoch: 195, Step: 4875, training loss: 2086.501, validation loss: 2087.321, Runtime: 17.1562 secs\nEpoch: 196, Step: 4900, training loss: 2086.138, validation loss: 2086.646, Runtime: 17.7375 secs\nEpoch: 197, Step: 4925, training loss: 2086.178, validation loss: 2086.050, Runtime: 17.6936 secs\nEpoch: 198, Step: 4950, training loss: 2086.246, validation loss: 2085.987, Runtime: 17.1806 secs\nEpoch: 199, Step: 4975, training loss: 2086.368, validation loss: 2086.245, Runtime: 17.4140 secs\nEpoch: 200, Step: 5000, training loss: 2085.867, validation loss: 2086.665, Runtime: 17.4369 secs\nEpoch: 201, Step: 5025, training loss: 2086.334, validation loss: 2086.649, Runtime: 17.3802 secs\nEpoch: 202, Step: 5050, training loss: 2085.952, validation loss: 2087.130, Runtime: 17.2247 secs\nEpoch: 203, Step: 5075, training loss: 2086.135, validation loss: 2086.370, Runtime: 17.4259 secs\nEpoch: 204, Step: 5100, training loss: 2086.846, validation loss: 2089.112, Runtime: 18.1112 secs\nLearning rate decreased to 0.004181\nEpoch: 205, Step: 5125, training loss: 2086.423, validation loss: 2086.894, Runtime: 17.2761 secs\nEpoch: 206, Step: 5150, training loss: 2086.302, validation loss: 2086.469, Runtime: 17.3358 secs\nEpoch: 207, Step: 5175, training loss: 2085.644, validation loss: 2086.401, Runtime: 17.4700 secs\nEpoch: 208, Step: 5200, training loss: 2085.258, validation loss: 2086.404, Runtime: 17.4251 secs\nEpoch: 209, Step: 5225, training loss: 2085.722, validation loss: 2088.842, Runtime: 17.3783 secs\nEpoch: 210, Step: 5250, training loss: 2086.933, validation loss: 2087.909, Runtime: 17.4375 secs\nLearning rate decreased to 0.003972\nEpoch: 211, Step: 5275, training loss: 2085.754, validation loss: 2085.928, Runtime: 17.3851 secs\nSaving checkpoint: 0.0123 s taken\nEpoch: 212, Step: 5300, training loss: 2085.492, validation loss: 2086.887, Runtime: 17.4158 secs\nEpoch: 213, Step: 5325, training loss: 2085.843, validation loss: 2086.955, Runtime: 17.3761 secs\nEpoch: 214, Step: 5350, training loss: 2085.433, validation loss: 2086.859, Runtime: 17.3186 secs\nEpoch: 215, Step: 5375, training loss: 2085.216, validation loss: 2085.859, Runtime: 17.1612 secs\nSaving checkpoint: 0.0126 s taken\nEpoch: 216, Step: 5400, training loss: 2085.358, validation loss: 2086.396, Runtime: 17.5248 secs\nEpoch: 217, Step: 5425, training loss: 2085.291, validation loss: 2085.998, Runtime: 17.3326 secs\nEpoch: 218, Step: 5450, training loss: 2085.043, validation loss: 2086.322, Runtime: 17.4176 secs\nEpoch: 219, Step: 5475, training loss: 2084.884, validation loss: 2086.243, Runtime: 17.3587 secs\nEpoch: 220, Step: 5500, training loss: 2085.550, validation loss: 2087.989, Runtime: 17.2265 secs\nLearning rate decreased to 0.003774\nEpoch: 221, Step: 5525, training loss: 2085.451, validation loss: 2085.695, Runtime: 17.3055 secs\nSaving checkpoint: 0.0124 s taken\nEpoch: 222, Step: 5550, training loss: 2085.442, validation loss: 2086.258, Runtime: 17.3763 secs\nEpoch: 223, Step: 5575, training loss: 2084.674, validation loss: 2085.641, Runtime: 17.2749 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 224, Step: 5600, training loss: 2084.752, validation loss: 2086.279, Runtime: 17.3453 secs\nEpoch: 225, Step: 5625, training loss: 2085.175, validation loss: 2085.552, Runtime: 17.2529 secs\nSaving checkpoint: 0.0145 s taken\nEpoch: 226, Step: 5650, training loss: 2084.658, validation loss: 2086.028, Runtime: 17.3257 secs\nEpoch: 227, Step: 5675, training loss: 2084.695, validation loss: 2085.877, Runtime: 17.7774 secs\nEpoch: 228, Step: 5700, training loss: 2084.962, validation loss: 2086.155, Runtime: 17.4118 secs\nEpoch: 229, Step: 5725, training loss: 2085.450, validation loss: 2086.354, Runtime: 17.3958 secs\nLearning rate decreased to 0.003585\nEpoch: 230, Step: 5750, training loss: 2084.917, validation loss: 2085.941, Runtime: 17.2994 secs\nEpoch: 231, Step: 5775, training loss: 2084.984, validation loss: 2085.947, Runtime: 17.3319 secs\nEpoch: 232, Step: 5800, training loss: 2084.439, validation loss: 2085.642, Runtime: 17.4672 secs\nEpoch: 233, Step: 5825, training loss: 2084.359, validation loss: 2086.044, Runtime: 17.4045 secs\nEpoch: 234, Step: 5850, training loss: 2084.576, validation loss: 2086.512, Runtime: 17.3132 secs\nEpoch: 235, Step: 5875, training loss: 2084.798, validation loss: 2085.927, Runtime: 17.4211 secs\nEpoch: 236, Step: 5900, training loss: 2084.393, validation loss: 2085.760, Runtime: 17.4050 secs\nEpoch: 237, Step: 5925, training loss: 2084.706, validation loss: 2085.530, Runtime: 17.3877 secs\nSaving checkpoint: 0.0132 s taken\nEpoch: 238, Step: 5950, training loss: 2084.273, validation loss: 2085.751, Runtime: 17.4115 secs\nEpoch: 239, Step: 5975, training loss: 2084.092, validation loss: 2086.116, Runtime: 17.2992 secs\nEpoch: 240, Step: 6000, training loss: 2084.437, validation loss: 2085.452, Runtime: 17.3304 secs\nSaving checkpoint: 0.0126 s taken\nEpoch: 241, Step: 6025, training loss: 2084.655, validation loss: 2085.819, Runtime: 17.3661 secs\nEpoch: 242, Step: 6050, training loss: 2084.343, validation loss: 2085.901, Runtime: 17.2353 secs\nEpoch: 243, Step: 6075, training loss: 2084.656, validation loss: 2085.640, Runtime: 17.4373 secs\nEpoch: 244, Step: 6100, training loss: 2084.085, validation loss: 2085.472, Runtime: 17.2923 secs\nEpoch: 245, Step: 6125, training loss: 2084.452, validation loss: 2086.590, Runtime: 17.3759 secs\nEpoch: 246, Step: 6150, training loss: 2084.839, validation loss: 2086.319, Runtime: 17.4653 secs\nLearning rate decreased to 0.003406\nEpoch: 247, Step: 6175, training loss: 2084.153, validation loss: 2085.692, Runtime: 17.4638 secs\nEpoch: 248, Step: 6200, training loss: 2083.812, validation loss: 2085.811, Runtime: 17.6271 secs\nEpoch: 249, Step: 6225, training loss: 2084.373, validation loss: 2085.303, Runtime: 17.2694 secs\nSaving checkpoint: 0.0125 s taken\nEpoch: 250, Step: 6250, training loss: 2084.273, validation loss: 2085.787, Runtime: 17.4027 secs\nEpoch: 251, Step: 6275, training loss: 2083.569, validation loss: 2085.365, Runtime: 17.4463 secs\nEpoch: 252, Step: 6300, training loss: 2083.994, validation loss: 2085.291, Runtime: 17.2883 secs\nSaving checkpoint: 0.0128 s taken\nEpoch: 253, Step: 6325, training loss: 2083.970, validation loss: 2085.545, Runtime: 17.4676 secs\nEpoch: 254, Step: 6350, training loss: 2083.948, validation loss: 2084.905, Runtime: 17.2826 secs\nSaving checkpoint: 0.0126 s taken\nEpoch: 255, Step: 6375, training loss: 2083.916, validation loss: 2085.267, Runtime: 17.3050 secs\nEpoch: 256, Step: 6400, training loss: 2083.534, validation loss: 2085.440, Runtime: 17.4014 secs\nEpoch: 257, Step: 6425, training loss: 2083.695, validation loss: 2085.386, Runtime: 17.4143 secs\nEpoch: 258, Step: 6450, training loss: 2083.827, validation loss: 2085.291, Runtime: 17.4052 secs\nEpoch: 259, Step: 6475, training loss: 2084.006, validation loss: 2086.559, Runtime: 17.4340 secs\nLearning rate decreased to 0.003235\nEpoch: 260, Step: 6500, training loss: 2083.629, validation loss: 2085.435, Runtime: 17.6401 secs\nEpoch: 261, Step: 6525, training loss: 2083.797, validation loss: 2085.190, Runtime: 17.4169 secs\nEpoch: 262, Step: 6550, training loss: 2083.571, validation loss: 2085.343, Runtime: 17.4104 secs\nEpoch: 263, Step: 6575, training loss: 2083.601, validation loss: 2085.712, Runtime: 17.2439 secs\nEpoch: 264, Step: 6600, training loss: 2083.736, validation loss: 2085.860, Runtime: 17.3231 secs\nEpoch: 265, Step: 6625, training loss: 2083.264, validation loss: 2085.426, Runtime: 17.4012 secs\nEpoch: 266, Step: 6650, training loss: 2083.207, validation loss: 2086.681, Runtime: 17.2680 secs\nEpoch: 267, Step: 6675, training loss: 2083.938, validation loss: 2085.295, Runtime: 17.4537 secs\nLearning rate decreased to 0.003074\nEpoch: 268, Step: 6700, training loss: 2083.356, validation loss: 2085.674, Runtime: 17.8439 secs\nEpoch: 269, Step: 6725, training loss: 2083.215, validation loss: 2085.275, Runtime: 17.2070 secs\nEpoch: 270, Step: 6750, training loss: 2083.416, validation loss: 2085.668, Runtime: 17.5627 secs\nEpoch: 271, Step: 6775, training loss: 2083.383, validation loss: 2085.717, Runtime: 17.4310 secs\nEpoch: 272, Step: 6800, training loss: 2083.093, validation loss: 2084.944, Runtime: 17.4457 secs\nEpoch: 273, Step: 6825, training loss: 2083.266, validation loss: 2085.338, Runtime: 17.3978 secs\nEpoch: 274, Step: 6850, training loss: 2083.223, validation loss: 2085.747, Runtime: 17.3445 secs\nEpoch: 275, Step: 6875, training loss: 2083.270, validation loss: 2085.205, Runtime: 17.3241 secs\nEpoch: 276, Step: 6900, training loss: 2083.073, validation loss: 2084.955, Runtime: 17.3937 secs\nEpoch: 277, Step: 6925, training loss: 2083.200, validation loss: 2086.093, Runtime: 17.4876 secs\nEpoch: 278, Step: 6950, training loss: 2083.354, validation loss: 2085.623, Runtime: 17.4250 secs\nLearning rate decreased to 0.002920\nEpoch: 279, Step: 6975, training loss: 2083.153, validation loss: 2086.289, Runtime: 17.4744 secs\nEpoch: 280, Step: 7000, training loss: 2082.597, validation loss: 2085.365, Runtime: 17.3977 secs\nEpoch: 281, Step: 7025, training loss: 2082.764, validation loss: 2084.969, Runtime: 17.2332 secs\nEpoch: 282, Step: 7050, training loss: 2083.312, validation loss: 2085.633, Runtime: 17.4483 secs\nEpoch: 283, Step: 7075, training loss: 2083.199, validation loss: 2085.469, Runtime: 17.3845 secs\nEpoch: 284, Step: 7100, training loss: 2082.970, validation loss: 2085.715, Runtime: 17.5090 secs\nEpoch: 285, Step: 7125, training loss: 2082.655, validation loss: 2085.106, Runtime: 17.5767 secs\nEpoch: 286, Step: 7150, training loss: 2082.778, validation loss: 2084.761, Runtime: 17.4798 secs\nSaving checkpoint: 0.0129 s taken\nEpoch: 287, Step: 7175, training loss: 2082.651, validation loss: 2085.166, Runtime: 17.4062 secs\nEpoch: 288, Step: 7200, training loss: 2083.041, validation loss: 2086.179, Runtime: 17.3023 secs\nEpoch: 289, Step: 7225, training loss: 2082.868, validation loss: 2085.720, Runtime: 17.3575 secs\nEpoch: 290, Step: 7250, training loss: 2082.602, validation loss: 2085.611, Runtime: 17.4047 secs\nEpoch: 291, Step: 7275, training loss: 2083.178, validation loss: 2085.080, Runtime: 17.3754 secs\nLearning rate decreased to 0.002774\nEpoch: 292, Step: 7300, training loss: 2082.759, validation loss: 2085.615, Runtime: 17.3619 secs\nEpoch: 293, Step: 7325, training loss: 2082.528, validation loss: 2085.469, Runtime: 17.2605 secs\nEpoch: 294, Step: 7350, training loss: 2082.599, validation loss: 2085.126, Runtime: 17.3832 secs\nEpoch: 295, Step: 7375, training loss: 2082.252, validation loss: 2085.410, Runtime: 17.2327 secs\nEpoch: 296, Step: 7400, training loss: 2082.368, validation loss: 2084.516, Runtime: 17.8021 secs\nSaving checkpoint: 0.0132 s taken\nEpoch: 297, Step: 7425, training loss: 2082.523, validation loss: 2085.124, Runtime: 17.4754 secs\nEpoch: 298, Step: 7450, training loss: 2082.692, validation loss: 2085.760, Runtime: 17.2666 secs\nEpoch: 299, Step: 7475, training loss: 2082.460, validation loss: 2085.246, Runtime: 17.3421 secs\nEpoch: 300, Step: 7500, training loss: 2082.548, validation loss: 2085.464, Runtime: 17.3970 secs\nEpoch: 301, Step: 7525, training loss: 2082.348, validation loss: 2085.351, Runtime: 17.4238 secs\nEpoch: 302, Step: 7550, training loss: 2082.332, validation loss: 2085.346, Runtime: 17.4198 secs\nEpoch: 303, Step: 7575, training loss: 2082.597, validation loss: 2085.133, Runtime: 17.2757 secs\nEpoch: 304, Step: 7600, training loss: 2082.353, validation loss: 2085.222, Runtime: 17.1473 secs\nEpoch: 305, Step: 7625, training loss: 2082.326, validation loss: 2085.467, Runtime: 17.3821 secs\nEpoch: 306, Step: 7650, training loss: 2082.367, validation loss: 2085.428, Runtime: 17.3402 secs\nEpoch: 307, Step: 7675, training loss: 2082.218, validation loss: 2084.735, Runtime: 17.3484 secs\nEpoch: 308, Step: 7700, training loss: 2082.220, validation loss: 2085.173, Runtime: 17.6708 secs\nEpoch: 309, Step: 7725, training loss: 2082.304, validation loss: 2085.266, Runtime: 17.5108 secs\nEpoch: 310, Step: 7750, training loss: 2082.162, validation loss: 2085.511, Runtime: 17.4043 secs\nEpoch: 311, Step: 7775, training loss: 2082.149, validation loss: 2085.124, Runtime: 17.2998 secs\nEpoch: 312, Step: 7800, training loss: 2082.319, validation loss: 2085.040, Runtime: 17.2150 secs\nEpoch: 313, Step: 7825, training loss: 2082.222, validation loss: 2085.511, Runtime: 17.4901 secs\nEpoch: 314, Step: 7850, training loss: 2082.283, validation loss: 2085.437, Runtime: 17.3623 secs\nEpoch: 315, Step: 7875, training loss: 2082.411, validation loss: 2085.661, Runtime: 17.2986 secs\nLearning rate decreased to 0.002635\nEpoch: 316, Step: 7900, training loss: 2082.214, validation loss: 2085.583, Runtime: 17.2870 secs\nEpoch: 317, Step: 7925, training loss: 2081.914, validation loss: 2085.167, Runtime: 17.3622 secs\nEpoch: 318, Step: 7950, training loss: 2082.126, validation loss: 2085.363, Runtime: 17.4624 secs\nEpoch: 319, Step: 7975, training loss: 2082.203, validation loss: 2084.302, Runtime: 17.3563 secs\nSaving checkpoint: 0.0133 s taken\nEpoch: 320, Step: 8000, training loss: 2082.077, validation loss: 2085.670, Runtime: 17.3627 secs\nEpoch: 321, Step: 8025, training loss: 2082.073, validation loss: 2085.230, Runtime: 17.4459 secs\nEpoch: 322, Step: 8050, training loss: 2082.270, validation loss: 2085.594, Runtime: 17.3989 secs\nLearning rate decreased to 0.002503\nEpoch: 323, Step: 8075, training loss: 2081.964, validation loss: 2086.282, Runtime: 17.4532 secs\nEpoch: 324, Step: 8100, training loss: 2081.978, validation loss: 2085.487, Runtime: 17.3470 secs\nEpoch: 325, Step: 8125, training loss: 2081.902, validation loss: 2086.038, Runtime: 17.2792 secs\nEpoch: 326, Step: 8150, training loss: 2081.632, validation loss: 2085.507, Runtime: 17.2168 secs\nEpoch: 327, Step: 8175, training loss: 2081.846, validation loss: 2084.861, Runtime: 17.3472 secs\nEpoch: 328, Step: 8200, training loss: 2081.828, validation loss: 2085.014, Runtime: 17.4539 secs\nEpoch: 329, Step: 8225, training loss: 2081.750, validation loss: 2085.386, Runtime: 17.3339 secs\nEpoch: 330, Step: 8250, training loss: 2081.775, validation loss: 2085.493, Runtime: 17.3618 secs\nEpoch: 331, Step: 8275, training loss: 2081.734, validation loss: 2085.192, Runtime: 17.2099 secs\nEpoch: 332, Step: 8300, training loss: 2081.373, validation loss: 2085.703, Runtime: 17.2939 secs\nEpoch: 333, Step: 8325, training loss: 2081.576, validation loss: 2084.927, Runtime: 17.3546 secs\nEpoch: 334, Step: 8350, training loss: 2081.427, validation loss: 2084.689, Runtime: 17.4806 secs\nEpoch: 335, Step: 8375, training loss: 2081.874, validation loss: 2085.482, Runtime: 17.5387 secs\nLearning rate decreased to 0.002378\nEpoch: 336, Step: 8400, training loss: 2081.344, validation loss: 2085.152, Runtime: 17.4123 secs\nEpoch: 337, Step: 8425, training loss: 2081.340, validation loss: 2085.823, Runtime: 17.9299 secs\nEpoch: 338, Step: 8450, training loss: 2081.989, validation loss: 2084.847, Runtime: 17.1671 secs\nEpoch: 339, Step: 8475, training loss: 2081.342, validation loss: 2085.253, Runtime: 17.4217 secs\nEpoch: 340, Step: 8500, training loss: 2081.403, validation loss: 2085.388, Runtime: 17.3611 secs\nEpoch: 341, Step: 8525, training loss: 2081.700, validation loss: 2085.640, Runtime: 17.5225 secs\nEpoch: 342, Step: 8550, training loss: 2081.662, validation loss: 2085.437, Runtime: 17.4949 secs\nEpoch: 343, Step: 8575, training loss: 2081.414, validation loss: 2085.335, Runtime: 17.5190 secs\nEpoch: 344, Step: 8600, training loss: 2081.695, validation loss: 2084.933, Runtime: 17.4322 secs\nEpoch: 345, Step: 8625, training loss: 2081.274, validation loss: 2084.771, Runtime: 17.2270 secs\nEpoch: 346, Step: 8650, training loss: 2081.465, validation loss: 2084.812, Runtime: 17.2981 secs\nEpoch: 347, Step: 8675, training loss: 2081.527, validation loss: 2085.214, Runtime: 17.3667 secs\nEpoch: 348, Step: 8700, training loss: 2081.278, validation loss: 2084.152, Runtime: 17.3398 secs\nSaving checkpoint: 0.0146 s taken\nEpoch: 349, Step: 8725, training loss: 2081.214, validation loss: 2085.321, Runtime: 17.3625 secs\nEpoch: 350, Step: 8750, training loss: 2081.249, validation loss: 2085.734, Runtime: 17.3519 secs\nEpoch: 351, Step: 8775, training loss: 2081.330, validation loss: 2085.230, Runtime: 17.3261 secs\nEpoch: 352, Step: 8800, training loss: 2081.605, validation loss: 2085.198, Runtime: 17.4698 secs\nLearning rate decreased to 0.002259\nEpoch: 353, Step: 8825, training loss: 2081.032, validation loss: 2085.615, Runtime: 17.3887 secs\nEpoch: 354, Step: 8850, training loss: 2081.177, validation loss: 2085.081, Runtime: 17.4038 secs\nEpoch: 355, Step: 8875, training loss: 2081.049, validation loss: 2085.027, Runtime: 17.3571 secs\nEpoch: 356, Step: 8900, training loss: 2081.175, validation loss: 2085.316, Runtime: 17.2807 secs\nEpoch: 357, Step: 8925, training loss: 2081.078, validation loss: 2084.960, Runtime: 17.3260 secs\nEpoch: 358, Step: 8950, training loss: 2081.129, validation loss: 2084.909, Runtime: 17.2837 secs\nEpoch: 359, Step: 8975, training loss: 2080.939, validation loss: 2084.892, Runtime: 17.4044 secs\nEpoch: 360, Step: 9000, training loss: 2080.972, validation loss: 2085.876, Runtime: 17.2574 secs\nEpoch: 361, Step: 9025, training loss: 2081.303, validation loss: 2084.543, Runtime: 17.2149 secs\nLearning rate decreased to 0.002146\nEpoch: 362, Step: 9050, training loss: 2080.560, validation loss: 2084.640, Runtime: 17.1153 secs\nEpoch: 363, Step: 9075, training loss: 2080.830, validation loss: 2084.806, Runtime: 17.2889 secs\nEpoch: 364, Step: 9100, training loss: 2080.966, validation loss: 2085.051, Runtime: 17.3882 secs\nEpoch: 365, Step: 9125, training loss: 2080.790, validation loss: 2085.370, Runtime: 17.2125 secs\nEpoch: 366, Step: 9150, training loss: 2080.901, validation loss: 2085.248, Runtime: 17.3404 secs\nEpoch: 367, Step: 9175, training loss: 2080.915, validation loss: 2085.455, Runtime: 17.3109 secs\nEpoch: 368, Step: 9200, training loss: 2080.795, validation loss: 2085.479, Runtime: 17.4264 secs\nEpoch: 369, Step: 9225, training loss: 2080.642, validation loss: 2084.879, Runtime: 17.4932 secs\nEpoch: 370, Step: 9250, training loss: 2080.931, validation loss: 2085.400, Runtime: 17.3950 secs\nEpoch: 371, Step: 9275, training loss: 2080.868, validation loss: 2085.394, Runtime: 17.3292 secs\nEpoch: 372, Step: 9300, training loss: 2080.731, validation loss: 2085.266, Runtime: 17.3159 secs\nEpoch: 373, Step: 9325, training loss: 2080.549, validation loss: 2084.781, Runtime: 17.4224 secs\nEpoch: 374, Step: 9350, training loss: 2080.735, validation loss: 2085.533, Runtime: 17.6613 secs\nEpoch: 375, Step: 9375, training loss: 2080.637, validation loss: 2085.507, Runtime: 17.4038 secs\nEpoch: 376, Step: 9400, training loss: 2081.009, validation loss: 2085.815, Runtime: 17.3260 secs\nLearning rate decreased to 0.002039\nEpoch: 377, Step: 9425, training loss: 2080.565, validation loss: 2085.008, Runtime: 17.4012 secs\nEpoch: 378, Step: 9450, training loss: 2080.648, validation loss: 2084.794, Runtime: 17.8411 secs\nEpoch: 379, Step: 9475, training loss: 2080.811, validation loss: 2084.953, Runtime: 17.4202 secs\nEpoch: 380, Step: 9500, training loss: 2080.445, validation loss: 2084.706, Runtime: 17.3149 secs\nEpoch: 381, Step: 9525, training loss: 2080.860, validation loss: 2085.349, Runtime: 17.3068 secs\nEpoch: 382, Step: 9550, training loss: 2080.778, validation loss: 2085.283, Runtime: 17.2569 secs\nEpoch: 383, Step: 9575, training loss: 2080.678, validation loss: 2085.996, Runtime: 17.4913 secs\nEpoch: 384, Step: 9600, training loss: 2080.970, validation loss: 2085.381, Runtime: 17.4390 secs\nLearning rate decreased to 0.001937\nEpoch: 385, Step: 9625, training loss: 2080.502, validation loss: 2084.421, Runtime: 17.3588 secs\nEpoch: 386, Step: 9650, training loss: 2080.390, validation loss: 2085.660, Runtime: 17.3450 secs\nEpoch: 387, Step: 9675, training loss: 2080.449, validation loss: 2084.825, Runtime: 17.4770 secs\nEpoch: 388, Step: 9700, training loss: 2080.507, validation loss: 2084.734, Runtime: 17.4325 secs\nEpoch: 389, Step: 9725, training loss: 2080.079, validation loss: 2085.518, Runtime: 17.4187 secs\nEpoch: 390, Step: 9750, training loss: 2080.611, validation loss: 2085.228, Runtime: 17.4313 secs\nEpoch: 391, Step: 9775, training loss: 2080.372, validation loss: 2085.340, Runtime: 17.3752 secs\nEpoch: 392, Step: 9800, training loss: 2080.418, validation loss: 2085.221, Runtime: 17.5405 secs\nEpoch: 393, Step: 9825, training loss: 2080.375, validation loss: 2085.379, Runtime: 17.3003 secs\nEpoch: 394, Step: 9850, training loss: 2080.478, validation loss: 2084.902, Runtime: 17.3757 secs\nEpoch: 395, Step: 9875, training loss: 2080.258, validation loss: 2084.728, Runtime: 17.2632 secs\nEpoch: 396, Step: 9900, training loss: 2080.461, validation loss: 2084.850, Runtime: 17.2542 secs\nEpoch: 397, Step: 9925, training loss: 2080.388, validation loss: 2084.858, Runtime: 17.2859 secs\nEpoch: 398, Step: 9950, training loss: 2079.959, validation loss: 2085.109, Runtime: 17.2827 secs\nEpoch: 399, Step: 9975, training loss: 2080.454, validation loss: 2084.521, Runtime: 17.3774 secs\nEpoch: 400, Step: 10000, training loss: 2080.302, validation loss: 2086.081, Runtime: 17.3412 secs\nEpoch: 401, Step: 10025, training loss: 2080.048, validation loss: 2084.601, Runtime: 17.3592 secs\nEpoch: 402, Step: 10050, training loss: 2080.454, validation loss: 2085.162, Runtime: 17.3909 secs\nEpoch: 403, Step: 10075, training loss: 2079.902, validation loss: 2085.023, Runtime: 17.4737 secs\nEpoch: 404, Step: 10100, training loss: 2080.463, validation loss: 2085.226, Runtime: 17.5336 secs\nLearning rate decreased to 0.001840\nEpoch: 405, Step: 10125, training loss: 2080.243, validation loss: 2085.052, Runtime: 17.3537 secs\nEpoch: 406, Step: 10150, training loss: 2080.441, validation loss: 2084.992, Runtime: 17.4125 secs\nEpoch: 407, Step: 10175, training loss: 2080.180, validation loss: 2084.990, Runtime: 17.3331 secs\nEpoch: 408, Step: 10200, training loss: 2079.803, validation loss: 2084.796, Runtime: 17.4092 secs\nEpoch: 409, Step: 10225, training loss: 2080.121, validation loss: 2085.029, Runtime: 17.3961 secs\nEpoch: 410, Step: 10250, training loss: 2080.217, validation loss: 2084.570, Runtime: 17.2754 secs\nEpoch: 411, Step: 10275, training loss: 2080.004, validation loss: 2084.688, Runtime: 17.4704 secs\nEpoch: 412, Step: 10300, training loss: 2079.759, validation loss: 2084.324, Runtime: 17.3561 secs\nEpoch: 413, Step: 10325, training loss: 2079.897, validation loss: 2085.207, Runtime: 17.4386 secs\nEpoch: 414, Step: 10350, training loss: 2079.932, validation loss: 2084.856, Runtime: 17.2792 secs\nEpoch: 415, Step: 10375, training loss: 2080.000, validation loss: 2084.353, Runtime: 17.3099 secs\nEpoch: 416, Step: 10400, training loss: 2079.917, validation loss: 2085.354, Runtime: 17.2875 secs\nEpoch: 417, Step: 10425, training loss: 2080.140, validation loss: 2085.221, Runtime: 17.3147 secs\nLearning rate decreased to 0.001748\nEpoch: 418, Step: 10450, training loss: 2080.245, validation loss: 2085.123, Runtime: 17.3971 secs\nEpoch: 419, Step: 10475, training loss: 2079.853, validation loss: 2084.967, Runtime: 17.3609 secs\nEpoch: 420, Step: 10500, training loss: 2079.942, validation loss: 2085.469, Runtime: 17.3123 secs\nEpoch: 421, Step: 10525, training loss: 2079.596, validation loss: 2085.071, Runtime: 17.3532 secs\nEpoch: 422, Step: 10550, training loss: 2079.712, validation loss: 2084.989, Runtime: 17.3488 secs\nEpoch: 423, Step: 10575, training loss: 2080.089, validation loss: 2085.103, Runtime: 17.3875 secs\nEpoch: 424, Step: 10600, training loss: 2079.811, validation loss: 2085.053, Runtime: 17.4778 secs\nEpoch: 425, Step: 10625, training loss: 2079.974, validation loss: 2085.368, Runtime: 17.3920 secs\nEpoch: 426, Step: 10650, training loss: 2079.843, validation loss: 2085.303, Runtime: 17.4598 secs\nEpoch: 427, Step: 10675, training loss: 2079.933, validation loss: 2084.529, Runtime: 17.3172 secs\nEpoch: 428, Step: 10700, training loss: 2079.946, validation loss: 2085.638, Runtime: 17.4026 secs\nEpoch: 429, Step: 10725, training loss: 2079.748, validation loss: 2084.540, Runtime: 17.2962 secs\nEpoch: 430, Step: 10750, training loss: 2079.707, validation loss: 2084.938, Runtime: 17.3379 secs\nEpoch: 431, Step: 10775, training loss: 2079.907, validation loss: 2084.966, Runtime: 17.4987 secs\nEpoch: 432, Step: 10800, training loss: 2079.721, validation loss: 2084.717, Runtime: 17.5183 secs\nEpoch: 433, Step: 10825, training loss: 2079.766, validation loss: 2085.978, Runtime: 17.5385 secs\nEpoch: 434, Step: 10850, training loss: 2079.816, validation loss: 2085.578, Runtime: 17.4218 secs\nEpoch: 435, Step: 10875, training loss: 2079.845, validation loss: 2085.161, Runtime: 17.9002 secs\nEpoch: 436, Step: 10900, training loss: 2079.633, validation loss: 2084.726, Runtime: 17.4327 secs\nEpoch: 437, Step: 10925, training loss: 2079.454, validation loss: 2084.859, Runtime: 17.5965 secs\nEpoch: 438, Step: 10950, training loss: 2079.760, validation loss: 2085.111, Runtime: 17.4530 secs\nEpoch: 439, Step: 10975, training loss: 2079.678, validation loss: 2084.636, Runtime: 17.3814 secs\nEpoch: 440, Step: 11000, training loss: 2079.646, validation loss: 2084.965, Runtime: 17.4234 secs\nEpoch: 441, Step: 11025, training loss: 2079.653, validation loss: 2085.218, Runtime: 17.4785 secs\nEpoch: 442, Step: 11050, training loss: 2079.911, validation loss: 2085.427, Runtime: 17.5559 secs\nLearning rate decreased to 0.001661\nEpoch: 443, Step: 11075, training loss: 2079.724, validation loss: 2084.649, Runtime: 17.4892 secs\nEpoch: 444, Step: 11100, training loss: 2079.554, validation loss: 2085.557, Runtime: 17.4829 secs\nEpoch: 445, Step: 11125, training loss: 2079.280, validation loss: 2085.169, Runtime: 17.4378 secs\nEpoch: 446, Step: 11150, training loss: 2079.608, validation loss: 2085.146, Runtime: 17.4361 secs\nEpoch: 447, Step: 11175, training loss: 2079.211, validation loss: 2085.143, Runtime: 17.4961 secs\nEpoch: 448, Step: 11200, training loss: 2079.696, validation loss: 2085.375, Runtime: 17.3641 secs\nEpoch: 449, Step: 11225, training loss: 2079.698, validation loss: 2085.152, Runtime: 17.3912 secs\nEpoch: 450, Step: 11250, training loss: 2079.291, validation loss: 2085.142, Runtime: 17.5164 secs\nEpoch: 451, Step: 11275, training loss: 2079.473, validation loss: 2085.224, Runtime: 17.4672 secs\nEpoch: 452, Step: 11300, training loss: 2079.223, validation loss: 2085.296, Runtime: 17.3564 secs\nEpoch: 453, Step: 11325, training loss: 2079.571, validation loss: 2084.892, Runtime: 17.5968 secs\nEpoch: 454, Step: 11350, training loss: 2079.335, validation loss: 2084.808, Runtime: 17.3346 secs\nEpoch: 455, Step: 11375, training loss: 2079.344, validation loss: 2084.839, Runtime: 17.3394 secs\nEpoch: 456, Step: 11400, training loss: 2079.429, validation loss: 2084.742, Runtime: 17.4895 secs\nEpoch: 457, Step: 11425, training loss: 2079.494, validation loss: 2085.061, Runtime: 17.8189 secs\nEpoch: 458, Step: 11450, training loss: 2079.367, validation loss: 2085.038, Runtime: 17.2024 secs\nEpoch: 459, Step: 11475, training loss: 2079.150, validation loss: 2085.079, Runtime: 17.4597 secs\nEpoch: 460, Step: 11500, training loss: 2079.494, validation loss: 2084.816, Runtime: 17.5079 secs\nEpoch: 461, Step: 11525, training loss: 2079.404, validation loss: 2085.425, Runtime: 17.4806 secs\nEpoch: 462, Step: 11550, training loss: 2079.428, validation loss: 2085.264, Runtime: 17.3274 secs\nEpoch: 463, Step: 11575, training loss: 2079.447, validation loss: 2085.227, Runtime: 17.3898 secs\nEpoch: 464, Step: 11600, training loss: 2079.334, validation loss: 2085.240, Runtime: 17.3822 secs\nEpoch: 465, Step: 11625, training loss: 2079.442, validation loss: 2085.489, Runtime: 17.4329 secs\nEpoch: 466, Step: 11650, training loss: 2079.226, validation loss: 2084.399, Runtime: 17.6466 secs\nEpoch: 467, Step: 11675, training loss: 2079.153, validation loss: 2085.402, Runtime: 17.3967 secs\nEpoch: 468, Step: 11700, training loss: 2079.461, validation loss: 2085.271, Runtime: 17.4329 secs\nLearning rate decreased to 0.001578\nEpoch: 469, Step: 11725, training loss: 2079.132, validation loss: 2084.999, Runtime: 17.4073 secs\nEpoch: 470, Step: 11750, training loss: 2079.449, validation loss: 2084.592, Runtime: 17.3623 secs\nEpoch: 471, Step: 11775, training loss: 2079.079, validation loss: 2084.704, Runtime: 17.4021 secs\nEpoch: 472, Step: 11800, training loss: 2079.131, validation loss: 2084.935, Runtime: 17.3859 secs\nEpoch: 473, Step: 11825, training loss: 2079.107, validation loss: 2085.156, Runtime: 17.5299 secs\nEpoch: 474, Step: 11850, training loss: 2079.230, validation loss: 2084.877, Runtime: 17.3433 secs\nEpoch: 475, Step: 11875, training loss: 2078.972, validation loss: 2084.882, Runtime: 17.3984 secs\nEpoch: 476, Step: 11900, training loss: 2079.204, validation loss: 2085.548, Runtime: 17.2669 secs\nEpoch: 477, Step: 11925, training loss: 2079.226, validation loss: 2085.219, Runtime: 17.4029 secs\nEpoch: 478, Step: 11950, training loss: 2078.970, validation loss: 2085.441, Runtime: 17.5248 secs\nEpoch: 479, Step: 11975, training loss: 2079.020, validation loss: 2085.321, Runtime: 17.3394 secs\nEpoch: 480, Step: 12000, training loss: 2079.308, validation loss: 2084.734, Runtime: 17.2683 secs\nLearning rate decreased to 0.001499\nEpoch: 481, Step: 12025, training loss: 2078.746, validation loss: 2085.402, Runtime: 17.2861 secs\nEpoch: 482, Step: 12050, training loss: 2079.108, validation loss: 2084.853, Runtime: 17.3627 secs\nEpoch: 483, Step: 12075, training loss: 2079.212, validation loss: 2085.214, Runtime: 17.4834 secs\nEpoch: 484, Step: 12100, training loss: 2078.893, validation loss: 2084.181, Runtime: 17.4727 secs\nEpoch: 485, Step: 12125, training loss: 2078.995, validation loss: 2085.533, Runtime: 17.4893 secs\nEpoch: 486, Step: 12150, training loss: 2079.199, validation loss: 2085.055, Runtime: 17.4219 secs\nEpoch: 487, Step: 12175, training loss: 2079.160, validation loss: 2084.697, Runtime: 17.3692 secs\nEpoch: 488, Step: 12200, training loss: 2078.899, validation loss: 2085.394, Runtime: 17.2669 secs\nEpoch: 489, Step: 12225, training loss: 2078.839, validation loss: 2085.381, Runtime: 17.3461 secs\nEpoch: 490, Step: 12250, training loss: 2078.901, validation loss: 2085.196, Runtime: 17.6438 secs\nEpoch: 491, Step: 12275, training loss: 2079.003, validation loss: 2085.125, Runtime: 17.3198 secs\nEpoch: 492, Step: 12300, training loss: 2079.058, validation loss: 2085.572, Runtime: 17.3759 secs\nEpoch: 493, Step: 12325, training loss: 2078.882, validation loss: 2085.020, Runtime: 17.3348 secs\nEpoch: 494, Step: 12350, training loss: 2079.024, validation loss: 2084.741, Runtime: 17.4109 secs\nEpoch: 495, Step: 12375, training loss: 2078.814, validation loss: 2085.148, Runtime: 17.4258 secs\nEpoch: 496, Step: 12400, training loss: 2079.003, validation loss: 2085.422, Runtime: 17.5519 secs\nEpoch: 497, Step: 12425, training loss: 2079.012, validation loss: 2085.005, Runtime: 17.4554 secs\nEpoch: 498, Step: 12450, training loss: 2079.004, validation loss: 2085.201, Runtime: 17.2492 secs\nEpoch: 499, Step: 12475, training loss: 2078.928, validation loss: 2085.009, Runtime: 17.5561 secs\nEpoch: 500, Step: 12500, training loss: 2078.737, validation loss: 2084.823, Runtime: 17.4496 secs\nEpoch: 501, Step: 12525, training loss: 2079.055, validation loss: 2085.295, Runtime: 17.5068 secs\nLearning rate decreased to 0.001424\nEpoch: 502, Step: 12550, training loss: 2078.853, validation loss: 2085.036, Runtime: 17.3897 secs\nEpoch: 503, Step: 12575, training loss: 2078.682, validation loss: 2085.476, Runtime: 17.4234 secs\nEpoch: 504, Step: 12600, training loss: 2078.911, validation loss: 2084.944, Runtime: 17.3570 secs\nEpoch: 505, Step: 12625, training loss: 2078.667, validation loss: 2084.762, Runtime: 17.3885 secs\nEpoch: 506, Step: 12650, training loss: 2078.670, validation loss: 2084.836, Runtime: 17.5369 secs\nEpoch: 507, Step: 12675, training loss: 2078.381, validation loss: 2085.440, Runtime: 17.3908 secs\nEpoch: 508, Step: 12700, training loss: 2078.471, validation loss: 2084.947, Runtime: 17.4832 secs\nEpoch: 509, Step: 12725, training loss: 2078.774, validation loss: 2085.788, Runtime: 17.3979 secs\nEpoch: 510, Step: 12750, training loss: 2078.698, validation loss: 2084.682, Runtime: 17.4578 secs\nEpoch: 511, Step: 12775, training loss: 2078.760, validation loss: 2085.196, Runtime: 17.4298 secs\nEpoch: 512, Step: 12800, training loss: 2078.742, validation loss: 2085.256, Runtime: 17.2816 secs\nEpoch: 513, Step: 12825, training loss: 2078.670, validation loss: 2084.743, Runtime: 17.3047 secs\nEpoch: 514, Step: 12850, training loss: 2078.366, validation loss: 2085.388, Runtime: 17.3358 secs\nEpoch: 515, Step: 12875, training loss: 2078.644, validation loss: 2085.020, Runtime: 17.3112 secs\nEpoch: 516, Step: 12900, training loss: 2078.663, validation loss: 2085.246, Runtime: 17.4432 secs\nEpoch: 517, Step: 12925, training loss: 2079.000, validation loss: 2084.933, Runtime: 17.3523 secs\nLearning rate decreased to 0.001353\nEpoch: 518, Step: 12950, training loss: 2078.623, validation loss: 2085.423, Runtime: 17.4018 secs\nEpoch: 519, Step: 12975, training loss: 2078.858, validation loss: 2085.154, Runtime: 17.4153 secs\nEpoch: 520, Step: 13000, training loss: 2078.329, validation loss: 2084.772, Runtime: 17.4028 secs\nEpoch: 521, Step: 13025, training loss: 2078.681, validation loss: 2085.056, Runtime: 17.2752 secs\nEpoch: 522, Step: 13050, training loss: 2078.538, validation loss: 2085.309, Runtime: 17.5368 secs\nEpoch: 523, Step: 13075, training loss: 2078.587, validation loss: 2085.239, Runtime: 17.4391 secs\nEpoch: 524, Step: 13100, training loss: 2078.608, validation loss: 2085.761, Runtime: 17.2646 secs\nEpoch: 525, Step: 13125, training loss: 2078.679, validation loss: 2085.325, Runtime: 17.3909 secs\nEpoch: 526, Step: 13150, training loss: 2078.737, validation loss: 2085.428, Runtime: 17.3359 secs\nLearning rate decreased to 0.001285\nEpoch: 527, Step: 13175, training loss: 2078.414, validation loss: 2085.096, Runtime: 17.4078 secs\nEpoch: 528, Step: 13200, training loss: 2078.320, validation loss: 2085.096, Runtime: 17.4098 secs\nEpoch: 529, Step: 13225, training loss: 2078.309, validation loss: 2084.352, Runtime: 17.3707 secs\nEpoch: 530, Step: 13250, training loss: 2078.373, validation loss: 2084.606, Runtime: 17.4923 secs\nEpoch: 531, Step: 13275, training loss: 2078.101, validation loss: 2085.382, Runtime: 17.3506 secs\nEpoch: 532, Step: 13300, training loss: 2078.405, validation loss: 2085.176, Runtime: 17.2746 secs\nEpoch: 533, Step: 13325, training loss: 2078.394, validation loss: 2085.233, Runtime: 17.3632 secs\nEpoch: 534, Step: 13350, training loss: 2078.518, validation loss: 2085.312, Runtime: 17.4210 secs\nLearning rate decreased to 0.001221\nEpoch: 535, Step: 13375, training loss: 2078.127, validation loss: 2085.346, Runtime: 17.4312 secs\nEpoch: 536, Step: 13400, training loss: 2078.276, validation loss: 2085.306, Runtime: 17.3820 secs\nEpoch: 537, Step: 13425, training loss: 2078.511, validation loss: 2085.207, Runtime: 17.4246 secs\nEpoch: 538, Step: 13450, training loss: 2078.134, validation loss: 2084.985, Runtime: 17.0895 secs\nEpoch: 539, Step: 13475, training loss: 2078.360, validation loss: 2085.653, Runtime: 17.2275 secs\nEpoch: 540, Step: 13500, training loss: 2078.215, validation loss: 2084.879, Runtime: 17.2542 secs\nEpoch: 541, Step: 13525, training loss: 2078.142, validation loss: 2085.448, Runtime: 17.3280 secs\nEpoch: 542, Step: 13550, training loss: 2078.322, validation loss: 2085.038, Runtime: 17.4475 secs\nEpoch: 543, Step: 13575, training loss: 2078.177, validation loss: 2085.428, Runtime: 17.4187 secs\nEpoch: 544, Step: 13600, training loss: 2078.152, validation loss: 2085.133, Runtime: 17.3816 secs\nEpoch: 545, Step: 13625, training loss: 2078.271, validation loss: 2085.369, Runtime: 17.4425 secs\nEpoch: 546, Step: 13650, training loss: 2078.020, validation loss: 2084.981, Runtime: 17.6624 secs\nEpoch: 547, Step: 13675, training loss: 2078.220, validation loss: 2084.750, Runtime: 17.4128 secs\nEpoch: 548, Step: 13700, training loss: 2078.122, validation loss: 2085.270, Runtime: 17.5214 secs\nEpoch: 549, Step: 13725, training loss: 2078.291, validation loss: 2085.513, Runtime: 17.4854 secs\nLearning rate decreased to 0.001160\nEpoch: 550, Step: 13750, training loss: 2078.110, validation loss: 2085.079, Runtime: 17.4074 secs\nEpoch: 551, Step: 13775, training loss: 2078.210, validation loss: 2084.824, Runtime: 17.3552 secs\nEpoch: 552, Step: 13800, training loss: 2078.148, validation loss: 2085.490, Runtime: 17.2295 secs\nEpoch: 553, Step: 13825, training loss: 2078.131, validation loss: 2085.322, Runtime: 17.4317 secs\nEpoch: 554, Step: 13850, training loss: 2078.260, validation loss: 2085.518, Runtime: 17.3137 secs\nEpoch: 555, Step: 13875, training loss: 2078.036, validation loss: 2085.378, Runtime: 17.3775 secs\nEpoch: 556, Step: 13900, training loss: 2078.239, validation loss: 2085.290, Runtime: 17.9813 secs\nEpoch: 557, Step: 13925, training loss: 2078.034, validation loss: 2084.551, Runtime: 17.3629 secs\nEpoch: 558, Step: 13950, training loss: 2078.031, validation loss: 2084.960, Runtime: 17.4641 secs\nEpoch: 559, Step: 13975, training loss: 2078.023, validation loss: 2085.361, Runtime: 17.4052 secs\nEpoch: 560, Step: 14000, training loss: 2078.244, validation loss: 2085.307, Runtime: 17.4638 secs\nEpoch: 561, Step: 14025, training loss: 2077.814, validation loss: 2085.154, Runtime: 17.4382 secs\nEpoch: 562, Step: 14050, training loss: 2078.000, validation loss: 2085.441, Runtime: 17.4989 secs\nEpoch: 563, Step: 14075, training loss: 2077.587, validation loss: 2085.224, Runtime: 17.4278 secs\nEpoch: 564, Step: 14100, training loss: 2078.071, validation loss: 2085.427, Runtime: 17.3547 secs\nEpoch: 565, Step: 14125, training loss: 2078.211, validation loss: 2085.016, Runtime: 17.3325 secs\nEpoch: 566, Step: 14150, training loss: 2077.913, validation loss: 2085.214, Runtime: 17.3539 secs\nEpoch: 567, Step: 14175, training loss: 2077.733, validation loss: 2085.291, Runtime: 17.2996 secs\nEpoch: 568, Step: 14200, training loss: 2077.926, validation loss: 2085.290, Runtime: 17.4623 secs\nEpoch: 569, Step: 14225, training loss: 2077.845, validation loss: 2085.188, Runtime: 17.3832 secs\nEpoch: 570, Step: 14250, training loss: 2077.728, validation loss: 2085.602, Runtime: 17.2423 secs\nEpoch: 571, Step: 14275, training loss: 2077.890, validation loss: 2085.451, Runtime: 17.1520 secs\nEpoch: 572, Step: 14300, training loss: 2078.047, validation loss: 2085.211, Runtime: 17.3906 secs\nLearning rate decreased to 0.001102\nEpoch: 573, Step: 14325, training loss: 2077.626, validation loss: 2085.372, Runtime: 17.5362 secs\nEpoch: 574, Step: 14350, training loss: 2077.712, validation loss: 2085.230, Runtime: 17.6373 secs\nEpoch: 575, Step: 14375, training loss: 2077.767, validation loss: 2085.285, Runtime: 17.3396 secs\nEpoch: 576, Step: 14400, training loss: 2078.131, validation loss: 2085.587, Runtime: 17.2316 secs\nEpoch: 577, Step: 14425, training loss: 2077.925, validation loss: 2085.046, Runtime: 17.3391 secs\nEpoch: 578, Step: 14450, training loss: 2077.924, validation loss: 2085.859, Runtime: 17.3738 secs\nEpoch: 579, Step: 14475, training loss: 2077.616, validation loss: 2085.103, Runtime: 17.2986 secs\nEpoch: 580, Step: 14500, training loss: 2077.744, validation loss: 2084.672, Runtime: 17.4952 secs\nEpoch: 581, Step: 14525, training loss: 2077.842, validation loss: 2085.042, Runtime: 17.5082 secs\nEpoch: 582, Step: 14550, training loss: 2077.948, validation loss: 2085.102, Runtime: 17.4321 secs\nEpoch: 583, Step: 14575, training loss: 2077.990, validation loss: 2085.119, Runtime: 17.2743 secs\nLearning rate decreased to 0.001047\nEpoch: 584, Step: 14600, training loss: 2077.680, validation loss: 2085.249, Runtime: 17.3303 secs\nEpoch: 585, Step: 14625, training loss: 2077.693, validation loss: 2085.399, Runtime: 17.2857 secs\nEpoch: 586, Step: 14650, training loss: 2077.745, validation loss: 2085.400, Runtime: 17.3081 secs\nEpoch: 587, Step: 14675, training loss: 2077.752, validation loss: 2084.633, Runtime: 17.3155 secs\nEpoch: 588, Step: 14700, training loss: 2077.571, validation loss: 2085.680, Runtime: 17.3801 secs\nEpoch: 589, Step: 14725, training loss: 2077.717, validation loss: 2085.075, Runtime: 17.3438 secs\nEpoch: 590, Step: 14750, training loss: 2077.432, validation loss: 2084.931, Runtime: 17.3301 secs\nEpoch: 591, Step: 14775, training loss: 2077.685, validation loss: 2085.592, Runtime: 17.3034 secs\nEpoch: 592, Step: 14800, training loss: 2077.462, validation loss: 2085.510, Runtime: 17.2299 secs\nEpoch: 593, Step: 14825, training loss: 2077.681, validation loss: 2085.300, Runtime: 17.3964 secs\nEpoch: 594, Step: 14850, training loss: 2077.506, validation loss: 2085.077, Runtime: 17.4641 secs\nEpoch: 595, Step: 14875, training loss: 2077.427, validation loss: 2085.615, Runtime: 17.2106 secs\nEpoch: 596, Step: 14900, training loss: 2077.747, validation loss: 2084.653, Runtime: 17.3673 secs\nLearning rate decreased to 0.000994\nEpoch: 597, Step: 14925, training loss: 2077.565, validation loss: 2085.230, Runtime: 17.2453 secs\nEpoch: 598, Step: 14950, training loss: 2077.545, validation loss: 2085.438, Runtime: 17.4056 secs\nEpoch: 599, Step: 14975, training loss: 2077.505, validation loss: 2085.680, Runtime: 17.4072 secs\nEpoch: 600, Step: 15000, training loss: 2077.743, validation loss: 2084.978, Runtime: 17.3449 secs\nEpoch: 601, Step: 15025, training loss: 2077.259, validation loss: 2084.958, Runtime: 17.4189 secs\nEpoch: 602, Step: 15050, training loss: 2077.263, validation loss: 2085.304, Runtime: 17.3758 secs\nEpoch: 603, Step: 15075, training loss: 2077.396, validation loss: 2085.747, Runtime: 17.5947 secs\nEpoch: 604, Step: 15100, training loss: 2077.611, validation loss: 2085.358, Runtime: 17.2404 secs\nEpoch: 605, Step: 15125, training loss: 2077.401, validation loss: 2085.383, Runtime: 17.3774 secs\nEpoch: 606, Step: 15150, training loss: 2077.184, validation loss: 2084.747, Runtime: 17.3810 secs\nEpoch: 607, Step: 15175, training loss: 2077.719, validation loss: 2085.313, Runtime: 17.4498 secs\nLearning rate decreased to 0.000945\nEpoch: 608, Step: 15200, training loss: 2077.800, validation loss: 2085.279, Runtime: 17.3417 secs\nEpoch: 609, Step: 15225, training loss: 2077.275, validation loss: 2085.242, Runtime: 17.7734 secs\nEpoch: 610, Step: 15250, training loss: 2077.242, validation loss: 2085.067, Runtime: 17.4167 secs\nEpoch: 611, Step: 15275, training loss: 2077.430, validation loss: 2084.994, Runtime: 17.3533 secs\nEpoch: 612, Step: 15300, training loss: 2077.450, validation loss: 2085.772, Runtime: 17.3522 secs\nEpoch: 613, Step: 15325, training loss: 2077.497, validation loss: 2085.627, Runtime: 17.3484 secs\nEpoch: 614, Step: 15350, training loss: 2077.712, validation loss: 2085.960, Runtime: 17.2829 secs\nEpoch: 615, Step: 15375, training loss: 2077.356, validation loss: 2085.229, Runtime: 17.4100 secs\nEpoch: 616, Step: 15400, training loss: 2077.180, validation loss: 2085.625, Runtime: 17.3313 secs\nEpoch: 617, Step: 15425, training loss: 2077.180, validation loss: 2085.373, Runtime: 17.3239 secs\nEpoch: 618, Step: 15450, training loss: 2077.365, validation loss: 2085.194, Runtime: 17.4427 secs\nEpoch: 619, Step: 15475, training loss: 2077.575, validation loss: 2085.825, Runtime: 17.3786 secs\nEpoch: 620, Step: 15500, training loss: 2077.175, validation loss: 2085.710, Runtime: 17.5891 secs\nEpoch: 621, Step: 15525, training loss: 2077.383, validation loss: 2085.556, Runtime: 17.2500 secs\nEpoch: 622, Step: 15550, training loss: 2077.239, validation loss: 2085.102, Runtime: 17.3343 secs\nEpoch: 623, Step: 15575, training loss: 2077.293, validation loss: 2085.403, Runtime: 17.4117 secs\nEpoch: 624, Step: 15600, training loss: 2077.409, validation loss: 2084.966, Runtime: 17.1967 secs\nEpoch: 625, Step: 15625, training loss: 2077.405, validation loss: 2085.168, Runtime: 17.3367 secs\nEpoch: 626, Step: 15650, training loss: 2077.265, validation loss: 2085.423, Runtime: 17.2208 secs\nEpoch: 627, Step: 15675, training loss: 2077.252, validation loss: 2085.522, Runtime: 17.3711 secs\nEpoch: 628, Step: 15700, training loss: 2077.378, validation loss: 2085.199, Runtime: 17.4143 secs\nEpoch: 629, Step: 15725, training loss: 2077.565, validation loss: 2085.647, Runtime: 17.4225 secs\nLearning rate decreased to 0.000897\nEpoch: 630, Step: 15750, training loss: 2077.243, validation loss: 2085.434, Runtime: 17.2654 secs\nEpoch: 631, Step: 15775, training loss: 2077.227, validation loss: 2085.462, Runtime: 17.3596 secs\nEpoch: 632, Step: 15800, training loss: 2077.044, validation loss: 2085.275, Runtime: 17.4099 secs\nEpoch: 633, Step: 15825, training loss: 2077.214, validation loss: 2085.401, Runtime: 17.4202 secs\nEpoch: 634, Step: 15850, training loss: 2077.446, validation loss: 2085.110, Runtime: 17.2972 secs\nEpoch: 635, Step: 15875, training loss: 2077.101, validation loss: 2085.831, Runtime: 17.8322 secs\nEpoch: 636, Step: 15900, training loss: 2077.274, validation loss: 2085.172, Runtime: 17.4851 secs\nEpoch: 637, Step: 15925, training loss: 2077.300, validation loss: 2086.066, Runtime: 17.2272 secs\nEpoch: 638, Step: 15950, training loss: 2077.017, validation loss: 2085.237, Runtime: 17.4308 secs\nEpoch: 639, Step: 15975, training loss: 2077.079, validation loss: 2085.553, Runtime: 17.2510 secs\nEpoch: 640, Step: 16000, training loss: 2077.414, validation loss: 2085.456, Runtime: 17.2102 secs\nEpoch: 641, Step: 16025, training loss: 2077.148, validation loss: 2085.420, Runtime: 17.3250 secs\nEpoch: 642, Step: 16050, training loss: 2077.296, validation loss: 2085.392, Runtime: 17.3562 secs\nEpoch: 643, Step: 16075, training loss: 2077.132, validation loss: 2085.409, Runtime: 17.5163 secs\nEpoch: 644, Step: 16100, training loss: 2077.312, validation loss: 2085.456, Runtime: 17.4498 secs\nEpoch: 645, Step: 16125, training loss: 2077.151, validation loss: 2085.714, Runtime: 17.6465 secs\nEpoch: 646, Step: 16150, training loss: 2077.114, validation loss: 2085.154, Runtime: 17.8054 secs\nEpoch: 647, Step: 16175, training loss: 2077.410, validation loss: 2085.177, Runtime: 17.3432 secs\nLearning rate decreased to 0.000853\nEpoch: 648, Step: 16200, training loss: 2077.305, validation loss: 2085.245, Runtime: 17.3694 secs\nEpoch: 649, Step: 16225, training loss: 2076.806, validation loss: 2085.078, Runtime: 17.2740 secs\nEpoch: 650, Step: 16250, training loss: 2076.978, validation loss: 2085.572, Runtime: 17.4010 secs\nEpoch: 651, Step: 16275, training loss: 2077.098, validation loss: 2085.465, Runtime: 17.3422 secs\nEpoch: 652, Step: 16300, training loss: 2077.017, validation loss: 2085.341, Runtime: 17.4453 secs\nEpoch: 653, Step: 16325, training loss: 2076.950, validation loss: 2085.265, Runtime: 17.4024 secs\nEpoch: 654, Step: 16350, training loss: 2076.912, validation loss: 2085.501, Runtime: 17.3677 secs\nEpoch: 655, Step: 16375, training loss: 2077.057, validation loss: 2085.806, Runtime: 17.4707 secs\nEpoch: 656, Step: 16400, training loss: 2077.022, validation loss: 2085.437, Runtime: 17.8765 secs\nEpoch: 657, Step: 16425, training loss: 2077.242, validation loss: 2084.945, Runtime: 17.4638 secs\nLearning rate decreased to 0.000810\nEpoch: 658, Step: 16450, training loss: 2077.088, validation loss: 2085.619, Runtime: 17.3948 secs\nEpoch: 659, Step: 16475, training loss: 2076.655, validation loss: 2085.152, Runtime: 17.4683 secs\nEpoch: 660, Step: 16500, training loss: 2076.979, validation loss: 2085.072, Runtime: 17.5307 secs\nEpoch: 661, Step: 16525, training loss: 2077.054, validation loss: 2085.258, Runtime: 17.4083 secs\nEpoch: 662, Step: 16550, training loss: 2076.928, validation loss: 2084.896, Runtime: 17.4629 secs\nEpoch: 663, Step: 16575, training loss: 2076.859, validation loss: 2085.386, Runtime: 17.3719 secs\nEpoch: 664, Step: 16600, training loss: 2076.930, validation loss: 2085.496, Runtime: 17.3659 secs\nEpoch: 665, Step: 16625, training loss: 2076.807, validation loss: 2084.979, Runtime: 17.3998 secs\nEpoch: 666, Step: 16650, training loss: 2076.958, validation loss: 2085.811, Runtime: 17.3891 secs\nEpoch: 667, Step: 16675, training loss: 2076.901, validation loss: 2085.005, Runtime: 17.4396 secs\nEpoch: 668, Step: 16700, training loss: 2077.093, validation loss: 2085.569, Runtime: 17.4250 secs\nLearning rate decreased to 0.000769\nEpoch: 669, Step: 16725, training loss: 2076.816, validation loss: 2085.569, Runtime: 17.5988 secs\nEpoch: 670, Step: 16750, training loss: 2076.752, validation loss: 2085.823, Runtime: 17.3776 secs\nEpoch: 671, Step: 16775, training loss: 2076.791, validation loss: 2085.476, Runtime: 17.5423 secs\nEpoch: 672, Step: 16800, training loss: 2076.722, validation loss: 2085.261, Runtime: 17.6595 secs\nEpoch: 673, Step: 16825, training loss: 2076.822, validation loss: 2085.309, Runtime: 17.6376 secs\nEpoch: 674, Step: 16850, training loss: 2076.751, validation loss: 2085.510, Runtime: 17.4300 secs\nEpoch: 675, Step: 16875, training loss: 2076.744, validation loss: 2085.687, Runtime: 17.4395 secs\nEpoch: 676, Step: 16900, training loss: 2076.843, validation loss: 2085.275, Runtime: 17.2661 secs\nLearning rate decreased to 0.000731\nEpoch: 677, Step: 16925, training loss: 2076.943, validation loss: 2086.125, Runtime: 17.8852 secs\nEpoch: 678, Step: 16950, training loss: 2076.453, validation loss: 2085.421, Runtime: 17.4508 secs\nEpoch: 679, Step: 16975, training loss: 2076.533, validation loss: 2085.663, Runtime: 17.3911 secs\nEpoch: 680, Step: 17000, training loss: 2076.933, validation loss: 2084.841, Runtime: 17.4102 secs\nEpoch: 681, Step: 17025, training loss: 2076.820, validation loss: 2085.075, Runtime: 17.4731 secs\nEpoch: 682, Step: 17050, training loss: 2076.607, validation loss: 2085.141, Runtime: 17.5682 secs\nEpoch: 683, Step: 17075, training loss: 2076.902, validation loss: 2085.750, Runtime: 17.3830 secs\nEpoch: 684, Step: 17100, training loss: 2076.695, validation loss: 2085.259, Runtime: 17.4081 secs\nEpoch: 685, Step: 17125, training loss: 2076.703, validation loss: 2085.684, Runtime: 17.3508 secs\nEpoch: 686, Step: 17150, training loss: 2076.874, validation loss: 2085.396, Runtime: 17.4252 secs\nEpoch: 687, Step: 17175, training loss: 2076.744, validation loss: 2085.163, Runtime: 17.5462 secs\nEpoch: 688, Step: 17200, training loss: 2076.630, validation loss: 2085.327, Runtime: 17.4314 secs\nEpoch: 689, Step: 17225, training loss: 2076.754, validation loss: 2085.277, Runtime: 17.3788 secs\nEpoch: 690, Step: 17250, training loss: 2076.521, validation loss: 2085.575, Runtime: 17.3919 secs\nEpoch: 691, Step: 17275, training loss: 2076.552, validation loss: 2085.537, Runtime: 17.3631 secs\nEpoch: 692, Step: 17300, training loss: 2076.646, validation loss: 2085.637, Runtime: 17.3602 secs\nEpoch: 693, Step: 17325, training loss: 2076.620, validation loss: 2085.419, Runtime: 17.5109 secs\nEpoch: 694, Step: 17350, training loss: 2076.708, validation loss: 2085.169, Runtime: 17.4409 secs\nEpoch: 695, Step: 17375, training loss: 2076.613, validation loss: 2084.898, Runtime: 17.4578 secs\nEpoch: 696, Step: 17400, training loss: 2076.439, validation loss: 2085.327, Runtime: 17.4239 secs\nEpoch: 697, Step: 17425, training loss: 2076.527, validation loss: 2085.626, Runtime: 17.4123 secs\nEpoch: 698, Step: 17450, training loss: 2076.546, validation loss: 2085.298, Runtime: 17.3343 secs\nEpoch: 699, Step: 17475, training loss: 2076.583, validation loss: 2085.922, Runtime: 17.2821 secs\nEpoch: 700, Step: 17500, training loss: 2076.431, validation loss: 2085.971, Runtime: 17.6071 secs\nEpoch: 701, Step: 17525, training loss: 2076.595, validation loss: 2085.741, Runtime: 17.4290 secs\nEpoch: 702, Step: 17550, training loss: 2076.718, validation loss: 2085.146, Runtime: 17.3735 secs\nLearning rate decreased to 0.000694\nEpoch: 703, Step: 17575, training loss: 2076.347, validation loss: 2084.885, Runtime: 17.4737 secs\nEpoch: 704, Step: 17600, training loss: 2076.596, validation loss: 2085.863, Runtime: 17.3297 secs\nEpoch: 705, Step: 17625, training loss: 2076.646, validation loss: 2085.958, Runtime: 17.3614 secs\nEpoch: 706, Step: 17650, training loss: 2076.748, validation loss: 2085.593, Runtime: 17.4260 secs\nEpoch: 707, Step: 17675, training loss: 2076.660, validation loss: 2085.222, Runtime: 17.4396 secs\nEpoch: 708, Step: 17700, training loss: 2076.736, validation loss: 2085.232, Runtime: 17.3295 secs\nEpoch: 709, Step: 17725, training loss: 2076.571, validation loss: 2085.692, Runtime: 17.2700 secs\nEpoch: 710, Step: 17750, training loss: 2076.436, validation loss: 2085.666, Runtime: 17.3678 secs\nEpoch: 711, Step: 17775, training loss: 2076.359, validation loss: 2085.547, Runtime: 17.3182 secs\nEpoch: 712, Step: 17800, training loss: 2076.477, validation loss: 2085.010, Runtime: 17.4070 secs\nEpoch: 713, Step: 17825, training loss: 2076.406, validation loss: 2085.365, Runtime: 17.5629 secs\nEpoch: 714, Step: 17850, training loss: 2076.479, validation loss: 2086.005, Runtime: 17.5275 secs\nEpoch: 715, Step: 17875, training loss: 2076.652, validation loss: 2085.130, Runtime: 17.5154 secs\nLearning rate decreased to 0.000660\nEpoch: 716, Step: 17900, training loss: 2076.614, validation loss: 2085.776, Runtime: 17.4676 secs\nEpoch: 717, Step: 17925, training loss: 2076.584, validation loss: 2085.796, Runtime: 17.5148 secs\nEpoch: 718, Step: 17950, training loss: 2076.337, validation loss: 2085.467, Runtime: 17.5257 secs\nEpoch: 719, Step: 17975, training loss: 2076.576, validation loss: 2085.510, Runtime: 17.7023 secs\nEpoch: 720, Step: 18000, training loss: 2076.558, validation loss: 2086.140, Runtime: 17.4078 secs\nEpoch: 721, Step: 18025, training loss: 2076.668, validation loss: 2085.572, Runtime: 17.4972 secs\nLearning rate decreased to 0.000627\nEpoch: 722, Step: 18050, training loss: 2076.383, validation loss: 2086.094, Runtime: 17.4236 secs\nEpoch: 723, Step: 18075, training loss: 2076.444, validation loss: 2084.936, Runtime: 17.3359 secs\nEpoch: 724, Step: 18100, training loss: 2076.404, validation loss: 2085.624, Runtime: 17.2653 secs\nEpoch: 725, Step: 18125, training loss: 2076.345, validation loss: 2085.280, Runtime: 17.3553 secs\nEpoch: 726, Step: 18150, training loss: 2076.097, validation loss: 2085.595, Runtime: 17.2678 secs\nEpoch: 727, Step: 18175, training loss: 2076.272, validation loss: 2084.960, Runtime: 17.4715 secs\nEpoch: 728, Step: 18200, training loss: 2076.321, validation loss: 2085.388, Runtime: 17.4663 secs\nEpoch: 729, Step: 18225, training loss: 2076.198, validation loss: 2085.648, Runtime: 17.3235 secs\nEpoch: 730, Step: 18250, training loss: 2076.587, validation loss: 2085.415, Runtime: 17.4665 secs\nLearning rate decreased to 0.000595\nEpoch: 731, Step: 18275, training loss: 2076.339, validation loss: 2085.831, Runtime: 17.8644 secs\nEpoch: 732, Step: 18300, training loss: 2076.407, validation loss: 2086.341, Runtime: 17.4843 secs\nEpoch: 733, Step: 18325, training loss: 2076.335, validation loss: 2085.816, Runtime: 17.3450 secs\nEpoch: 734, Step: 18350, training loss: 2076.484, validation loss: 2085.339, Runtime: 17.3323 secs\nEpoch: 735, Step: 18375, training loss: 2076.077, validation loss: 2086.175, Runtime: 17.3088 secs\nEpoch: 736, Step: 18400, training loss: 2076.239, validation loss: 2086.016, Runtime: 17.4115 secs\nEpoch: 737, Step: 18425, training loss: 2076.441, validation loss: 2085.954, Runtime: 17.3480 secs\nEpoch: 738, Step: 18450, training loss: 2076.173, validation loss: 2084.893, Runtime: 17.4765 secs\nEpoch: 739, Step: 18475, training loss: 2076.219, validation loss: 2085.517, Runtime: 17.2841 secs\nEpoch: 740, Step: 18500, training loss: 2076.185, validation loss: 2085.359, Runtime: 17.3962 secs\nEpoch: 741, Step: 18525, training loss: 2076.499, validation loss: 2086.475, Runtime: 17.3861 secs\nLearning rate decreased to 0.000566\nEpoch: 742, Step: 18550, training loss: 2076.111, validation loss: 2085.211, Runtime: 17.4488 secs\nEpoch: 743, Step: 18575, training loss: 2076.177, validation loss: 2085.428, Runtime: 17.4312 secs\nEpoch: 744, Step: 18600, training loss: 2076.336, validation loss: 2085.340, Runtime: 17.5233 secs\nEpoch: 745, Step: 18625, training loss: 2076.312, validation loss: 2085.628, Runtime: 17.2938 secs\nEpoch: 746, Step: 18650, training loss: 2076.375, validation loss: 2086.366, Runtime: 17.2071 secs\nEpoch: 747, Step: 18675, training loss: 2076.149, validation loss: 2085.910, Runtime: 17.3372 secs\nEpoch: 748, Step: 18700, training loss: 2076.184, validation loss: 2085.146, Runtime: 17.4312 secs\nEpoch: 749, Step: 18725, training loss: 2076.127, validation loss: 2085.748, Runtime: 17.5125 secs\nEpoch: 750, Step: 18750, training loss: 2076.056, validation loss: 2085.565, Runtime: 17.4285 secs\nEpoch: 751, Step: 18775, training loss: 2076.078, validation loss: 2085.570, Runtime: 17.4908 secs\nEpoch: 752, Step: 18800, training loss: 2076.069, validation loss: 2085.623, Runtime: 17.3951 secs\nEpoch: 753, Step: 18825, training loss: 2076.289, validation loss: 2085.367, Runtime: 17.4365 secs\nLearning rate decreased to 0.000537\nEpoch: 754, Step: 18850, training loss: 2076.323, validation loss: 2085.574, Runtime: 17.3122 secs\nEpoch: 755, Step: 18875, training loss: 2076.075, validation loss: 2085.779, Runtime: 17.4337 secs\nEpoch: 756, Step: 18900, training loss: 2076.100, validation loss: 2085.219, Runtime: 17.4872 secs\nEpoch: 757, Step: 18925, training loss: 2076.304, validation loss: 2085.146, Runtime: 17.3890 secs\nEpoch: 758, Step: 18950, training loss: 2076.057, validation loss: 2085.583, Runtime: 17.3766 secs\nEpoch: 759, Step: 18975, training loss: 2075.885, validation loss: 2086.243, Runtime: 17.4474 secs\nEpoch: 760, Step: 19000, training loss: 2076.065, validation loss: 2085.765, Runtime: 17.5078 secs\nEpoch: 761, Step: 19025, training loss: 2076.012, validation loss: 2085.991, Runtime: 17.3071 secs\nEpoch: 762, Step: 19050, training loss: 2076.205, validation loss: 2085.883, Runtime: 17.4803 secs\nEpoch: 763, Step: 19075, training loss: 2076.009, validation loss: 2085.876, Runtime: 17.4627 secs\nEpoch: 764, Step: 19100, training loss: 2076.061, validation loss: 2085.952, Runtime: 17.3479 secs\nEpoch: 765, Step: 19125, training loss: 2076.170, validation loss: 2085.856, Runtime: 17.5807 secs\nEpoch: 766, Step: 19150, training loss: 2076.172, validation loss: 2085.523, Runtime: 17.5672 secs\nEpoch: 767, Step: 19175, training loss: 2076.244, validation loss: 2085.823, Runtime: 17.5112 secs\nLearning rate decreased to 0.000510\nEpoch: 768, Step: 19200, training loss: 2075.891, validation loss: 2085.481, Runtime: 17.3093 secs\nEpoch: 769, Step: 19225, training loss: 2075.883, validation loss: 2085.793, Runtime: 17.4069 secs\nEpoch: 770, Step: 19250, training loss: 2076.231, validation loss: 2085.385, Runtime: 17.4820 secs\nEpoch: 771, Step: 19275, training loss: 2076.271, validation loss: 2085.747, Runtime: 17.3268 secs\nEpoch: 772, Step: 19300, training loss: 2076.146, validation loss: 2085.386, Runtime: 17.4357 secs\nEpoch: 773, Step: 19325, training loss: 2076.155, validation loss: 2085.674, Runtime: 17.3520 secs\nEpoch: 774, Step: 19350, training loss: 2076.095, validation loss: 2085.353, Runtime: 17.3606 secs\nEpoch: 775, Step: 19375, training loss: 2076.163, validation loss: 2085.653, Runtime: 17.2359 secs\nEpoch: 776, Step: 19400, training loss: 2076.047, validation loss: 2085.656, Runtime: 17.3990 secs\nEpoch: 777, Step: 19425, training loss: 2076.067, validation loss: 2085.586, Runtime: 17.4656 secs\nEpoch: 778, Step: 19450, training loss: 2076.095, validation loss: 2085.711, Runtime: 17.3578 secs\nEpoch: 779, Step: 19475, training loss: 2075.945, validation loss: 2085.086, Runtime: 17.3948 secs\nEpoch: 780, Step: 19500, training loss: 2076.151, validation loss: 2086.125, Runtime: 17.3746 secs\nEpoch: 781, Step: 19525, training loss: 2076.117, validation loss: 2085.459, Runtime: 17.4544 secs\nEpoch: 782, Step: 19550, training loss: 2076.061, validation loss: 2085.771, Runtime: 17.3938 secs\nEpoch: 783, Step: 19575, training loss: 2076.271, validation loss: 2085.579, Runtime: 17.3062 secs\nLearning rate decreased to 0.000485\nEpoch: 784, Step: 19600, training loss: 2075.931, validation loss: 2085.562, Runtime: 17.3609 secs\nEpoch: 785, Step: 19625, training loss: 2075.792, validation loss: 2085.721, Runtime: 17.3811 secs\nEpoch: 786, Step: 19650, training loss: 2075.697, validation loss: 2085.206, Runtime: 17.2651 secs\nEpoch: 787, Step: 19675, training loss: 2075.911, validation loss: 2085.658, Runtime: 17.5258 secs\nEpoch: 788, Step: 19700, training loss: 2076.096, validation loss: 2086.042, Runtime: 17.6211 secs\nEpoch: 789, Step: 19725, training loss: 2076.021, validation loss: 2085.887, Runtime: 17.6424 secs\nEpoch: 790, Step: 19750, training loss: 2075.958, validation loss: 2085.384, Runtime: 17.2753 secs\nEpoch: 791, Step: 19775, training loss: 2076.024, validation loss: 2085.735, Runtime: 17.4720 secs\nEpoch: 792, Step: 19800, training loss: 2075.779, validation loss: 2085.596, Runtime: 17.2455 secs\nEpoch: 793, Step: 19825, training loss: 2075.839, validation loss: 2085.599, Runtime: 17.5369 secs\nEpoch: 794, Step: 19850, training loss: 2075.915, validation loss: 2085.653, Runtime: 17.3590 secs\nEpoch: 795, Step: 19875, training loss: 2075.958, validation loss: 2086.394, Runtime: 17.5098 secs\nEpoch: 796, Step: 19900, training loss: 2075.892, validation loss: 2085.486, Runtime: 17.4073 secs\nEpoch: 797, Step: 19925, training loss: 2075.946, validation loss: 2084.788, Runtime: 17.4299 secs\nEpoch: 798, Step: 19950, training loss: 2076.192, validation loss: 2085.255, Runtime: 17.4424 secs\nLearning rate decreased to 0.000461\nEpoch: 799, Step: 19975, training loss: 2075.808, validation loss: 2085.231, Runtime: 17.4458 secs\nEpoch: 800, Step: 20000, training loss: 2075.874, validation loss: 2085.376, Runtime: 17.5100 secs\nEpoch: 801, Step: 20025, training loss: 2075.846, validation loss: 2085.975, Runtime: 17.3635 secs\nEpoch: 802, Step: 20050, training loss: 2076.014, validation loss: 2085.511, Runtime: 17.3601 secs\nEpoch: 803, Step: 20075, training loss: 2075.931, validation loss: 2085.923, Runtime: 17.4244 secs\nEpoch: 804, Step: 20100, training loss: 2075.540, validation loss: 2086.352, Runtime: 17.4421 secs\nEpoch: 805, Step: 20125, training loss: 2075.713, validation loss: 2085.686, Runtime: 17.4301 secs\nEpoch: 806, Step: 20150, training loss: 2075.889, validation loss: 2085.368, Runtime: 17.3970 secs\nEpoch: 807, Step: 20175, training loss: 2076.069, validation loss: 2085.906, Runtime: 17.2879 secs\nLearning rate decreased to 0.000438\nEpoch: 808, Step: 20200, training loss: 2075.856, validation loss: 2085.683, Runtime: 17.3948 secs\nEpoch: 809, Step: 20225, training loss: 2075.757, validation loss: 2085.015, Runtime: 17.3777 secs\nEpoch: 810, Step: 20250, training loss: 2075.735, validation loss: 2085.696, Runtime: 17.4616 secs\nEpoch: 811, Step: 20275, training loss: 2075.546, validation loss: 2085.625, Runtime: 17.3573 secs\nEpoch: 812, Step: 20300, training loss: 2075.755, validation loss: 2085.543, Runtime: 17.3958 secs\nEpoch: 813, Step: 20325, training loss: 2075.526, validation loss: 2085.472, Runtime: 17.3139 secs\nEpoch: 814, Step: 20350, training loss: 2075.564, validation loss: 2085.681, Runtime: 17.4993 secs\nEpoch: 815, Step: 20375, training loss: 2075.763, validation loss: 2085.499, Runtime: 17.5001 secs\nLearning rate decreased to 0.000416\nEpoch: 816, Step: 20400, training loss: 2075.358, validation loss: 2085.664, Runtime: 17.4280 secs\nEpoch: 817, Step: 20425, training loss: 2075.756, validation loss: 2085.616, Runtime: 17.4123 secs\nEpoch: 818, Step: 20450, training loss: 2075.664, validation loss: 2085.784, Runtime: 17.4057 secs\nEpoch: 819, Step: 20475, training loss: 2075.761, validation loss: 2085.108, Runtime: 17.3550 secs\nEpoch: 820, Step: 20500, training loss: 2075.875, validation loss: 2085.683, Runtime: 17.9248 secs\nEpoch: 821, Step: 20525, training loss: 2075.912, validation loss: 2085.728, Runtime: 17.2927 secs\nLearning rate decreased to 0.000395\nEpoch: 822, Step: 20550, training loss: 2075.584, validation loss: 2085.441, Runtime: 17.4596 secs\nEpoch: 823, Step: 20575, training loss: 2075.763, validation loss: 2085.684, Runtime: 17.3920 secs\nEpoch: 824, Step: 20600, training loss: 2075.726, validation loss: 2085.930, Runtime: 17.2376 secs\nEpoch: 825, Step: 20625, training loss: 2075.770, validation loss: 2085.555, Runtime: 17.2349 secs\nEpoch: 826, Step: 20650, training loss: 2075.618, validation loss: 2085.835, Runtime: 17.2862 secs\nEpoch: 827, Step: 20675, training loss: 2075.840, validation loss: 2085.790, Runtime: 17.4213 secs\nEpoch: 828, Step: 20700, training loss: 2075.880, validation loss: 2085.613, Runtime: 17.4905 secs\nLearning rate decreased to 0.000375\nEpoch: 829, Step: 20725, training loss: 2075.637, validation loss: 2085.647, Runtime: 17.5623 secs\nEpoch: 830, Step: 20750, training loss: 2075.203, validation loss: 2085.618, Runtime: 17.2595 secs\nEpoch: 831, Step: 20775, training loss: 2075.445, validation loss: 2085.633, Runtime: 17.4690 secs\nEpoch: 832, Step: 20800, training loss: 2075.764, validation loss: 2085.264, Runtime: 17.5627 secs\nEpoch: 833, Step: 20825, training loss: 2075.530, validation loss: 2085.996, Runtime: 17.6230 secs\nEpoch: 834, Step: 20850, training loss: 2075.618, validation loss: 2085.870, Runtime: 17.2889 secs\nEpoch: 835, Step: 20875, training loss: 2075.628, validation loss: 2085.485, Runtime: 17.1887 secs\nEpoch: 836, Step: 20900, training loss: 2075.719, validation loss: 2085.491, Runtime: 17.2916 secs\nEpoch: 837, Step: 20925, training loss: 2075.571, validation loss: 2085.667, Runtime: 17.4470 secs\nEpoch: 838, Step: 20950, training loss: 2075.665, validation loss: 2085.984, Runtime: 17.4414 secs\nEpoch: 839, Step: 20975, training loss: 2075.639, validation loss: 2086.456, Runtime: 17.4017 secs\nEpoch: 840, Step: 21000, training loss: 2075.549, validation loss: 2086.077, Runtime: 17.4372 secs\nEpoch: 841, Step: 21025, training loss: 2075.575, validation loss: 2085.464, Runtime: 17.4712 secs\nEpoch: 842, Step: 21050, training loss: 2075.695, validation loss: 2084.902, Runtime: 17.3599 secs\nEpoch: 843, Step: 21075, training loss: 2075.590, validation loss: 2085.894, Runtime: 17.4655 secs\nEpoch: 844, Step: 21100, training loss: 2075.685, validation loss: 2085.896, Runtime: 17.3892 secs\nEpoch: 845, Step: 21125, training loss: 2075.406, validation loss: 2085.810, Runtime: 17.3165 secs\nEpoch: 846, Step: 21150, training loss: 2075.604, validation loss: 2085.216, Runtime: 17.3661 secs\nEpoch: 847, Step: 21175, training loss: 2075.671, validation loss: 2085.837, Runtime: 17.2671 secs\nEpoch: 848, Step: 21200, training loss: 2075.519, validation loss: 2085.605, Runtime: 17.3854 secs\nEpoch: 849, Step: 21225, training loss: 2075.699, validation loss: 2085.978, Runtime: 17.3879 secs\nLearning rate decreased to 0.000356\nEpoch: 850, Step: 21250, training loss: 2075.591, validation loss: 2086.074, Runtime: 17.5110 secs\nEpoch: 851, Step: 21275, training loss: 2075.926, validation loss: 2085.308, Runtime: 17.6822 secs\nEpoch: 852, Step: 21300, training loss: 2075.673, validation loss: 2085.746, Runtime: 17.4931 secs\nEpoch: 853, Step: 21325, training loss: 2075.762, validation loss: 2085.523, Runtime: 17.3524 secs\nEpoch: 854, Step: 21350, training loss: 2075.450, validation loss: 2085.883, Runtime: 17.2301 secs\nEpoch: 855, Step: 21375, training loss: 2075.520, validation loss: 2085.737, Runtime: 17.2962 secs\nEpoch: 856, Step: 21400, training loss: 2075.716, validation loss: 2085.783, Runtime: 17.4326 secs\nEpoch: 857, Step: 21425, training loss: 2075.775, validation loss: 2085.486, Runtime: 17.4099 secs\nEpoch: 858, Step: 21450, training loss: 2075.501, validation loss: 2086.500, Runtime: 17.8878 secs\nEpoch: 859, Step: 21475, training loss: 2075.728, validation loss: 2085.866, Runtime: 17.3710 secs\nEpoch: 860, Step: 21500, training loss: 2075.535, validation loss: 2085.322, Runtime: 17.4379 secs\nEpoch: 861, Step: 21525, training loss: 2075.442, validation loss: 2085.813, Runtime: 17.4988 secs\nEpoch: 862, Step: 21550, training loss: 2075.562, validation loss: 2085.842, Runtime: 17.4660 secs\nEpoch: 863, Step: 21575, training loss: 2075.266, validation loss: 2085.491, Runtime: 17.3982 secs\nEpoch: 864, Step: 21600, training loss: 2075.352, validation loss: 2085.532, Runtime: 17.4687 secs\nEpoch: 865, Step: 21625, training loss: 2075.313, validation loss: 2085.757, Runtime: 17.4346 secs\nEpoch: 866, Step: 21650, training loss: 2075.523, validation loss: 2085.446, Runtime: 17.3564 secs\nEpoch: 867, Step: 21675, training loss: 2075.664, validation loss: 2085.637, Runtime: 17.5335 secs\nLearning rate decreased to 0.000339\nEpoch: 868, Step: 21700, training loss: 2075.601, validation loss: 2085.906, Runtime: 17.4927 secs\nEpoch: 869, Step: 21725, training loss: 2075.565, validation loss: 2085.912, Runtime: 17.4713 secs\nEpoch: 870, Step: 21750, training loss: 2075.459, validation loss: 2086.004, Runtime: 17.4064 secs\nEpoch: 871, Step: 21775, training loss: 2075.289, validation loss: 2085.407, Runtime: 17.3773 secs\nEpoch: 872, Step: 21800, training loss: 2075.567, validation loss: 2086.105, Runtime: 17.5273 secs\nEpoch: 873, Step: 21825, training loss: 2075.359, validation loss: 2085.802, Runtime: 17.3610 secs\nEpoch: 874, Step: 21850, training loss: 2075.531, validation loss: 2086.000, Runtime: 17.4013 secs\nEpoch: 875, Step: 21875, training loss: 2075.532, validation loss: 2085.388, Runtime: 17.5071 secs\nEpoch: 876, Step: 21900, training loss: 2075.556, validation loss: 2086.382, Runtime: 17.4612 secs\nEpoch: 877, Step: 21925, training loss: 2075.518, validation loss: 2085.625, Runtime: 17.4280 secs\nEpoch: 878, Step: 21950, training loss: 2075.588, validation loss: 2086.203, Runtime: 17.4005 secs\nLearning rate decreased to 0.000322\nEpoch: 879, Step: 21975, training loss: 2075.526, validation loss: 2086.171, Runtime: 17.4384 secs\nEpoch: 880, Step: 22000, training loss: 2075.885, validation loss: 2085.845, Runtime: 17.5261 secs\nEpoch: 881, Step: 22025, training loss: 2075.471, validation loss: 2085.815, Runtime: 17.3874 secs\nEpoch: 882, Step: 22050, training loss: 2075.216, validation loss: 2085.186, Runtime: 17.4139 secs\nEpoch: 883, Step: 22075, training loss: 2075.186, validation loss: 2085.606, Runtime: 17.5676 secs\nEpoch: 884, Step: 22100, training loss: 2075.478, validation loss: 2086.266, Runtime: 17.4049 secs\nEpoch: 885, Step: 22125, training loss: 2075.365, validation loss: 2085.815, Runtime: 17.3638 secs\nEpoch: 886, Step: 22150, training loss: 2075.225, validation loss: 2085.901, Runtime: 17.5597 secs\nEpoch: 887, Step: 22175, training loss: 2075.499, validation loss: 2085.562, Runtime: 17.2036 secs\nLearning rate decreased to 0.000306\nEpoch: 888, Step: 22200, training loss: 2075.371, validation loss: 2085.874, Runtime: 17.3723 secs\nEpoch: 889, Step: 22225, training loss: 2075.369, validation loss: 2085.678, Runtime: 17.4198 secs\nEpoch: 890, Step: 22250, training loss: 2075.369, validation loss: 2085.666, Runtime: 17.4794 secs\nEpoch: 891, Step: 22275, training loss: 2075.460, validation loss: 2085.766, Runtime: 17.4714 secs\nEpoch: 892, Step: 22300, training loss: 2075.459, validation loss: 2085.425, Runtime: 17.1427 secs\nEpoch: 893, Step: 22325, training loss: 2075.540, validation loss: 2085.315, Runtime: 17.3859 secs\nLearning rate decreased to 0.000290\nEpoch: 894, Step: 22350, training loss: 2075.294, validation loss: 2086.199, Runtime: 17.3989 secs\nEpoch: 895, Step: 22375, training loss: 2075.327, validation loss: 2085.869, Runtime: 17.5053 secs\nEpoch: 896, Step: 22400, training loss: 2075.254, validation loss: 2085.774, Runtime: 17.4725 secs\nEpoch: 897, Step: 22425, training loss: 2075.279, validation loss: 2085.866, Runtime: 17.4827 secs\nEpoch: 898, Step: 22450, training loss: 2075.219, validation loss: 2085.805, Runtime: 17.5849 secs\nEpoch: 899, Step: 22475, training loss: 2075.384, validation loss: 2085.492, Runtime: 17.4619 secs\nEpoch: 900, Step: 22500, training loss: 2075.134, validation loss: 2085.840, Runtime: 17.3840 secs\nEpoch: 901, Step: 22525, training loss: 2075.222, validation loss: 2086.265, Runtime: 17.3600 secs\nEpoch: 902, Step: 22550, training loss: 2075.183, validation loss: 2085.774, Runtime: 17.4392 secs\nEpoch: 903, Step: 22575, training loss: 2075.302, validation loss: 2085.738, Runtime: 17.5777 secs\nEpoch: 904, Step: 22600, training loss: 2075.137, validation loss: 2085.977, Runtime: 17.3802 secs\nEpoch: 905, Step: 22625, training loss: 2075.369, validation loss: 2086.141, Runtime: 17.4196 secs\nEpoch: 906, Step: 22650, training loss: 2075.098, validation loss: 2086.028, Runtime: 17.3317 secs\nEpoch: 907, Step: 22675, training loss: 2075.456, validation loss: 2085.975, Runtime: 17.7799 secs\nLearning rate decreased to 0.000276\nEpoch: 908, Step: 22700, training loss: 2075.281, validation loss: 2085.515, Runtime: 17.8206 secs\nEpoch: 909, Step: 22725, training loss: 2075.278, validation loss: 2086.132, Runtime: 17.2556 secs\nEpoch: 910, Step: 22750, training loss: 2075.352, validation loss: 2085.865, Runtime: 17.5098 secs\nEpoch: 911, Step: 22775, training loss: 2075.171, validation loss: 2085.366, Runtime: 17.3368 secs\nEpoch: 912, Step: 22800, training loss: 2075.172, validation loss: 2085.143, Runtime: 17.5010 secs\nEpoch: 913, Step: 22825, training loss: 2075.208, validation loss: 2085.662, Runtime: 17.3132 secs\nEpoch: 914, Step: 22850, training loss: 2075.222, validation loss: 2086.679, Runtime: 17.5483 secs\nEpoch: 915, Step: 22875, training loss: 2075.043, validation loss: 2086.089, Runtime: 17.3891 secs\nEpoch: 916, Step: 22900, training loss: 2075.234, validation loss: 2085.833, Runtime: 17.4720 secs\nEpoch: 917, Step: 22925, training loss: 2075.444, validation loss: 2085.294, Runtime: 17.4054 secs\nLearning rate decreased to 0.000262\nEpoch: 918, Step: 22950, training loss: 2075.254, validation loss: 2085.870, Runtime: 17.3936 secs\nEpoch: 919, Step: 22975, training loss: 2075.272, validation loss: 2086.142, Runtime: 17.4623 secs\nEpoch: 920, Step: 23000, training loss: 2075.368, validation loss: 2085.765, Runtime: 17.3294 secs\nEpoch: 921, Step: 23025, training loss: 2075.267, validation loss: 2085.945, Runtime: 17.3000 secs\nEpoch: 922, Step: 23050, training loss: 2075.053, validation loss: 2085.702, Runtime: 17.5126 secs\nEpoch: 923, Step: 23075, training loss: 2075.297, validation loss: 2085.940, Runtime: 17.3764 secs\nEpoch: 924, Step: 23100, training loss: 2075.474, validation loss: 2085.468, Runtime: 17.4334 secs\nLearning rate decreased to 0.000249\nEpoch: 925, Step: 23125, training loss: 2075.114, validation loss: 2085.783, Runtime: 17.3410 secs\nEpoch: 926, Step: 23150, training loss: 2075.217, validation loss: 2086.658, Runtime: 17.5377 secs\nEpoch: 927, Step: 23175, training loss: 2075.199, validation loss: 2085.872, Runtime: 17.6217 secs\nEpoch: 928, Step: 23200, training loss: 2075.324, validation loss: 2086.021, Runtime: 17.5363 secs\nEpoch: 929, Step: 23225, training loss: 2075.084, validation loss: 2085.906, Runtime: 17.4457 secs\nEpoch: 930, Step: 23250, training loss: 2075.403, validation loss: 2085.789, Runtime: 17.5056 secs\nEpoch: 931, Step: 23275, training loss: 2075.262, validation loss: 2085.612, Runtime: 17.4592 secs\nEpoch: 932, Step: 23300, training loss: 2075.323, validation loss: 2086.337, Runtime: 17.3344 secs\nEpoch: 933, Step: 23325, training loss: 2075.206, validation loss: 2085.690, Runtime: 17.3934 secs\nEpoch: 934, Step: 23350, training loss: 2075.224, validation loss: 2085.205, Runtime: 17.4391 secs\nEpoch: 935, Step: 23375, training loss: 2075.117, validation loss: 2085.797, Runtime: 17.5343 secs\nEpoch: 936, Step: 23400, training loss: 2075.250, validation loss: 2085.974, Runtime: 17.5543 secs\nEpoch: 937, Step: 23425, training loss: 2075.072, validation loss: 2085.899, Runtime: 17.3106 secs\nEpoch: 938, Step: 23450, training loss: 2075.150, validation loss: 2086.071, Runtime: 17.5575 secs\nEpoch: 939, Step: 23475, training loss: 2075.311, validation loss: 2085.998, Runtime: 17.4680 secs\nLearning rate decreased to 0.000236\nEpoch: 940, Step: 23500, training loss: 2074.835, validation loss: 2086.041, Runtime: 17.2728 secs\nEpoch: 941, Step: 23525, training loss: 2075.011, validation loss: 2085.911, Runtime: 17.4113 secs\nEpoch: 942, Step: 23550, training loss: 2075.195, validation loss: 2085.599, Runtime: 17.4266 secs\nEpoch: 943, Step: 23575, training loss: 2075.187, validation loss: 2085.736, Runtime: 17.4938 secs\nEpoch: 944, Step: 23600, training loss: 2075.126, validation loss: 2085.350, Runtime: 17.4511 secs\nEpoch: 945, Step: 23625, training loss: 2075.133, validation loss: 2086.073, Runtime: 17.4164 secs\nEpoch: 946, Step: 23650, training loss: 2075.004, validation loss: 2085.696, Runtime: 17.5031 secs\nEpoch: 947, Step: 23675, training loss: 2075.254, validation loss: 2085.476, Runtime: 17.5978 secs\nLearning rate decreased to 0.000225\nEpoch: 948, Step: 23700, training loss: 2075.158, validation loss: 2085.628, Runtime: 17.2494 secs\nEpoch: 949, Step: 23725, training loss: 2075.321, validation loss: 2085.845, Runtime: 17.4197 secs\nEpoch: 950, Step: 23750, training loss: 2074.825, validation loss: 2085.998, Runtime: 17.5258 secs\nEpoch: 951, Step: 23775, training loss: 2075.235, validation loss: 2085.762, Runtime: 17.3288 secs\nEpoch: 952, Step: 23800, training loss: 2075.081, validation loss: 2085.771, Runtime: 17.5218 secs\nEpoch: 953, Step: 23825, training loss: 2075.152, validation loss: 2085.762, Runtime: 17.4128 secs\nEpoch: 954, Step: 23850, training loss: 2075.188, validation loss: 2085.917, Runtime: 17.3098 secs\nEpoch: 955, Step: 23875, training loss: 2075.309, validation loss: 2085.675, Runtime: 17.4832 secs\nEpoch: 956, Step: 23900, training loss: 2074.952, validation loss: 2086.111, Runtime: 17.3366 secs\nEpoch: 957, Step: 23925, training loss: 2075.045, validation loss: 2085.734, Runtime: 17.3646 secs\nEpoch: 958, Step: 23950, training loss: 2075.110, validation loss: 2085.988, Runtime: 17.4440 secs\nEpoch: 959, Step: 23975, training loss: 2075.167, validation loss: 2085.772, Runtime: 17.3252 secs\nEpoch: 960, Step: 24000, training loss: 2075.000, validation loss: 2086.154, Runtime: 17.5126 secs\nEpoch: 961, Step: 24025, training loss: 2075.417, validation loss: 2086.059, Runtime: 17.4213 secs\nLearning rate decreased to 0.000213\nEpoch: 962, Step: 24050, training loss: 2075.147, validation loss: 2085.969, Runtime: 17.4703 secs\nEpoch: 963, Step: 24075, training loss: 2075.082, validation loss: 2086.099, Runtime: 17.3285 secs\nEpoch: 964, Step: 24100, training loss: 2075.022, validation loss: 2085.933, Runtime: 17.4780 secs\nEpoch: 965, Step: 24125, training loss: 2075.167, validation loss: 2086.380, Runtime: 17.2920 secs\nEpoch: 966, Step: 24150, training loss: 2075.111, validation loss: 2086.195, Runtime: 17.2385 secs\nEpoch: 967, Step: 24175, training loss: 2075.274, validation loss: 2086.097, Runtime: 17.5335 secs\nEpoch: 968, Step: 24200, training loss: 2074.828, validation loss: 2085.634, Runtime: 17.2363 secs\nEpoch: 969, Step: 24225, training loss: 2075.263, validation loss: 2085.919, Runtime: 17.2896 secs\nEpoch: 970, Step: 24250, training loss: 2075.219, validation loss: 2085.870, Runtime: 17.2633 secs\nEpoch: 971, Step: 24275, training loss: 2075.098, validation loss: 2086.251, Runtime: 17.3234 secs\nEpoch: 972, Step: 24300, training loss: 2075.098, validation loss: 2086.332, Runtime: 17.4634 secs\nEpoch: 973, Step: 24325, training loss: 2074.962, validation loss: 2085.603, Runtime: 17.3684 secs\nEpoch: 974, Step: 24350, training loss: 2074.894, validation loss: 2086.403, Runtime: 17.4056 secs\nEpoch: 975, Step: 24375, training loss: 2075.162, validation loss: 2085.802, Runtime: 17.4313 secs\nEpoch: 976, Step: 24400, training loss: 2075.245, validation loss: 2086.259, Runtime: 17.4528 secs\nLearning rate decreased to 0.000203\nEpoch: 977, Step: 24425, training loss: 2075.212, validation loss: 2085.564, Runtime: 17.3666 secs\nEpoch: 978, Step: 24450, training loss: 2075.310, validation loss: 2085.385, Runtime: 17.6306 secs\nEpoch: 979, Step: 24475, training loss: 2075.137, validation loss: 2085.802, Runtime: 17.9376 secs\nEpoch: 980, Step: 24500, training loss: 2075.121, validation loss: 2085.869, Runtime: 17.5492 secs\nEpoch: 981, Step: 24525, training loss: 2074.970, validation loss: 2085.732, Runtime: 17.5477 secs\nEpoch: 982, Step: 24550, training loss: 2075.033, validation loss: 2086.608, Runtime: 17.2729 secs\nEpoch: 983, Step: 24575, training loss: 2075.036, validation loss: 2085.763, Runtime: 17.2641 secs\nEpoch: 984, Step: 24600, training loss: 2075.247, validation loss: 2086.247, Runtime: 17.3933 secs\nEpoch: 985, Step: 24625, training loss: 2074.962, validation loss: 2086.044, Runtime: 17.4278 secs\nEpoch: 986, Step: 24650, training loss: 2074.898, validation loss: 2086.184, Runtime: 17.4062 secs\nEpoch: 987, Step: 24675, training loss: 2075.101, validation loss: 2085.316, Runtime: 17.4010 secs\nEpoch: 988, Step: 24700, training loss: 2075.217, validation loss: 2085.754, Runtime: 17.4474 secs\nEpoch: 989, Step: 24725, training loss: 2074.974, validation loss: 2085.924, Runtime: 17.4960 secs\nEpoch: 990, Step: 24750, training loss: 2075.161, validation loss: 2085.729, Runtime: 17.6180 secs\nEpoch: 991, Step: 24775, training loss: 2074.826, validation loss: 2085.637, Runtime: 17.5796 secs\nEpoch: 992, Step: 24800, training loss: 2074.859, validation loss: 2086.342, Runtime: 17.4604 secs\nEpoch: 993, Step: 24825, training loss: 2075.291, validation loss: 2085.925, Runtime: 17.4314 secs\nLearning rate decreased to 0.000193\nEpoch: 994, Step: 24850, training loss: 2074.966, validation loss: 2085.510, Runtime: 17.3955 secs\nEpoch: 995, Step: 24875, training loss: 2075.118, validation loss: 2085.814, Runtime: 17.4168 secs\nEpoch: 996, Step: 24900, training loss: 2075.121, validation loss: 2085.447, Runtime: 17.4161 secs\nEpoch: 997, Step: 24925, training loss: 2075.019, validation loss: 2085.850, Runtime: 17.3483 secs\nEpoch: 998, Step: 24950, training loss: 2074.876, validation loss: 2085.645, Runtime: 17.4385 secs\nEpoch: 999, Step: 24975, training loss: 2075.097, validation loss: 2086.390, Runtime: 17.4161 secs\nEpoch: 1000, Step: 25000, training loss: 2075.021, validation loss: 2085.924, Runtime: 17.3863 secs\nEpoch: 1001, Step: 25025, training loss: 2074.908, validation loss: 2085.331, Runtime: 17.4042 secs\nEpoch: 1002, Step: 25050, training loss: 2074.826, validation loss: 2085.784, Runtime: 17.3146 secs\nEpoch: 1003, Step: 25075, training loss: 2075.253, validation loss: 2085.474, Runtime: 17.3083 secs\nLearning rate decreased to 0.000183\nEpoch: 1004, Step: 25100, training loss: 2074.809, validation loss: 2086.452, Runtime: 17.2120 secs\nEpoch: 1005, Step: 25125, training loss: 2074.787, validation loss: 2085.907, Runtime: 17.4503 secs\nEpoch: 1006, Step: 25150, training loss: 2074.788, validation loss: 2086.273, Runtime: 17.2808 secs\nEpoch: 1007, Step: 25175, training loss: 2075.022, validation loss: 2085.975, Runtime: 17.3685 secs\nEpoch: 1008, Step: 25200, training loss: 2075.000, validation loss: 2086.039, Runtime: 17.3506 secs\nEpoch: 1009, Step: 25225, training loss: 2074.897, validation loss: 2085.945, Runtime: 17.4306 secs\nEpoch: 1010, Step: 25250, training loss: 2074.858, validation loss: 2086.248, Runtime: 17.2876 secs\nEpoch: 1011, Step: 25275, training loss: 2074.857, validation loss: 2086.149, Runtime: 17.4400 secs\nEpoch: 1012, Step: 25300, training loss: 2075.090, validation loss: 2086.353, Runtime: 17.3057 secs\nLearning rate decreased to 0.000174\nEpoch: 1013, Step: 25325, training loss: 2074.971, validation loss: 2086.084, Runtime: 17.3441 secs\nEpoch: 1014, Step: 25350, training loss: 2074.860, validation loss: 2086.076, Runtime: 17.8802 secs\nEpoch: 1015, Step: 25375, training loss: 2074.788, validation loss: 2086.124, Runtime: 17.3560 secs\nEpoch: 1016, Step: 25400, training loss: 2075.058, validation loss: 2085.937, Runtime: 17.3865 secs\nEpoch: 1017, Step: 25425, training loss: 2075.133, validation loss: 2086.056, Runtime: 17.4132 secs\nEpoch: 1018, Step: 25450, training loss: 2074.936, validation loss: 2086.374, Runtime: 17.3065 secs\nEpoch: 1019, Step: 25475, training loss: 2075.078, validation loss: 2085.704, Runtime: 17.3105 secs\nEpoch: 1020, Step: 25500, training loss: 2074.926, validation loss: 2086.245, Runtime: 17.4453 secs\nEpoch: 1021, Step: 25525, training loss: 2074.847, validation loss: 2086.317, Runtime: 17.3614 secs\nEpoch: 1022, Step: 25550, training loss: 2075.184, validation loss: 2086.069, Runtime: 17.4484 secs\nLearning rate decreased to 0.000165\nEpoch: 1023, Step: 25575, training loss: 2075.169, validation loss: 2086.113, Runtime: 17.3033 secs\nEpoch: 1024, Step: 25600, training loss: 2074.909, validation loss: 2085.811, Runtime: 17.4407 secs\nEpoch: 1025, Step: 25625, training loss: 2075.074, validation loss: 2086.131, Runtime: 17.5841 secs\nEpoch: 1026, Step: 25650, training loss: 2074.868, validation loss: 2085.794, Runtime: 17.7026 secs\nEpoch: 1027, Step: 25675, training loss: 2074.961, validation loss: 2086.459, Runtime: 17.4151 secs\nEpoch: 1028, Step: 25700, training loss: 2074.705, validation loss: 2086.081, Runtime: 17.4018 secs\nEpoch: 1029, Step: 25725, training loss: 2074.937, validation loss: 2086.573, Runtime: 17.4374 secs\nEpoch: 1030, Step: 25750, training loss: 2074.827, validation loss: 2085.862, Runtime: 17.4217 secs\nEpoch: 1031, Step: 25775, training loss: 2074.882, validation loss: 2085.929, Runtime: 17.4324 secs\nEpoch: 1032, Step: 25800, training loss: 2074.766, validation loss: 2085.601, Runtime: 17.4311 secs\nEpoch: 1033, Step: 25825, training loss: 2074.987, validation loss: 2086.900, Runtime: 17.4260 secs\nLearning rate decreased to 0.000157\nEpoch: 1034, Step: 25850, training loss: 2074.913, validation loss: 2085.623, Runtime: 17.4292 secs\nEpoch: 1035, Step: 25875, training loss: 2074.844, validation loss: 2085.782, Runtime: 17.4321 secs\nEpoch: 1036, Step: 25900, training loss: 2074.630, validation loss: 2086.220, Runtime: 17.5064 secs\nEpoch: 1037, Step: 25925, training loss: 2074.753, validation loss: 2086.338, Runtime: 17.3647 secs\nEpoch: 1038, Step: 25950, training loss: 2074.873, validation loss: 2086.007, Runtime: 17.5290 secs\nEpoch: 1039, Step: 25975, training loss: 2074.702, validation loss: 2086.456, Runtime: 17.4306 secs\nEpoch: 1040, Step: 26000, training loss: 2075.077, validation loss: 2086.356, Runtime: 17.4636 secs\nLearning rate decreased to 0.000149\nEpoch: 1041, Step: 26025, training loss: 2074.579, validation loss: 2085.520, Runtime: 17.4370 secs\nEpoch: 1042, Step: 26050, training loss: 2074.901, validation loss: 2086.264, Runtime: 17.2904 secs\nEpoch: 1043, Step: 26075, training loss: 2074.721, validation loss: 2086.188, Runtime: 17.3319 secs\nEpoch: 1044, Step: 26100, training loss: 2074.847, validation loss: 2085.844, Runtime: 17.3120 secs\nEpoch: 1045, Step: 26125, training loss: 2074.656, validation loss: 2086.124, Runtime: 17.5072 secs\nEpoch: 1046, Step: 26150, training loss: 2074.690, validation loss: 2085.898, Runtime: 17.4314 secs\nEpoch: 1047, Step: 26175, training loss: 2074.726, validation loss: 2086.621, Runtime: 17.4054 secs\nEpoch: 1048, Step: 26200, training loss: 2074.792, validation loss: 2086.078, Runtime: 17.5218 secs\nEpoch: 1049, Step: 26225, training loss: 2074.946, validation loss: 2085.871, Runtime: 17.3935 secs\nLearning rate decreased to 0.000142\nEpoch: 1050, Step: 26250, training loss: 2074.822, validation loss: 2085.709, Runtime: 17.4247 secs\nEpoch: 1051, Step: 26275, training loss: 2075.064, validation loss: 2085.838, Runtime: 17.3713 secs\nEpoch: 1052, Step: 26300, training loss: 2074.943, validation loss: 2086.004, Runtime: 17.3385 secs\nEpoch: 1053, Step: 26325, training loss: 2074.894, validation loss: 2086.286, Runtime: 17.4922 secs\nEpoch: 1054, Step: 26350, training loss: 2074.721, validation loss: 2085.994, Runtime: 17.3899 secs\nEpoch: 1055, Step: 26375, training loss: 2074.749, validation loss: 2086.053, Runtime: 17.7061 secs\nEpoch: 1056, Step: 26400, training loss: 2074.932, validation loss: 2085.974, Runtime: 17.5916 secs\nEpoch: 1057, Step: 26425, training loss: 2074.707, validation loss: 2086.079, Runtime: 17.8829 secs\nEpoch: 1058, Step: 26450, training loss: 2074.807, validation loss: 2086.084, Runtime: 17.3259 secs\nEpoch: 1059, Step: 26475, training loss: 2074.833, validation loss: 2085.738, Runtime: 17.4459 secs\nEpoch: 1060, Step: 26500, training loss: 2074.841, validation loss: 2086.035, Runtime: 17.5085 secs\nEpoch: 1061, Step: 26525, training loss: 2074.849, validation loss: 2086.104, Runtime: 17.4559 secs\nEpoch: 1062, Step: 26550, training loss: 2074.515, validation loss: 2085.703, Runtime: 17.4400 secs\nEpoch: 1063, Step: 26575, training loss: 2074.698, validation loss: 2085.963, Runtime: 17.4102 secs\nEpoch: 1064, Step: 26600, training loss: 2074.862, validation loss: 2086.115, Runtime: 17.3568 secs\nLearning rate decreased to 0.000135\nEpoch: 1065, Step: 26625, training loss: 2074.938, validation loss: 2086.278, Runtime: 17.4339 secs\nEpoch: 1066, Step: 26650, training loss: 2074.814, validation loss: 2085.776, Runtime: 17.2190 secs\nEpoch: 1067, Step: 26675, training loss: 2074.958, validation loss: 2086.063, Runtime: 17.2602 secs\nEpoch: 1068, Step: 26700, training loss: 2074.693, validation loss: 2086.014, Runtime: 17.2680 secs\nEpoch: 1069, Step: 26725, training loss: 2074.794, validation loss: 2086.137, Runtime: 17.3617 secs\nEpoch: 1070, Step: 26750, training loss: 2074.857, validation loss: 2086.089, Runtime: 17.3232 secs\nEpoch: 1071, Step: 26775, training loss: 2075.129, validation loss: 2085.332, Runtime: 17.1384 secs\nLearning rate decreased to 0.000128\nEpoch: 1072, Step: 26800, training loss: 2074.611, validation loss: 2085.606, Runtime: 17.3348 secs\nEpoch: 1073, Step: 26825, training loss: 2074.615, validation loss: 2086.510, Runtime: 17.4466 secs\nEpoch: 1074, Step: 26850, training loss: 2074.794, validation loss: 2085.814, Runtime: 17.4212 secs\nEpoch: 1075, Step: 26875, training loss: 2074.825, validation loss: 2086.700, Runtime: 17.3427 secs\nEpoch: 1076, Step: 26900, training loss: 2074.775, validation loss: 2086.118, Runtime: 17.5599 secs\nEpoch: 1077, Step: 26925, training loss: 2074.888, validation loss: 2086.293, Runtime: 17.5273 secs\nEpoch: 1078, Step: 26950, training loss: 2074.805, validation loss: 2085.858, Runtime: 17.3381 secs\nEpoch: 1079, Step: 26975, training loss: 2074.787, validation loss: 2086.289, Runtime: 17.4264 secs\nEpoch: 1080, Step: 27000, training loss: 2074.852, validation loss: 2086.368, Runtime: 17.2803 secs\nEpoch: 1081, Step: 27025, training loss: 2074.736, validation loss: 2085.748, Runtime: 17.5274 secs\nEpoch: 1082, Step: 27050, training loss: 2074.933, validation loss: 2085.971, Runtime: 17.4327 secs\nLearning rate decreased to 0.000121\nEpoch: 1083, Step: 27075, training loss: 2074.848, validation loss: 2085.526, Runtime: 17.3743 secs\nEpoch: 1084, Step: 27100, training loss: 2074.725, validation loss: 2086.249, Runtime: 17.3650 secs\nEpoch: 1085, Step: 27125, training loss: 2074.686, validation loss: 2085.968, Runtime: 17.3724 secs\nEpoch: 1086, Step: 27150, training loss: 2074.665, validation loss: 2086.592, Runtime: 17.7533 secs\nEpoch: 1087, Step: 27175, training loss: 2074.562, validation loss: 2086.016, Runtime: 17.3260 secs\nEpoch: 1088, Step: 27200, training loss: 2074.748, validation loss: 2086.358, Runtime: 17.5767 secs\nEpoch: 1089, Step: 27225, training loss: 2074.749, validation loss: 2085.325, Runtime: 17.5893 secs\nEpoch: 1090, Step: 27250, training loss: 2074.765, validation loss: 2086.099, Runtime: 17.4301 secs\nLearning rate decreased to 0.000115\nEpoch: 1091, Step: 27275, training loss: 2074.994, validation loss: 2085.575, Runtime: 17.3921 secs\nEpoch: 1092, Step: 27300, training loss: 2074.617, validation loss: 2086.187, Runtime: 17.4713 secs\nEpoch: 1093, Step: 27325, training loss: 2074.782, validation loss: 2085.802, Runtime: 17.4974 secs\nEpoch: 1094, Step: 27350, training loss: 2074.656, validation loss: 2086.265, Runtime: 17.3455 secs\nEpoch: 1095, Step: 27375, training loss: 2074.704, validation loss: 2086.368, Runtime: 17.4143 secs\nEpoch: 1096, Step: 27400, training loss: 2074.765, validation loss: 2086.413, Runtime: 17.5121 secs\nEpoch: 1097, Step: 27425, training loss: 2074.876, validation loss: 2086.222, Runtime: 17.2081 secs\nEpoch: 1098, Step: 27450, training loss: 2074.705, validation loss: 2085.636, Runtime: 17.3991 secs\nEpoch: 1099, Step: 27475, training loss: 2074.734, validation loss: 2085.812, Runtime: 17.3809 secs\nEpoch: 1100, Step: 27500, training loss: 2074.632, validation loss: 2085.924, Runtime: 17.6541 secs\nEpoch: 1101, Step: 27525, training loss: 2074.726, validation loss: 2085.919, Runtime: 17.4984 secs\nEpoch: 1102, Step: 27550, training loss: 2074.702, validation loss: 2085.820, Runtime: 17.6566 secs\nEpoch: 1103, Step: 27575, training loss: 2074.911, validation loss: 2085.743, Runtime: 17.5479 secs\nLearning rate decreased to 0.000110\nEpoch: 1104, Step: 27600, training loss: 2074.610, validation loss: 2085.843, Runtime: 17.9597 secs\nEpoch: 1105, Step: 27625, training loss: 2074.750, validation loss: 2086.333, Runtime: 17.4771 secs\nEpoch: 1106, Step: 27650, training loss: 2074.776, validation loss: 2085.849, Runtime: 17.3429 secs\nEpoch: 1107, Step: 27675, training loss: 2074.647, validation loss: 2086.360, Runtime: 17.3002 secs\nEpoch: 1108, Step: 27700, training loss: 2074.710, validation loss: 2085.783, Runtime: 17.2358 secs\nEpoch: 1109, Step: 27725, training loss: 2074.791, validation loss: 2085.951, Runtime: 17.3902 secs\nEpoch: 1110, Step: 27750, training loss: 2074.740, validation loss: 2086.250, Runtime: 17.4648 secs\nEpoch: 1111, Step: 27775, training loss: 2074.626, validation loss: 2086.379, Runtime: 17.3709 secs\nEpoch: 1112, Step: 27800, training loss: 2074.480, validation loss: 2085.885, Runtime: 17.5185 secs\nEpoch: 1113, Step: 27825, training loss: 2074.727, validation loss: 2086.111, Runtime: 17.4905 secs\nEpoch: 1114, Step: 27850, training loss: 2074.585, validation loss: 2086.170, Runtime: 17.4020 secs\nEpoch: 1115, Step: 27875, training loss: 2075.094, validation loss: 2086.154, Runtime: 17.4450 secs\nLearning rate decreased to 0.000104\nEpoch: 1116, Step: 27900, training loss: 2074.391, validation loss: 2086.452, Runtime: 17.4850 secs\nEpoch: 1117, Step: 27925, training loss: 2074.506, validation loss: 2085.505, Runtime: 17.5494 secs\nEpoch: 1118, Step: 27950, training loss: 2074.691, validation loss: 2085.676, Runtime: 17.4137 secs\nEpoch: 1119, Step: 27975, training loss: 2074.675, validation loss: 2086.129, Runtime: 17.3997 secs\nEpoch: 1120, Step: 28000, training loss: 2074.770, validation loss: 2085.673, Runtime: 17.3645 secs\nEpoch: 1121, Step: 28025, training loss: 2074.811, validation loss: 2086.383, Runtime: 17.4349 secs\nEpoch: 1122, Step: 28050, training loss: 2074.781, validation loss: 2085.776, Runtime: 17.3871 secs\nEpoch: 1123, Step: 28075, training loss: 2074.627, validation loss: 2086.220, Runtime: 17.2820 secs\nEpoch: 1124, Step: 28100, training loss: 2074.482, validation loss: 2085.743, Runtime: 17.3236 secs\nEpoch: 1125, Step: 28125, training loss: 2074.661, validation loss: 2086.203, Runtime: 17.4280 secs\nEpoch: 1126, Step: 28150, training loss: 2074.742, validation loss: 2086.057, Runtime: 17.5172 secs\nEpoch: 1127, Step: 28175, training loss: 2074.611, validation loss: 2085.985, Runtime: 17.4261 secs\nEpoch: 1128, Step: 28200, training loss: 2074.652, validation loss: 2085.811, Runtime: 17.7220 secs\nEpoch: 1129, Step: 28225, training loss: 2074.737, validation loss: 2086.690, Runtime: 17.3222 secs\nEpoch: 1130, Step: 28250, training loss: 2074.475, validation loss: 2085.887, Runtime: 17.2786 secs\nEpoch: 1131, Step: 28275, training loss: 2074.724, validation loss: 2085.784, Runtime: 17.3274 secs\nEpoch: 1132, Step: 28300, training loss: 2074.744, validation loss: 2086.470, Runtime: 17.4087 secs\nLearning rate decreased to 0.000099\nEpoch: 1133, Step: 28325, training loss: 2074.768, validation loss: 2085.958, Runtime: 17.6448 secs\nEpoch: 1134, Step: 28350, training loss: 2074.689, validation loss: 2085.870, Runtime: 17.3092 secs\nEpoch: 1135, Step: 28375, training loss: 2074.719, validation loss: 2086.275, Runtime: 17.2635 secs\nEpoch: 1136, Step: 28400, training loss: 2074.808, validation loss: 2085.847, Runtime: 17.4449 secs\nEpoch: 1137, Step: 28425, training loss: 2074.573, validation loss: 2086.399, Runtime: 17.4479 secs\nEpoch: 1138, Step: 28450, training loss: 2074.570, validation loss: 2086.118, Runtime: 17.4628 secs\nEpoch: 1139, Step: 28475, training loss: 2074.690, validation loss: 2086.599, Runtime: 17.3566 secs\nEpoch: 1140, Step: 28500, training loss: 2074.833, validation loss: 2086.312, Runtime: 17.4730 secs\nLearning rate decreased to 0.000094\nEpoch: 1141, Step: 28525, training loss: 2074.859, validation loss: 2085.938, Runtime: 17.3842 secs\nEpoch: 1142, Step: 28550, training loss: 2074.582, validation loss: 2086.267, Runtime: 17.3405 secs\nEpoch: 1143, Step: 28575, training loss: 2074.782, validation loss: 2085.844, Runtime: 17.3838 secs\nEpoch: 1144, Step: 28600, training loss: 2074.480, validation loss: 2086.050, Runtime: 17.4861 secs\nEpoch: 1145, Step: 28625, training loss: 2075.000, validation loss: 2086.394, Runtime: 17.3385 secs\nEpoch: 1146, Step: 28650, training loss: 2074.763, validation loss: 2086.272, Runtime: 17.4070 secs\nEpoch: 1147, Step: 28675, training loss: 2074.663, validation loss: 2086.067, Runtime: 17.3638 secs\nEpoch: 1148, Step: 28700, training loss: 2074.930, validation loss: 2085.699, Runtime: 17.4491 secs\nEpoch: 1149, Step: 28725, training loss: 2074.599, validation loss: 2086.241, Runtime: 17.4291 secs\nEpoch: 1150, Step: 28750, training loss: 2074.667, validation loss: 2086.023, Runtime: 17.3900 secs\nEpoch: 1151, Step: 28775, training loss: 2074.907, validation loss: 2086.015, Runtime: 17.3230 secs\nEpoch: 1152, Step: 28800, training loss: 2074.677, validation loss: 2086.424, Runtime: 17.3544 secs\nEpoch: 1153, Step: 28825, training loss: 2074.566, validation loss: 2086.331, Runtime: 17.3084 secs\nEpoch: 1154, Step: 28850, training loss: 2074.529, validation loss: 2086.374, Runtime: 17.4010 secs\nEpoch: 1155, Step: 28875, training loss: 2074.784, validation loss: 2086.285, Runtime: 17.5508 secs\nEpoch: 1156, Step: 28900, training loss: 2074.621, validation loss: 2085.963, Runtime: 17.4366 secs\nEpoch: 1157, Step: 28925, training loss: 2074.559, validation loss: 2086.212, Runtime: 17.3546 secs\nEpoch: 1158, Step: 28950, training loss: 2074.522, validation loss: 2086.469, Runtime: 17.3723 secs\nEpoch: 1159, Step: 28975, training loss: 2074.672, validation loss: 2086.109, Runtime: 17.4711 secs\nEpoch: 1160, Step: 29000, training loss: 2074.591, validation loss: 2086.211, Runtime: 17.4798 secs\nEpoch: 1161, Step: 29025, training loss: 2074.563, validation loss: 2086.085, Runtime: 17.3094 secs\nEpoch: 1162, Step: 29050, training loss: 2074.906, validation loss: 2085.544, Runtime: 17.4548 secs\nLearning rate decreased to 0.000089\nEpoch: 1163, Step: 29075, training loss: 2074.360, validation loss: 2085.838, Runtime: 17.4395 secs\nEpoch: 1164, Step: 29100, training loss: 2074.570, validation loss: 2085.897, Runtime: 17.4305 secs\nEpoch: 1165, Step: 29125, training loss: 2074.835, validation loss: 2086.338, Runtime: 17.3309 secs\nEpoch: 1166, Step: 29150, training loss: 2074.669, validation loss: 2085.949, Runtime: 17.4389 secs\nEpoch: 1167, Step: 29175, training loss: 2074.640, validation loss: 2086.031, Runtime: 17.4507 secs\nEpoch: 1168, Step: 29200, training loss: 2074.715, validation loss: 2085.759, Runtime: 17.2789 secs\nEpoch: 1169, Step: 29225, training loss: 2074.679, validation loss: 2085.487, Runtime: 17.2982 secs\nEpoch: 1170, Step: 29250, training loss: 2074.716, validation loss: 2085.988, Runtime: 17.4991 secs\nEpoch: 1171, Step: 29275, training loss: 2074.720, validation loss: 2086.619, Runtime: 17.5066 secs\nEpoch: 1172, Step: 29300, training loss: 2074.506, validation loss: 2086.029, Runtime: 17.4566 secs\nEpoch: 1173, Step: 29325, training loss: 2074.676, validation loss: 2085.786, Runtime: 17.3791 secs\nEpoch: 1174, Step: 29350, training loss: 2074.526, validation loss: 2085.882, Runtime: 17.4323 secs\nEpoch: 1175, Step: 29375, training loss: 2074.645, validation loss: 2086.131, Runtime: 17.2612 secs\nEpoch: 1176, Step: 29400, training loss: 2074.661, validation loss: 2086.158, Runtime: 17.2730 secs\nEpoch: 1177, Step: 29425, training loss: 2074.558, validation loss: 2085.556, Runtime: 17.3511 secs\nEpoch: 1178, Step: 29450, training loss: 2074.641, validation loss: 2086.111, Runtime: 17.4123 secs\nEpoch: 1179, Step: 29475, training loss: 2074.742, validation loss: 2086.005, Runtime: 17.4611 secs\nLearning rate decreased to 0.000085\nEpoch: 1180, Step: 29500, training loss: 2074.786, validation loss: 2086.616, Runtime: 17.3850 secs\nEpoch: 1181, Step: 29525, training loss: 2074.704, validation loss: 2085.902, Runtime: 17.4977 secs\nEpoch: 1182, Step: 29550, training loss: 2074.720, validation loss: 2085.651, Runtime: 17.4816 secs\nEpoch: 1183, Step: 29575, training loss: 2074.611, validation loss: 2086.943, Runtime: 17.3657 secs\nEpoch: 1184, Step: 29600, training loss: 2074.929, validation loss: 2086.375, Runtime: 17.4041 secs\nEpoch: 1185, Step: 29625, training loss: 2074.697, validation loss: 2086.160, Runtime: 17.3874 secs\nEpoch: 1186, Step: 29650, training loss: 2074.546, validation loss: 2086.078, Runtime: 17.3794 secs\nEpoch: 1187, Step: 29675, training loss: 2074.651, validation loss: 2085.943, Runtime: 17.1615 secs\nEpoch: 1188, Step: 29700, training loss: 2074.453, validation loss: 2085.617, Runtime: 17.3632 secs\nEpoch: 1189, Step: 29725, training loss: 2074.747, validation loss: 2085.706, Runtime: 17.3629 secs\nEpoch: 1190, Step: 29750, training loss: 2074.778, validation loss: 2085.846, Runtime: 17.4561 secs\nEpoch: 1191, Step: 29775, training loss: 2074.603, validation loss: 2086.305, Runtime: 17.6105 secs\nEpoch: 1192, Step: 29800, training loss: 2074.626, validation loss: 2086.303, Runtime: 17.3531 secs\nEpoch: 1193, Step: 29825, training loss: 2074.533, validation loss: 2086.333, Runtime: 17.5217 secs\nEpoch: 1194, Step: 29850, training loss: 2074.804, validation loss: 2086.030, Runtime: 17.3378 secs\nLearning rate decreased to 0.000081\nEpoch: 1195, Step: 29875, training loss: 2074.632, validation loss: 2085.842, Runtime: 17.4468 secs\nEpoch: 1196, Step: 29900, training loss: 2074.648, validation loss: 2086.039, Runtime: 17.4975 secs\nEpoch: 1197, Step: 29925, training loss: 2074.485, validation loss: 2086.054, Runtime: 17.8603 secs\nEpoch: 1198, Step: 29950, training loss: 2074.612, validation loss: 2085.807, Runtime: 17.4684 secs\nEpoch: 1199, Step: 29975, training loss: 2074.471, validation loss: 2086.936, Runtime: 17.2944 secs\nEpoch: 1200, Step: 30000, training loss: 2074.756, validation loss: 2086.266, Runtime: 17.3082 secs\nEpoch: 1201, Step: 30025, training loss: 2074.423, validation loss: 2085.567, Runtime: 17.1715 secs\nEpoch: 1202, Step: 30050, training loss: 2074.410, validation loss: 2085.639, Runtime: 17.4557 secs\nEpoch: 1203, Step: 30075, training loss: 2074.676, validation loss: 2086.144, Runtime: 17.2663 secs\nEpoch: 1204, Step: 30100, training loss: 2074.417, validation loss: 2086.624, Runtime: 17.4467 secs\nEpoch: 1205, Step: 30125, training loss: 2074.796, validation loss: 2086.178, Runtime: 17.4860 secs\nLearning rate decreased to 0.000077\nEpoch: 1206, Step: 30150, training loss: 2074.452, validation loss: 2086.467, Runtime: 17.3671 secs\nEpoch: 1207, Step: 30175, training loss: 2074.556, validation loss: 2085.857, Runtime: 17.5267 secs\nEpoch: 1208, Step: 30200, training loss: 2074.455, validation loss: 2086.250, Runtime: 17.4203 secs\nEpoch: 1209, Step: 30225, training loss: 2074.487, validation loss: 2086.133, Runtime: 17.5326 secs\nEpoch: 1210, Step: 30250, training loss: 2074.580, validation loss: 2086.167, Runtime: 17.5059 secs\nEpoch: 1211, Step: 30275, training loss: 2074.366, validation loss: 2085.877, Runtime: 17.3910 secs\nEpoch: 1212, Step: 30300, training loss: 2074.500, validation loss: 2086.774, Runtime: 17.4234 secs\nEpoch: 1213, Step: 30325, training loss: 2074.449, validation loss: 2086.021, Runtime: 17.4563 secs\nEpoch: 1214, Step: 30350, training loss: 2074.483, validation loss: 2086.247, Runtime: 17.4905 secs\nEpoch: 1215, Step: 30375, training loss: 2074.575, validation loss: 2086.065, Runtime: 17.5094 secs\nEpoch: 1216, Step: 30400, training loss: 2074.504, validation loss: 2086.871, Runtime: 17.6058 secs\nEpoch: 1217, Step: 30425, training loss: 2074.731, validation loss: 2086.091, Runtime: 17.5643 secs\nLearning rate decreased to 0.000073\nEpoch: 1218, Step: 30450, training loss: 2074.600, validation loss: 2085.819, Runtime: 17.6851 secs\nEpoch: 1219, Step: 30475, training loss: 2074.517, validation loss: 2086.146, Runtime: 17.7522 secs\nEpoch: 1220, Step: 30500, training loss: 2074.470, validation loss: 2086.020, Runtime: 17.4519 secs\nEpoch: 1221, Step: 30525, training loss: 2074.656, validation loss: 2085.689, Runtime: 17.4845 secs\nEpoch: 1222, Step: 30550, training loss: 2074.615, validation loss: 2086.360, Runtime: 17.4294 secs\nEpoch: 1223, Step: 30575, training loss: 2074.715, validation loss: 2086.069, Runtime: 17.3887 secs\nEpoch: 1224, Step: 30600, training loss: 2074.445, validation loss: 2086.319, Runtime: 17.4867 secs\nEpoch: 1225, Step: 30625, training loss: 2074.389, validation loss: 2085.573, Runtime: 17.2766 secs\nEpoch: 1226, Step: 30650, training loss: 2074.523, validation loss: 2085.913, Runtime: 17.4370 secs\nEpoch: 1227, Step: 30675, training loss: 2074.329, validation loss: 2086.818, Runtime: 17.3510 secs\nEpoch: 1228, Step: 30700, training loss: 2074.460, validation loss: 2086.082, Runtime: 17.5247 secs\nEpoch: 1229, Step: 30725, training loss: 2074.596, validation loss: 2086.378, Runtime: 17.4612 secs\nEpoch: 1230, Step: 30750, training loss: 2074.557, validation loss: 2085.875, Runtime: 17.4154 secs\nEpoch: 1231, Step: 30775, training loss: 2074.777, validation loss: 2085.854, Runtime: 17.5793 secs\nLearning rate decreased to 0.000069\nEpoch: 1232, Step: 30800, training loss: 2074.449, validation loss: 2085.531, Runtime: 17.4273 secs\nEpoch: 1233, Step: 30825, training loss: 2074.424, validation loss: 2086.380, Runtime: 17.3130 secs\nEpoch: 1234, Step: 30850, training loss: 2074.733, validation loss: 2085.526, Runtime: 17.1719 secs\nEpoch: 1235, Step: 30875, training loss: 2074.433, validation loss: 2085.842, Runtime: 17.2833 secs\nEpoch: 1236, Step: 30900, training loss: 2074.735, validation loss: 2085.932, Runtime: 17.4434 secs\nEpoch: 1237, Step: 30925, training loss: 2074.526, validation loss: 2085.756, Runtime: 17.4875 secs\nEpoch: 1238, Step: 30950, training loss: 2074.563, validation loss: 2086.063, Runtime: 17.4141 secs\nEpoch: 1239, Step: 30975, training loss: 2074.652, validation loss: 2086.813, Runtime: 17.4574 secs\nEpoch: 1240, Step: 31000, training loss: 2074.597, validation loss: 2086.491, Runtime: 17.4703 secs\nEpoch: 1241, Step: 31025, training loss: 2074.729, validation loss: 2086.225, Runtime: 17.4350 secs\nEpoch: 1242, Step: 31050, training loss: 2074.520, validation loss: 2085.904, Runtime: 17.3822 secs\nEpoch: 1243, Step: 31075, training loss: 2074.526, validation loss: 2085.810, Runtime: 18.1746 secs\nEpoch: 1244, Step: 31100, training loss: 2074.527, validation loss: 2085.867, Runtime: 17.2761 secs\nEpoch: 1245, Step: 31125, training loss: 2074.530, validation loss: 2085.913, Runtime: 17.3640 secs\nEpoch: 1246, Step: 31150, training loss: 2074.609, validation loss: 2085.679, Runtime: 17.4319 secs\nEpoch: 1247, Step: 31175, training loss: 2074.425, validation loss: 2086.044, Runtime: 17.4517 secs\nEpoch: 1248, Step: 31200, training loss: 2074.649, validation loss: 2086.312, Runtime: 17.4854 secs\nLearning rate decreased to 0.000066\nEpoch: 1249, Step: 31225, training loss: 2074.502, validation loss: 2086.577, Runtime: 17.4995 secs\nEpoch: 1250, Step: 31250, training loss: 2074.650, validation loss: 2086.908, Runtime: 17.4703 secs\nEpoch: 1251, Step: 31275, training loss: 2074.610, validation loss: 2086.113, Runtime: 17.3602 secs\nEpoch: 1252, Step: 31300, training loss: 2074.853, validation loss: 2085.950, Runtime: 17.4139 secs\nEpoch: 1253, Step: 31325, training loss: 2074.630, validation loss: 2086.366, Runtime: 17.4087 secs\nEpoch: 1254, Step: 31350, training loss: 2074.442, validation loss: 2086.013, Runtime: 17.3527 secs\nEpoch: 1255, Step: 31375, training loss: 2074.558, validation loss: 2086.422, Runtime: 17.3937 secs\nEpoch: 1256, Step: 31400, training loss: 2074.298, validation loss: 2085.599, Runtime: 17.3725 secs\nEpoch: 1257, Step: 31425, training loss: 2074.353, validation loss: 2086.015, Runtime: 17.4509 secs\nEpoch: 1258, Step: 31450, training loss: 2074.282, validation loss: 2086.245, Runtime: 17.3427 secs\nEpoch: 1259, Step: 31475, training loss: 2074.653, validation loss: 2085.361, Runtime: 17.3628 secs\nLearning rate decreased to 0.000062\nEpoch: 1260, Step: 31500, training loss: 2074.619, validation loss: 2086.620, Runtime: 17.4215 secs\nEpoch: 1261, Step: 31525, training loss: 2074.390, validation loss: 2086.029, Runtime: 17.3668 secs\nEpoch: 1262, Step: 31550, training loss: 2074.669, validation loss: 2086.524, Runtime: 17.3814 secs\nEpoch: 1263, Step: 31575, training loss: 2074.533, validation loss: 2086.793, Runtime: 17.1203 secs\nEpoch: 1264, Step: 31600, training loss: 2074.446, validation loss: 2086.071, Runtime: 17.2340 secs\nEpoch: 1265, Step: 31625, training loss: 2074.488, validation loss: 2086.211, Runtime: 17.2795 secs\nEpoch: 1266, Step: 31650, training loss: 2074.276, validation loss: 2086.491, Runtime: 17.3065 secs\nEpoch: 1267, Step: 31675, training loss: 2074.336, validation loss: 2085.742, Runtime: 17.4619 secs\nEpoch: 1268, Step: 31700, training loss: 2074.572, validation loss: 2085.899, Runtime: 17.4130 secs\nEpoch: 1269, Step: 31725, training loss: 2074.683, validation loss: 2086.195, Runtime: 17.4493 secs\nLearning rate decreased to 0.000059\nEpoch: 1270, Step: 31750, training loss: 2074.351, validation loss: 2086.342, Runtime: 17.5724 secs\nEpoch: 1271, Step: 31775, training loss: 2074.431, validation loss: 2085.723, Runtime: 17.6353 secs\nEpoch: 1272, Step: 31800, training loss: 2074.427, validation loss: 2085.891, Runtime: 17.5526 secs\nEpoch: 1273, Step: 31825, training loss: 2074.493, validation loss: 2085.550, Runtime: 17.7220 secs\nEpoch: 1274, Step: 31850, training loss: 2074.435, validation loss: 2086.209, Runtime: 17.3520 secs\nEpoch: 1275, Step: 31875, training loss: 2074.581, validation loss: 2086.363, Runtime: 17.3599 secs\nEpoch: 1276, Step: 31900, training loss: 2074.427, validation loss: 2085.846, Runtime: 17.4758 secs\nEpoch: 1277, Step: 31925, training loss: 2074.435, validation loss: 2086.190, Runtime: 17.4788 secs\nEpoch: 1278, Step: 31950, training loss: 2074.506, validation loss: 2086.345, Runtime: 17.5477 secs\nEpoch: 1279, Step: 31975, training loss: 2074.465, validation loss: 2086.142, Runtime: 17.3526 secs\nEpoch: 1280, Step: 32000, training loss: 2074.545, validation loss: 2086.124, Runtime: 17.3602 secs\nEpoch: 1281, Step: 32025, training loss: 2074.438, validation loss: 2085.883, Runtime: 17.4288 secs\nEpoch: 1282, Step: 32050, training loss: 2074.437, validation loss: 2086.497, Runtime: 17.5002 secs\nEpoch: 1283, Step: 32075, training loss: 2074.471, validation loss: 2086.058, Runtime: 17.4262 secs\nEpoch: 1284, Step: 32100, training loss: 2074.375, validation loss: 2085.761, Runtime: 17.4184 secs\nEpoch: 1285, Step: 32125, training loss: 2074.525, validation loss: 2085.947, Runtime: 17.5244 secs\nEpoch: 1286, Step: 32150, training loss: 2074.593, validation loss: 2085.712, Runtime: 17.4749 secs\nLearning rate decreased to 0.000056\nEpoch: 1287, Step: 32175, training loss: 2074.387, validation loss: 2085.994, Runtime: 17.3340 secs\nEpoch: 1288, Step: 32200, training loss: 2074.549, validation loss: 2085.837, Runtime: 17.4643 secs\nEpoch: 1289, Step: 32225, training loss: 2074.607, validation loss: 2086.286, Runtime: 17.3494 secs\nEpoch: 1290, Step: 32250, training loss: 2074.576, validation loss: 2086.409, Runtime: 17.2228 secs\nEpoch: 1291, Step: 32275, training loss: 2074.304, validation loss: 2086.211, Runtime: 17.2845 secs\nEpoch: 1292, Step: 32300, training loss: 2074.603, validation loss: 2086.089, Runtime: 17.3873 secs\nEpoch: 1293, Step: 32325, training loss: 2074.562, validation loss: 2086.410, Runtime: 17.6033 secs\nEpoch: 1294, Step: 32350, training loss: 2074.504, validation loss: 2085.879, Runtime: 17.3969 secs\nEpoch: 1295, Step: 32375, training loss: 2074.511, validation loss: 2086.305, Runtime: 17.4834 secs\nEpoch: 1296, Step: 32400, training loss: 2074.688, validation loss: 2086.013, Runtime: 17.4969 secs\nLearning rate decreased to 0.000053\nEpoch: 1297, Step: 32425, training loss: 2074.241, validation loss: 2086.168, Runtime: 17.4596 secs\nEpoch: 1298, Step: 32450, training loss: 2074.411, validation loss: 2086.123, Runtime: 17.4590 secs\nEpoch: 1299, Step: 32475, training loss: 2074.404, validation loss: 2086.378, Runtime: 17.3811 secs\nEpoch: 1300, Step: 32500, training loss: 2074.498, validation loss: 2086.308, Runtime: 17.3756 secs\nEpoch: 1301, Step: 32525, training loss: 2074.474, validation loss: 2085.933, Runtime: 17.4724 secs\nEpoch: 1302, Step: 32550, training loss: 2074.515, validation loss: 2086.364, Runtime: 17.4316 secs\nEpoch: 1303, Step: 32575, training loss: 2074.593, validation loss: 2085.974, Runtime: 17.2909 secs\nLearning rate decreased to 0.000051\nEpoch: 1304, Step: 32600, training loss: 2074.708, validation loss: 2086.358, Runtime: 17.4591 secs\nEpoch: 1305, Step: 32625, training loss: 2074.491, validation loss: 2086.879, Runtime: 17.4053 secs\nEpoch: 1306, Step: 32650, training loss: 2074.301, validation loss: 2086.619, Runtime: 17.3930 secs\nEpoch: 1307, Step: 32675, training loss: 2074.422, validation loss: 2086.163, Runtime: 17.4518 secs\nEpoch: 1308, Step: 32700, training loss: 2074.637, validation loss: 2086.657, Runtime: 17.4699 secs\nEpoch: 1309, Step: 32725, training loss: 2074.493, validation loss: 2086.799, Runtime: 17.4835 secs\nEpoch: 1310, Step: 32750, training loss: 2074.383, validation loss: 2086.146, Runtime: 17.3809 secs\nEpoch: 1311, Step: 32775, training loss: 2074.456, validation loss: 2085.935, Runtime: 17.3026 secs\nEpoch: 1312, Step: 32800, training loss: 2074.494, validation loss: 2086.208, Runtime: 17.4367 secs\nEpoch: 1313, Step: 32825, training loss: 2074.293, validation loss: 2086.248, Runtime: 17.3537 secs\nEpoch: 1314, Step: 32850, training loss: 2074.421, validation loss: 2086.386, Runtime: 17.2441 secs\nEpoch: 1315, Step: 32875, training loss: 2074.419, validation loss: 2085.948, Runtime: 17.3234 secs\nEpoch: 1316, Step: 32900, training loss: 2074.648, validation loss: 2086.325, Runtime: 17.4192 secs\nLearning rate decreased to 0.000048\nEpoch: 1317, Step: 32925, training loss: 2074.551, validation loss: 2086.760, Runtime: 17.4783 secs\nEpoch: 1318, Step: 32950, training loss: 2074.503, validation loss: 2086.678, Runtime: 17.4391 secs\nEpoch: 1319, Step: 32975, training loss: 2074.576, validation loss: 2086.250, Runtime: 17.6676 secs\nEpoch: 1320, Step: 33000, training loss: 2074.255, validation loss: 2086.120, Runtime: 17.3831 secs\nEpoch: 1321, Step: 33025, training loss: 2074.462, validation loss: 2086.340, Runtime: 17.4207 secs\nEpoch: 1322, Step: 33050, training loss: 2074.513, validation loss: 2085.997, Runtime: 17.4383 secs\nEpoch: 1323, Step: 33075, training loss: 2074.313, validation loss: 2086.285, Runtime: 17.4128 secs\nEpoch: 1324, Step: 33100, training loss: 2074.500, validation loss: 2086.068, Runtime: 17.4193 secs\nEpoch: 1325, Step: 33125, training loss: 2074.773, validation loss: 2085.913, Runtime: 17.3555 secs\nLearning rate decreased to 0.000046\nEpoch: 1326, Step: 33150, training loss: 2074.477, validation loss: 2086.763, Runtime: 17.9397 secs\nEpoch: 1327, Step: 33175, training loss: 2074.577, validation loss: 2086.630, Runtime: 17.3100 secs\nEpoch: 1328, Step: 33200, training loss: 2074.306, validation loss: 2085.751, Runtime: 17.5260 secs\nEpoch: 1329, Step: 33225, training loss: 2074.247, validation loss: 2085.777, Runtime: 17.5340 secs\nEpoch: 1330, Step: 33250, training loss: 2074.567, validation loss: 2085.627, Runtime: 17.3199 secs\nEpoch: 1331, Step: 33275, training loss: 2074.595, validation loss: 2085.652, Runtime: 17.3550 secs\nEpoch: 1332, Step: 33300, training loss: 2074.450, validation loss: 2086.663, Runtime: 17.4537 secs\nEpoch: 1333, Step: 33325, training loss: 2074.195, validation loss: 2086.326, Runtime: 17.3383 secs\nEpoch: 1334, Step: 33350, training loss: 2074.486, validation loss: 2086.472, Runtime: 17.5018 secs\nEpoch: 1335, Step: 33375, training loss: 2074.490, validation loss: 2086.329, Runtime: 17.3990 secs\nEpoch: 1336, Step: 33400, training loss: 2074.585, validation loss: 2086.591, Runtime: 17.4995 secs\nEpoch: 1337, Step: 33425, training loss: 2074.464, validation loss: 2085.714, Runtime: 17.3756 secs\nEpoch: 1338, Step: 33450, training loss: 2074.372, validation loss: 2086.089, Runtime: 17.3908 secs\nEpoch: 1339, Step: 33475, training loss: 2074.525, validation loss: 2085.961, Runtime: 17.3363 secs\nEpoch: 1340, Step: 33500, training loss: 2074.396, validation loss: 2085.785, Runtime: 17.3407 secs\nEpoch: 1341, Step: 33525, training loss: 2074.289, validation loss: 2085.918, Runtime: 17.2970 secs\nEpoch: 1342, Step: 33550, training loss: 2074.562, validation loss: 2086.033, Runtime: 17.4277 secs\nEpoch: 1343, Step: 33575, training loss: 2074.785, validation loss: 2086.146, Runtime: 17.3246 secs\nLearning rate decreased to 0.000044\nEpoch: 1344, Step: 33600, training loss: 2074.421, validation loss: 2085.810, Runtime: 17.3888 secs\nEpoch: 1345, Step: 33625, training loss: 2074.517, validation loss: 2085.696, Runtime: 17.3742 secs\nEpoch: 1346, Step: 33650, training loss: 2074.490, validation loss: 2085.764, Runtime: 17.2726 secs\nEpoch: 1347, Step: 33675, training loss: 2074.340, validation loss: 2086.259, Runtime: 17.3815 secs\nEpoch: 1348, Step: 33700, training loss: 2074.306, validation loss: 2085.854, Runtime: 17.4537 secs\nEpoch: 1349, Step: 33725, training loss: 2074.495, validation loss: 2086.108, Runtime: 17.5157 secs\nEpoch: 1350, Step: 33750, training loss: 2074.703, validation loss: 2086.481, Runtime: 17.5185 secs\nLearning rate decreased to 0.000041\nEpoch: 1351, Step: 33775, training loss: 2074.723, validation loss: 2085.944, Runtime: 17.8124 secs\nEpoch: 1352, Step: 33800, training loss: 2074.391, validation loss: 2086.053, Runtime: 17.3504 secs\nEpoch: 1353, Step: 33825, training loss: 2074.565, validation loss: 2085.616, Runtime: 17.3072 secs\nEpoch: 1354, Step: 33850, training loss: 2074.346, validation loss: 2086.301, Runtime: 17.5379 secs\nEpoch: 1355, Step: 33875, training loss: 2074.660, validation loss: 2086.141, Runtime: 17.4617 secs\nEpoch: 1356, Step: 33900, training loss: 2074.254, validation loss: 2086.182, Runtime: 17.6225 secs\nEpoch: 1357, Step: 33925, training loss: 2074.424, validation loss: 2086.238, Runtime: 17.4120 secs\nEpoch: 1358, Step: 33950, training loss: 2074.576, validation loss: 2086.615, Runtime: 17.3764 secs\nEpoch: 1359, Step: 33975, training loss: 2074.385, validation loss: 2086.482, Runtime: 17.3601 secs\nEpoch: 1360, Step: 34000, training loss: 2074.474, validation loss: 2086.087, Runtime: 17.2942 secs\nEpoch: 1361, Step: 34025, training loss: 2074.480, validation loss: 2086.375, Runtime: 17.2550 secs\nEpoch: 1362, Step: 34050, training loss: 2074.533, validation loss: 2086.827, Runtime: 17.1645 secs\nEpoch: 1363, Step: 34075, training loss: 2074.490, validation loss: 2086.507, Runtime: 17.2628 secs\nEpoch: 1364, Step: 34100, training loss: 2074.656, validation loss: 2086.173, Runtime: 17.5462 secs\nLearning rate decreased to 0.000039\nEpoch: 1365, Step: 34125, training loss: 2074.598, validation loss: 2085.748, Runtime: 17.3049 secs\nEpoch: 1366, Step: 34150, training loss: 2074.171, validation loss: 2086.336, Runtime: 17.2874 secs\nEpoch: 1367, Step: 34175, training loss: 2074.489, validation loss: 2086.155, Runtime: 17.4991 secs\nEpoch: 1368, Step: 34200, training loss: 2074.339, validation loss: 2086.615, Runtime: 17.3259 secs\nEpoch: 1369, Step: 34225, training loss: 2074.480, validation loss: 2085.985, Runtime: 17.4223 secs\nEpoch: 1370, Step: 34250, training loss: 2074.415, validation loss: 2086.475, Runtime: 17.4507 secs\nEpoch: 1371, Step: 34275, training loss: 2074.500, validation loss: 2086.431, Runtime: 17.3256 secs\nEpoch: 1372, Step: 34300, training loss: 2074.251, validation loss: 2086.085, Runtime: 17.6242 secs\nEpoch: 1373, Step: 34325, training loss: 2074.697, validation loss: 2086.489, Runtime: 17.4789 secs\nLearning rate decreased to 0.000037\nEpoch: 1374, Step: 34350, training loss: 2074.595, validation loss: 2086.044, Runtime: 17.4947 secs\nEpoch: 1375, Step: 34375, training loss: 2074.335, validation loss: 2086.135, Runtime: 17.3620 secs\nEpoch: 1376, Step: 34400, training loss: 2074.397, validation loss: 2086.075, Runtime: 17.3678 secs\nEpoch: 1377, Step: 34425, training loss: 2074.407, validation loss: 2086.381, Runtime: 17.4078 secs\nEpoch: 1378, Step: 34450, training loss: 2074.539, validation loss: 2086.252, Runtime: 17.2584 secs\nEpoch: 1379, Step: 34475, training loss: 2074.393, validation loss: 2086.476, Runtime: 17.4170 secs\nEpoch: 1380, Step: 34500, training loss: 2074.441, validation loss: 2086.104, Runtime: 17.5655 secs\nEpoch: 1381, Step: 34525, training loss: 2074.498, validation loss: 2085.923, Runtime: 17.4033 secs\nEpoch: 1382, Step: 34550, training loss: 2074.688, validation loss: 2086.178, Runtime: 17.3640 secs\nLearning rate decreased to 0.000035\nEpoch: 1383, Step: 34575, training loss: 2074.383, validation loss: 2086.787, Runtime: 17.3856 secs\nEpoch: 1384, Step: 34600, training loss: 2074.487, validation loss: 2085.702, Runtime: 17.4425 secs\nEpoch: 1385, Step: 34625, training loss: 2074.370, validation loss: 2086.233, Runtime: 17.4148 secs\nEpoch: 1386, Step: 34650, training loss: 2074.511, validation loss: 2086.333, Runtime: 17.5276 secs\nEpoch: 1387, Step: 34675, training loss: 2074.519, validation loss: 2086.270, Runtime: 17.3011 secs\nEpoch: 1388, Step: 34700, training loss: 2074.460, validation loss: 2086.248, Runtime: 17.3984 secs\nEpoch: 1389, Step: 34725, training loss: 2074.645, validation loss: 2085.394, Runtime: 17.3350 secs\nLearning rate decreased to 0.000034\nEpoch: 1390, Step: 34750, training loss: 2074.263, validation loss: 2085.955, Runtime: 17.4378 secs\nEpoch: 1391, Step: 34775, training loss: 2074.401, validation loss: 2086.529, Runtime: 17.4089 secs\nEpoch: 1392, Step: 34800, training loss: 2074.432, validation loss: 2086.209, Runtime: 17.4760 secs\nEpoch: 1393, Step: 34825, training loss: 2074.432, validation loss: 2086.394, Runtime: 17.3825 secs\nEpoch: 1394, Step: 34850, training loss: 2074.327, validation loss: 2086.241, Runtime: 17.4112 secs\nEpoch: 1395, Step: 34875, training loss: 2074.382, validation loss: 2086.325, Runtime: 17.3638 secs\nEpoch: 1396, Step: 34900, training loss: 2074.599, validation loss: 2086.115, Runtime: 17.4238 secs\nLearning rate decreased to 0.000032\nEpoch: 1397, Step: 34925, training loss: 2074.449, validation loss: 2085.743, Runtime: 17.4546 secs\nEpoch: 1398, Step: 34950, training loss: 2074.543, validation loss: 2085.616, Runtime: 17.3588 secs\nEpoch: 1399, Step: 34975, training loss: 2074.531, validation loss: 2085.678, Runtime: 17.3806 secs\nEpoch: 1400, Step: 35000, training loss: 2074.368, validation loss: 2085.867, Runtime: 17.6352 secs\nEpoch: 1401, Step: 35025, training loss: 2074.353, validation loss: 2085.797, Runtime: 17.5601 secs\nEpoch: 1402, Step: 35050, training loss: 2074.465, validation loss: 2086.352, Runtime: 17.5046 secs\nEpoch: 1403, Step: 35075, training loss: 2074.027, validation loss: 2086.542, Runtime: 17.4633 secs\nEpoch: 1404, Step: 35100, training loss: 2074.448, validation loss: 2086.547, Runtime: 17.6105 secs\nEpoch: 1405, Step: 35125, training loss: 2074.266, validation loss: 2086.430, Runtime: 17.3539 secs\nEpoch: 1406, Step: 35150, training loss: 2074.283, validation loss: 2085.671, Runtime: 17.4082 secs\nEpoch: 1407, Step: 35175, training loss: 2074.250, validation loss: 2086.395, Runtime: 17.3249 secs\nEpoch: 1408, Step: 35200, training loss: 2074.461, validation loss: 2085.695, Runtime: 17.3045 secs\nEpoch: 1409, Step: 35225, training loss: 2074.425, validation loss: 2085.641, Runtime: 17.3848 secs\nEpoch: 1410, Step: 35250, training loss: 2074.500, validation loss: 2085.708, Runtime: 17.3790 secs\nLearning rate decreased to 0.000030\nEpoch: 1411, Step: 35275, training loss: 2074.471, validation loss: 2086.335, Runtime: 17.4913 secs\nEpoch: 1412, Step: 35300, training loss: 2074.374, validation loss: 2086.342, Runtime: 17.4841 secs\nEpoch: 1413, Step: 35325, training loss: 2074.354, validation loss: 2086.904, Runtime: 17.3383 secs\nEpoch: 1414, Step: 35350, training loss: 2074.299, validation loss: 2086.666, Runtime: 17.3846 secs\nEpoch: 1415, Step: 35375, training loss: 2074.690, validation loss: 2086.345, Runtime: 17.4153 secs\nEpoch: 1416, Step: 35400, training loss: 2074.314, validation loss: 2086.316, Runtime: 17.5065 secs\nEpoch: 1417, Step: 35425, training loss: 2074.412, validation loss: 2085.943, Runtime: 17.4264 secs\nEpoch: 1418, Step: 35450, training loss: 2074.453, validation loss: 2086.364, Runtime: 17.4097 secs\nEpoch: 1419, Step: 35475, training loss: 2074.337, validation loss: 2086.235, Runtime: 17.3838 secs\nEpoch: 1420, Step: 35500, training loss: 2074.472, validation loss: 2086.164, Runtime: 17.3781 secs\nEpoch: 1421, Step: 35525, training loss: 2074.517, validation loss: 2086.143, Runtime: 17.6832 secs\nEpoch: 1422, Step: 35550, training loss: 2074.287, validation loss: 2086.103, Runtime: 17.3970 secs\nEpoch: 1423, Step: 35575, training loss: 2074.261, validation loss: 2086.238, Runtime: 17.4776 secs\nEpoch: 1424, Step: 35600, training loss: 2074.611, validation loss: 2086.429, Runtime: 17.3373 secs\nLearning rate decreased to 0.000029\nEpoch: 1425, Step: 35625, training loss: 2074.379, validation loss: 2086.171, Runtime: 17.4657 secs\nEpoch: 1426, Step: 35650, training loss: 2074.446, validation loss: 2085.521, Runtime: 17.4427 secs\nEpoch: 1427, Step: 35675, training loss: 2074.502, validation loss: 2085.759, Runtime: 17.3732 secs\nEpoch: 1428, Step: 35700, training loss: 2074.398, validation loss: 2085.852, Runtime: 17.3163 secs\nEpoch: 1429, Step: 35725, training loss: 2074.320, validation loss: 2086.222, Runtime: 17.3495 secs\nEpoch: 1430, Step: 35750, training loss: 2074.244, validation loss: 2085.861, Runtime: 17.3255 secs\nEpoch: 1431, Step: 35775, training loss: 2074.355, validation loss: 2086.153, Runtime: 17.4824 secs\nEpoch: 1432, Step: 35800, training loss: 2074.101, validation loss: 2086.256, Runtime: 17.4608 secs\nEpoch: 1433, Step: 35825, training loss: 2074.184, validation loss: 2086.104, Runtime: 17.5219 secs\nEpoch: 1434, Step: 35850, training loss: 2074.392, validation loss: 2086.523, Runtime: 17.3037 secs\nEpoch: 1435, Step: 35875, training loss: 2074.507, validation loss: 2086.324, Runtime: 17.3654 secs\nLearning rate decreased to 0.000027\nEpoch: 1436, Step: 35900, training loss: 2074.194, validation loss: 2085.670, Runtime: 17.3521 secs\nEpoch: 1437, Step: 35925, training loss: 2074.374, validation loss: 2086.671, Runtime: 17.2922 secs\nEpoch: 1438, Step: 35950, training loss: 2074.445, validation loss: 2086.967, Runtime: 17.3667 secs\nEpoch: 1439, Step: 35975, training loss: 2074.403, validation loss: 2085.959, Runtime: 17.4394 secs\nEpoch: 1440, Step: 36000, training loss: 2074.661, validation loss: 2086.343, Runtime: 17.4065 secs\nEpoch: 1441, Step: 36025, training loss: 2074.562, validation loss: 2086.535, Runtime: 17.3387 secs\nEpoch: 1442, Step: 36050, training loss: 2074.442, validation loss: 2086.580, Runtime: 17.4270 secs\nEpoch: 1443, Step: 36075, training loss: 2074.084, validation loss: 2085.608, Runtime: 17.4030 secs\nEpoch: 1444, Step: 36100, training loss: 2074.218, validation loss: 2086.345, Runtime: 17.4292 secs\nEpoch: 1445, Step: 36125, training loss: 2074.414, validation loss: 2086.453, Runtime: 17.2932 secs\nEpoch: 1446, Step: 36150, training loss: 2074.351, validation loss: 2086.421, Runtime: 17.4109 secs\nEpoch: 1447, Step: 36175, training loss: 2074.491, validation loss: 2086.501, Runtime: 17.4089 secs\nEpoch: 1448, Step: 36200, training loss: 2074.183, validation loss: 2086.561, Runtime: 17.4256 secs\nEpoch: 1449, Step: 36225, training loss: 2074.341, validation loss: 2086.038, Runtime: 17.4234 secs\nEpoch: 1450, Step: 36250, training loss: 2074.680, validation loss: 2086.632, Runtime: 17.4215 secs\nLearning rate decreased to 0.000026\nEpoch: 1451, Step: 36275, training loss: 2074.440, validation loss: 2085.998, Runtime: 17.3779 secs\nEpoch: 1452, Step: 36300, training loss: 2074.476, validation loss: 2086.349, Runtime: 17.3710 secs\nEpoch: 1453, Step: 36325, training loss: 2074.301, validation loss: 2085.913, Runtime: 17.5104 secs\nEpoch: 1454, Step: 36350, training loss: 2074.325, validation loss: 2086.339, Runtime: 17.5382 secs\nEpoch: 1455, Step: 36375, training loss: 2074.207, validation loss: 2086.062, Runtime: 17.3388 secs\nEpoch: 1456, Step: 36400, training loss: 2074.580, validation loss: 2086.486, Runtime: 17.4128 secs\nEpoch: 1457, Step: 36425, training loss: 2074.326, validation loss: 2085.881, Runtime: 17.4783 secs\nEpoch: 1458, Step: 36450, training loss: 2074.308, validation loss: 2086.546, Runtime: 17.7863 secs\nEpoch: 1459, Step: 36475, training loss: 2074.188, validation loss: 2086.378, Runtime: 17.4499 secs\nEpoch: 1460, Step: 36500, training loss: 2074.371, validation loss: 2085.441, Runtime: 17.3922 secs\nEpoch: 1461, Step: 36525, training loss: 2074.271, validation loss: 2086.255, Runtime: 17.3875 secs\nEpoch: 1462, Step: 36550, training loss: 2074.481, validation loss: 2086.336, Runtime: 17.3947 secs\nEpoch: 1463, Step: 36575, training loss: 2074.375, validation loss: 2086.001, Runtime: 17.3987 secs\nEpoch: 1464, Step: 36600, training loss: 2074.334, validation loss: 2086.562, Runtime: 17.4441 secs\nEpoch: 1465, Step: 36625, training loss: 2074.485, validation loss: 2086.291, Runtime: 17.3038 secs\nLearning rate decreased to 0.000025\nEpoch: 1466, Step: 36650, training loss: 2074.350, validation loss: 2086.478, Runtime: 17.4614 secs\nEpoch: 1467, Step: 36675, training loss: 2074.219, validation loss: 2085.610, Runtime: 17.2729 secs\nEpoch: 1468, Step: 36700, training loss: 2074.211, validation loss: 2086.569, Runtime: 17.4914 secs\nEpoch: 1469, Step: 36725, training loss: 2074.319, validation loss: 2086.185, Runtime: 17.4116 secs\nEpoch: 1470, Step: 36750, training loss: 2074.326, validation loss: 2085.881, Runtime: 17.4354 secs\nEpoch: 1471, Step: 36775, training loss: 2074.274, validation loss: 2086.396, Runtime: 17.4444 secs\nEpoch: 1472, Step: 36800, training loss: 2074.459, validation loss: 2086.202, Runtime: 17.8119 secs\nLearning rate decreased to 0.000024\nEpoch: 1473, Step: 36825, training loss: 2074.435, validation loss: 2085.802, Runtime: 17.4320 secs\nEpoch: 1474, Step: 36850, training loss: 2074.296, validation loss: 2085.964, Runtime: 17.4800 secs\nEpoch: 1475, Step: 36875, training loss: 2074.167, validation loss: 2086.351, Runtime: 17.4642 secs\nEpoch: 1476, Step: 36900, training loss: 2074.163, validation loss: 2085.663, Runtime: 17.4478 secs\nEpoch: 1477, Step: 36925, training loss: 2074.398, validation loss: 2086.172, Runtime: 17.3876 secs\nEpoch: 1478, Step: 36950, training loss: 2074.211, validation loss: 2086.250, Runtime: 17.2816 secs\nEpoch: 1479, Step: 36975, training loss: 2074.362, validation loss: 2086.447, Runtime: 17.3200 secs\nEpoch: 1480, Step: 37000, training loss: 2074.516, validation loss: 2085.988, Runtime: 17.4575 secs\nLearning rate decreased to 0.000022\nEpoch: 1481, Step: 37025, training loss: 2074.399, validation loss: 2086.607, Runtime: 17.4498 secs\nEpoch: 1482, Step: 37050, training loss: 2074.540, validation loss: 2085.771, Runtime: 17.3599 secs\nEpoch: 1483, Step: 37075, training loss: 2074.499, validation loss: 2085.756, Runtime: 17.4331 secs\nEpoch: 1484, Step: 37100, training loss: 2074.460, validation loss: 2085.912, Runtime: 17.3680 secs\nEpoch: 1485, Step: 37125, training loss: 2074.472, validation loss: 2086.073, Runtime: 17.4234 secs\nEpoch: 1486, Step: 37150, training loss: 2074.445, validation loss: 2086.298, Runtime: 17.2297 secs\nEpoch: 1487, Step: 37175, training loss: 2074.175, validation loss: 2085.605, Runtime: 17.4351 secs\nEpoch: 1488, Step: 37200, training loss: 2074.441, validation loss: 2086.281, Runtime: 17.4687 secs\nEpoch: 1489, Step: 37225, training loss: 2074.103, validation loss: 2086.321, Runtime: 17.4958 secs\nEpoch: 1490, Step: 37250, training loss: 2074.492, validation loss: 2085.842, Runtime: 17.3997 secs\nLearning rate decreased to 0.000021\nEpoch: 1491, Step: 37275, training loss: 2074.536, validation loss: 2086.412, Runtime: 17.3781 secs\nEpoch: 1492, Step: 37300, training loss: 2074.472, validation loss: 2085.987, Runtime: 17.4314 secs\nEpoch: 1493, Step: 37325, training loss: 2074.230, validation loss: 2086.035, Runtime: 17.5429 secs\nEpoch: 1494, Step: 37350, training loss: 2074.149, validation loss: 2086.453, Runtime: 17.4607 secs\nEpoch: 1495, Step: 37375, training loss: 2074.381, validation loss: 2086.500, Runtime: 17.4338 secs\nEpoch: 1496, Step: 37400, training loss: 2074.542, validation loss: 2086.009, Runtime: 17.4127 secs\nLearning rate decreased to 0.000020\nEpoch: 1497, Step: 37425, training loss: 2074.527, validation loss: 2086.089, Runtime: 17.9187 secs\nEpoch: 1498, Step: 37450, training loss: 2074.203, validation loss: 2086.107, Runtime: 17.5474 secs\nEpoch: 1499, Step: 37475, training loss: 2074.615, validation loss: 2086.153, Runtime: 17.8948 secs\nEpoch: 1500, Step: 37500, training loss: 2074.391, validation loss: 2085.671, Runtime: 17.5449 secs\nEpoch: 1501, Step: 37525, training loss: 2074.395, validation loss: 2086.334, Runtime: 17.3675 secs\nEpoch: 1502, Step: 37550, training loss: 2074.147, validation loss: 2086.360, Runtime: 17.4619 secs\nEpoch: 1503, Step: 37575, training loss: 2074.337, validation loss: 2085.932, Runtime: 17.3765 secs\nEpoch: 1504, Step: 37600, training loss: 2074.158, validation loss: 2086.096, Runtime: 17.4277 secs\nEpoch: 1505, Step: 37625, training loss: 2074.347, validation loss: 2086.390, Runtime: 17.3212 secs\nEpoch: 1506, Step: 37650, training loss: 2074.074, validation loss: 2086.419, Runtime: 17.3485 secs\nEpoch: 1507, Step: 37675, training loss: 2074.348, validation loss: 2086.344, Runtime: 17.5845 secs\nEpoch: 1508, Step: 37700, training loss: 2074.419, validation loss: 2086.272, Runtime: 17.7339 secs\nLearning rate decreased to 0.000019\nEpoch: 1509, Step: 37725, training loss: 2074.455, validation loss: 2086.030, Runtime: 17.4286 secs\nEpoch: 1510, Step: 37750, training loss: 2074.344, validation loss: 2086.362, Runtime: 17.4799 secs\nEpoch: 1511, Step: 37775, training loss: 2074.467, validation loss: 2085.782, Runtime: 17.5073 secs\nEpoch: 1512, Step: 37800, training loss: 2074.595, validation loss: 2086.005, Runtime: 17.4655 secs\nEpoch: 1513, Step: 37825, training loss: 2074.301, validation loss: 2086.551, Runtime: 17.3644 secs\nEpoch: 1514, Step: 37850, training loss: 2074.314, validation loss: 2086.359, Runtime: 17.6791 secs\nEpoch: 1515, Step: 37875, training loss: 2074.397, validation loss: 2086.148, Runtime: 17.4197 secs\nEpoch: 1516, Step: 37900, training loss: 2074.446, validation loss: 2086.408, Runtime: 17.4700 secs\nEpoch: 1517, Step: 37925, training loss: 2074.396, validation loss: 2086.383, Runtime: 17.3329 secs\nEpoch: 1518, Step: 37950, training loss: 2074.505, validation loss: 2086.626, Runtime: 17.2747 secs\nEpoch: 1519, Step: 37975, training loss: 2074.371, validation loss: 2086.188, Runtime: 17.3665 secs\nEpoch: 1520, Step: 38000, training loss: 2074.424, validation loss: 2086.345, Runtime: 17.4076 secs\nEpoch: 1521, Step: 38025, training loss: 2074.591, validation loss: 2085.875, Runtime: 17.3627 secs\nLearning rate decreased to 0.000018\nEpoch: 1522, Step: 38050, training loss: 2074.461, validation loss: 2086.297, Runtime: 17.3233 secs\nEpoch: 1523, Step: 38075, training loss: 2074.474, validation loss: 2086.621, Runtime: 17.3718 secs\nEpoch: 1524, Step: 38100, training loss: 2074.354, validation loss: 2086.145, Runtime: 17.5394 secs\nEpoch: 1525, Step: 38125, training loss: 2074.427, validation loss: 2086.780, Runtime: 17.4402 secs\nEpoch: 1526, Step: 38150, training loss: 2074.447, validation loss: 2086.319, Runtime: 17.4488 secs\nEpoch: 1527, Step: 38175, training loss: 2074.706, validation loss: 2086.371, Runtime: 17.2469 secs\nLearning rate decreased to 0.000017\nEpoch: 1528, Step: 38200, training loss: 2074.482, validation loss: 2086.018, Runtime: 17.3231 secs\nEpoch: 1529, Step: 38225, training loss: 2074.240, validation loss: 2085.916, Runtime: 17.4615 secs\nEpoch: 1530, Step: 38250, training loss: 2074.260, validation loss: 2086.034, Runtime: 17.7167 secs\nEpoch: 1531, Step: 38275, training loss: 2074.372, validation loss: 2085.892, Runtime: 17.2659 secs\nEpoch: 1532, Step: 38300, training loss: 2074.279, validation loss: 2086.672, Runtime: 17.3324 secs\nEpoch: 1533, Step: 38325, training loss: 2074.645, validation loss: 2086.251, Runtime: 17.4606 secs\nEpoch: 1534, Step: 38350, training loss: 2074.291, validation loss: 2086.011, Runtime: 17.4458 secs\nEpoch: 1535, Step: 38375, training loss: 2074.220, validation loss: 2086.608, Runtime: 17.5106 secs\nEpoch: 1536, Step: 38400, training loss: 2074.468, validation loss: 2086.399, Runtime: 17.3145 secs\nEpoch: 1537, Step: 38425, training loss: 2074.445, validation loss: 2085.440, Runtime: 17.4322 secs\nEpoch: 1538, Step: 38450, training loss: 2074.302, validation loss: 2085.956, Runtime: 17.4742 secs\nEpoch: 1539, Step: 38475, training loss: 2074.645, validation loss: 2086.269, Runtime: 17.3779 secs\nLearning rate decreased to 0.000016\nEpoch: 1540, Step: 38500, training loss: 2074.257, validation loss: 2086.322, Runtime: 17.3593 secs\nEpoch: 1541, Step: 38525, training loss: 2074.287, validation loss: 2086.102, Runtime: 17.2546 secs\nEpoch: 1542, Step: 38550, training loss: 2074.401, validation loss: 2086.450, Runtime: 17.2534 secs\nEpoch: 1543, Step: 38575, training loss: 2074.395, validation loss: 2086.313, Runtime: 17.4693 secs\nEpoch: 1544, Step: 38600, training loss: 2074.473, validation loss: 2086.168, Runtime: 17.9017 secs\nEpoch: 1545, Step: 38625, training loss: 2074.319, validation loss: 2085.571, Runtime: 17.3560 secs\nEpoch: 1546, Step: 38650, training loss: 2074.391, validation loss: 2086.009, Runtime: 17.2662 secs\nEpoch: 1547, Step: 38675, training loss: 2074.304, validation loss: 2086.477, Runtime: 17.3674 secs\nEpoch: 1548, Step: 38700, training loss: 2074.161, validation loss: 2086.499, Runtime: 17.2969 secs\nEpoch: 1549, Step: 38725, training loss: 2074.352, validation loss: 2086.099, Runtime: 17.4227 secs\nEpoch: 1550, Step: 38750, training loss: 2074.493, validation loss: 2086.158, Runtime: 17.2588 secs\nLearning rate decreased to 0.000016\nEpoch: 1551, Step: 38775, training loss: 2074.345, validation loss: 2086.165, Runtime: 17.3836 secs\nEpoch: 1552, Step: 38800, training loss: 2074.215, validation loss: 2086.435, Runtime: 17.4677 secs\nEpoch: 1553, Step: 38825, training loss: 2074.380, validation loss: 2086.578, Runtime: 17.1095 secs\nEpoch: 1554, Step: 38850, training loss: 2074.275, validation loss: 2086.090, Runtime: 17.4037 secs\nEpoch: 1555, Step: 38875, training loss: 2074.302, validation loss: 2085.655, Runtime: 17.3527 secs\nEpoch: 1556, Step: 38900, training loss: 2074.433, validation loss: 2085.714, Runtime: 17.4477 secs\nEpoch: 1557, Step: 38925, training loss: 2074.186, validation loss: 2086.406, Runtime: 17.4458 secs\nEpoch: 1558, Step: 38950, training loss: 2074.349, validation loss: 2086.726, Runtime: 17.5001 secs\nEpoch: 1559, Step: 38975, training loss: 2074.240, validation loss: 2086.103, Runtime: 17.4267 secs\nEpoch: 1560, Step: 39000, training loss: 2074.327, validation loss: 2086.407, Runtime: 17.3985 secs\nEpoch: 1561, Step: 39025, training loss: 2074.407, validation loss: 2085.735, Runtime: 17.4439 secs\nEpoch: 1562, Step: 39050, training loss: 2074.364, validation loss: 2086.332, Runtime: 17.4884 secs\nEpoch: 1563, Step: 39075, training loss: 2074.078, validation loss: 2086.109, Runtime: 17.4180 secs\nEpoch: 1564, Step: 39100, training loss: 2074.190, validation loss: 2085.903, Runtime: 17.4465 secs\nEpoch: 1565, Step: 39125, training loss: 2074.490, validation loss: 2085.890, Runtime: 17.4140 secs\nLearning rate decreased to 0.000015\nEpoch: 1566, Step: 39150, training loss: 2074.228, validation loss: 2086.879, Runtime: 17.4493 secs\nEpoch: 1567, Step: 39175, training loss: 2074.130, validation loss: 2086.191, Runtime: 17.3191 secs\nEpoch: 1568, Step: 39200, training loss: 2074.466, validation loss: 2085.827, Runtime: 17.3071 secs\nEpoch: 1569, Step: 39225, training loss: 2074.291, validation loss: 2086.189, Runtime: 17.4314 secs\nEpoch: 1570, Step: 39250, training loss: 2074.425, validation loss: 2086.075, Runtime: 17.3825 secs\nEpoch: 1571, Step: 39275, training loss: 2074.303, validation loss: 2086.488, Runtime: 17.3309 secs\nEpoch: 1572, Step: 39300, training loss: 2074.382, validation loss: 2085.802, Runtime: 17.2732 secs\nEpoch: 1573, Step: 39325, training loss: 2074.293, validation loss: 2086.226, Runtime: 17.3971 secs\nEpoch: 1574, Step: 39350, training loss: 2074.328, validation loss: 2086.410, Runtime: 17.3574 secs\nEpoch: 1575, Step: 39375, training loss: 2074.322, validation loss: 2086.366, Runtime: 17.4705 secs\nEpoch: 1576, Step: 39400, training loss: 2074.138, validation loss: 2086.010, Runtime: 17.4283 secs\nEpoch: 1577, Step: 39425, training loss: 2074.431, validation loss: 2086.583, Runtime: 17.3656 secs\nLearning rate decreased to 0.000014\nEpoch: 1578, Step: 39450, training loss: 2074.427, validation loss: 2086.371, Runtime: 17.4468 secs\nEpoch: 1579, Step: 39475, training loss: 2074.301, validation loss: 2085.808, Runtime: 17.2613 secs\nEpoch: 1580, Step: 39500, training loss: 2074.121, validation loss: 2086.492, Runtime: 17.3124 secs\nEpoch: 1581, Step: 39525, training loss: 2074.237, validation loss: 2086.295, Runtime: 17.3967 secs\nEpoch: 1582, Step: 39550, training loss: 2074.488, validation loss: 2086.566, Runtime: 17.3536 secs\nEpoch: 1583, Step: 39575, training loss: 2074.292, validation loss: 2085.915, Runtime: 17.4623 secs\nEpoch: 1584, Step: 39600, training loss: 2074.380, validation loss: 2085.856, Runtime: 17.2739 secs\nEpoch: 1585, Step: 39625, training loss: 2074.293, validation loss: 2086.331, Runtime: 17.2905 secs\nEpoch: 1586, Step: 39650, training loss: 2074.513, validation loss: 2086.426, Runtime: 17.4721 secs\nLearning rate decreased to 0.000013\nEpoch: 1587, Step: 39675, training loss: 2074.577, validation loss: 2086.476, Runtime: 17.4157 secs\nEpoch: 1588, Step: 39700, training loss: 2074.333, validation loss: 2086.589, Runtime: 17.4989 secs\nEpoch: 1589, Step: 39725, training loss: 2074.287, validation loss: 2085.946, Runtime: 17.5389 secs\nEpoch: 1590, Step: 39750, training loss: 2074.217, validation loss: 2086.161, Runtime: 17.4990 secs\nEpoch: 1591, Step: 39775, training loss: 2074.364, validation loss: 2086.063, Runtime: 17.3773 secs\nEpoch: 1592, Step: 39800, training loss: 2074.337, validation loss: 2086.112, Runtime: 17.4747 secs\nEpoch: 1593, Step: 39825, training loss: 2074.093, validation loss: 2085.966, Runtime: 17.3852 secs\nEpoch: 1594, Step: 39850, training loss: 2074.445, validation loss: 2086.272, Runtime: 17.4213 secs\nLearning rate decreased to 0.000013\nEpoch: 1595, Step: 39875, training loss: 2074.309, validation loss: 2086.282, Runtime: 17.3996 secs\nEpoch: 1596, Step: 39900, training loss: 2074.399, validation loss: 2086.072, Runtime: 17.4565 secs\nEpoch: 1597, Step: 39925, training loss: 2074.302, validation loss: 2086.535, Runtime: 17.3022 secs\nEpoch: 1598, Step: 39950, training loss: 2074.168, validation loss: 2086.311, Runtime: 17.2455 secs\nEpoch: 1599, Step: 39975, training loss: 2074.642, validation loss: 2085.980, Runtime: 17.4064 secs\nEpoch: 1600, Step: 40000, training loss: 2074.355, validation loss: 2086.024, Runtime: 17.5482 secs\nEpoch: 1601, Step: 40025, training loss: 2074.278, validation loss: 2086.457, Runtime: 17.3656 secs\nEpoch: 1602, Step: 40050, training loss: 2074.388, validation loss: 2086.023, Runtime: 17.3825 secs\nEpoch: 1603, Step: 40075, training loss: 2074.397, validation loss: 2086.394, Runtime: 17.3484 secs\nEpoch: 1604, Step: 40100, training loss: 2074.263, validation loss: 2086.995, Runtime: 17.3358 secs\nEpoch: 1605, Step: 40125, training loss: 2074.183, validation loss: 2086.322, Runtime: 17.2805 secs\nEpoch: 1606, Step: 40150, training loss: 2074.273, validation loss: 2086.330, Runtime: 17.8128 secs\nEpoch: 1607, Step: 40175, training loss: 2074.376, validation loss: 2086.484, Runtime: 17.3262 secs\nEpoch: 1608, Step: 40200, training loss: 2074.509, validation loss: 2086.434, Runtime: 17.2976 secs\nLearning rate decreased to 0.000012\nEpoch: 1609, Step: 40225, training loss: 2074.330, validation loss: 2085.792, Runtime: 17.2698 secs\nEpoch: 1610, Step: 40250, training loss: 2074.494, validation loss: 2086.082, Runtime: 17.4054 secs\nEpoch: 1611, Step: 40275, training loss: 2074.144, validation loss: 2086.337, Runtime: 17.5179 secs\nEpoch: 1612, Step: 40300, training loss: 2074.291, validation loss: 2086.753, Runtime: 17.2929 secs\nEpoch: 1613, Step: 40325, training loss: 2074.688, validation loss: 2086.297, Runtime: 17.3270 secs\nEpoch: 1614, Step: 40350, training loss: 2074.287, validation loss: 2086.196, Runtime: 17.4414 secs\nEpoch: 1615, Step: 40375, training loss: 2074.491, validation loss: 2086.376, Runtime: 17.3211 secs\nEpoch: 1616, Step: 40400, training loss: 2074.392, validation loss: 2085.629, Runtime: 17.3692 secs\nEpoch: 1617, Step: 40425, training loss: 2074.379, validation loss: 2085.968, Runtime: 17.4159 secs\nEpoch: 1618, Step: 40450, training loss: 2074.454, validation loss: 2086.378, Runtime: 17.5482 secs\nEpoch: 1619, Step: 40475, training loss: 2074.369, validation loss: 2085.552, Runtime: 17.2738 secs\nEpoch: 1620, Step: 40500, training loss: 2074.336, validation loss: 2086.583, Runtime: 17.4051 secs\nEpoch: 1621, Step: 40525, training loss: 2074.336, validation loss: 2086.455, Runtime: 17.4310 secs\nEpoch: 1622, Step: 40550, training loss: 2074.305, validation loss: 2086.107, Runtime: 17.4193 secs\nEpoch: 1623, Step: 40575, training loss: 2074.320, validation loss: 2086.314, Runtime: 17.4164 secs\nEpoch: 1624, Step: 40600, training loss: 2074.443, validation loss: 2085.669, Runtime: 17.3420 secs\nEpoch: 1625, Step: 40625, training loss: 2074.312, validation loss: 2085.735, Runtime: 17.4259 secs\nEpoch: 1626, Step: 40650, training loss: 2074.286, validation loss: 2086.301, Runtime: 17.3187 secs\nEpoch: 1627, Step: 40675, training loss: 2074.664, validation loss: 2086.794, Runtime: 17.4514 secs\nLearning rate decreased to 0.000011\nEpoch: 1628, Step: 40700, training loss: 2074.535, validation loss: 2086.569, Runtime: 17.4684 secs\nEpoch: 1629, Step: 40725, training loss: 2074.368, validation loss: 2086.290, Runtime: 17.3113 secs\nEpoch: 1630, Step: 40750, training loss: 2074.311, validation loss: 2086.350, Runtime: 17.4251 secs\nEpoch: 1631, Step: 40775, training loss: 2074.076, validation loss: 2086.391, Runtime: 17.7905 secs\nEpoch: 1632, Step: 40800, training loss: 2074.025, validation loss: 2086.100, Runtime: 17.4608 secs\nEpoch: 1633, Step: 40825, training loss: 2074.306, validation loss: 2086.035, Runtime: 17.4149 secs\nEpoch: 1634, Step: 40850, training loss: 2074.114, validation loss: 2086.462, Runtime: 17.5210 secs\nEpoch: 1635, Step: 40875, training loss: 2074.266, validation loss: 2086.376, Runtime: 17.6115 secs\nEpoch: 1636, Step: 40900, training loss: 2074.408, validation loss: 2086.402, Runtime: 17.3915 secs\nLearning rate decreased to 0.000011\nEpoch: 1637, Step: 40925, training loss: 2074.456, validation loss: 2085.869, Runtime: 17.4546 secs\nEpoch: 1638, Step: 40950, training loss: 2074.213, validation loss: 2086.060, Runtime: 17.4931 secs\nEpoch: 1639, Step: 40975, training loss: 2074.180, validation loss: 2086.115, Runtime: 17.3166 secs\nEpoch: 1640, Step: 41000, training loss: 2074.101, validation loss: 2086.024, Runtime: 17.7589 secs\nEpoch: 1641, Step: 41025, training loss: 2074.423, validation loss: 2086.085, Runtime: 17.3371 secs\nEpoch: 1642, Step: 41050, training loss: 2074.310, validation loss: 2086.052, Runtime: 17.4618 secs\nEpoch: 1643, Step: 41075, training loss: 2074.392, validation loss: 2085.971, Runtime: 17.3652 secs\nEpoch: 1644, Step: 41100, training loss: 2074.341, validation loss: 2086.062, Runtime: 17.4821 secs\nEpoch: 1645, Step: 41125, training loss: 2074.391, validation loss: 2085.951, Runtime: 17.4468 secs\nEpoch: 1646, Step: 41150, training loss: 2074.345, validation loss: 2086.271, Runtime: 17.4131 secs\nEpoch: 1647, Step: 41175, training loss: 2074.331, validation loss: 2086.177, Runtime: 17.3760 secs\nEpoch: 1648, Step: 41200, training loss: 2074.400, validation loss: 2086.010, Runtime: 17.3875 secs\nLearning rate decreased to 0.000010\nEpoch: 1649, Step: 41225, training loss: 2074.094, validation loss: 2086.418, Runtime: 17.4398 secs\nEpoch: 1650, Step: 41250, training loss: 2074.300, validation loss: 2085.857, Runtime: 17.3888 secs\nEpoch: 1651, Step: 41275, training loss: 2074.212, validation loss: 2086.157, Runtime: 17.6161 secs\nEpoch: 1652, Step: 41300, training loss: 2074.065, validation loss: 2086.751, Runtime: 17.3781 secs\nEpoch: 1653, Step: 41325, training loss: 2074.400, validation loss: 2085.960, Runtime: 17.3265 secs\nEpoch: 1654, Step: 41350, training loss: 2074.352, validation loss: 2086.264, Runtime: 17.4073 secs\nEpoch: 1655, Step: 41375, training loss: 2074.246, validation loss: 2086.205, Runtime: 17.2920 secs\nEpoch: 1656, Step: 41400, training loss: 2074.187, validation loss: 2086.447, Runtime: 17.4270 secs\nEpoch: 1657, Step: 41425, training loss: 2074.355, validation loss: 2085.937, Runtime: 17.4316 secs\nEpoch: 1658, Step: 41450, training loss: 2074.370, validation loss: 2086.950, Runtime: 17.4762 secs\nEpoch: 1659, Step: 41475, training loss: 2074.316, validation loss: 2085.911, Runtime: 17.4606 secs\nEpoch: 1660, Step: 41500, training loss: 2074.166, validation loss: 2086.148, Runtime: 17.3242 secs\nEpoch: 1661, Step: 41525, training loss: 2074.163, validation loss: 2085.881, Runtime: 17.5342 secs\nEpoch: 1662, Step: 41550, training loss: 2074.400, validation loss: 2086.326, Runtime: 17.4161 secs\nLearning rate decreased to 0.000010\n...training complete.\n" ], [ "model.load_checkpoint('best')", "_____no_output_____" ], [ "model.plot_summary(valid_data, valid_truth)", "_____no_output_____" ], [ "results_dict = model.plot_recon_rsquared(valid_data, valid_truth, train_data, train_truth)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Analyze a large dataset with Google BigQuery\n\n**Learning Objectives**\n\n1. Access an ecommerce dataset\n1. Look at the dataset metadata\n1. Remove duplicate entries\n1. Write and execute queries\n\n\n## Introduction \nBigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.\n\nWe have a publicly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into a table in BigQuery. In this lab, you use a copy of that dataset. Sample scenarios are provided, from which you look at the data and ways to remove duplicate information. The lab then steps you through further analysis the data.\n\nBigQuery can be accessed by its own browser-based interface, Google Data Studio, and many third party tools. In this lab you will use the BigQuery directly in notebook cells using the iPython magic command `%%bigquery`.\n\nThe steps you will follow in the lab are analogous to what you would do to prepare data for use in advanced ML operations. You will follow the notebook to experiment with the BigQuery queries provided to analyze the data.", "_____no_output_____" ], [ "### Set up the notebook environment\n\n__VERY IMPORTANT__: In the cell below you must replace the text `<YOUR PROJECT>` with you GCP project id.", "_____no_output_____" ] ], [ [ "import os\n\nimport pandas as pd\n\nPROJECT = \"<YOUR PROJECT>\" #TODO Replace with your project id\n\nos.environ[\"PROJECT\"] = PROJECT\n\npd.options.display.max_columns = 50", "_____no_output_____" ] ], [ [ "## Explore eCommerce data and identify duplicate records\n\nScenario: You were provided with Google Analytics logs for an eCommerce website in a BigQuery dataset. The data analyst team created a new BigQuery table of all the raw eCommerce visitor session data. This data tracks user interactions, location, device types, time on page, and details of any transaction. Your ultimate plan is to use this data in an ML capacity to create a model that delivers highly accurate predictions of user behavior to support tailored marketing campaigns.\n\nFirst, a few notes on BigQuery within a python notebook context. Any cell that starts with `%%bigquery` (the BigQuery Magic) will be interpreted as a SQL query that is executed on BigQuery, and the result is printed to our notebook.\n\nBigQuery supports [two flavors](https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#comparison_of_legacy_and_standard_sql) of SQL syntax: legacy SQL and standard SQL. The preferred is standard SQL because it complies with the official SQL:2011 standard. To instruct BigQuery to interpret our syntax as such we start the query with `#standardSQL`.\n\nOur first query is accessing the BigQuery Information Schema which stores all object-related metadata. In this case we want to see metadata details for the \"all_sessions_raw\" table. \n\nTip: To run the current cell you can click the cell and hit **shift enter**", "_____no_output_____" ], [ "TODO 2", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardsql\nSELECT * \nEXCEPT \n (table_catalog, table_schema, is_generated, generation_expression, is_stored, \n is_updatable, is_hidden, is_system_defined, is_partitioning_column, clustering_ordinal_position)\nFROM `data-to-insights.ecommerce.INFORMATION_SCHEMA.COLUMNS`\nWHERE table_name=\"all_sessions_raw\"", "_____no_output_____" ] ], [ [ "Next examine how many rows are in the table.", "_____no_output_____" ], [ "TODO 1", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT \n#standardSQL\nSELECT count(*)\nFROM `data-to-insights.ecommerce.all_sessions_raw`", "_____no_output_____" ] ], [ [ "Now take a quick at few rows of data in the table.", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT \n#standardSQL\nSELECT *\nFROM `data-to-insights.ecommerce.all_sessions_raw`\nLIMIT 7", "_____no_output_____" ] ], [ [ "### Identify duplicate rows\n\nSeeing a sample amount of data may give you greater intuition for what is included in the dataset. But since the table is quite large, a preview is not likely to render meaningful results. As you scan and scroll through the sample rows you see there is no singular field that uniquely identifies a row, so you need advanced logic to identify duplicate rows.\n\nThe query below uses the SQL GROUP BY function on every field and counts (COUNT) where there are rows that have the same values across every field.\n\nIf every field is unique, the COUNT will return 1 as there are no other groupings of rows with the exact same value for all fields.\nIf there is a row with the same values for all fields, they will be grouped together and the COUNT will be greater than 1. The last part of the query is an aggregation filter using HAVING to only show the results that have a COUNT of duplicates greater than 1.\nRun the following query to find duplicate records across all columns.", "_____no_output_____" ], [ "TODO 3", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT \n#standardSQL\nSELECT count(*) AS num_duplicate_rows, \n * \nFROM `data-to-insights.ecommerce.all_sessions_raw` \nGROUP BY fullvisitorid, \n channelgrouping, \n time, \n country, \n city, \n totaltransactionrevenue, \n transactions, \n timeonsite, \n pageviews, \n sessionqualitydim, \n date, \n visitid, \n type, \n productrefundamount, \n productquantity, \n productprice, \n productrevenue, \n productsku, \n v2productname, \n v2productcategory, \n productvariant, \n currencycode, \n itemquantity, \n itemrevenue, \n transactionrevenue, \n transactionid, \n pagetitle, \n searchkeyword, \n pagepathlevel1, \n ecommerceaction_type, \n ecommerceaction_step, \n ecommerceaction_option \nHAVING num_duplicate_rows > 1; ", "_____no_output_____" ] ], [ [ "As you can see there are quite a few \"duplicate\" records (615) when analyzed with these parameters.\n\nIn your own datasets, even if you have a unique key, it is still beneficial to confirm the uniqueness of the rows with COUNT, GROUP BY, and HAVING before you begin your analysis.", "_____no_output_____" ], [ "## Analyze the new all_sessions table\n\nIn this section you use a deduplicated table called all_sessions.\n\nScenario: Your data analyst team has provided you with a relevant query, and your schema experts have identified the key fields that must be unique for each record per your schema.\n\nRun the query to confirm that no duplicates exist, this time against the \"all_sessions\" table:", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT fullvisitorid, # the unique visitor ID \n visitid, # a visitor can have multiple visits \n date, # session date stored as string YYYYMMDD \n time, # time of the individual site hit (can be 0 or more) \n v2productname, # not unique since a product can have variants like Color \n productsku, # unique for each product \n type, # visit and/or event trigger \n ecommerceaction_type, # maps to ‘add to cart', ‘completed checkout' \n ecommerceaction_step, \n ecommerceaction_option, \n transactionrevenue, # revenue of the order \n transactionid, # unique identifier for revenue bearing transaction \n count(*) AS row_count \nFROM `data-to-insights.ecommerce.all_sessions` \nGROUP BY 1, \n 2, \n 3, \n 4, \n 5, \n 6, \n 7, \n 8, \n 9, \n 10, \n 11, \n 12 \nHAVING row_count > 1 # find duplicates \n", "_____no_output_____" ] ], [ [ "The query returns zero records indicating no duplicates exist.", "_____no_output_____" ], [ "## Write basic SQL against the eCommerce data (TODO 4)\n\nIn this section, you query for insights on the ecommerce dataset.\n\nA good first path of analysis is to find the total unique visitors\nThe query below determines the total views by counting product_views and the number of unique visitors by counting fullVisitorID.", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT count(*) AS product_views, \n count(DISTINCT fullvisitorid) AS unique_visitors \nFROM `data-to-insights.ecommerce.all_sessions`; ", "_____no_output_____" ] ], [ [ "The next query shows total unique visitors(fullVisitorID) by the referring site (channelGrouping):", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT count(DISTINCT fullvisitorid) AS unique_visitors, \n channelgrouping \nFROM `data-to-insights.ecommerce.all_sessions` \nGROUP BY 2 \nORDER BY 2 DESC;", "_____no_output_____" ] ], [ [ "To find deeper insights in the data, the next query lists the five products with the most views (product_views) from unique visitors. The query counts number of times a product (v2ProductName) was viewed (product_views), puts the list in descending order, and lists the top 5 entries:", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT count(*) AS product_views, \n ( v2productname ) AS ProductName \nFROM `data-to-insights.ecommerce.all_sessions` \nWHERE type = 'PAGE' \nGROUP BY v2productname \nORDER BY product_views DESC \nLIMIT 5;", "_____no_output_____" ] ], [ [ "Now expand your previous query to include the total number of distinct products ordered and the total number of total units ordered (productQuantity):", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT count(*) AS product_views, \n count(productquantity) AS orders, \n sum(productquantity) AS quantity_product_ordered, \n v2productname \nFROM `data-to-insights.ecommerce.all_sessions` \nWHERE type = 'PAGE' \nGROUP BY v2productname \nORDER BY product_views DESC \nLIMIT 5; ", "_____no_output_____" ] ], [ [ "Lastly, expand the query to include the average amount of product per order (total number of units ordered/total number of orders, or `SUM(productQuantity)/COUNT(productQuantity)`).", "_____no_output_____" ] ], [ [ "%%bigquery --project $PROJECT\n#standardSQL\nSELECT count(*) AS product_views, \n count(productquantity) AS orders, \n sum(productquantity) AS quantity_product_ordered, \n sum(productquantity) / Count(productquantity) AS avg_per_order, \n v2productname AS productName \nFROM `data-to-insights.ecommerce.all_sessions` \nWHERE type = 'PAGE' \nGROUP BY v2productname \nORDER BY product_views DESC \nLIMIT 5; ", "_____no_output_____" ] ], [ [ "You can see that among these top 5 products by product views that the 22 oz YouTube Bottle Infuser had the highest avg_per_order with 9.38 units per order.", "_____no_output_____" ], [ "You have completed this lab exercise. In this situation the \"all_sessions\" was provided to you with the deduplicated records. In the course of your own future analysis you may have to create this on your own using BigQuery and the `create table DATASET.TABLE2 as select * from DATASET.TABLE1` syntax.", "_____no_output_____" ], [ "Copyright 2019 Google Inc.\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\nhttp://www.apache.org/licenses/LICENSE-2.0\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "library(tidyverse)\nlibrary(skimr)", "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.2.1 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.2\n\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 2.1.3 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 0.8.3\n\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 1.3.1 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.4.0\n── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n\nAttaching package: ‘skimr’\n\nThe following object is masked from ‘package:stats’:\n\n filter\n\n" ] ], [ [ "download the dataset from https://www.ncdc.noaa.gov/cag/global/time-series", "_____no_output_____" ], [ "# Data wrangling #", "_____no_output_____" ], [ "Normalize the column name :change the `value` to `Surface Temperature in Africa` in each dataframe", "_____no_output_____" ] ], [ [ "Africa1 <- read_csv(file = \"Africa.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "Africa <- Africa1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nAfrica2 <- Africa %>% rename(\"Surface Temperature in Africa\" = \"SurfaceTemperature\")", "_____no_output_____" ], [ "North_America1 <- read_csv(file = \"North America.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "North_America <- North_America1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nNorth_America2 <- North_America %>% rename(\"Surface Temperature in North America\" = \"SurfaceTemperature\")", "_____no_output_____" ], [ "South_America1 <- read_csv(file = \"South America.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "South_America <- South_America1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nSouth_America2 <- South_America %>% rename(\"Surface Temperature in South America\" = \"SurfaceTemperature\")", "_____no_output_____" ], [ "Europe1 <- read_csv(file = \"Europe.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "Europe <- Europe1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nEurope2 <- Europe %>% rename(\"Surface Temperature in Europe\" = \"SurfaceTemperature\")", "_____no_output_____" ], [ "Asia1 <- read_csv(file = \"Asia.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "Asia <- Asia1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nAsia2 <- Asia %>% rename(\"Surface Temperature in Asia\" = \"SurfaceTemperature\")", "_____no_output_____" ], [ "Oceania1 <- read_csv(file = \"Oceania.csv\")", "Parsed with column specification:\ncols(\n Year = \u001b[32mcol_double()\u001b[39m,\n Value = \u001b[32mcol_double()\u001b[39m\n)\n" ], [ "Oceania <- Oceania1 %>% rename(\"SurfaceTemperature\" = \"Value\")\nOceania2 <- Oceania %>% rename(\"Surface Temperature in Oceania\" = \"SurfaceTemperature\")", "_____no_output_____" ] ], [ [ "Join together!", "_____no_output_____" ] ], [ [ "climate_df <- Africa2 %>% \n full_join(North_America2) %>%\n full_join(South_America2) %>%\n full_join(Europe2) %>%\n full_join(Asia2) %>%\n full_join(Oceania2)", "Joining, by = \"Year\"\nJoining, by = \"Year\"\nJoining, by = \"Year\"\nJoining, by = \"Year\"\nJoining, by = \"Year\"\n" ] ], [ [ "check about the types of the columns, the missing values, and output a quick summary of the dataset.", "_____no_output_____" ] ], [ [ "glimpse(climate_df)", "Observations: 109\nVariables: 7\n$ Year \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m 1910, 1911, 1912, 1913, 1914, …\n$ `Surface Temperature in Africa` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m -0.38, -0.57, -0.24, -0.11, -0…\n$ `Surface Temperature in North America` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m -0.13, -0.40, -0.73, -0.67, -0…\n$ `Surface Temperature in South America` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m -0.43, -0.57, -0.25, -0.16, -0…\n$ `Surface Temperature in Europe` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m 0.01, -0.16, -0.71, -0.14, -0.…\n$ `Surface Temperature in Asia` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m -0.59, -0.58, -0.88, -0.42, 0.…\n$ `Surface Temperature in Oceania` \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m -0.28, -0.47, -0.05, -0.60, 0.…\n" ], [ "summary(climate_df) ", "_____no_output_____" ], [ "climate_df %>%\n skim() %>%\n kable()", "Skim summary statistics \n n obs: 109 \n n variables: 7 \n\nVariable type: numeric\n\n| variable | missing | complete | n | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |\n|--------------------------------------|---------|----------|-----|------|-------|-------|-------|------|------|------|----------|\n| Surface Temperature in Africa | 0 | 109 | 109 | 0.17 | 0.49 | -0.68 | -0.19 | 0.02 | 0.43 | 1.52 | ▂▇▇▅▂▃▂▁ |\n| Surface Temperature in Asia | 0 | 109 | 109 | 0.2 | 0.61 | -0.88 | -0.26 | 0.05 | 0.67 | 1.7 | ▂▇▇▆▂▃▃▁ |\n| Surface Temperature in Europe | 0 | 109 | 109 | 0.19 | 0.64 | -1.07 | -0.16 | 0.06 | 0.58 | 1.87 | ▂▅▇▆▃▃▂▁ |\n| Surface Temperature in North America | 0 | 109 | 109 | 0.17 | 0.61 | -1.3 | -0.29 | 0.1 | 0.5 | 1.92 | ▁▂▇▇▆▂▂▁ |\n| Surface Temperature in Oceania | 0 | 109 | 109 | 0.14 | 0.49 | -0.89 | -0.25 | 0.11 | 0.44 | 1.33 | ▁▃▇▆▆▃▂▂ |\n| Surface Temperature in South America | 0 | 109 | 109 | 0.16 | 0.5 | -0.87 | -0.19 | 0.07 | 0.47 | 1.48 | ▂▃▇▆▃▃▂▁ |\n| Year | 0 | 109 | 109 | 1964 | 31.61 | 1910 | 1937 | 1964 | 1991 | 2018 | ▇▇▇▇▇▇▇▇ |\n" ], [ "write_csv(climate_df,\"Climate.csv\")", "_____no_output_____" ] ], [ [ "# Data analysis#", "_____no_output_____" ], [ "choose the data from 1950 to 2018 for ploting", "_____no_output_____" ] ], [ [ "Africa$pos = Africa$SurfaceTemperature >= 0", "_____no_output_____" ], [ "Africa_climate_plot <- Africa %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in Africa\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nAfrica_climate_plot", "_____no_output_____" ], [ "ggsave(Africa_climate_plot,filename = \"Africa climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ], [ "North_America$pos = North_America$SurfaceTemperature >= 0", "_____no_output_____" ], [ "North_America_climate_plot <- North_America %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in North America\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nNorth_America_climate_plot", "_____no_output_____" ], [ "ggsave(North_America_climate_plot,filename = \"North America climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ], [ "South_America$pos = South_America$SurfaceTemperature >= 0", "_____no_output_____" ], [ "South_America_climate_plot <- South_America %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in South America\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nSouth_America_climate_plot", "_____no_output_____" ], [ "ggsave(South_America_climate_plot,filename = \"South America climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ], [ "Europe$pos = Europe$SurfaceTemperature >= 0", "_____no_output_____" ], [ "Europe_climate_plot <- Europe %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in Europe\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nEurope_climate_plot", "_____no_output_____" ], [ "ggsave(Europe_climate_plot,filename = \"Europe climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ], [ "Asia$pos = Asia$SurfaceTemperature >= 0", "_____no_output_____" ], [ "Asia_climate_plot <- Asia %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in Asia\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nAsia_climate_plot", "_____no_output_____" ], [ "ggsave(Asia_climate_plot,filename = \"Asia climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ], [ "Oceania$pos = Oceania$SurfaceTemperature >= 0", "_____no_output_____" ], [ "Oceania_climate_plot <- Oceania %>% \n filter( Year >= 1950) %>%\n ggplot(aes( \n x = Year, \n y = SurfaceTemperature,\n fill = pos)) + \n labs(title = \"Time Series of Surface Temperature Anomalies in Oceania\") +\n scale_x_continuous(breaks=seq(1950, 2020, 10)) +\n scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +\n geom_bar(stat = \"identity\",position = \"identity\", colour = \"black\", size = 0.05) +\n xlab(\"Year\") + ylab (\"Surface Temperature ( ºC )\") +\n theme_light()+\n theme(plot.title = element_text(hjust = 0.5)) +\n scale_fill_manual(values = c(\"#CCEEFF\", \"#FFDDDD\"), guide = FALSE) \nOceania_climate_plot", "_____no_output_____" ], [ "ggsave(Oceania_climate_plot,filename = \"Oceania climate plot.jpg\",width = 12,height = 9)", "_____no_output_____" ] ], [ [ "Put all plots together", "_____no_output_____" ] ], [ [ "library(ggpubr)", "Loading required package: magrittr\n\nAttaching package: ‘magrittr’\n\nThe following object is masked from ‘package:purrr’:\n\n set_names\n\nThe following object is masked from ‘package:tidyr’:\n\n extract\n\n" ], [ "general_plot <- ggarrange(Africa_climate_plot, Asia_climate_plot, \n Europe_climate_plot, South_America_climate_plot, \n North_America_climate_plot, Oceania_climate_plot, ncol = 2, nrow = 3)\ngeneral_plot", "_____no_output_____" ], [ "ggsave(general_plot,filename = \"Climate general plot.jpg\",width = 12,height = 9)", "_____no_output_____" ] ] ]

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[ [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import h5py\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport brownian\nfrom brownian import u\nfrom brownian import Cantilever\n\n%matplotlib inline", "_____no_output_____" ], [ "f = h5py.File('brownian173033.h5', 'r') ", "_____no_output_____" ], [ "freq = f['x'][()]\nPSD = f['y'][()]\nPSD_std = f['y_std'][()]\nPSD_err = PSD_std / (f['y'].attrs['n_avg']**0.5) # Standard error", "_____no_output_____" ], [ "m = brownian.make_mask(freq, 70000, 71000)\nplt.semilogy(freq[m], PSD[m])", "_____no_output_____" ], [ "cantilever = Cantilever(f_c=70.5*u.kHz,\n Q=28000*u.dimensionless,\n k_c=3.5*u.N/u.m)\nbmf = brownian.BrownianMotionFitter(freq, PSD, PSD_err, 298, cantilever)", "_____no_output_____" ], [ "bmf.calc_fit(70300, 70800)", "\nResiduals\n-------------------------------------\n Mean: -8.21e-02\n Std. dev.: 2.06e-01\n" ], [ "fig, ax = bmf.plot_fit()", "_____no_output_____" ], [ "ax.set_xlim(70540, 70580)\nax.set_ylim(1e-7, 1e-4)\nfig", "_____no_output_____" ], [ "bmf.plot_residuals()", "_____no_output_____" ], [ "bmf.plot_reduced_residuals()", "_____no_output_____" ], [ "bmf.plot_cdf()", "_____no_output_____" ], [ "print(bmf.report())", "\n Input\n -----------------------------------------------\n Temperature T: 298 kelvin\n\n Estimates\n -----------------------------------------------\n Spring constant k_c: 3.5 newton/meter\n Resonance frequency f_c: 70.5 kilohertz\n Quality factor Q: 28000 dimensionless\n \n Fitting\n -----------------------------------------------\n Fit frequency min f_min: 70300 hertz\n Fit frequency max f_max: 70800 hertz\n\n Results\n -----------------------------------------------\n Resonance frequency f_c: 70561.35(4) hertz\n Spring constant k_c: 22.3(8) newton/meter\n Quality Factor Q: 2.9(1)×10⁴ dimensionless\n Detector Noise : 7.38(7)×10⁻⁹ nanometer²/hertz\n \n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "#define the function to find the roots of\ndef function_for_roots(x):\n a = 1.01\n b = -3.04\n c = 2.07\n return a*x**2 +b*x +c", "_____no_output_____" ], [ "#define the derivative\ndef derivative_for_root(x):\n a = 1.01\n b = -3.04\n return 2*a*x +b", "_____no_output_____" ], [ "# define to check our initial guesses\ndef check_initial_values(f, x_min, x_max, tol):\n \n y_min = f(x_min)\n y_max = f(x_max)\n \n #check that x_min and x_max contain a zero crossing\n if(y_min*y_max>=0.0):\n print(\"No zero crossing found in the range = \",x_min, x_max)\n s = \"f(%f) = %f, f(%f) = %f\" % (x_min, y_min, x_max, y_max)\n print(s)\n return 0\n \n #if x_min is a root, then return flag ==1\n if(np.fabs(y_min)<tol):\n return 1\n \n #if x_max is a root, then return flag ==2\n if(np.fabs(y_max)<tol):\n return 2\n \n #if we reach this point, the bracket is valid and we will return 3\n return 3", "_____no_output_____" ], [ "#define function that uses bisection search to find a root\ndef bisection_root_finding(f, x_min_start, x_max_start, tol):\n \n x_min = x_min_start #min x\n x_max = x_max_start #max x\n x_mid = 0.0 #mid point\n \n y_min = f(x_min) #function value at x_min\n y_max = f(x_max) #...x_max\n y_mid = 0.0 #...mid point\n \n imax = 10000 #set a maximum number of iterations\n i = 0 #iteration counter\n \n #check the initial values\n flag = check_initial_values(f,x_min,x_max,tol)\n if(flag==0):\n print(\"Error in bisection_root_finding().\")\n raise ValueError('Initial values invalid',x_min,x_max)\n elif(flag==1):\n return x_min\n elif(flag==2):\n return x_max\n \n #if we reach here, then we need to conduct the search\n \n #set a flag\n flag = 1\n \n #enter a while loop\n while(flag):\n x_mid = 0.5*(x_min+x_max) #mid point\n y_mid = f(x_mid) #function value at x_mid\n \n #check if x_mid is a root\n if(np.fabs(y_mid)<tol):\n flag = 0\n else:\n #x_mid is not a root\n \n #if the product of the function at the midpoint\n #and at one of the end points is greater than\n #zero, replace this end point\n if(f(x_min)*f(x_mid)>0):\n #replace x_min with x_mid\n x_min = x_mid\n else:\n #replace x_max with x_mid\n x_max = x_mid\n \n \n #print out the iteration\n print(x_min,f(x_min),x_max,f(x_max))\n \n #count the iteration\n i += 1\n \n #if we have exceeded the max number\n #of iterations, exit\n if(i>=imax):\n print(\"Exceeded max number of iterations = \",i)\n s = \"Min bracket f(%f) = %f\" % (x_min, f(x_min))\n print(s)\n s = \"Max bracket f(%f) = %f\" % (x_max, f(x_max))\n print(s)\n s = \"Mid bracket f(%f) = %f\" % (x_mid, f(x_mid))\n print(s)\n raise StopIteration('Stopping iterations after ',i)\n \n #we are done!\n return x_mid", "_____no_output_____" ], [ "x_min = 0.0\nx_max = 1.5\ntolerance = 1.0e-6\n\n#print the initial guess\nprint(x_min,function_for_roots(x_min))\nprint(x_max,function_for_roots(x_max))\n\nx_root = bisection_root_finding(function_for_roots,x_min,x_max,tolerance)\ny_root = function_for_roots(x_root)\n\ns = \"Root found with y(%f) = %f\" % (x_root,y_root)\nprint(s)", "_____no_output_____" ], [ "def newton_raphson_root_finding(f, dfdx, x_start, tol):\n\n \n flag = 1\n \n imax = 10000\n \n i = 0\n \n #define the new and old guesses\n x_old = x_start\n x_new = 0.0\n y_new = 0.0\n \n #start the loop\n while(flag):\n \n #make a new guess\n x_new = x_old - f(x_old)/dfdx(x_old)\n \n print(x_new,x_old,f(x_old),dfdx(x_old))\n \n #if the abs value of the new function value\n #is < tol, then stop\n y_new = f(x_new)\n if(np.fabs(y_new)<tol):\n flag = 0 \n else:\n \n #plot\n fig = plt.figure(figsize =(7,7))\n xarr = function_for_roots(x)\n yarr = f(xarr)\n plt.plot(xarr,yarr, color = \"orange\")\n plt.axis([x_min,x_max,f(x_min),f(x_max)])\n plt.xlim([0,3])\n plt.ylim([-0.5,2.1])\n \n xapprox = np.linspace(0, 3, 1000)\n plt.plot(xapprox, color = \"black\")\n plt.suptitle('f(x) vs. x')\n plt.show()\n \n #save the result\n x_old = x_new\n #increment the iteration\n i += 1\n \n if(i>=imax):\n print(\"Max iterations reached.\")\n raise StopIteration('Stopping iterations after ',i)\n \n return x_new", "_____no_output_____" ], [ "x_start = 0.5\ntolerance = 1.0e-6\n\n#print the initial guess\nprint(x_start,function_for_roots(x_start))\n\nx_root = newton_raphson_root_finding(function_for_roots,derivative_for_root,x_start,tolerance)\ny_root = function_for_roots(x_root)\n \ns = \"Root found with y(%f) = %f\" % (x_root,y_root)\nprint(s)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import sys\nimport os\n\nSOURCE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__name__)))\nsys.path.insert(0, SOURCE_DIR)", "_____no_output_____" ], [ "import malaya\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)", "_____no_output_____" ], [ "malaya.__home__", "_____no_output_____" ], [ "string1 = \"Yang Berhormat Dato Sri Haji Mohammad Najib bin Tun Haji Abdul Razak ialah ahli politik Malaysia dan merupakan bekas Perdana Menteri Malaysia ke-6 yang mana beliau menjawat jawatan dari 3 April 2009 hingga 9 Mei 2018. Beliau juga pernah berkhidmat sebagai bekas Menteri Kewangan dan merupakan Ahli Parlimen Pekan Pahang\"\nstring2 = \"Pahang ialah negeri yang ketiga terbesar di Malaysia Terletak di lembangan Sungai Pahang yang amat luas negeri Pahang bersempadan dengan Kelantan di utara Perak Selangor serta Negeri Sembilan di barat Johor di selatan dan Terengganu dan Laut China Selatan di timur.\"", "_____no_output_____" ], [ "model = malaya.knowledge_graph.transformer()\nquantized_model = malaya.knowledge_graph.transformer(quantized = True)", "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/knowledge-graph-generator/base/model.pb HTTP/1.1\" 200 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/bpe/sp10m.cased.ms-en.model HTTP/1.1\" 200 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/knowledge-graph-generator/base/version HTTP/1.1\" 404 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\n" ], [ "r = model.greedy_decoder([string1, string2])\nr[0]", "WARNING:root:1\n" ], [ "model = malaya.knowledge_graph.transformer(model = 'large')\nquantized_model = malaya.knowledge_graph.transformer(model = 'large', quantized = True)", "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/knowledge-graph-generator/large/model.pb HTTP/1.1\" 200 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/bpe/sp10m.cased.ms-en.model HTTP/1.1\" 200 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\nDEBUG:urllib3.connectionpool:https://f000.backblazeb2.com:443 \"HEAD /file/malaya-model/knowledge-graph-generator/large/version HTTP/1.1\" 404 0\nDEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): f000.backblazeb2.com:443\n" ], [ "r = model.greedy_decoder([string1, string2])\nr[0]", "WARNING:root:1\n" ], [ "malaya.utils.delete_cache('knowledge-graph-generator')", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "import xarray as xr\nimport gcsfs\nimport intake\nimport numpy as np\nimport matplotlib\n#import cmocean\nimport stats\nimport stats as st\nfrom matplotlib import pyplot as plt\n%matplotlib inline\nimport ecco_v4_tools as ecco\n# Import plotting libraries\nimport importlib\nimport llcmapping\nimportlib.reload(llcmapping)\nfrom llcmapping import LLCMapper\nimportlib.reload(ecco)\nimportlib.reload(ecco.tile_plot_proj)\nimport glob", "/glade/u/home/patrizio/miniconda3/envs/pangeo/lib/python3.7/site-packages/tqdm/autonotebook/__init__.py:18: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n" ], [ "matplotlib.rcParams.update({'font.size': 16})\nmatplotlib.rcParams.update({'axes.titlesize': 16})\nmatplotlib.rcParams.update({'figure.figsize': (10,8)})\nmatplotlib.rcParams.update({'lines.linewidth': 2})\nmatplotlib.rcParams.update({'legend.fontsize': 18})\nmatplotlib.rcParams.update({'mathtext.fontset': 'cm'})\nmatplotlib.rcParams.update({'ytick.major.size': 3})\nmatplotlib.rcParams.update({'axes.labelsize': 16})\nmatplotlib.rcParams.update({'ytick.labelsize': 16})\nmatplotlib.rcParams.update({'xtick.labelsize': 16})", "_____no_output_____" ], [ "fin = '/glade/work/patrizio/ECCO/' \nfout = '/glade/scratch/patrizio/figs/'", "_____no_output_____" ], [ "fcoords = glob.glob(fin + 'fields/*coords.nc')[0]\nfsnp = glob.glob(fin + 'fields/*snp.nc')\nfvars = set(glob.glob(fin + 'fields/*.nc')) - set(glob.glob(fin + 'fields/*coords.nc')) - set(glob.glob(fin + 'fields/*snp.nc'))\nfvars = list(fvars)\nfvars ", "_____no_output_____" ], [ "coords = xr.open_dataset(fcoords)\ncoords", "_____no_output_____" ], [ "ntchunk=288\n\nds_snp = xr.open_mfdataset(fsnp,concat_dim=None)\nds_snp = ds_snp.chunk({'time':ntchunk-1})", "_____no_output_____" ], [ "ds = xr.open_mfdataset(fvars,concat_dim=None)\nds", "_____no_output_____" ], [ "ds_MXLDEPTH = xr.open_zarr(fin + 'ecco-data/MXLDEPTH', chunks={'time':ntchunk})\nmxldepth = ds_MXLDEPTH.MXLDEPTH", "_____no_output_____" ], [ "mxldepth", "_____no_output_____" ] ], [ [ "For some reason the mixed layer depth coordinate indices are displaced by +1 in relation to the ECCO data stored on Pangeo. The coordinates need to be matched for future calculations. ", "_____no_output_____" ] ], [ [ "mxldepth.coords['i'] = coords['i']\nmxldepth.coords['j'] = coords['j']", "_____no_output_____" ] ], [ [ "Calculate climatological mean mixed layer depth. We will be using this later to mask grid points outside of the mixed layer. ", "_____no_output_____" ] ], [ [ "mxldepth_clim=mxldepth.mean(dim='time').load()\n#mxldepth_clim=mxldepth.mean(dim='time').persist()", "_____no_output_____" ] ], [ [ "Make a mask of points outside the ocean mixed layer:", "_____no_output_____" ] ], [ [ "mxlpoints = np.abs(coords['Z']) <= mxldepth_clim", "_____no_output_____" ], [ "# Flag for low-pass filtering\nlowpass=True\n\n# Filter requirements\norder = 5\nfs = 1 # sample rate, (cycles per month)\nTn = 12*3.\ncutoff = 1/Tn # desired cutoff frequency of the filter (cycles per month)\n\n# Face numbers to analyze\n# 0: Southern Ocean (Atlantic)\n# 1: South Atlantic Ocean / Africa \n# 2: East North Atlantic / Europe\n# 3: Southern Ocean (Indian)\n# 4: Indian Ocean\n# 5: Asia\n# 6: Arctic\n# 7: North Pacific (central)\n# 8: West South Pacific\n# 9: Southern Ocean (West Pacific)\n# 10: North America / West North Atlantic\n# 11: East South Pacific / South America\n# 12: Southern Ocean(East Pacific)\n#facen = [5,7]\n\n#Note: longitude bounds can either be 0 < bounds < 360, or -180 < bounds < 180. \n#The only requirement is that the left longitude bound is less than the right bound \n#(along date line must use 0 < bounds < 360).\n#(along prime meridian must use -180 < bounds < 180)\n\n# Complete global \n#facen=[0,1,2,3,4,5,6,7,8,9,10,11,12]\n#bnds = [0,359.9,-90,90]\n\n#facen=[]\n#bnds = [0,359.9,-90,90]\n\n# Global (excluding polar regions)\n#facen=[1,2,4,5,7,8,10,11]\n#bnds = [0,359.9,-58,70]\n\n#Southern Ocean (Atlantic)\n#facen=[0]\n#bnds = [-20,20,-58,-90]\n\n#1: South Atlantic Ocean / Africa\n#facen=[1]\n#bnds = [-38,30,-58,10]\n\n#2: East North Atlantic \n#facen=[2]\n#bnds = [-38,30,10,70]\n\n#3: Southern Ocean (Indian)\n#facen=[3]\n#bnds = [60,143,-58,-90]\n\n#4: Indian Ocean\n#facen=[4]\n#bnds = [60,143,-58,10]\n\n#7: North Pacific (central)\n#facen=[7]\n#bnds = [145,230,10,70]\n\n#8: West South Pacific\n#facen=[8]\n#bnds = [145,230,-58,10]\n\n#11: East South Pacific\n#facen=[11]\n#bnds = [-128,-38,-58,10]\n\n#2, 10: North Atlantic\nfacen=[2,10]\nbnds = [-80,0,10,70]\n\n#5,7,10: North Pacific\n#facen=[5,7,10]\n#bnds = [100,270,10,70]\n\n#4,5,7,8,10,11: Pacific\n#facen=[4,5,7,8,10,11]\n#bnds = [100,300,-70,70]\n\n#5,7,8,10,11: Tropical Pacific\n#facen=[5,7,8,10,11]\n#bnds = [145,290,-15,15]\n\n#5,7: KOE\n#facen=[5,7]\n#bnds = [120,180,15,60]", "_____no_output_____" ], [ "rho0 = 1029 #sea-water density (kg/m^3)\nc_p = 3994 #sea-water heat capacity (J/kg/K)", "_____no_output_____" ], [ "coords=coords.isel(face=facen)", "_____no_output_____" ], [ "# Vertical grid spacing\ndrF = coords.drF\nhFacC = coords.hFacC\n#rA = coords.rA.isel(face=facen).load()\n#vol = drF*hFacC*rA.load()", "_____no_output_____" ], [ "c_o = rho0*c_p*drF*hFacC", "_____no_output_____" ], [ "T = ds_snp.T.isel(face=facen)\nadv_ConvH = ds.adv_ConvH.isel(face=facen)\ndif_ConvH = ds.dif_ConvH.isel(face=facen)\nforcH = ds.forcH.isel(face=facen)", "_____no_output_____" ], [ "dt = coords.time_snp[1:].load()\ndt = dt.rename({'time_snp': 'time'})\n# delta t in seconds. Note: divide by 10**9 to convert nanoseconds to seconds\ndt.values = [float(t)/10**9 for t in np.diff(coords.time_snp)]\n\n# time axis of dt should be the same as of the monthly averages\ndt.time.values = coords.time[1:-1].values", "_____no_output_____" ], [ "lons = coords.XC\nlats = coords.YC", "_____no_output_____" ], [ "T_anom, T_clim = st.anom(T) \nC_adv_anom, C_adv_clim = st.anom(adv_ConvH)\nC_dif_anom, C_dif_clim = st.anom(dif_ConvH)\nC_forc_anom, C_forc_clim = st.anom(forcH)\ntotalH_anom = C_adv_anom + C_dif_anom + C_forc_anom", "_____no_output_____" ], [ "T_anom = T_anom.chunk({'time':ntchunk-1})\nC_adv_anom = C_adv_anom.chunk({'time':ntchunk})\nC_dif_anom = C_dif_anom.chunk({'time':ntchunk})\nC_forc_anom = C_forc_anom.chunk({'time':ntchunk})", "_____no_output_____" ], [ "if lowpass:\n \n T_anom = T_anom.chunk({'time':288, 'j':10, 'i':10})\n \n C_adv_anom = C_adv_anom.chunk({'time':288, 'j':10, 'i':10})\n C_dif_anom = C_dif_anom.chunk({'time':288, 'j':10, 'i':10})\n C_forc_anom = C_forc_anom.chunk({'time':288, 'j':10, 'i':10})\n \n T_anom = stats.butter_lowpass_filter_xr(T_anom, cutoff, fs, order)\n \n C_adv_anom = stats.butter_lowpass_filter_xr(C_adv_anom, cutoff, fs, order)\n C_dif_anom = stats.butter_lowpass_filter_xr(C_dif_anom, cutoff, fs, order)\n C_forc_anom = stats.butter_lowpass_filter_xr(C_forc_anom, cutoff, fs, order)\n \n totalH_anom = C_adv_anom + C_dif_anom + C_forc_anom", "_____no_output_____" ], [ "%time T_anom.load()\n%time C_adv_anom.load()\n%time C_dif_anom.load()\n%time C_forc_anom.load()", "_____no_output_____" ], [ "tendH_perMonth = (T_anom.shift(time=-1)-T_anom)[:-1]", "_____no_output_____" ], [ "# Make sure time axis is the same as for the monthly variables\ntendH_perMonth.time.values = coords.time[1:-1].values\n\n# Convert tendency from 1/month to 1/s\ntendH_perSec = tendH_perMonth/dt\ntendH_perSec = tendH_perSec.transpose('face','time', 'k', 'j', 'i')", "_____no_output_____" ], [ "# Define tendH array with correct dimensions\ntendH_anom = xr.DataArray(np.nan*np.zeros([len(facen),np.shape(tendH_perSec)[1]+2,50,90,90]),\n coords={'face': facen, 'time': range(np.shape(tendH_perSec)[1]+2),'k': np.array(range(0,50)),\n 'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['face', 'time','k', 'j','i'])\n\ntendH_anom.time.values = coords.time.values", "_____no_output_____" ], [ "tendH_anom", "_____no_output_____" ], [ "tendH_anom.nbytes/1e9", "_____no_output_____" ], [ "# Add coordinates#\ntendH_anom['XC'] = lons\ntendH_anom['YC'] = lats\ntendH_anom['Z'] = coords.Z\n\n# Total tendency (degC/s)\ntendH_anom.values[:,1:-1,:] = tendH_perSec.values\n%time tendH_anom.load()\n#%time tendH.persist()", "_____no_output_____" ], [ "# Convert from degC/s to W/m^2\ntendH_anom = c_o*tendH_anom\ntendH_anom = tendH_anom.transpose('time','face', 'k', 'j', 'i')", "_____no_output_____" ], [ "face=0\nk = 0\nj = 15\ni = 15\n\nplt.figure(figsize=(14,10))\nplt.subplot(2, 1, 1)\nplt.plot(tendH_anom.time, tendH_anom.isel(face=face,k=k,j=j,i=i), lw=4, color='K', marker='.',label='total tendency')\nplt.plot(C_forc_anom.time, C_forc_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C0', marker='.',label='forcing')\nplt.plot(C_adv_anom.time, C_adv_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C1', marker='.',label='advection')\nplt.axhline(0,color='k',lw=1)\nplt.plot(C_dif_anom.time, C_dif_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C2',label='diffusion')\nplt.setp(plt.gca(), 'xticklabels',[])\nplt.legend(loc='best',frameon=False,fontsize=14)\n\nplt.subplot(2, 1, 2)\nplt.plot(totalH_anom.time, totalH_anom.isel(face=face,k=k,j=j,i=i), lw=4, color='red', marker='.',label='RHS')\nplt.plot(tendH_anom.time, tendH_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='blue', marker='.',label='LHS')\nplt.plot(tendH_anom.time, (totalH_anom-tendH_anom).isel(face=face,k=k,j=j,i=i), lw=2, color='k', marker='.',label='RHS - LHS')\nplt.legend(loc='best',frameon=False,fontsize=14)\nplt.savefig(fout + 'sstbudget_anom_ts.png')", "_____no_output_____" ], [ "T_var = T_anom.var(dim='time')\n%time T_var.load()\n#%time T_var.persist()", "_____no_output_____" ], [ "tendH_anom = tendH_anom/c_o", "_____no_output_____" ], [ "#tendH_anom = tendH_anom.transpose('time','face', 'k', 'j', 'i')\ncov_adv = st.cov(tendH_anom, C_adv_anom)\ncov_dif = st.cov(tendH_anom, C_dif_anom)\ncov_forc = st.cov(tendH_anom, C_forc_anom)", "_____no_output_____" ], [ "cov_adv.nbytes/1e9", "_____no_output_____" ], [ "%time cov_adv.load()\n%time cov_dif.load()\n%time cov_forc.load()", "_____no_output_____" ], [ "deltat = dt.mean()\ndeltat.compute()", "_____no_output_____" ], [ "r_1 = st.cor(T_anom, T_anom,lagx=1).compute()\nr_1", "_____no_output_____" ], [ "fac = (deltat**2/(2*c_o*(1-r_1)))\nfac.load()", "_____no_output_____" ], [ "T_var_sum = fac*(cov_adv + cov_dif + cov_forc)", "_____no_output_____" ], [ "%time T_var_sum.load()\n#%time T_var_sum.persist()", "_____no_output_____" ], [ "mapper = LLCMapper(coords)", "_____no_output_____" ], [ "k=0\nmapper(T_var.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0)\nmapper(T_var_sum.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0)", "_____no_output_____" ] ], [ [ "The temperature variance budget is clearly balanced! Let's take a look at the contribution due to each term.", "_____no_output_____" ] ], [ [ "T_var_adv = fac*cov_adv\nT_var_dif = fac*cov_dif\nT_var_forc = fac*cov_forc", "_____no_output_____" ], [ "vmin=-1.0\nvmax=1.0\nsstmax=1.6\nif lowpass:\n sstmax=0.5\n vmin=-0.5\n vmax=0.5", "_____no_output_____" ] ], [ [ "### Contributions to temperature variance from advection, diffusion and surface forcing", "_____no_output_____" ] ], [ [ "k=0\nmapper(T_var_sum.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=sstmax)\nplt.title(r'temperature variance (K$^2$)')\nplt.savefig(fout + 'Tvar_sum.png')\nmapper(T_var_adv.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'advective contribution (K$^2$)')\nplt.savefig(fout + 'Tvar_adv.png')\nmapper(T_var_dif.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'diffusive contribution (K$^2$)')\nplt.savefig(fout + 'Tvar_dif.png')\nmapper(T_var_forc.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'surface forcing contribution (K$^2$)')\nplt.savefig(fout + 'Tvar_forc.png')", "_____no_output_____" ] ], [ [ "### Contributions to ocean mixed layer temperature variance from advection, diffusion and surface forcing", "_____no_output_____" ] ], [ [ "mxlpoints = mxlpoints.isel(face=facen)\ndelz = drF*hFacC\ndelz=delz.where(mxlpoints)\ndelz_sum = delz.sum(dim='k')", "_____no_output_____" ], [ "mxlpoints", "_____no_output_____" ], [ "weights = delz/delz_sum", "_____no_output_____" ], [ "T_var_mxl = (weights*T_var).where(mxlpoints).sum(dim='k')", "_____no_output_____" ], [ "T_var_adv_mxl = (weights*T_var_adv).where(mxlpoints).sum(dim='k')\nT_var_dif_mxl = (weights*T_var_dif).where(mxlpoints).sum(dim='k')\nT_var_forc_mxl = (weights*T_var_forc).where(mxlpoints).sum(dim='k')", "_____no_output_____" ], [ "T_var_sum_mxl = T_var_adv_mxl + T_var_dif_mxl + T_var_forc_mxl", "_____no_output_____" ], [ "#f, axes = plt.subplots(2,2,figsize=(16,12))\n#f.tight_layout()\nmapper(T_var_sum_mxl, bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=sstmax)\nplt.title(r'temperature variance (K$^2$)')\nplt.savefig(fout + 'Tmxlvar_sum.png')\n\nmapper(T_var_adv_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'advective contribution (K$^2$)')\nplt.savefig(fout + 'Tmxlvar_adv.png')\n\nmapper(T_var_dif_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'diffusive contribution (K$^2$)')\nplt.savefig(fout + 'Tmxlvar_dif.png')\n\nmapper(T_var_forc_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'surface forcing contribution (K$^2$)')\nplt.savefig(fout + 'Tmxlvar_forc.png')", "_____no_output_____" ], [ "#mapper(T_var_sum_mxl, bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0)\n#plt.title(r'temperature variance (K$^2$)')\n#plt.savefig(fout + 'Tmxlvar_sum.png')\nmapper(T_var_adv_mxl + T_var_dif_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)\nplt.title(r'ocean dynamics (advective + diffusive) contribution (K$^2$)')\nplt.savefig(fout + 'Tmxlvar_ocndyn.png')\n#mapper(T_var_forc_mxl, bnds=bnds, cmap='RdBu_r', vmin=-1.0,vmax=1.0)\n#plt.title(r'surface forcing contribution (K$^2$)')\n#plt.savefig(fout + 'Tmxlvar_forc.png')", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import pickle\nimport tensorflow as tf\nimport os\nimport numpy as np\nfrom src.utils import DatasetUtils\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport plotly.offline as ply\nimport plotly.graph_objs as go\nkeras = tf.keras\nply.init_notebook_mode(connected=True)", "c:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n from ._conv import register_converters as _register_converters\n" ], [ "model1 = keras.models.load_model('./models/noisy/convnet.h5')", "_____no_output_____" ], [ "model2 = keras.models.load_model('./models/noisy/resnet.h5')", "_____no_output_____" ], [ "model3 = keras.models.load_model('./models/noisy/residualresnet.h5')", "_____no_output_____" ], [ "utils = DatasetUtils()", "_____no_output_____" ], [ "classes = utils.get_label_encoder().classes_", "_____no_output_____" ], [ "test_data, test_labels = utils.get_dataset_and_encoded_labels('test_data.npy', 'test_labels.npy')", "_____no_output_____" ], [ "all_predictions1 = model1.predict(test_data)\nall_predictions2 = model2.predict(test_data)\nall_predictions3 = model3.predict(test_data)", "_____no_output_____" ], [ "test_labels = classes[np.argmax(test_labels, axis=-1)]", "_____no_output_____" ], [ "all_pred_labels1 = classes[np.argmax(all_predictions1, axis=-1)]\nall_pred_labels2 = classes[np.argmax(all_predictions2, axis=-1)]\nall_pred_labels3 = classes[np.argmax(all_predictions3, axis=-1)]", "_____no_output_____" ], [ "cm1 = confusion_matrix(test_labels, all_pred_labels1, labels=classes)\ncm1 = cm1.astype('float') / cm1.sum(axis=1)[:, np.newaxis]\ncm2 = confusion_matrix(test_labels, all_pred_labels2, labels=classes)\ncm2 = cm2.astype('float') / cm2.sum(axis=1)[:, np.newaxis]\ncm3 = confusion_matrix(test_labels, all_pred_labels3, labels=classes)\ncm3 = cm3.astype('float') / cm3.sum(axis=1)[:, np.newaxis]", "_____no_output_____" ], [ "per_class_accuracy1 = dict()\nfor i, label in enumerate(classes):\n per_class_accuracy1[label] = cm1[i, i] * 100\nper_class_accuracy2 = dict()\nfor i, label in enumerate(classes):\n per_class_accuracy2[label] = cm2[i, i] * 100\nper_class_accuracy3 = dict()\nfor i, label in enumerate(classes):\n per_class_accuracy3[label] = cm3[i, i] * 100", "_____no_output_____" ], [ "sorted_per_class_accuracy1 = sorted(per_class_accuracy1.items(), key=lambda x: x[1], reverse=True)\nsorted_per_class_accuracy2 = sorted(per_class_accuracy2.items(), key=lambda x: x[1], reverse=True)\nsorted_per_class_accuracy3 = sorted(per_class_accuracy3.items(), key=lambda x: x[1], reverse=True)", "_____no_output_____" ], [ "sorted_per_class_accuracy3", "_____no_output_____" ], [ "trace1 = go.Histogram(x=list(per_class_accuracy1.values()),\n name='ConvNet',\n opacity=0.8)\ntrace2 = go.Histogram(x=list(per_class_accuracy2.values()),\n name='DenseNet',\n opacity=0.8)\ntrace3 = go.Histogram(x=list(per_class_accuracy3.values()),\n name='Recurrent-DenseNet',\n opacity=0.8)\ndata = [trace1, trace2, trace3]\nlayout = go.Layout()\nply.iplot(dict(data=data, layout=layout))", "_____no_output_____" ], [ "sorted_per_class_acc = sorted(per_class_accuracy.items(), key=lambda x: x[1], reverse=True)", "_____no_output_____" ] ], [ [ "speakers = os.listdir('./speaker_spectrograms/')\nspeaker_pred = dict()\nfor speaker in speakers:\n spects = np.load('./speaker_spectrograms/' + speaker)\n spects = spects.reshape(spects.shape+(1,))\n pred = model.predict(spects)\n pred = np.argmax(pred, axis=-1)\n pred_labels = classes[pred]\n speaker_pred[speaker.split('.')[0]] = pred_labels\nwith open('./per_speaker_pred.pkl', 'wb') as handle:\n pickle.dump(speaker_pred, handle, protocol=pickle.HIGHEST_PROTOCOL)", "_____no_output_____" ] ], [ [ "speaker_pred = pickle.load(open('./per_speaker_pred.pkl', 'rb'))", "_____no_output_____" ], [ "speaker_gt = pickle.load(open('./per_speaker_gt.pkl', 'rb'))", "_____no_output_____" ], [ "per_speaker = dict()\nfor speaker in os.listdir('./speaker_spectrograms/'):\n speaker = speaker.split('.')[0]\n pred = np.array(speaker_pred[speaker])\n gt = np.array(speaker_gt[speaker])\n per_label = dict()\n for label in np.unique(gt):\n label_idx = np.where(gt == label)\n acc = np.sum(np.core.defchararray.equal(pred[label_idx], gt[label_idx])) / len(label_idx[0])\n per_label[label] = acc * 100\n per_speaker[speaker] = per_label", "_____no_output_____" ], [ "list(per_speaker.values())[0]", "_____no_output_____" ], [ "per_speaker_acc = dict()\nfor speaker in os.listdir('./speaker_spectrograms/'):\n speaker = speaker.split('.')[0]\n pred = speaker_pred[speaker]\n gt = speaker_gt[speaker]\n acc = np.sum(np.core.defchararray.equal(pred, gt)) / len(pred)\n per_speaker_acc[speaker] = acc * 100\nsorted_per_speaker_acc = sorted(per_speaker_acc.items(), key=lambda x: x[1], reverse=True)", "_____no_output_____" ], [ "class_names = []\nclass_accs = []\nper_class_accuracy_list = np.full((len(classes), len(per_speaker)), np.nan)\nfor index, item in enumerate(sorted_per_class_acc):\n class_names.append(item[0])\n class_accs.append(item[1])\n for i, speaker in enumerate(list(per_speaker.values())):\n if item[0] in speaker.keys():\n per_class_accuracy_list[index, i] = speaker[item[0]]", "_____no_output_____" ], [ "boxprops = dict(linestyle='-', linewidth=1.0, color='k')\nmedianprops = dict(linestyle='-', linewidth=1.0, color='k')\nwhiskerprops = dict(linestyle='-', linewidth=1.0, color='k')\ncapprops = dict(linestyle='-', linewidth=1.0, color='k')\nplt.boxplot(per_class_accuracy_list.T,\n patch_artist = True,\n boxprops=boxprops,\n capprops=capprops,\n medianprops=medianprops,\n whiskerprops=whiskerprops,\n whis=\"range\")", "c:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\numpy\\lib\\function_base.py:4291: RuntimeWarning: Invalid value encountered in percentile\n interpolation=interpolation)\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\numpy\\core\\_methods.py:29: RuntimeWarning: invalid value encountered in reduce\n return umr_minimum(a, axis, None, out, keepdims)\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\numpy\\core\\_methods.py:26: RuntimeWarning: invalid value encountered in reduce\n return umr_maximum(a, axis, None, out, keepdims)\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1872: RuntimeWarning: invalid value encountered in less_equal\n wiskhi = np.compress(x <= hival, x)\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1879: RuntimeWarning: invalid value encountered in greater_equal\n wisklo = np.compress(x >= loval, x)\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1887: RuntimeWarning: invalid value encountered in less\n np.compress(x < stats['whislo'], x),\nc:\\users\\aakaas~1\\docume~1\\nyu\\projects\\speech~1\\env\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1888: RuntimeWarning: invalid value encountered in greater\n np.compress(x > stats['whishi'], x)\n" ], [ "class_names", "_____no_output_____" ], [ "fig, ax = plt.subplots(1, 1, sharex=True, figsize=(5, 3))\n\nboxprops = dict(linestyle='-', linewidth=1.0, color='k')\nmedianprops = dict(linestyle='-', linewidth=1.0, color='k')\nwhiskerprops = dict(linestyle='-', linewidth=1.0, color='k')\ncapprops = dict(linestyle='-', linewidth=1.0, color='k')\nbplot = ax.boxplot(\n [100 * ,\n 100 * none_accs,\n 100 * all_accs],\n patch_artist = True,\n boxprops=boxprops,\n capprops=capprops,\n medianprops=medianprops,\n whiskerprops=whiskerprops,\n whis=\"range\");", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0", "MIT" ]

[ "Apache-2.0", "MIT" ]

[ "Apache-2.0", "MIT" ]

[ [ [ "# Tweepy streamer\n", "_____no_output_____" ], [ " ## Find Top tweeting user:\n\n - Find User who is tweeting a lot.\n - Find top 50 across the world.\n", "_____no_output_____" ], [ "Since this is streaming application, we will use python logging module to log. [Further read.](https://www.webcodegeeks.com/python/python-logging-example/)", "_____no_output_____" ] ], [ [ "import logging # python logging module\n\n# basic format for logging\nlogFormat = \"%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s\"\n\n# logs will be stored in tweepy.log\nlogging.basicConfig(filename='tweepytopuser.log', level=logging.INFO, \n format=logFormat, datefmt=\"%Y-%m-%d %H:%M:%S\")\n", "_____no_output_____" ] ], [ [ "## Authentication and Authorisation\n\nCreate an app in twitter [here](https://apps.twitter.com/). Copy the necessary keys and access tokens, which will be used here in our code. \n\nThe authorization is done using Oauth, An open protocol to allow secure authorization in a simple and standard method from web, mobile and desktop applications. [Further read](https://oauth.net/). \n\nWe will use Tweepy a python module. Tweepy is open-sourced, hosted on [GitHub](https://github.com/tweepy/tweepy) and enables Python to communicate with Twitter platform and use its API. Tweepy supports oauth authentication. Authentication is handled by the tweepy.AuthHandler class.", "_____no_output_____" ] ], [ [ "import tweepy # importing all the modules required\nimport socket # will be used to create sockets\nimport json # manipulate json\n\nfrom httplib import IncompleteRead", "_____no_output_____" ], [ "# Keep these tokens secret, as anyone can have full access to your\n# twitter account, using these tokens\n\nconsumerKey = \"#\"\nconsumerSecret = \"#\"\n\naccessToken = \"#-#\"\naccessTokenSecret = \"#\"\n", "_____no_output_____" ] ], [ [ "Post this step, we will have full access to twitter api's", "_____no_output_____" ] ], [ [ "# Performing the authentication and authorization, post this step \n# we will have full access to twitter api's\ndef connectToTwitter():\n \"\"\"Connect to twitter.\"\"\"\n try:\n auth = tweepy.OAuthHandler(consumerKey, consumerSecret)\n auth.set_access_token(accessToken, accessTokenSecret)\n\n api = tweepy.API(auth)\n logging.info(\"Successfully logged in to twitter.\")\n return api, auth\n except Exception as e:\n logging.info(\"Something went wrong in oauth, please check your tokens.\")\n logging.error(e)\n", "_____no_output_____" ] ], [ [ "## Streaming with tweepy\n\nThe Twitter streaming API is used to download twitter messages in real time. We use streaming api instead of rest api because, the REST api is used to pull data from twitter but the streaming api pushes messages to a persistent session. This allows the streaming api to download more data in real time than could be done using the REST API.\n\nIn Tweepy, an instance of tweepy.Stream establishes a streaming session and routes messages to StreamListener instance. The on_data method of a stream listener receives all messages and calls functions according to the message type. \n\nBut the on_data method is only a stub, so we need to implement the functionality by subclassing StreamListener. \n\nUsing the streaming api has three steps.\n\n1. Create a class inheriting from StreamListener\n2. Using that class create a Stream object\n3. Connect to the Twitter API using the Stream.\n\n", "_____no_output_____" ] ], [ [ "# Tweet listner class which subclasses from tweepy.StreamListener\nclass TweetListner(tweepy.StreamListener):\n \"\"\"Twitter stream listner\"\"\"\n \n def __init__(self, csocket):\n self.clientSocket = csocket\n \n def dataProcessing(self, data):\n \"\"\"Process the data, before sending to spark streaming\n \"\"\"\n sendData = {} # data that is sent to spark streamer\n user = data.get(\"user\", {})\n name = user.get(\"name\", \"undefined\").encode('utf-8')\n sendData[\"name\"] = name\n #data_string = \"{}:{}\".format(name, followersCount) \n self.clientSocket.send(json.dumps(sendData) + u\"\\n\") # append new line character, so that spark recognizes it\n logging.debug(json.dumps(sendData))\n \n def on_data(self, raw_data):\n \"\"\" Called when raw data is received from connection.\n return False to stop stream and close connection.\n \"\"\"\n try:\n data = json.loads(raw_data)\n self.dataProcessing(data)\n #self.clientSocket.send(json.dumps(sendData) + u\"\\n\") # Because the connection was breaking\n return True\n except Exception as e:\n logging.error(\"An unhandled exception has occured, check your data processing\")\n logging.error(e)\n raise e\n \n def on_error(self, status_code):\n \"\"\"Called when a non-200 status code is returned\"\"\"\n logging.error(\"A non-200 status code is returned\")\n return True\n ", "_____no_output_____" ], [ "# Creating a proxy socket\ndef createProxySocket(host, port):\n \"\"\" Returns a socket which can be used to connect\n to spark.\n \"\"\"\n try:\n s = socket.socket() # initialize socket instance\n s.bind((host, port)) # bind to the given host and port \n s.listen(5) # Enable a server to accept connections.\n logging.info(\"Listening on the port {}\".format(port))\n cSocket, address = s.accept() # waiting for a connection\n logging.info(\"Received Request from: {}\".format(address))\n return cSocket\n except socket.error as e: \n if e.errno == socket.errno.EADDRINUSE: # Address in use\n logging.error(\"The given host:port {}:{} is already in use\"\\\n .format(host, port))\n logging.info(\"Trying on port: {}\".format(port + 1))\n return createProxySocket(host, port + 1)\n", "_____no_output_____" ] ], [ [ "## Drawbacks of twitter streaming API\n\nThe major drawback of the Streaming API is that Twitter’s Steaming API provides only a sample of tweets that are occurring. The actual percentage of total tweets users receive with Twitter’s Streaming API varies heavily based on the criteria users request and the current traffic. Studies have estimated that using Twitter’s Streaming API users can expect to receive anywhere from 1% of the tweets to over 40% of tweets in near real-time. The reason that you do not receive all of the tweets from the Twitter Streaming API is simply because Twitter doesn’t have the current infrastructure to support it, and they don’t want to; hence, the Twitter Firehose. [Ref](https://brightplanet.com/2013/06/twitter-firehose-vs-twitter-api-whats-the-difference-and-why-should-you-care/)\n\nSo we will use a hack i.e. get the top trending topics and use that to filter data.\n", "_____no_output_____" ] ], [ [ "if __name__ == \"__main__\":\n try:\n api, auth = connectToTwitter() # connecting to twitter\n # Global information is available by using 1 as the WOEID\n # woeid = getWOEIDForTrendsAvailable(api, \"Worldwide\") # get the woeid of the worldwide\n \n host = \"localhost\"\n port = 8600\n cSocket = createProxySocket(host, port) # Creating a socket\n \n while True:\n try:\n # Connect/reconnect the stream\n tweetStream = tweepy.Stream(auth, TweetListner(cSocket)) # Stream the twitter data\n # DON'T run this approach async or you'll just create a ton of streams!\n tweetStream.filter(track=\"iphone\") # Filter on trending topics\n except IncompleteRead:\n # Oh well, reconnect and keep trucking\n continue\n except KeyboardInterrupt:\n # Or however you want to exit this loop\n tweetStream.disconnect()\n break\n except Exception as e:\n logging.error(\"Unhandled exception has occured\")\n logging.error(e)\n continue\n \n except KeyboardInterrupt: # Keyboard interrupt called\n logging.error(\"KeyboardInterrupt was hit\")\n except Exception as e:\n logging.error(\"Unhandled exception has occured\")\n logging.error(e)\n", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Analizando información de IMDB con Keras\n\nYa aprendiste cómo se construye una red neuronal. ¡Ahora es tu turno! En este reto, vas a construir una red neuronal que logra predecir si hay un sentimiento positivo o negativo en un review.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport keras\nfrom keras.datasets import imdb\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.preprocessing.text import Tokenizer\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nnp.random.seed(42)", "_____no_output_____" ] ], [ [ "## Paso 1. Cargar la información", "_____no_output_____" ] ], [ [ "# IMDB ya es un dataset que es parte de Keras, así que lo tenemos fácil!\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)\n\nprint(x_train.shape)\nprint(x_test.shape)", "_____no_output_____" ] ], [ [ "## Paso 2. Comprender la información\n\nEsta vez la información ya esta preprocesada, por lo cuál es mucho más fácil trabajar con ella. Todas las palabras han sido transformadas a números, y cada review es un vector con las palabras que contine. El output es el sentimiento, donde 1 es un sentimiento positivo y 0 un sentimiento negativo", "_____no_output_____" ] ], [ [ "print(x_train[0])\nprint(y_train[0])", "_____no_output_____" ] ], [ [ "## Paso 3. Modificar la información para la red neuronal\n\n### One-hot encoding\n\nTenemos un vector con números, pero queremos convertirlo en muchos vectores con valor 0 ó 1. Por ejemplo, si el vector preprocesado contiene el número 14, entonces el vector procesado, en la entrada 14, será 1. Haremos lo mismo para la salida. Estamos trabajando con 50mil datos, así que se puede tardar unos segundos.", "_____no_output_____" ] ], [ [ "# One-hot encoding the output into vector mode, each of length 1000\ntokenizer = Tokenizer(num_words=1000)\nx_train = tokenizer.sequences_to_matrix(x_train, mode='binary')\nx_test = tokenizer.sequences_to_matrix(x_test, mode='binary')\nprint(x_train[0])", "_____no_output_____" ], [ "# One-hot encoding the output\nnum_classes = 2\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\nprint(y_train.shape)\nprint(y_test.shape)", "_____no_output_____" ] ], [ [ "## Paso 4. Construimos Arquitectura del Modelo\n\nConstruye un modelo secuencial. Siéntete libre de explorar y experimentar.", "_____no_output_____" ] ], [ [ "## TODO: Construye un modelo secuencial\n\n\n## TODO: Compila el modelo con un optimizador y una función de pérdida", "_____no_output_____" ] ], [ [ "## Paso 5. Entrenamos el modelo", "_____no_output_____" ] ], [ [ "## TODO: Corre el modelo. Experimenta con diferentes tamaños de batch y número de epochs. \n# Usa verbose=2 para ver cómo va progresando el modelo", "_____no_output_____" ] ], [ [ "## Paso 6. Evaluamos el modelo\n\n¿Crees poder llegar a más de 80%? ¿Qué tal arriba de 85%?", "_____no_output_____" ] ], [ [ "score = model.evaluate(x_test, y_test, verbose=0)\nprint(\"Accuracy: \", score[1])", "_____no_output_____" ] ], [ [ "# SOLUCIONES", "_____no_output_____" ], [ "No las veas antes de intentar tú primero", "_____no_output_____" ], [ "Ya intentaste tú primero?", "_____no_output_____" ], [ "# Intenta primero", "_____no_output_____" ] ], [ [ "## TODO: Construye un modelo secuencial\nmodel = Sequential()\nmodel.add(Dense(512, activation='relu', input_dim=1000))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.summary()\n\n## TODO: Compila el modelo con un optimizador y una función de pérdida\nmodel.compile(loss='categorical_crossentropy',\n optimizer='rmsprop',\n metrics=['accuracy'])\n\n## TODO: Corre el modelo. Experimenta con diferentes tamaños de batch y número de epochs. \n# Usa verbose=2 para ver cómo va progresando el modelo\nmodel.fit(x_train, y_train,\n batch_size=32,\n epochs=10,\n validation_data=(x_test, y_test), \n verbose=2)", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/zerotodeeplearning/ztdl-masterclasses/blob/master/notebooks/Pre-trained_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "## Learn with us: www.zerotodeeplearning.com\n\nCopyright © 2021: Zero to Deep Learning ® Catalit LLC.", "_____no_output_____" ] ], [ [ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.", "_____no_output_____" ] ], [ [ "# Pre-trained Models", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport tensorflow as tf\nimport os\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator", "_____no_output_____" ], [ "# sports_images_path = tf.keras.utils.get_file(\n# 'sports_images',\n# 'https://archive.org/download/ztdl_sports_images/sports_images.tgz',\n# untar=True)", "_____no_output_____" ], [ "![[ ! -f sports_images.tar.gz ]] && gsutil cp gs://ztdl-datasets/sports_images.tar.gz .\n![[ ! -d sports_images ]] && echo \"Extracting images...\" && tar zxf sports_images.tar.gz\nsports_images_path = './sports_images'", "_____no_output_____" ], [ "train_path = os.path.join(sports_images_path, 'train')\ntest_path = os.path.join(sports_images_path, 'test')", "_____no_output_____" ], [ "batch_size = 16\nimg_size = 224", "_____no_output_____" ], [ "train_datagen = ImageDataGenerator() \\\n .flow_from_directory(train_path, \n target_size = (img_size, img_size),\n batch_size = batch_size,\n class_mode = 'sparse')", "_____no_output_____" ], [ "try:\n assert(train_datagen.samples == 11414)\nexcept:\n raise Exception(\"Found less images than expected. Please remove the files and download again.\")", "_____no_output_____" ], [ "classes_dict = train_datagen.class_indices\nclasses = list(classes_dict.keys())\nclasses", "_____no_output_____" ], [ "batch, labels = train_datagen.next()", "_____no_output_____" ], [ "batch.shape", "_____no_output_____" ], [ "labels.shape", "_____no_output_____" ], [ "plt.figure(figsize=(10, 10))\nfor i in range(len(batch)):\n plt.subplot(4, 4, i+1)\n plt.imshow(batch[i].astype('int'))\n plt.title(classes[int(labels[i])])\n plt.axis('off')\n\nplt.tight_layout()", "_____no_output_____" ] ], [ [ "### Pre-trained model\n\nLet's use a Resnet50 model to classify the images without any training.", "_____no_output_____" ] ], [ [ "from PIL import Image\nfrom io import BytesIO\nfrom IPython.display import HTML\nimport base64", "_____no_output_____" ], [ "from tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.applications.resnet50 import preprocess_input as preprocess_input_resnet50\nfrom tensorflow.keras.applications.resnet50 import decode_predictions as decode_predictions_resnet50", "_____no_output_____" ], [ "model = ResNet50(weights='imagenet')", "_____no_output_____" ], [ "batch_preprocessed = preprocess_input_resnet50(batch.copy())", "_____no_output_____" ], [ "predictions = model.predict(batch_preprocessed)", "_____no_output_____" ], [ "decoded_top_3 = decode_predictions_resnet50(predictions, top=3)", "_____no_output_____" ], [ "def image_formatter(a):\n im = Image.fromarray(a)\n im.thumbnail((28, 28), Image.LANCZOS)\n with BytesIO() as buffer:\n im.save(buffer, 'jpeg')\n im_base64 = base64.b64encode(buffer.getvalue()).decode()\n return f'<img src=\"data:image/jpeg;base64,{im_base64}\">'", "_____no_output_____" ], [ "def display_batch(batch, decoded_top_3):\n res = []\n for i, top3 in enumerate(decoded_top_3):\n im = image_formatter(batch[i].astype('uint8'))\n cl = classes[int(labels[i])]\n line = [im, cl]\n for item in top3:\n line = line + list(item[1:])\n res.append(line)\n\n\n res_df = pd.DataFrame(res,\n columns=['image', 'ground_truth',\n 'top_1', 'prob_1',\n 'top_2', 'prob_2',\n 'top_3', 'prob_3'])\n\n \n return res_df.style.bar(color='lightgreen', vmin=0, vmax=1)", "_____no_output_____" ], [ "display_batch(batch, decoded_top_3)", "_____no_output_____" ] ], [ [ "### Exercise 1:\n\nUse a different pre-trained model from the ones provided at: https://keras.io/applications/\n\nDo the predictions match?\n\nYou will need to:\n- import the pre-trained model\n- import the corresponding `preprocess_input` and `decode_predictions`\n- check the correct imput shape for your chosen model and possibly re-load a new batch with updated image size\n- pipe the batch through the predict function and display the results", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "\n# Stochastic examples\n\n\nThis example is designed to show how to use the stochatic optimization\nalgorithms for descrete and semicontinous measures from the POT library.\n\n\n", "_____no_output_____" ] ], [ [ "# Author: Kilian Fatras <[email protected]>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport numpy as np\nimport ot\nimport ot.plot", "_____no_output_____" ] ], [ [ "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n\n", "_____no_output_____" ] ], [ [ "print(\"------------SEMI-DUAL PROBLEM------------\")", "------------SEMI-DUAL PROBLEM------------\n" ] ], [ [ "DISCRETE CASE\nSample two discrete measures for the discrete case\n---------------------------------------------\n\nDefine 2 discrete measures a and b, the points where are defined the source\nand the target measures and finally the cost matrix c.\n\n", "_____no_output_____" ] ], [ [ "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)", "_____no_output_____" ] ], [ [ "Call the \"SAG\" method to find the transportation matrix in the discrete case\n---------------------------------------------\n\nDefine the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n", "_____no_output_____" ] ], [ [ "method = \"SAG\"\nsag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax)\nprint(sag_pi)", "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n" ] ], [ [ "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n", "_____no_output_____" ] ], [ [ "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)", "_____no_output_____" ] ], [ [ "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\ncase\n---------------------------------------------\n\nDefine the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n", "_____no_output_____" ] ], [ [ "method = \"ASGD\"\nasgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax, log=log)\nprint(log_asgd['alpha'], log_asgd['beta'])\nprint(asgd_pi)", "[3.75309361 7.63288278 3.76418767 2.53747778 1.70389504 3.53981297\n 2.67663944] [-2.49164966 -2.25281897 -0.77666675 5.52113539]\n[[2.19699465e-02 1.03185982e-01 1.76983379e-02 2.87611188e-06]\n [1.20688044e-01 1.49823131e-02 1.50635578e-03 5.68043045e-03]\n [3.01194583e-03 7.75764779e-02 6.22686313e-02 8.78225379e-08]\n [2.28707628e-02 3.52120795e-02 8.44977549e-02 2.76545693e-04]\n [1.19721129e-02 1.10087991e-03 1.53333937e-02 1.14450756e-01]\n [2.65247890e-02 1.33140544e-03 2.66861405e-03 1.12332334e-01]\n [3.71512413e-02 2.86513804e-02 7.53932500e-02 1.66127118e-03]]\n" ] ], [ [ "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n", "_____no_output_____" ] ], [ [ "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)", "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" ] ], [ [ "PLOT TRANSPORTATION MATRIX\n#############################################################################\n\n", "_____no_output_____" ], [ "Plot SAG results\n----------------\n\n", "_____no_output_____" ] ], [ [ "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\npl.show()", "_____no_output_____" ] ], [ [ "Plot ASGD results\n-----------------\n\n", "_____no_output_____" ] ], [ [ "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\npl.show()", "_____no_output_____" ] ], [ [ "Plot Sinkhorn results\n---------------------\n\n", "_____no_output_____" ] ], [ [ "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()", "_____no_output_____" ] ], [ [ "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n\n", "_____no_output_____" ] ], [ [ "print(\"------------DUAL PROBLEM------------\")", "------------DUAL PROBLEM------------\n" ] ], [ [ "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n", "_____no_output_____" ] ], [ [ "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 100000\nlr = 0.1\nbatch_size = 3\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)", "_____no_output_____" ] ], [ [ "Call the \"SGD\" dual method to find the transportation matrix in the\nsemicontinous case\n---------------------------------------------\n\nCall ot.solve_dual_entropic and plot the results.\n\n", "_____no_output_____" ] ], [ [ "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n batch_size, numItermax,\n lr, log=log)\nprint(log_sgd['alpha'], log_sgd['beta'])\nprint(sgd_dual_pi)", "[ 1.67648902 5.3770004 1.70385554 0.4276547 -0.77206786 1.0474898\n 0.54202203] [-0.23723788 -0.20259434 1.30855788 8.06179985]\n[[2.62451875e-02 1.00499531e-01 1.78515577e-02 4.57450829e-06]\n [1.20510690e-01 1.21972758e-02 1.27002374e-03 7.55197481e-03]\n [3.65708350e-03 7.67963231e-02 6.38381061e-02 1.41974930e-07]\n [2.64286344e-02 3.31748063e-02 8.24445965e-02 4.25479786e-04]\n [9.59295422e-03 7.19190875e-04 1.03739180e-02 1.22100712e-01]\n [2.09087627e-02 8.55676046e-04 1.77617241e-03 1.17896019e-01]\n [4.18792948e-02 2.63326297e-02 7.17598381e-02 2.49335733e-03]]\n" ] ], [ [ "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n", "_____no_output_____" ] ], [ [ "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)", "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" ] ], [ [ "Plot SGD results\n-----------------\n\n", "_____no_output_____" ] ], [ [ "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\npl.show()", "_____no_output_____" ] ], [ [ "Plot Sinkhorn results\n---------------------\n\n", "_____no_output_____" ] ], [ [ "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "https://xavierbourretsicotte.github.io/LDA_QDA.html ", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport matplotlib.colors as colors\n# from mpl_toolkits.mplot3d import Axes3D\n# from mpl_toolkits import mplot3d\nfrom sklearn import linear_model, datasets\nimport seaborn as sns\nimport itertools\n\n%matplotlib inline\nsns.set()\n#plt.style.use('seaborn-white')", "_____no_output_____" ], [ "def multivariate_gaussian_pdf(X,MU,SIGMA):\n '''Returns the pdf of a nultivariate gaussian distribution\n - X, MU are p x 1 vectors\n - SIGMA is a p x p matrix'''\n #Initialize and reshape\n X = X.reshape(-1,1)\n MU = MU.reshape(-1,1)\n p,_ = SIGMA.shape\n\n #Compute values\n SIGMA_inv = np.linalg.inv(SIGMA)\n denominator = np.sqrt((2 * np.pi)**p * np.linalg.det(SIGMA))\n exponent = -(1/2) * ((X - MU).T @ SIGMA_inv @ (X - MU))\n \n #Return result\n return float((1. / denominator) * np.exp(exponent) ) \n\ndef calculate_boundary(X,MU_k,MU_l, SIGMA,pi_k,pi_l): \n return (np.log(pi_k / pi_l) - 1/2 * (MU_k + MU_l).T @ np.linalg.inv(SIGMA)@(MU_k - MU_l) + X.T @ np.linalg.inv(SIGMA)@ (MU_k - MU_l)).flatten()[0] \n\n\n\ndef QDA_score(X,MU_k,SIGMA,pi_k): \n #Returns the value of the linear discriminant score function for a given class \"k\" and \n # a given x value X\n \n SIGMA_inv = np.linalg.inv(SIGMA)\n \n return (np.log(pi_k) - 1/2 * np.log(np.linalg.det(SIGMA_inv)) - 1/2 * (X - MU_k).T @ SIGMA_inv @ (X - MU_k)).flatten()[0] \n\ndef predict_QDA_class(X,MU_list,SIGMA_list,pi_list): \n #Returns the class for which the the linear discriminant score function is largest\n scores_list = []\n classes = len(MU_list)\n \n for p in range(classes):\n score = QDA_score(X.reshape(-1,1),MU_list[p].reshape(-1,1),SIGMA_list[p],pi_list[p]) \n scores_list.append(score)\n \n return np.argmax(scores_list)", "_____no_output_____" ], [ "iris = sns.load_dataset(\"iris\")\nsns.pairplot(iris, hue=\"species\")", "_____no_output_____" ], [ "iris = iris.rename(index = str, columns = {'sepal_length':'1_sepal_length','sepal_width':'2_sepal_width', 'petal_length':'3_petal_length', 'petal_width':'4_petal_width'})\nsns.FacetGrid(iris, hue=\"species\", size=6) .map(plt.scatter,\"1_sepal_length\", \"2_sepal_width\", ) .add_legend()\nplt.title('Scatter plot')\ndf1 = iris[[\"1_sepal_length\", \"2_sepal_width\",'species']]", "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n warnings.warn(msg, UserWarning)\n" ] ], [ [ "Visualizing the gaussian estimations and the boundary lines", "_____no_output_____" ] ], [ [ "#Estimating the parameters\nmu_list = np.split(df1.groupby('species').mean().values,[1,2])\nsigma = df1.cov().values\npi_list = df1.iloc[:,2].value_counts().values / len(df1)\n\n# Our 2-dimensional distribution will be over variables X and Y\nN = 100\nX = np.linspace(3, 8, N)\nY = np.linspace(1.5, 5, N)\nX, Y = np.meshgrid(X, Y)\n\n#fig = plt.figure(figsize = (10,10))\n#ax = fig.gca()\ncolor_list = ['Blues','Greens','Reds']\nmy_norm = colors.Normalize(vmin=-1.,vmax=1.)\n\ng = sns.FacetGrid(iris, hue=\"species\", size=10, palette = 'colorblind') .map(plt.scatter,\"1_sepal_length\", \"2_sepal_width\", ) .add_legend()\nmy_ax = g.ax\n\nfor i,v in enumerate(itertools.combinations([0,1,2],2)):\n mu = mu_list[i]\n Sigma = sigma\n\n#Computing the cost function for each theta combination\n zz = np.array( [multivariate_gaussian_pdf( np.array([xx,yy]).reshape(-1,1), mu, Sigma) \n for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )\n \n bb = np.array( [ calculate_boundary(np.array([xx,yy]).reshape(-1,1),mu_list[v[0]].reshape(-1,1),mu_list[v[1]].reshape(-1,1), sigma , .33,.33)\n for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )\n \n#Reshaping the cost values \n Z = zz.reshape(X.shape)\n B = bb.reshape(X.shape)\n\n#Plot the result in 3D\n my_ax.contour( X, Y, Z, 3,cmap = color_list[i] , norm = my_norm, alpha = .3)\n\n my_ax.contour( X, Y, B , levels = [0] ,cmap = color_list[i] , norm = my_norm)\n\n \n\n# Adjust the limits, ticks and view angle\nmy_ax.set_xlabel('X')\nmy_ax.set_ylabel('Y')\nmy_ax.set_title('LDA: gaussians of each class and boundary lines')\n\nplt.show()", "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n warnings.warn(msg, UserWarning)\n" ] ], [ [ "Visualizing the Gaussian estimations with different covariance matrices", "_____no_output_____" ] ], [ [ "#Estimating the parameters\nmu_list = np.split(df1.groupby('species').mean().values,[1,2])\nsigma_list = np.split(df1.groupby('species').cov().values,[2,4], axis = 0)\npi_list = df1.iloc[:,2].value_counts().values / len(df1)\n\n# Our 2-dimensional distribution will be over variables X and Y\nN = 100\nX = np.linspace(3, 8, N)\nY = np.linspace(1.5, 5, N)\nX, Y = np.meshgrid(X, Y)\n\n#fig = plt.figure(figsize = (10,10))\n#ax = fig.gca()\ncolor_list = ['Blues','Greens','Reds']\nmy_norm = colors.Normalize(vmin=-1.,vmax=1.)\n\ng = sns.FacetGrid(iris, hue=\"species\", size=10, palette = 'colorblind') .map(plt.scatter, \"1_sepal_length\", \"2_sepal_width\",) .add_legend()\nmy_ax = g.ax\n\nfor i in range(3):\n mu = mu_list[i]\n Sigma = sigma_list[i]\n\n#Computing the cost function for each theta combination\n zz = np.array( [multivariate_gaussian_pdf( np.array([xx,yy]).reshape(-1,1), mu, Sigma) \n for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )\n#Reshaping the cost values \n Z = zz.reshape(X.shape)\n Zm = np.ma.masked_array(Z, Z < 0.15)\n\n#Plot the result in 3D\n my_ax.contour( X, Y, Z, 15, alpha = .3 ,cmap = color_list[i], norm = my_norm)\n my_ax.pcolor(X,Y,Zm, alpha = .1, cmap = color_list[i], norm = my_norm)\n\n\n# Adjust the limits, ticks and view angle\nmy_ax.set_xlabel('X')\nmy_ax.set_ylabel('Y')\nmy_ax.set_title('Multivariate Gaussians with different Sigma ')\n\nplt.show()", "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n warnings.warn(msg, UserWarning)\n" ] ], [ [ "Visualizing the quadratic boundary curves", "_____no_output_____" ] ], [ [ "#Estimating the parameters\nmu_list = np.split(df1.groupby('species').mean().values,[1,2])\nsigma_list = np.split(df1.groupby('species').cov().values,[2,4], axis = 0)\npi_list = df1.iloc[:,2].value_counts().values / len(df1)\n\n# Our 2-dimensional distribution will be over variables X and Y\nN = 200\nX = np.linspace(4, 8, N)\nY = np.linspace(1.5, 5, N)\nX, Y = np.meshgrid(X, Y)\n\n#fig = plt.figure(figsize = (10,10))\n#ax = fig.gca()\ncolor_list = ['Blues','Greens','Reds']\nmy_norm = colors.Normalize(vmin=-1.,vmax=1.)\n\ng = sns.FacetGrid(iris, hue=\"species\", size=10, palette = 'colorblind') .map(plt.scatter, \"1_sepal_length\", \"2_sepal_width\",) .add_legend()\nmy_ax = g.ax\n\n\n#Computing the predicted class function for each value on the grid\nzz = np.array( [predict_QDA_class( np.array([xx,yy]).reshape(-1,1), mu_list, sigma_list, pi_list) \n for xx, yy in zip(np.ravel(X), np.ravel(Y)) ] )\n \n#Reshaping the predicted class into the meshgrid shape\nZ = zz.reshape(X.shape)\n\n\n#Plot the filled and boundary contours\nmy_ax.contourf( X, Y, Z, 2, alpha = .1, colors = ('blue','green','red'))\nmy_ax.contour( X, Y, Z, 2, alpha = 1, colors = ('blue','green','red'))\n\n# Addd axis and title\nmy_ax.set_xlabel('X')\nmy_ax.set_ylabel('Y')\nmy_ax.set_title('QDA and boundaries')\n\nplt.show()", "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n warnings.warn(msg, UserWarning)\n" ] ], [ [ "QDA Accuracy", "_____no_output_____" ] ], [ [ "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nX_data = df1.iloc[:,0:2]\ny_labels = df1.iloc[:,2].replace({'setosa':0,'versicolor':1,'virginica':2}).copy()\n\nqda = QuadraticDiscriminantAnalysis(store_covariance=True)\nqda.fit(X_data,y_labels)\n\n#Numpy accuracy\ny_pred = np.array( [predict_QDA_class( np.array([xx,yy]).reshape(-1,1), mu_list, sigma_list, pi_list) \n for xx, yy in zip(np.ravel(X_data.values[:,0]), np.ravel(X_data.values[:,1])) ] )\ndisplay(np.mean(y_pred == y_labels))\n\n#predict_QDA_class( np.array([xx,yy]).reshape(-1,1), mu_list, sigma_list, pi_list) \n\n#Sklearn accuracy\ndisplay(qda.score(X_data,y_labels))", "_____no_output_____" ] ] ]

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[ [ [ "###### 03 Geometric Machine Learning for Shape Analysis\n\n## E) Unsupervised Learning: Dimension Reduction\n\n$\\color{#003660}{\\text{Nina Miolane - Assistant Professor}}$ @ BioShape Lab @ UCSB ECE", "_____no_output_____" ], [ "<center><img src=\"figs/03_dimred.png\" width=750px alt=\"default\"/></center>", "_____no_output_____" ], [ "# This Unit\n\n- **Unit 1 (Geometry - Math!)**: Differential Geometry for Engineers\n- **Unit 2 (Shapes)**: Computational Representations of Biomedical Shapes\n- **Unit 3 (Machine Learning)**: **Geometric Machine Learning for Shape Analysis**\n - A) Mean and Covariance\n - B) Supervised Learning: Classification\n - C) Supervised Learning: Regression\n - D) Unsupervised Learning: Clustering\n - **E) Unsupervised Learning: Dimension Reduction**\n - Motivation: Dimension Reduction on Optic Nerve Heads (5 landmarks)\n - Traditional Principal Component Analysis (PCA)\n - Dimension Reduction Method 1: Tangent PCA\n - Dimension Reduction Method 2: Principal Geodesic Analysis\n- **Unit 4 (Deep Learning)**: Geometric Deep Learning for Shape Analysis\n\n$\\rightarrow$ We explain the machine learning algorithms and statistics used in these real-world scenarios.", "_____no_output_____" ], [ "# Overview of Machine Learning (ML)\n\nMachine Learning is divided into two principal categories of algorithms: supervised and unsupervised learning algorithms. Both learn from data.\n\n$\\color{#EF5645}{\\text{Definition}}$: **Unsupervised learning** refers to the task of discovering any naturally occuring patterns in a dataset of data points $x$. We say that the model is:\n- a clustering: if we want to find groups (clusters),\n- a dimension reduction: if we want to find the main sources of variations.\n\n", "_____no_output_____" ], [ "# Why Dimension Reduction\n\n- Some data are (very) high dimensional\n- Dimension reduction: Extract a low dimensional structure for:\n - Visualization\n - More efficient use of resources (memory)\n - Downstream tasks: fewer dimensions -> better generalization.\n \n<center><img src=\"figs/03_dimred.png\" width=850px alt=\"default\"/></center>", "_____no_output_____" ], [ "# Dimension Reduction\n\n\n\n$\\color{#EF5645}{\\text{Given}}$:\n- dataset $X_1, . . . , X_n$ in a data space of dimension $D$\n- integer $d < D$,\n\n$\\color{#EF5645}{\\text{Goal}}$:\n - Find representations $z_1, ..., z_n$ of the data points,\n - that belong to a lower-dimensional space of dimension $d < D$,\n - that are \"representative\" of the $X_1, ..., X_n$.\n\n\n<center><img src=\"figs/03_pca.png\" width=250px alt=\"default\"/></center>", "_____no_output_____" ], [ "# Motivation: Dimension Reduction for Optical Nerve Heads", "_____no_output_____" ], [ "$\\color{#EF5645}{\\text{Question}}$: Are the shapes of optic nerve heads split into two clusters: healthy versus glaucoma? --> _Can we visualize the dataset?_\n\nData acquired with a Heidelberg Retina Tomograph - Patrangenaru and Ellingson (2015):\n- 11 Rhesus monkeys\n- 22 images of monkeys’ eyes:\n - an experimental glaucoma was introduced in one eye, \n - while the second eye was kept as control.\n \n$\\rightarrow$ On each image, 5 anatomical \"landmarks\" were recorded.\n\n<center><img src=\"figs/01_optic_nerves.png\" width=400px alt=\"default\"/></center>\n<center>Comparison of optic nerve heads in monkeys with and without glaucoma.</center>", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport matplotlib.patches as mpatches\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")", "_____no_output_____" ], [ "import geomstats.datasets.utils as data_utils\n\nnerves, labels, monkeys = data_utils.load_optical_nerves()\n# Keep the 5 landmarks\nprint(nerves.shape)\nprint(labels)\nprint(monkeys)", "(22, 5, 3)\n[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1]\n[ 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10]\n" ] ], [ [ "Plot two optical shapes: ", "_____no_output_____" ] ], [ [ "two_nerves = nerves[monkeys == 0]\nprint(two_nerves.shape)\n\ntwo_labels = labels[monkeys == 0]\nprint(two_labels)", "(2, 5, 3)\n[0 1]\n" ], [ "label_to_str = {0: \"Normal nerve\", 1: \"Glaucoma nerve\"}\nlabel_to_color = {\n 0: (102 / 255, 178 / 255, 255 / 255, 1.0),\n 1: (255 / 255, 178 / 255, 102 / 255, 1.0),\n}", "_____no_output_____" ], [ "fig = plt.figure(); ax = Axes3D(fig); ax.set_xlim((2000, 4000)); ax.set_ylim((1000, 5000)); ax.set_zlim((-600, 200))\n\nfor nerve, label in zip(two_nerves, two_labels):\n x = nerve[:, 0]\n y = nerve[:, 1]\n z = nerve[:, 2]\n\n verts = [list(zip(x, y, z))]\n\n poly = Poly3DCollection(verts, alpha=0.5)\n color = label_to_color[int(label)]\n poly.set_color(colors.rgb2hex(color))\n poly.set_edgecolor(\"k\")\n ax.add_collection3d(poly)\n\npatch_0 = mpatches.Patch(color=label_to_color[0], label=label_to_str[0], alpha=0.5)\npatch_1 = mpatches.Patch(color=label_to_color[1], label=label_to_str[1], alpha=0.5)\nplt.legend(handles=[patch_0, patch_1], prop={\"size\": 20})\nplt.show()", "_____no_output_____" ] ], [ [ "## Refresher: Traditional Principal Component Analysis", "_____no_output_____" ], [ "$\\color{#EF5645}{\\text{Principal Component Analysis (PCA)}}$ is an:\n- orthogonal projection of the data (belonging to a vector space $\\mathbb{R}^D$),\n- into a (lower dimensional) linear subspace $\\mathbb{R}^d$, $d < D$, \n- so that the variance of the projected data is maximized.", "_____no_output_____" ], [ "<center><img src=\"figs/03_pcadims.png\" width=800px alt=\"default\"/></center>\n\n$\\color{#EF5645}{\\text{Notations}}$: $D$ original dimension, $d$ dimension of lower-dimensional subspace.", "_____no_output_____" ], [ "## PCA: Intuition", "_____no_output_____" ], [ "What is the 1-dimensional linear subspace that maximizes the variance of the projected data?\n\n<center><img src=\"figs/03_apca1.png\" width=400px alt=\"default\"/></center>", "_____no_output_____" ], [ "<center><img src=\"figs/03_apca2.png\" width=400px alt=\"default\"/></center>", "_____no_output_____" ], [ "<center><img src=\"figs/03_apca3.png\" width=400px alt=\"default\"/></center>", "_____no_output_____" ], [ "## PCA: Mathematical Notations\n\nFind a orthonormal basis $\\left\\{v_{1}, \\ldots, v_{D}\\right\\}$ of $\\mathbb{R}^{D}$, which satisfies the recursive relationship\n$$\n\\begin{gathered}\nv_{1}=\\underset{\\|v\\|=1}{\\arg \\max } \\sum_{i=1}^{n}\\left(v \\cdot x_{i}\\right)^{2} \\\\\nv_{k}=\\underset{\\|v\\|=1}{\\arg \\max } \\sum_{i=1}^{n} \\sum_{j=1}^{k-1}\\left(v_{j} \\cdot x_{i}\\right)^{2}+\\left(v \\cdot x_{i}\\right)^{2}\n\\end{gathered}\n$$\n\nThe $x_i$ are centered at the mean in the equations above.\n\n<center><img src=\"figs/03_apca3.png\" width=300px alt=\"default\"/></center>\n\n", "_____no_output_____" ], [ "- The subspace $V_{k}=\\operatorname{span}\\left(\\left\\{v_{1}, \\ldots, v_{k}\\right\\}\\right)$ is:\n - the $k$-dimensional subspace \n - that maximizes the variance \n - of the data projected to that subspace: $\\pi_{V_1}(x_i) = v \\cdot x_{i}$\n ", "_____no_output_____" ], [ "$\\color{#047C91}{\\text{Exercise}}$: Consider the two projections below. Which maximizes the variance?\n\n\n<center><img src=\"figs/03_var.png\" width=700px alt=\"default\"/></center>", "_____no_output_____" ], [ "## PCA: Method\n\nThe basis $\\left\\{v_{k}\\right\\}$ is computed as the set of ordered eigenvectors of the sample covariance matrix of the data.\n \n$\\color{#6D7D33}{\\text{Method}}$: Given data $\\left\\{X_{1}, \\ldots, X_n\\right\\}$:\n- Compute covariance matrix $\\Sigma$, where $\\quad \\overline{\\mathbf{x}}=\\frac{1}{n} \\sum_{i=1}^{n} X_{i}$:\n\n$$\\Sigma=\\frac{1}{n} \\sum_{i=1}^{n}\\left(\\mathbf{x}_{i}-\\overline{\\mathbf{x}}\\right)(\\mathbf{x}_i-\\overline{\\mathbf{x}})^{T} \\quad$$ \n- Compute eigenvectors, eigenvalues of $\\Sigma$:\n - Eigenvectors: principal components (PCs)\n - Eigenvalues: orders PCs", "_____no_output_____" ], [ "## PCA: Explanation\n\n$\\color{#EF5645}{\\text{Goal (Rewritten)}}$: Maximize $\\quad \\mathbf{u}^{\\top} \\mathbf{X X}^{\\top} \\mathbf{u}$\ns.t $\\quad \\mathbf{u}^{\\top} \\mathbf{u}=1$, where $\\Sigma = \\mathbf{X X}^{\\top}$\n\n$\\color{#6D7D33}{\\text{Method}}$:\n- Construct Lagrangian $\\mathbf{u}^{\\top} \\mathbf{X X}^{\\top} \\mathbf{u}-\\lambda \\mathbf{u}^{\\top} \\mathbf{u}$\n- Set partial derivatives to zero\n$$\n\\mathbf{X X}^{\\top} \\mathbf{u}-\\lambda \\mathbf{u}=\\left(\\mathbf{X X}^{\\top}-\\lambda \\mathrm{I}\\right) \\mathbf{u}=\\mathbf{0}\n$$\n\nAs $\\mathbf{u} \\neq \\mathbf{0}$ then $\\mathbf{u}$ must be an eigenvector of $XX^{\\top}$ with eigenvalue $\\lambda$", "_____no_output_____" ], [ "# How Many Principal Components (PCs) ?\n\nMaximum number of PCs:\n- For $D$ original dimensions, sample covariance matrix is $D \\times D$, and has up to $D$ eigenvectors.\n- Maximum number: $D$ PCs.\n\nInteresting number of PCs:\n- Ignore the components of lesser significance, i.e. small eigenvalues.\n- Interesting number: $d$ PCs.\n\n<center><img src=\"figs/03_pcs.png\" width=400px alt=\"default\"/></center>\n", "_____no_output_____" ], [ "## PCA: Two Interpretations\n\n$\\color{#EF5645}{\\text{Maximum Variance Direction:}}$\n projection captures maximum variance in the data\n$$\n\\frac{1}{n} \\sum_{i=1}^{n}\\left(\\mathbf{v}^{T} \\mathbf{x}_{i}\\right)^{2}=\\mathbf{v}^{T} \\mathbf{X X}^{T} \\mathbf{v}\n$$\n\n$\\color{#EF5645}{\\text{Minimum Reconstruction Error:}}$ projection yields minimum mean square error\n$$\n\\frac{1}{n} \\sum_{i=1}^{n}\\left\\|\\mathbf{x}_{i}-\\left(\\mathbf{v}^{T} \\mathbf{x}_{i}\\right) \\mathbf{v}\\right\\|^{2}\n$$\n\n<center><img src=\"figs/03_orthogonal.png\" width=400px alt=\"default\"/></center>\n", "_____no_output_____" ], [ "## Dimension Reduction Method 1: Tangent Principal Component Analysis", "_____no_output_____" ], [ "### Recall: \"Trick\": Tangent Space at the Fréchet Mean\n\nThe Fréchet mean gives us a way of transforming our non-linear data into vectors!\n1. Compute the Fréchet mean $\\bar{x}$ of the data points\n2. Consider the tangent space $T_\\bar{x}M$of the manifold $M$ at $\\bar{x}$\n3. Compute the Logarithms of the data points at $\\bar{x}$\n\n$\\rightarrow$ Get a dataset on a vector space, and apply classical machine learning on it.", "_____no_output_____" ], [ "## Tangent Principal Component Analysis\n\n= Apply PCA on the tangent space at the Fréchet mean.\n\nThe next slides illustrate the use of tangent PCA on:\n- the hyperbolic space (synthetic data)\n- Kendall shape space (optical nerve head data)", "_____no_output_____" ], [ "## On the Hyperbolic Space", "_____no_output_____" ] ], [ [ "from geomstats.geometry.hyperboloid import Hyperboloid\nfrom geomstats.learning.frechet_mean import FrechetMean\nfrom geomstats.learning.pca import TangentPCA\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport geomstats.visualization as viz", "_____no_output_____" ] ], [ [ "1. Set-up\n\n- $\\color{#EF5645}{\\text{Decide on the model:}}$ We use tangent PCA\n- $\\color{#EF5645}{\\text{Decide on a loss function:}}$ Minimize -variance", "_____no_output_____" ] ], [ [ "# Synthetic data\nhyperbolic_plane = Hyperboloid(dim=2)\ndata = hyperbolic_plane.random_point(n_samples=140)\n\n# Set-up\nmean = FrechetMean(metric=hyperbolic_plane.metric)\ntpca = TangentPCA(metric=hyperbolic_plane.metric, n_components=2)", "_____no_output_____" ] ], [ [ "2. $\\color{#EF5645}{\\text{Split dataset into train / test sets:}}$ \n - Train $X_1, ..., X_{n_\\text{train}}$: build the algorithm\n - Test $X_{n_\\text{train}+1}, ..., X_n$: assess its performances.", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import train_test_split\n\ntrain, test = train_test_split(data)\nprint(train.shape)\nprint(test.shape)", "(105, 3)\n(35, 3)\n" ] ], [ [ "3. $\\color{#EF5645}{\\text{Train:}}$ Build the algorithm", "_____no_output_____" ] ], [ [ "mean.fit(train)\nmean_estimate = mean.estimate_\n\ntpca = tpca.fit(train, base_point=mean_estimate)\ntangent_projected_data = tpca.transform(train)", "_____no_output_____" ] ], [ [ "4. $\\color{#EF5645}{\\text{Test:}}$ Assess its performances", "_____no_output_____" ] ], [ [ "geodesic_0 = hyperbolic_plane.metric.geodesic(\n initial_point=mean_estimate, initial_tangent_vec=tpca.components_[0]\n )\ngeodesic_1 = hyperbolic_plane.metric.geodesic(\n initial_point=mean_estimate, initial_tangent_vec=tpca.components_[1]\n )\n\nn_steps = 100\nt = np.linspace(-1, 1, n_steps)\ngeodesic_points_0 = geodesic_0(t)\ngeodesic_points_1 = geodesic_1(t)", "_____no_output_____" ], [ "fig = plt.figure(figsize=(15, 5))\nax_var = fig.add_subplot(121)\nxticks = np.arange(1, 2 + 1, 1); ax_var.xaxis.set_ticks(xticks)\nax_var.set_title(\"Explained variance\"); ax_var.set_xlabel(\"Number of Principal Components\")\nax_var.set_ylim((0, 1))\nax_var.plot(xticks, tpca.explained_variance_ratio_)\n\nax = fig.add_subplot(122)\n\nviz.plot(\n mean_estimate, ax, space=\"H2_poincare_disk\", color=\"darkgreen\", s=10\n)\nviz.plot(geodesic_points_0, ax, space=\"H2_poincare_disk\", linewidth=2)\nviz.plot(geodesic_points_1, ax, space=\"H2_poincare_disk\", linewidth=2)\nviz.plot(data, ax, space=\"H2_poincare_disk\", color=\"black\", alpha=0.7)\n\nax.set_aspect(\"equal\")\nplt.show()", "_____no_output_____" ] ], [ [ "## On Kendall Shape Spaces", "_____no_output_____" ] ], [ [ "from geomstats.geometry.pre_shape import PreShapeSpace, KendallShapeMetric\n\nm_ambient = 3\nk_landmarks = 5\n\npreshape = PreShapeSpace(m_ambient=m_ambient, k_landmarks=k_landmarks)\nmatrices_metric = preshape.embedding_metric\n\n\nnerves_preshape = preshape.projection(nerves)\nprint(nerves_preshape.shape)\nprint(preshape.belongs(nerves_preshape))\nprint(np.isclose(matrices_metric.norm(nerves_preshape), 1.0))", "(22, 5, 3)\n[ True True True True True True True True True True True True\n True True True True True True True True True True]\n[ True True True True True True True True True True True True\n True True True True True True True True True True]\n" ], [ "base_point = nerves_preshape[0]\n\nnerves_shape = preshape.align(point=nerves_preshape, base_point=base_point)", "_____no_output_____" ] ], [ [ "1. Set-up\n\n- $\\color{#EF5645}{\\text{Decide on the model:}}$ We use tangent PCA\n- $\\color{#EF5645}{\\text{Decide on a loss function:}}$ Minimize -variance", "_____no_output_____" ] ], [ [ "kendall_metric = KendallShapeMetric(m_ambient=m_ambient, k_landmarks=k_landmarks)\n\ntpca = TangentPCA(kendall_metric)", "_____no_output_____" ] ], [ [ "2. $\\color{#EF5645}{\\text{Split dataset into train / test sets:}}$ \n - Train $X_1, ..., X_{n_\\text{train}}$: build the algorithm\n - Test $X_{n_\\text{train}+1}, ..., X_n$: assess its performances.", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import train_test_split\n\ntrain_nerves_shape = nerves_shape[:18]\ntest_nerves_shape = nerves_shape[18:]\n\nprint(train_nerves_shape.shape)\nprint(test_nerves_shape.shape)\n", "(18, 5, 3)\n(4, 5, 3)\n" ] ], [ [ "3. $\\color{#EF5645}{\\text{Train:}}$ Build the algorithm", "_____no_output_____" ] ], [ [ "tpca.fit(train_nerves_shape)\n\nplt.plot(tpca.explained_variance_ratio_)\nplt.xlabel(\"Number of principal tangent components\", size=14)\nplt.ylabel(\"Fraction of explained variance\", size=14);", "_____no_output_____" ] ], [ [ "Two principal components describe around 60% of the variance. We plot the data projected in the tangent space defined by these two principal components.", "_____no_output_____" ], [ "4. $\\color{#EF5645}{\\text{Test:}}$ Assess its performances\n\n- We project the whole dataset on the principal components.", "_____no_output_____" ] ], [ [ "X = tpca.transform(nerves_shape)\nplt.figure(figsize=(11, 11))\nfor label, col in label_to_color.items():\n mask = labels == label\n plt.scatter(X[mask, 0], X[mask, 1], color=col, s=100, label=label_to_str[label])\nplt.legend(fontsize=14)\nfor label, x, y in zip(monkeys, X[:, 0], X[:, 1]):\n plt.annotate(label, xy=(x, y), xytext=(-20, 20), textcoords=\"offset points\", ha=\"right\", va=\"bottom\", bbox=dict(boxstyle=\"round,pad=0.5\", fc=\"white\", alpha=0.5), arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=0\"))\nplt.show()", "_____no_output_____" ] ], [ [ "## Dimension Reduction Method 2: Principal Geodesic Analysis\n", "_____no_output_____" ], [ "- Variance. Following the work of Fréchet, we define the sample variance of the data as the expected value of the squared Riemannian distance from the mean.\n- Geodesic subspaces. The lower-dimensional subspaces in PCA are linear subspaces. For general manifolds we extend the concept of a linear subspace to that of a geodesic submanifold.\n- Projection. In PCA the data is projected onto linear subspaces. We define a projection operator for geodesic submanifolds, and show how it may be efficiently approximated.", "_____no_output_____" ], [ "$\\color{#EF5645}{\\text{Principal Geodesic Analysis (PGA)}}$ is an:\n- $\\color{#EF5645}{\\text{orthogonal projection}}$ of the data\n- into a (lower dimensional) $\\color{#EF5645}{\\text{geodesic subspace}}$, \n- so that the variance of the projected data is maximized.", "_____no_output_____" ], [ "## Geodesic Subspace\n\n$\\color{#EF5645}{\\text{A submanifold $N$ of a manifold $M$}}$ is a subset of $M$ that is also a manifold.\n\nIn general, if $N$ is a submanifold of a manifold $M$, geodesics of $N$ are not necessarily geodesics of $M$.\n\n- $\\color{#047C91}{\\text{Example}}$: $S^2$ as a submanifold of $\\mathbb{R}^3$.", "_____no_output_____" ], [ "$\\color{#EF5645}{\\text{A submanifold $H$ of $M$ is said to be geodesic at $x \\in H$}}$ if all geodesics of $H$ _passing through $x$_ are also geodesics of $M$. \n\n- $\\color{#047C91}{\\text{Example}}$: A linear subspace of $\\mathbb{R}^{D}$ is a submanifold geodesic at 0.\n\n$\\color{#EF5645}{\\text{Remark}}$: Submanifolds geodesic at $x$ preserve distances to $x.$ This is an essential property for PGA because variance is defined as the average squared distance to the mean. Thus submanifolds geodesic at the mean will be the generalizations of the linear subspaces of PCA.", "_____no_output_____" ], [ "## Projection\n\n$\\color{#EF5645}{\\text{The projection of a point $x \\in M$}}$ onto a geodesic submanifold $H$ of $M$ is defined as the point on $H$ that is nearest to $x$ in Riemannian distance. Thus we define the projection operator $\\pi_{H}: M \\rightarrow H$ as\n$$\n\\pi_{H}(x)=\\underset{y \\in H}{\\arg \\min } d(x, y)^{2}\n$$\n\n\n<center><img src=\"figs/03_proj.png\" width=400px alt=\"default\"/></center>", "_____no_output_____" ], [ "## PGA: Mathematical Notations\n\nThe principal geodesic submanifolds are the images of the $V_{k}$ under the exponential map: $H_{k}=\\operatorname{Exp}_{\\mu}\\left(V_{k}\\right)$. The first principal direction is chosen to maximize the projected variance along the corresponding geodesic:\n$$\nv_{1}=\\underset{\\|v\\|=1}{\\arg \\max } \\sum_{i=1}^{n}\\left\\|\\log _{\\mu}\\left(\\pi_{H}\\left(x_{i}\\right)\\right)\\right\\|^{2},\n$$\nwhere $H=\\operatorname{Exp}_{\\mu}(\\operatorname{span}(\\{v\\}) \\cap U)$.\nThe remaining principal directions are then defined recursively as\n$$\n\\begin{aligned}\n&v_{k}=\\underset{\\|v\\|=1}{\\arg \\max } \\sum_{i=1}^{n}\\left\\|\\log _{\\mu}\\left(\\pi_{H}\\left(x_{i}\\right)\\right)\\right\\|^{2} \\\\\n&\\text { where } H=\\operatorname{Exp}_{\\mu}\\left(\\operatorname{span}\\left(\\left\\{v_{1}, \\ldots, v_{k-1}, v\\right\\}\\right) \\cap U\\right) .\n\\end{aligned}\n$$", "_____no_output_____" ] ], [ [ "- The subspace $V_{k}=\\operatorname{span}\\left(\\left\\{v_{1}, \\ldots, v_{k}\\right\\}\\right)$ is:\n - the $k$-dimensional subspace \n - that maximizes the variance \n - of the data projected to that subspace: $\\pi_{V_1}(x_i) = v \\cdot x_{i}$", "_____no_output_____" ] ], [ [ "<center><img src=\"figs/03_pga_alg.png\" width=900px alt=\"default\"/></center>\n", "_____no_output_____" ], [ "## Example on the Sphere\n\nThe following code runs PGA on the sphere. Note that you need to specify pytorch backend for automatic differentiation.\n\nhttps://github.com/nguigs/geomstats/blob/nguigs-pga/examples/pga-s2.py", "_____no_output_____" ], [ "# This Unit\n\n- **Unit 1 (Geometry - Math!)**: Differential Geometry for Engineers\n- **Unit 2 (Shapes)**: Computational Representations of Biomedical Shapes\n- **Unit 3 (Machine Learning)**: **Geometric Machine Learning for Shape Analysis**\n - A) Mean and Covariance\n - B) Supervised Learning: Classification\n - C) Supervised Learning: Regression\n - D) Unsupervised Learning: Clustering**\n - **E) Unsupervised Learning: Dimension Reduction**\n - Motivation: Dimension Reduction on Optic Nerve Heads (5 landmarks)\n - Traditional Principal Component Analysis (PCA)\n - Dimension Reduction Method 1: Tangent PCA\n - Dimension Reduction Method 2: Principal Geodesic Analysis\n- **Unit 4 (Deep Learning)**: Geometric Deep Learning for Shape Analysis\n\n$\\rightarrow$ We explain the machine learning algorithms and statistics used in these real-world scenarios.", "_____no_output_____" ] ] ]

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[ [ [ "Label Detection. Face Detection and Comparison, Celebrity Recognition, Image moderation, Text in image detection", "_____no_output_____" ] ], [ [ "import cv2\nimport boto3\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "# Helpers\ndef show_image(filename):\n image = cv2.imread(filename)\n plt.imshow(image)\n plt.show()\n \n# Change color channels\ndef show_image_rgb(filename):\n image = cv2.imread(filename)\n plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n plt.show()\n \ndef image_encoder(image_array):\n #image_uint8 = image_array.astype(np.uint8)\n ret, buf = cv2.imencode('.jpg', image_array)\n encoded = {\n 'Bytes':buf.tobytes()\n }\n return encoded", "_____no_output_____" ], [ "def detect_image_entities(filename):\n rekognition = boto3.client('rekognition')\n # Read image array\n image = cv2.imread(filename)\n # Encode Image\n encoded = image_encoder(image)\n # Send to rekognition\n response = rekognition.detect_labels(\n Image = encoded\n )\n return response['Labels']", "_____no_output_____" ], [ "filename = 'new-york-city.jpg'\nshow_image(filename)\ndetect_image_entities(filename)", "_____no_output_____" ], [ "def detect_image_text(filename):\n rekognition = boto3.client('rekognition')\n # Read image array\n image = cv2.imread(filename)\n # Encode Image\n encoded = image_encoder(image)\n # Send to rekognition\n response = rekognition.detect_text(\n Image = encoded\n )\n return response['TextDetections']", "_____no_output_____" ], [ "filename = 'innovation.jpg'\nshow_image(filename)\ndetect_image_text(filename)", "_____no_output_____" ], [ "def analyze_face(filename):\n rekognition = boto3.client('rekognition')\n # Read image array\n image = cv2.imread(filename)\n # Encode Image\n encoded = image_encoder(image)\n # Send to rekognition\n response = rekognition.detect_faces(\n Image = encoded,\n Attributes=[\n 'ALL',\n ]\n )\n return response['FaceDetails']", "_____no_output_____" ], [ "filename = 'harry_megan.JPG'\nshow_image_rgb(filename)\nanalyze_face(filename)", "_____no_output_____" ], [ "def detect_celebrity(filename):\n rekognition = boto3.client('rekognition')\n # Read image array\n image = cv2.imread(filename)\n # Encode Image\n encoded = image_encoder(image)\n # Send to rekognition\n response = rekognition.recognize_celebrities(\n Image = encoded\n )\n return response['CelebrityFaces']", "_____no_output_____" ], [ "filename = 'elon.jpg'\nshow_image_rgb(filename)\ndetect_celebrity(filename)", "_____no_output_____" ], [ "def compare_faces(filename1,filename2):\n rekognition = boto3.client('rekognition')\n # Read image array\n image1 = cv2.imread(filename1)\n image2 = cv2.imread(filename2)\n # Encode Image\n encoded1 = image_encoder(image1)\n encoded2 = image_encoder(image2)\n # Send to rekognition\n response = rekognition.compare_faces(\n SourceImage = encoded1,\n TargetImage = encoded2\n )\n return response['SourceImageFace'], response['FaceMatches'], response['UnmatchedFaces']", "_____no_output_____" ], [ "filename1 = 'obama1.jpg'\nfilename2 = 'obama2.jpg'\n\nshow_image_rgb(filename1)\nshow_image_rgb(filename2)\n\ncompare_faces(filename1,filename2)", "_____no_output_____" ] ] ]

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[ [ [ "# In this notebook I'm generating the movements and the states variables (torques and angles) in order to produce this movement using the 2dof simulator.\n\n### Some of the algorithms (or inspiration) to simulate the 2dof arm came from: http://www.gribblelab.org/compneuro/", "_____no_output_____" ], [ "# Here starts the 2 joint arm study", "_____no_output_____" ], [ "# Main functions to the 2 joint arm simulation", "_____no_output_____" ] ], [ [ "# Makes possible to show the output from matplotlib inline\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\n# Makes the figures in the PNG format:\n# For more information see %config InlineBackend\n%config InlineBackend.figure_formats=set([u'png'])\n\nplt.rcParams['figure.figsize'] = 20, 10\n\nimport numpy\nimport sys\nimport save_load_file as slf", "_____no_output_____" ], [ "# Loads the modules and starts the object to be used with the parallel processing iPython stuff...\n\n# Remember to execute at the shell: ipcluster start -n 4\n# or from the iPython notebook interface!\n# from IPython.parallel import Client\nfrom ipyparallel import Client\n\n# When using the ipython in my desktop, launch the cluster in the right profile :)\ncli = Client()\n\n# lbview = cli.load_balanced_view()\ndview = cli[:]", "_____no_output_____" ], [ "%%file simulation_2DoF_Arm_physics.py\n\n# 2 DOF Simulator module\n# Strongly based on http://www.gribblelab.org/compneuro/index.html\n\nimport numpy\n\n# forward kinematics\ndef joints_to_hand(A,aparams):\n \"\"\"\n Given joint angles A=(a1,a2) and anthropometric params aparams,\n returns hand position H=(hx,hy) and elbow position E=(ex,ey)\n Note1: A must be type matrix (or array([[a1,a2],...]))\n Note2: If A has multiple lines, H and E will have the same number of lines.\n \"\"\"\n l1 = aparams['l1']\n l2 = aparams['l2']\n n = numpy.shape(A)[0]\n E = numpy.zeros((n,2))\n H = numpy.zeros((n,2))\n for i in range(n):\n E[i,0] = l1 * numpy.cos(A[i,0])\n E[i,1] = l1 * numpy.sin(A[i,0])\n H[i,0] = E[i,0] + (l2 * numpy.cos(A[i,0]+A[i,1]))\n H[i,1] = E[i,1] + (l2 * numpy.sin(A[i,0]+A[i,1]))\n return H,E\n\n\n# I could do the inverse kinematics using all the possible values (workspace) of the arm and creating a numpy array. Then I \n# could use the argmin trick to find the value.\n# In order to solve the problem when multiple solutions appear I could use the minimum jerk criterion. The user should enter\n# the actual position and the next one, then the system solves according to the one that uses mininum energy.\n# One curious thing about inverse kinematics is that as human beings we cannot do a inverse kinematic of our hand position\n# without taking in account the actual position. The function, below, doesn't care about the actual position, and that is why \n# more than one solution appears.\n# So, I don't think the brain solves the problem of multiple solutions. Who solves this problem is the morphology of the limbs.\n# It is impossible to change trajectories instantaneously, therefore the continuity of the movements is guaranteed.\n# Summary: there are no positions, but trajectories :)\n\n# inverse kinematics\ndef hand_to_joints(H,aparams):\n \"\"\"\n Given hand position H=(hx,hy) and anthropometric params aparams,\n returns joint angles A=(a1,a2)\n Note1: H must be type matrix (or array([[hx,hy],...]))\n Note2: If H has multiple lines, A will have the same number of lines.\n \"\"\"\n l1 = aparams['l1']\n l2 = aparams['l2']\n n = numpy.shape(H)[0]\n A = numpy.zeros((n,2))\n for i in range(n):\n A[i,1] = numpy.arccos(((H[i,0]*H[i,0])+(H[i,1]*H[i,1])-(l1*l1)-(l2*l2))/(2.0*l1*l2))\n A[i,0] = numpy.arctan2(H[i,1],H[i,0]) - numpy.arctan2((l2*numpy.sin(A[i,1])),(l1+(l2*numpy.cos(A[i,1]))))\n# if A[i,0] < 0:\n# print \"<0:\",A[i,0]\n# A[i,0] = A[i,0] + pi\n# elif A[i,0] > pi:\n# print \">0:\",A[i,0]\n# A[i,0] = A[i,0] - pi\n return A\n\n\n# inverse kinematics\ndef hand_to_joints(H,aparams,ang_error=0.01):\n \"\"\"\n Given hand position H=(hx,hy) and anthropometric params aparams,\n returns joint angles A=(a1,a2)\n Note1: H must be type matrix (or array([[hx,hy],...]))\n Note2: If H has multiple lines, A will have the same number of lines.\n \"\"\"\n l1 = aparams['l1']\n l2 = aparams['l2']\n n = numpy.shape(H)[0]\n A = numpy.zeros((n,2))\n t_bias=[0,0] \n for i in range(n):\n A[i,1] = numpy.arccos(((H[i,0]*H[i,0])+(H[i,1]*H[i,1])-(l1*l1)-(l2*l2))/(2.0*l1*l2)) + t_bias[1]\n A[i,0] = numpy.arctan2(H[i,1],H[i,0]) - numpy.arctan2((l2*numpy.sin(A[i,1])),(l1+(l2*numpy.cos(A[i,1])))) + t_bias[0]\n if i>0:\n # Here I'm trying to avoid descontinuity problems when there's a 2pi difference between them!\n if 0<=abs(abs((A[i,1]-A[i-1,1])/numpy.pi)-2)<=ang_error:\n print \"Correction on Joint 2:\",(A[i,1],A[i-1,1])\n if (A[i,1]-A[i-1,1])>0:\n A[i,1]-=2*numpy.pi\n t_bias[1]-=2*numpy.pi\n else:\n A[i,1]+=2*numpy.pi\n t_bias[1]+=2*numpy.pi \n \n if 0<=abs(abs((A[i,0]-A[i-1,0])/numpy.pi)-2)<=ang_error:\n print \"Correction on Joint 1:\",(A[i,0],A[i-1,0])\n if (A[i,0]-A[i-1,0])>0:\n A[i,0]-=2*numpy.pi\n t_bias[0]-=2*numpy.pi\n else:\n A[i,0]+=2*numpy.pi\n t_bias[0]+=2*numpy.pi\n return A\n\n\n# Generates the movements according to:\n# Flash, Tamar and Neville Hogan. 1985. The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model. The Journal of Neuroscience 5 (7): 1688-1703\ndef cartesian_movement_generation_training(xstart,ystart,xdest,ydest,MT,t):\n '''\n xstart,ystart: initial position of the trajectory\n xdest,ydest: final position of the trajectory\n MT: total time spent doing the trajectory\n t: current time\n \n returns a matrix: [[x0,y0],[x1,y1],...]\n '''\n x_t=xstart+(xstart-xdest)*(15*(t/MT)**4-6*(t/MT)**5-10*(t/MT)**3)\n y_t=ystart+(ystart-ydest)*(15*(t/MT)**4-6*(t/MT)**5-10*(t/MT)**3) \n return numpy.array([x_t,y_t]).T\n\n\n# Used to generate the velocities and the accelerations using the position and time vectors\ndef derivator(v,t):\n return numpy.array([(v[i+1]-v[i])/(t[i+1]-t[i]) for i in range(len(t)-1)])\n\n\ndef twojointarm_torques(state, t, aparams):\n \"\"\"\n Calculates the necessaries torques to generate the accelerations\n \"\"\"\n import numpy\n\n a1,a2,a1d,a2d,a1dd,a2dd = state # joint_angle_a1,joint_angle_a2,joint_vel_a1,joint_vel_a2,joint_acc_a1,joint_acc_a2\n\n l1,l2 = aparams['l1'], aparams['l2'] # lenght link 1 and 2\n m1,m2 = aparams['m1'], aparams['m2'] # mass link 1 and 2\n i1,i2 = aparams['i1'], aparams['i2'] # moment of inertia link 1 and 2\n lc1,lc2 = aparams['lc1'], aparams['lc2'] # distance to the center of mass of link 1 and 2\n\n M11 = i1 + i2 + (m1*lc1*lc1) + (m2*((l1*l1) + (lc2*lc2) + (2*l1*lc2*numpy.cos(a2))))\n M12 = i2 + (m2*((lc2*lc2) + (l1*lc2*numpy.cos(a2))))\n M21 = M12\n M22 = i2 + (m2*lc2*lc2)\n M = numpy.matrix([[M11,M12],[M21,M22]]) # H matrix\n\n C1 = -(m2*l1*a2d*a2d*lc2*numpy.sin(a2)) - (2*m2*l1*a1d*a2d*lc2*numpy.sin(a2))\n C2 = m2*l1*a1d*a1d*lc2*numpy.sin(a2)\n C = numpy.matrix([[C1],[C2]])\n\n ACC = numpy.array([[a1dd],[a2dd]])\n\n T = M*ACC + C\n\n return numpy.array([T[0,0],T[1,0]])\n\n\n# forward dynamics equations of our two-joint arm\ndef twojointarm(state, t, aparams, torque):\n import numpy\n \n \"\"\"\n two-joint arm in plane\n X is fwd(+) and back(-)\n Y is up(+) and down(-)\n shoulder angle a1 relative to Y vert, +ve counter-clockwise\n elbow angle a2 relative to upper arm, +ve counter-clockwise\n \"\"\"\n a1,a2,a1d,a2d = state # joint_angle_a1, joint_angle_a2, joint_velocity_a1, joint_velocity_a2\n\n l1,l2 = aparams['l1'], aparams['l2'] # lenght link 1 and 2\n m1,m2 = aparams['m1'], aparams['m2'] # mass link 1 and 2\n i1,i2 = aparams['i1'], aparams['i2'] # moment of inertia link 1 and 2\n lc1,lc2 = aparams['lc1'], aparams['lc2'] # distance to the center of mass of link 1 and 2\n\n M11 = i1 + i2 + (m1*lc1*lc1) + (m2*((l1*l1) + (lc2*lc2) + (2*l1*lc2*numpy.cos(a2))))\n M12 = i2 + (m2*((lc2*lc2) + (l1*lc2*numpy.cos(a2))))\n M21 = M12\n M22 = i2 + (m2*lc2*lc2)\n M = numpy.matrix([[M11,M12],[M21,M22]]) # H matrix\n\n C1 = -(m2*l1*a2d*a2d*lc2*numpy.sin(a2)) - (2*m2*l1*a1d*a2d*lc2*numpy.sin(a2))\n C2 = m2*l1*a1d*a1d*lc2*numpy.sin(a2)\n C = numpy.matrix([[C1],[C2]])\n\n T = numpy.matrix([[torque[0]],[torque[1]]])\n\n ACC = numpy.linalg.inv(M) * (T-C) # calculates the accelerations of joints 1 and 2\n\n a1dd,a2dd = ACC[0,0], ACC[1,0]\n\n return [a1d, a2d, a1dd, a2dd] # It returns the first and second derivatives of the joints\n\n\ndef animatearm(state,t,aparams):\n \"\"\"\n animate the twojointarm\n \"\"\"\n import matplotlib.pyplot as plt\n import numpy\n import time\n\n A = state[:,[0,1]] # Gets the angles a1 and a2 from the states matrix\n A[:,0] = A[:,0]\n H,E = joints_to_hand(A,aparams)\n l1,l2 = aparams['l1'], aparams['l2']\n plt.figure()\n plt.plot(0,0,'b.')\n plt.plot(H[:,0],H[:,1],'g.-');\n p1, = plt.plot(E[0,0],E[0,1],'b.')\n p2, = plt.plot(H[0,0],H[0,1],'b.')\n p3, = plt.plot((0,E[0,0],H[0,0]),(0,E[0,1],H[0,1]),'b-')\n plt.xlim([-l1-l2, l1+l2])\n plt.ylim([-l1-l2, l1+l2])\n dt = t[1]-t[0]\n tt = plt.title(\"Click on this plot to continue...\")\n plt.ginput(1)\n for i in xrange(0,numpy.shape(state)[0]):\n time.sleep(0.05)\n p1.set_xdata((E[i,0]))\n p1.set_ydata((E[i,1]))\n p2.set_xdata((H[i,0]))\n p2.set_ydata((H[i,1]))\n p3.set_xdata((0,E[i,0],H[i,0]))\n p3.set_ydata((0,E[i,1],H[i,1]))\n tt.set_text(\"Current time:%4.2f sec - click to next slide!\" % (i*dt))\n plt.draw()\n tt.set_text(\"Current time:%4.2f sec - finished!\" % ((numpy.shape(state)[0]-1)*dt))\n plt.draw() \n\n \ndef animatearm_JS(state,t,aparams):\n \"\"\"\n animate the twojointarm\n \"\"\"\n import matplotlib.pyplot as plt\n import numpy\n from JSAnimation import IPython_display\n from matplotlib import animation\n\n\n A = state[:,[0,1]] # Gets the angles a1 and a2 from the states matrix\n A[:,0] = A[:,0]\n H,E = joints_to_hand(A,aparams)\n l1,l2 = aparams['l1'], aparams['l2']\n\n # Set up the axes, making sure the axis ratio is equal\n# ax = fig.add_axes([0, 0, 1, 1], xlim=(-0.02, 13.02), ylim=(-0.02, 5.02),\n# xticks=range(14), yticks=range(6), aspect='equal', frameon=False)\n\n fig = plt.figure(figsize=(6, 6),dpi=100)\n ax = plt.axes(xlim=(-1, 1), ylim=(-1, 1), aspect='equal')\n ax.plot(0,0,'b.')\n ax.plot(H[:,0],H[:,1],'g.-');\n p1, = ax.plot(E[0,0],E[0,1],'b.')\n p2, = ax.plot(H[0,0],H[0,1],'b.')\n p3, = ax.plot((0,E[0,0],H[0,0]),(0,E[0,1],H[0,1]),'b-') \n \n def init():\n p1.set_data([],[])\n p2.set_data([],[])\n p3.set_data([],[]) \n return p1,p2,p3\n \n def animate(i):\n p1.set_data([E[i,0]],[E[i,1]])\n p2.set_data(H[i,0],H[i,1])\n p3.set_data((0,E[i,0],H[i,0]),(0,E[i,1],H[i,1]))\n return p1,p2,p3\n anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(E[:,0]), interval=20, blit=True)\n return anim\n # In order to make the JSAnimation to work it is necessary that the function returns the animation object!\n \n \ndef odeint_arms(twojointarm, state, t, aparams, torque):\n '''\n twojointarm: function object. Must receive (state,t,aparams,torque) and return [a1d,a2d,a1dd,a2dd]\n state: current states => [a1(t),a2(t),a1d(t),a2d(t)]\n t: array([t,t+1]) => current time step and next (t+1)\n returns next states [a1(t+1),a2(t+1),a1d(t+1),a2d(t+1)]\n '''\n from scipy.integrate import odeint\n\n return odeint(twojointarm, state, t, args=(aparams,torque))\n\n\ndef moving_average (values,window=6):\n weights = numpy.repeat(1.0, window)/window\n sma = numpy.convolve(numpy.concatenate((numpy.zeros(int((window-1)/2.0)),values,numpy.zeros((window-1)-int((window-1)/2.0)))), weights, 'valid')\n # I should try the function numpy.lib.pad instead of concatenating manually\n return sma\n\n\ndef moving_average (values, window=6):\n weights = numpy.repeat(1.0, window)/window\n sma = numpy.convolve(values, weights, 'valid')\n # I should try the function numpy.lib.pad instead of concatenating manually\n return numpy.lib.pad(sma, (int((window-1)/2.0),(window-1)-int((window-1)/2.0)), 'edge')", "Overwriting simulation_2DoF_Arm_physics.py\n" ], [ "from simulation_2DoF_Arm_physics import *", "_____no_output_____" ], [ "@dview.parallel(block=True)\ndef generate_trajectories(sim_inputs):\n import numpy\n import sys\n import save_load_file as slf\n \n import simulation_2DoF_Arm_physics\n reload(sys.modules['simulation_2DoF_Arm_physics']) # Makes sure the interpreter is going to reload the module\n s2ap = simulation_2DoF_Arm_physics\n\n tji,positions,sim_params = sim_inputs\n \n xstart,ystart = positions[0]\n xdest,ydest = positions[1]\n\n sim_set,base_dir,MT,time_step,Ninput,aparams = sim_params\n \n t_mov=numpy.arange(0, MT, time_step) # t starts in 0s and steps time_step(s) until reaches MT(s)\n\n # Generates the movements according to:\n # Flash, Tamar, and Neville Hogan. 1985\n H_path=s2ap.cartesian_movement_generation_training(xstart, ystart, xdest, ydest, MT, t_mov)\n\n # These are the values teta1 and teta2 can have because the system limits the resolution.\n # According to Joshi/Maass paper there are 50 neurons to code the positions of each variable.\n #\n #\n teta1=numpy.linspace(-numpy.pi/6,numpy.pi,num=Ninput)\n teta2=numpy.linspace(0,numpy.pi,num=Ninput)\n teta1_teta2 = numpy.array([teta1,teta2]).T # This is the matrix to use with the function\n # to generate the x,y values of the workspace\n\n # Joint's workspace: all the possible combinations between teta1 and teta2.\n teta_workspace = numpy.array([[t1,t2] for t1 in teta1 for t2 in teta2])\n\n # Arm's workspace: x,y points that the arm (endpoint) can reach\n H_workspace = s2ap.joints_to_hand(teta_workspace,aparams)[0] # I'm getting the first because it returns the elbow's positions too.\n\n # Generate the joint's positions according to the ORIGINAL (X,Y) values.\n # I'm using the traditional geometric way to do the inverse kinematics here. I need to\n # implement the minimum jerk way to generate the joint's positions taking into account the movement's dynamics.\n\n Joints=s2ap.hand_to_joints(H_path, aparams,ang_error=0.1)\n\n # Here I'm extending the Joints matrix because I need two extra positions to calculate the accelerations.\n # Consequently, because the trajectory always finish with velocity ZERO, keeping the same position seems a good choice. \n Joints_extended=numpy.concatenate((Joints,[Joints[-1],Joints[-1]]))\n\n # But the time array (t_mov) must be extended too:\n t_mov_extended=numpy.concatenate((t_mov,[t_mov[-1]+time_step],[t_mov[-1]+2*time_step]))\n\n\n # Joint's velocities\n teta1_d=s2ap.derivator(Joints_extended[:,0],t_mov_extended)\n teta2_d=s2ap.derivator(Joints_extended[:,1],t_mov_extended)\n \n\n # Joint's accelerations\n teta1_dd=s2ap.derivator(teta1_d,t_mov_extended[:251])\n teta2_dd=s2ap.derivator(teta2_d,t_mov_extended[:251])\n\n #\n # WITH ORIGINAL JOINT'S VELOCITIES\n #\n\n # And generates a matrix with [teta1,teta2,teta1d,teta2d,teta1dd,teta2dd]\n # Using this matrix I will generate the torques.\n states_mov = numpy.array([[Joints[:,0][i],Joints[:,1][i],teta1_d[i],teta2_d[i],teta1_dd[i],teta2_dd[i]] for i in range(len(Joints[:,0]))])\n\n\n # Applying the function to all lines of the states_mov matrix, I generate the torques matrix\n T_mov=numpy.array([s2ap.twojointarm_torques(states_mov[i], t_mov, aparams) for i in range(numpy.shape(states_mov)[0])])\n\n # Here I calculate the states using the calculated torques, just to make sure it is working!\n state = states_mov[0,[0,1,2,3]] # This is the initial state. The rest of the states will be generated dynamically \n # according to the input torques.\n state_v2 = [state]\n t = t_mov\n torque = T_mov\n for i in range(len(t)-1):\n print \"state:\",state\n state = s2ap.odeint_arms(s2ap.twojointarm, state, [t[i], t[i+1]], aparams, torque[i])[1]\n state_v2.append(state)\n\n state_v2=numpy.array(state_v2)\n\n # slf.save_to_file_gz([numpy.array(state_v2),numpy.array(torque)],\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(tji)+\".gzpickle\")\n \n return tji", "_____no_output_____" ] ], [ [ "## End of the main functions!", "_____no_output_____" ], [ "# Adjusting the parameters:", "_____no_output_____" ] ], [ [ "# Experiment identifier\nsim_sets = [\"set_A\", \"set_B\", \"set_C\", \"set_D\"]\n\nsim_set = sim_sets[0]\n\n# Base dir to save / access\nbase_dir = \"2DofArm_simulation_data\"\n\n# List with all trajectories to be generated\n# [[[start_x,start_y],[final_x,final_y]],...]\ntrajectories = [[[0.75,0.25],[0.0,0.5]], [[0.25,0.60],[-0.25,0.60]], [[-0.10,0.75],[-0.10,0.25]],[[-0.75,0.50],[-0.40,0.00]]]\n\n\n# The values below must match the ones used with the SNN simulation:\n\n# Total time spent during the movement (in seconds)\nMT = 0.5\n\n# Simulation time step (in seconds)\ntime_step = 2/1000.0\n\n# Number of neurons at the input layer (defines the resolution of the system)\nNinput = 50\n\n\n# Arm parametres used with the 2 dof arm simulator\n# (according to Joshi/Maass 2006 paper)\naparams = {\n 'l1' : 0.5, # metres\n 'l2' : 0.5,\n 'lc1' : 0.25,\n 'lc2' : 0.25,\n 'm1' : 1.0, # kg\n 'm2' : 1.0,\n 'i1' : 0.03, # kg*m*m\n 'i2' : 0.03\n}\n\n# Variable only used to pass the parameters\nsim_params = sim_set,base_dir,MT,time_step,Ninput,aparams", "_____no_output_____" ], [ "%time results = generate_trajectories.map([(tji,positions,sim_params) for tji,positions in zip(range(1,len(trajectories)+1),trajectories)])", "CPU times: user 15.6 ms, sys: 6.15 ms, total: 21.7 ms\nWall time: 1.69 s\n" ] ] ]

[ "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Simple function to print the sum of 2 numbers\n\ndef simple_sum(a,b):\n print(a + b)\n \nsimple_sum(2,9)\nsimple_sum(7,8)\nsimple_sum(10,15)", "_____no_output_____" ], [ "# Simple function to sum 2 numbers\n\ndef simple_sum(a,b):\n return(a + b)\n\nprint(simple_sum(2,9))", "_____no_output_____" ], [ "# Function to verify if a number is pair\n\ndef is_pair(x):\n return (x % 2 == 0)\n \nprint(is_pair(2))\nprint(is_pair(3))", "_____no_output_____" ], [ "# Using a function inside another function\n\ndef is_pair(x):\n return (x % 2 == 0)\n\ndef even_or_odd(x):\n if is_pair(x):\n return \"pair\"\n else:\n return \"odd\"\n \nprint(even_or_odd(4))\nprint(even_or_odd(5))", "_____no_output_____" ], [ "# Function to search for a value into a list\n\ndef search(list, value):\n for x,e in enumerate(list):\n if e == value:\n return x\n return None\n \nL = [10,20,25,30]\n\nprint(search(L,25))\nprint(search(L,27))\n", "_____no_output_____" ], [ "# Function to calculate the mean of all elements on a list\n\nL = [10,20,25,30]\ndef simple_sum(L):\n total = 0\n for e in L:\n total += e\n return total\n \ndef mean(L):\n return (simple_sum(L) / len(L))\n\nprint(mean(L))", "_____no_output_____" ], [ "# Function to print traces below the name of the course\n\ncourse = \"biomedical informatics\"\ndef print_course():\n print(course)\n print(\"-\" * len(course))\nprint_course()", "_____no_output_____" ], [ "# Function to change a value locally on a function\n\na = 5\n\ndef change_and_print():\n a = 7\n print (\"'A' inside function: %d\" %a)\n \nprint (\"'A' before changing: %d\" %a)\nchange_and_print()\nprint (\"'A' after changing: %d\" %a)", "_____no_output_____" ], [ "# Function to change a value globally on a function\n\na = 5\ndef change_and_print():\n global a\n a = 7\n print (\"'A' inside function: %d\" %a)\n \nprint (\"'A' before changing: %d\" %a)\nchange_and_print()\nprint (\"'A' after changing: %d\" %a)", "_____no_output_____" ], [ "# Function to print the factorial of a number\n\ndef factorial(n):\n if n == 0 or n == 1:\n return 1\n else:\n return n * factorial(n - 1)\n \nprint(factorial(5))", "_____no_output_____" ], [ "# Function passing default parameters\n\ndef trace(n = 40, character = \"-\"):\n print(character * n)\n \ntrace()\ntrace(character = \"*\")\ntrace(10)\ntrace(character = \"?\")", "_____no_output_____" ], [ "# Packing parameters\n\ndef simple_sum(a,b):\n print(a + b)\n \nL = [2,3]\nsimple_sum(*L)", "_____no_output_____" ], [ "# Unpacking parameters\n\ndef simple_sum(*args):\n s = 0\n for x in args:\n s += x\n return s\nprint(simple_sum(1,2))\nprint(simple_sum(2))\nprint(simple_sum(5,6,7,8))\nprint(simple_sum(9,10,20,30,40))", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>\n", "_____no_output_____" ], [ "# Plotly - Create Waterfall chart (Advanced)\n<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Plotly/Create%20Waterfall%20chart%20%28Advanced%29.ipynb\" target=\"_parent\"><img src=\"https://img.shields.io/badge/-Open%20in%20Naas-success?labelColor=000000&logo=data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iMTAyNHB4IiBoZWlnaHQ9IjEwMjRweCIgdmlld0JveD0iMCAwIDEwMjQgMTAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayIgdmVyc2lvbj0iMS4xIj4KIDwhLS0gR2VuZXJhdGVkIGJ5IFBpeGVsbWF0b3IgUHJvIDIuMC41IC0tPgogPGRlZnM+CiAgPHRleHQgaWQ9InN0cmluZyIgdHJhbnNmb3JtPSJtYXRyaXgoMS4wIDAuMCAwLjAgMS4wIDIyOC4wIDU0LjUpIiBmb250LWZhbWlseT0iQ29tZm9ydGFhLVJlZ3VsYXIsIENvbWZvcnRhYSIgZm9udC1zaXplPSI4MDAiIHRleHQtZGVjb3JhdGlvbj0ibm9uZSIgZmlsbD0iI2ZmZmZmZiIgeD0iMS4xOTk5OTk5OTk5OTk5ODg2IiB5PSI3MDUuMCI+bjwvdGV4dD4KIDwvZGVmcz4KIDx1c2UgaWQ9Im4iIHhsaW5rOmhyZWY9IiNzdHJpbmciLz4KPC9zdmc+Cg==\"/></a>", "_____no_output_____" ], [ "**Tags:** #plotly #chart #warterfall #dataviz", "_____no_output_____" ], [ "## Input", "_____no_output_____" ], [ "### Install packages", "_____no_output_____" ] ], [ [ "!pip install numpy\n!pip install matplotlib", "_____no_output_____" ] ], [ [ "### Import library", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import FuncFormatter", "_____no_output_____" ] ], [ [ "## Model", "_____no_output_____" ], [ "### Create the waterfall chart", "_____no_output_____" ] ], [ [ "#Use python 2.7+ syntax to format currency\ndef money(x, pos):\n 'The two args are the value and tick position'\n return \"${:,.0f}\".format(x)\nformatter = FuncFormatter(money)\n\n#Data to plot. Do not include a total, it will be calculated\nindex = ['sales','returns','credit fees','rebates','late charges','shipping']\ndata = {'amount': [350000,-30000,-7500,-25000,95000,-7000]}\n\n#Store data and create a blank series to use for the waterfall\ntrans = pd.DataFrame(data=data,index=index)\nblank = trans.amount.cumsum().shift(1).fillna(0)\n\n#Get the net total number for the final element in the waterfall\ntotal = trans.sum().amount\ntrans.loc[\"net\"]= total\nblank.loc[\"net\"] = total\n\n#The steps graphically show the levels as well as used for label placement\nstep = blank.reset_index(drop=True).repeat(3).shift(-1)\nstep[1::3] = np.nan\n\n#When plotting the last element, we want to show the full bar,\n#Set the blank to 0\nblank.loc[\"net\"] = 0\n\n#Plot and label\nmy_plot = trans.plot(kind='bar', stacked=True, bottom=blank,legend=None, figsize=(10, 5), title=\"2014 Sales Waterfall\")\nmy_plot.plot(step.index, step.values,'k')\nmy_plot.set_xlabel(\"Transaction Types\")\n\n#Format the axis for dollars\nmy_plot.yaxis.set_major_formatter(formatter)\n\n#Get the y-axis position for the labels\ny_height = trans.amount.cumsum().shift(1).fillna(0)\n\n#Get an offset so labels don't sit right on top of the bar\nmax = trans.max()\nneg_offset = max / 25\npos_offset = max / 50\nplot_offset = int(max / 15)\n\n#Start label loop\nloop = 0\nfor index, row in trans.iterrows():\n # For the last item in the list, we don't want to double count\n if row['amount'] == total:\n y = y_height[loop]\n else:\n y = y_height[loop] + row['amount']\n # Determine if we want a neg or pos offset\n if row['amount'] > 0:\n y += pos_offset\n else:\n y -= neg_offset\n my_plot.annotate(\"{:,.0f}\".format(row['amount']),(loop,y),ha=\"center\")\n loop+=1", "_____no_output_____" ] ], [ [ "## Output", "_____no_output_____" ], [ "### Display result", "_____no_output_____" ] ], [ [ "#Scale up the y axis so there is room for the labels\nmy_plot.set_ylim(0,blank.max()+int(plot_offset))\n#Rotate the labels\nmy_plot.set_xticklabels(trans.index,rotation=0)\nmy_plot.get_figure().savefig(\"waterfall.png\",dpi=200,bbox_inches='tight')", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)\n\n# Toggle cell visibility\n\nfrom IPython.display import HTML\ntag = HTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide()\n } else {\n $('div.input').show()\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\nToggle cell visibility <a href=\"javascript:code_toggle()\">here</a>.''')\ndisplay(tag)\n\n# Hide the code completely\n\n# from IPython.display import HTML\n# tag = HTML('''<style>\n# div.input {\n# display:none;\n# }\n# </style>''')\n# display(tag)\n", "_____no_output_____" ] ], [ [ "## State feedback control for the mass-spring-damper system\n\nGiven the mass-spring-damper system, we want to control it in order to have a step response with zero error at steady state and a settling time for 5% tolerance band of less than 6 s.\n\nThe system's equations written in state space form are:\n\n$$\n\\begin{bmatrix}\n\\dot{x_1} \\\\\n\\dot{x_2}\n\\end{bmatrix}=\\underbrace{\\begin{bmatrix}\n0 && 1 \\\\\n-\\frac{k}{m} && -\\frac{c}{m}\n\\end{bmatrix}}_{A}\\begin{bmatrix}\nx_1 \\\\\nx_2\n\\end{bmatrix}+\\underbrace{\\begin{bmatrix}\n0 \\\\\n\\frac{1}{m}\n\\end{bmatrix}}_{B}u,\n$$\n\nwith $m=5$ kg, $k=2$ N/m, $c=1$ Ns/m, $x_1$ representing the position and $x_2$ the velocity. By defining the gain matrix of the state feedback as $K=\\begin{bmatrix}k_1&k_2\\end{bmatrix}^T$ and substituting it in $A-BK$ we obtain:\n\n$$\nA-BK = \\begin{bmatrix}0&1\\\\-\\frac{2}{5}-\\frac{k_1}{5}&-\\frac{1}{5}-\\frac{k_2}{5}\\end{bmatrix}\\,.\n$$\n\nNote that the system is in canonical controllability form, the characteristic polynomial is\n\n$$\n\\lambda^2+(\\frac{k_2}{5}+\\frac{1}{5})\\lambda+(\\frac{k_1}{5}+\\frac{2}{5})\n$$\n\nand imposing the roots to be equal to $\\lambda_{1,2}=-1$ rad/s $\\left((\\lambda+1)^2=\\lambda^2+2\\lambda+1\\right)$ we find the values $k_1 = 3$ and $k_2=9$.\n\nIn order to reach zero steady-state error, it is possible to simply adjust the closed-loop gain: we multiply the reference input $u_{ref}$ by the inverse of the closed-loop gain to have the closed-loop transfer function staying at $0$ dB at low frequencies.\n\nThe static gain is calculated as $G(0)=C(-A+BK)^{-1}B$ with $C=\\begin{bmatrix}1&0\\end{bmatrix}$.\n\nThe final controlled system, that is still SISO from the input $u_{ref}$ to the position $x_1$, is:\n\n$$\n\\begin{cases}\n\\begin{bmatrix}\n\\dot{x_1} \\\\\n\\dot{x_2}\n\\end{bmatrix}=\\underbrace{\\begin{bmatrix}\n0 && 1 \\\\\n-1 && -2\n\\end{bmatrix}}_{A-BK}\\begin{bmatrix}\nx_1 \\\\\nx_2\n\\end{bmatrix}+\\underbrace{\\begin{bmatrix}\n0 \\\\\n\\frac{1}{5}\n\\end{bmatrix}}_{B}\\frac{1}{0.2}u_{\\text{ref}} \\\\\ny = \\begin{bmatrix}1&0\\end{bmatrix}\\begin{bmatrix}\nx_1 \\\\\nx_2\n\\end{bmatrix}\n\\end{cases}\n$$\n\n### How to use this notebook?\nTry to change the eigenvalues and adjust the reference signal gain to achieve zero steady-state error. ", "_____no_output_____" ] ], [ [ "%matplotlib inline\nimport control as control\nimport numpy\nimport sympy as sym\nfrom IPython.display import display, Markdown\nimport ipywidgets as widgets\nimport matplotlib.pyplot as plt\n\n\n#print a matrix latex-like\ndef bmatrix(a):\n \"\"\"Returns a LaTeX bmatrix - by Damir Arbula (ICCT project)\n\n :a: numpy array\n :returns: LaTeX bmatrix as a string\n \"\"\"\n if len(a.shape) > 2:\n raise ValueError('bmatrix can at most display two dimensions')\n lines = str(a).replace('[', '').replace(']', '').splitlines()\n rv = [r'\\begin{bmatrix}']\n rv += [' ' + ' & '.join(l.split()) + r'\\\\' for l in lines]\n rv += [r'\\end{bmatrix}']\n return '\\n'.join(rv)\n\n\n# Display formatted matrix: \ndef vmatrix(a):\n if len(a.shape) > 2:\n raise ValueError('bmatrix can at most display two dimensions')\n lines = str(a).replace('[', '').replace(']', '').splitlines()\n rv = [r'\\begin{vmatrix}']\n rv += [' ' + ' & '.join(l.split()) + r'\\\\' for l in lines]\n rv += [r'\\end{vmatrix}']\n return '\\n'.join(rv)\n\n\n#matrixWidget is a matrix looking widget built with a VBox of HBox(es) that returns a numPy array as value !\nclass matrixWidget(widgets.VBox):\n def updateM(self,change):\n for irow in range(0,self.n):\n for icol in range(0,self.m):\n self.M_[irow,icol] = self.children[irow].children[icol].value\n #print(self.M_[irow,icol])\n self.value = self.M_\n\n def dummychangecallback(self,change):\n pass\n \n \n def __init__(self,n,m):\n self.n = n\n self.m = m\n self.M_ = numpy.matrix(numpy.zeros((self.n,self.m)))\n self.value = self.M_\n widgets.VBox.__init__(self,\n children = [\n widgets.HBox(children = \n [widgets.FloatText(value=0.0, layout=widgets.Layout(width='90px')) for i in range(m)]\n ) \n for j in range(n)\n ])\n \n #fill in widgets and tell interact to call updateM each time a children changes value\n for irow in range(0,self.n):\n for icol in range(0,self.m):\n self.children[irow].children[icol].value = self.M_[irow,icol]\n self.children[irow].children[icol].observe(self.updateM, names='value')\n #value = Unicode('[email protected]', help=\"The email value.\").tag(sync=True)\n self.observe(self.updateM, names='value', type= 'All')\n \n def setM(self, newM):\n #disable callbacks, change values, and reenable\n self.unobserve(self.updateM, names='value', type= 'All')\n for irow in range(0,self.n):\n for icol in range(0,self.m):\n self.children[irow].children[icol].unobserve(self.updateM, names='value')\n self.M_ = newM\n self.value = self.M_\n for irow in range(0,self.n):\n for icol in range(0,self.m):\n self.children[irow].children[icol].value = self.M_[irow,icol]\n for irow in range(0,self.n):\n for icol in range(0,self.m):\n self.children[irow].children[icol].observe(self.updateM, names='value')\n self.observe(self.updateM, names='value', type= 'All') \n\n #self.children[irow].children[icol].observe(self.updateM, names='value')\n\n \n#overlaod class for state space systems that DO NOT remove \"useless\" states (what \"professor\" of automatic control would do this?)\nclass sss(control.StateSpace):\n def __init__(self,*args):\n #call base class init constructor\n control.StateSpace.__init__(self,*args)\n #disable function below in base class\n def _remove_useless_states(self):\n pass", "_____no_output_____" ], [ "# Preparatory cell\n\nA = numpy.matrix([[0,1],[-2/5,-1/5]])\nB = numpy.matrix('0; 1')\nC = numpy.matrix('1 0')\nX0 = numpy.matrix('0; 0')\nK = numpy.matrix([3,9])\n\nAw = matrixWidget(2,2)\nAw.setM(A)\nBw = matrixWidget(2,1)\nBw.setM(B)\nCw = matrixWidget(1,2)\nCw.setM(C)\nX0w = matrixWidget(2,1)\nX0w.setM(X0)\nKw = matrixWidget(1,2)\nKw.setM(K)\n\n\neig1c = matrixWidget(1,1)\neig2c = matrixWidget(2,1)\neig1c.setM(numpy.matrix([-1])) \neig2c.setM(numpy.matrix([[-1],[0]]))", "_____no_output_____" ], [ "# Misc\n\n#create dummy widget \nDW = widgets.FloatText(layout=widgets.Layout(width='0px', height='0px'))\n\n#create button widget\nSTART = widgets.Button(\n description='Test',\n disabled=False,\n button_style='', # 'success', 'info', 'warning', 'danger' or ''\n tooltip='Test',\n icon='check'\n)\n \ndef on_start_button_clicked(b):\n #This is a workaround to have intreactive_output call the callback:\n # force the value of the dummy widget to change\n if DW.value> 0 :\n DW.value = -1\n else: \n DW.value = 1\n pass\nSTART.on_click(on_start_button_clicked)\n\n# Define type of method \nselm = widgets.Dropdown(\n options= ['Set K', 'Set the eigenvalues'],\n value= 'Set the eigenvalues',\n description='',\n disabled=False\n)\n\n# Define the number of complex eigenvalues for the observer\nselc = widgets.Dropdown(\n options= ['0 complex eigenvalues', '2 complex eigenvalues'],\n value= '0 complex eigenvalues',\n description='Eigenvalues:',\n disabled=False\n)\n\n#define type of ipout \nselu = widgets.Dropdown(\n options=['impulse', 'step', 'sinusoid', 'square wave'],\n value='step',\n description='Type of reference:',\n style = {'description_width': 'initial'},\n disabled=False\n)\n# Define the values of the input\nu = widgets.FloatSlider(\n value=1,\n min=0,\n max=20.0,\n step=0.1,\n description='Input reference:',\n style = {'description_width': 'initial'},\n disabled=False,\n continuous_update=False,\n orientation='horizontal',\n readout=True,\n readout_format='.1f',\n)\nperiod = widgets.FloatSlider(\n value=1,\n min=0.01,\n max=4,\n step=0.01,\n description='Period: ',\n disabled=False,\n continuous_update=False,\n orientation='horizontal',\n readout=True,\n readout_format='.2f',\n)\ngain = widgets.FloatText(\n value=0.2,\n description='Inverse reference gain:',\n style = {'description_width': 'initial'},\n disabled=False\n)\nm = widgets.FloatSlider(\n value=5,\n min=0.1,\n max=10.0,\n step=0.1,\n description='m [kg]:',\n disabled=False,\n continuous_update=False,\n orientation='horizontal',\n readout=True,\n readout_format='.1f',\n)\nk = widgets.FloatSlider(\n value=2,\n min=0,\n max=10.0,\n step=0.1,\n description='k [N/m]:',\n disabled=False,\n continuous_update=False,\n orientation='horizontal',\n readout=True,\n readout_format='.1f',\n)\nc = widgets.FloatSlider(\n value=1,\n min=0,\n max=10.0,\n step=0.1,\n description='c [Ns/m]:',\n disabled=False,\n continuous_update=False,\n orientation='horizontal',\n readout=True,\n readout_format='.1f',\n)", "_____no_output_____" ], [ "# Support functions\n\ndef eigen_choice(selc):\n if selc == '0 complex eigenvalues':\n eig1c.children[0].children[0].disabled = False\n eig2c.children[1].children[0].disabled = True\n eigc = 0\n if selc == '2 complex eigenvalues':\n eig1c.children[0].children[0].disabled = True\n eig2c.children[1].children[0].disabled = False\n eigc = 2\n return eigc\n\ndef method_choice(selm):\n if selm == 'Set K':\n method = 1\n selc.disabled = True\n if selm == 'Set the eigenvalues':\n method = 2\n selc.disabled = False\n return method", "_____no_output_____" ], [ "def main_callback(m, k, c, gain, X0w, K, eig1c, eig2c, u, period, selm, selc, selu, DW):\n A, B = numpy.matrix([[0,1],[-k/m,-c/m]]), numpy.matrix([[0],[1/m]])\n sols = numpy.linalg.eig(A)\n eigc = eigen_choice(selc)\n method = method_choice(selm)\n \n if method == 1:\n sol = numpy.linalg.eig(A-B*K)\n if method == 2:\n if eigc == 0:\n K = control.acker(A, B, [eig1c[0,0], eig2c[0,0]])\n Kw.setM(K) \n if eigc == 2:\n K = control.acker(A, B, [numpy.complex(eig2c[0,0],eig2c[1,0]), \n numpy.complex(eig2c[0,0],-eig2c[1,0])])\n Kw.setM(K)\n sol = numpy.linalg.eig(A-B*K)\n print('The system\\'s eigenvalues are:',round(sols[0][0],4),'and',round(sols[0][1],4))\n print('The controlled system\\'s eigenvalues are:',round(sol[0][0],4),'and',round(sol[0][1],4))\n \n sys1 = sss(A-B*K,B,C,0)\n sg = control.evalfr(sys1,0)\n print('The static gain of the controlled system is: %f' %sg)\n if gain != 0:\n sys = sss(A-B*K,B*1/gain,C,0)\n else:\n print('The inverse gain setted is 0 and it is changed to 1')\n sys = sss(A-B*K,B,C,0)\n T = numpy.linspace(0, 10, 1000)\n \n if selu == 'impulse': #selu\n U = [0 for t in range(0,len(T))]\n U[0] = u\n T, yout, xout = control.forced_response(sys,T,U,X0w)\n if selu == 'step':\n U = [u for t in range(0,len(T))]\n T, yout, xout = control.forced_response(sys,T,U,X0w)\n if selu == 'sinusoid':\n U = u*numpy.sin(2*numpy.pi/period*T)\n T, yout, xout = control.forced_response(sys,T,U,X0w)\n if selu == 'square wave':\n U = u*numpy.sign(numpy.sin(2*numpy.pi/period*T))\n T, yout, xout = control.forced_response(sys,T,U,X0w)\n \n fig = plt.figure(num='Bode plot', figsize=(16,10))\n control.bode_plot(sys)\n fig.suptitle('Bode plot', fontsize=16)\n \n plt.figure(num='Simulation', figsize=(16,4))\n plt.title('Position input response')\n plt.ylabel('position vs ref')\n plt.plot(T,xout[0],T,U,'r--')\n plt.xlabel('$t$ [s]')\n plt.axvline(x=0,color='black',linewidth=0.8)\n plt.axhline(y=0,color='black',linewidth=0.8)\n plt.legend(['position','Reference'])\n plt.grid()\n\n \nalltogether = widgets.VBox([widgets.HBox([selm, \n selc, \n selu]),\n widgets.Label(' ',border=3),\n widgets.HBox([widgets.Label('K:',border=3), Kw, \n widgets.Label(' ',border=3),\n widgets.Label(' ',border=3),\n widgets.Label('Eigenvalues:',border=3), \n eig1c, \n eig2c, \n widgets.Label(' ',border=3),\n widgets.Label(' ',border=3),\n widgets.Label('X0:',border=3), X0w]),\n widgets.Label(' ',border=3),\n widgets.HBox([u, \n period, \n START]),\n widgets.Label(' ',border=3),\n widgets.HBox([m,\n k,\n c,\n gain])])\nout = widgets.interactive_output(main_callback, {'m':m, 'k':k, 'c':c, 'gain':gain, 'X0w':X0w, 'K':Kw, 'eig1c':eig1c, 'eig2c':eig2c, \n 'u':u, 'period':period, 'selm':selm, 'selc':selc, 'selu':selu, 'DW':DW})\nout.layout.height = '1050px'\ndisplay(out, alltogether)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Operations for indexing, splitting, slicing and iterating over a dataset", "_____no_output_____" ] ], [ [ "import numpy as np", "_____no_output_____" ] ], [ [ "#### Indexing", "_____no_output_____" ] ], [ [ "dataset = np.genfromtxt('../Datasets/normal_distribution_splittable.csv', delimiter=',')", "_____no_output_____" ], [ "# Mean of the second row\nsecond_row = dataset[1]\nnp.mean(second_row)", "_____no_output_____" ], [ "# Mean of the last row\nlast_row = dataset[-1]\nnp.mean(last_row)", "_____no_output_____" ], [ "# Mean of the first value of the first row\nfirst_val_first_row = dataset[0][0]\nprint(np.mean(first_val_first_row))\nprint(first_val_first_row)", "99.14931546\n99.14931546\n" ], [ "# Index the value of the last element in the second last row\nlast_val_second_last_row = dataset[-2, -1]\nnp.mean(last_val_second_last_row)", "_____no_output_____" ] ], [ [ "#### Slicing", "_____no_output_____" ] ], [ [ "# Create a 2x2 matrix that starts in the second row and second column\nsubsection_2x2 = dataset[1:3, 1:3]\nnp.mean(subsection_2x2)", "_____no_output_____" ], [ "# Get every element in the 5th row, but only get every second element of that row\nevery_other_elem = dataset[4, ::2]\nprint(dataset[4])\nprint(every_other_elem)\nprint(np.mean(every_other_elem))", "[101.20862522 103.5730309 100.28690912 105.85269352 93.37126331\n 108.57980357 100.79478953 94.20019732 96.10020311]\n[101.20862522 100.28690912 93.37126331 100.79478953 96.10020311]\n98.35235805800001\n" ], [ "# Revesed last row of the dataset\nreversed_last_row = dataset[-1, ::-1]\nprint(dataset[-1])\nprint(reversed_last_row)\nprint(np.mean(reversed_last_row))", "[ 94.11176915 99.62387832 104.51786419 97.62787811 93.97853495\n 98.75108352 106.05042487 100.07721494 106.89005002]\n[106.89005002 100.07721494 106.05042487 98.75108352 93.97853495\n 97.62787811 104.51786419 99.62387832 94.11176915]\n100.18096645222222\n" ] ], [ [ "#### Splitting", "_____no_output_____" ] ], [ [ "# Split horizontally the dataset in three equal subsets\nhor_splits = np.hsplit(dataset,(3))", "_____no_output_____" ], [ "# Split the first third in 2 equal vertically parts\nver_splits = np.vsplit(hor_splits[0],(2))", "_____no_output_____" ], [ "print(\"Dataset\", dataset.shape)\nprint(\"Subset\", ver_splits[0].shape)", "Dataset (24, 9)\nSubset (12, 3)\n" ] ], [ [ "#### Iterating", "_____no_output_____" ] ], [ [ "# Iterate over the whole dataset using nditer\ncurr_index = 0\nfor x in np.nditer(dataset):\n print(x, curr_index)\n curr_index += 1", "99.14931546 0\n104.03852715 1\n107.43534677 2\n97.85230675 3\n98.74986914 4\n98.80833412 5\n96.81964892 6\n98.56783189 7\n101.34745901 8\n92.02628776 9\n97.10439252 10\n99.32066924 11\n97.24584816 12\n92.9267508 13\n92.65657752 14\n105.7197853 15\n101.23162942 16\n93.87155456 17\n95.66253664 18\n95.17750125 19\n90.93318132 20\n110.18889465 21\n98.80084371 22\n105.95297652 23\n98.37481387 24\n106.54654286 25\n107.22482426 26\n91.37294597 27\n100.96781394 28\n100.40118279 29\n113.42090475 30\n105.48508838 31\n91.6604946 32\n106.1472841 33\n95.08715803 34\n103.40412146 35\n101.20862522 36\n103.5730309 37\n100.28690912 38\n105.85269352 39\n93.37126331 40\n108.57980357 41\n100.79478953 42\n94.20019732 43\n96.10020311 44\n102.80387079 45\n98.29687616 46\n93.24376389 47\n97.24130034 48\n89.03452725 49\n96.2832753 50\n104.60344836 51\n101.13442416 52\n97.62787811 53\n106.71751618 54\n102.97585605 55\n98.45723272 56\n100.72418901 57\n106.39798503 58\n95.46493436 59\n94.35373179 60\n106.83273763 61\n100.07721494 62\n96.02548256 63\n102.82360856 64\n106.47551845 65\n101.34745901 66\n102.45651798 67\n98.74767493 68\n97.57544275 69\n92.5748759 70\n91.37294597 71\n105.30350449 72\n92.87730812 73\n103.19258339 74\n104.40518318 75\n101.29326772 76\n100.85447132 77\n101.2226037 78\n106.03868807 79\n97.85230675 80\n110.44484313 81\n93.87155456 82\n101.5363647 83\n97.65393524 84\n92.75048583 85\n101.72074646 86\n96.96851209 87\n103.29147111 88\n99.14931546 89\n101.3514185 90\n100.37372248 91\n106.6471081 92\n100.61742813 93\n105.0320535 94\n99.35999981 95\n98.87007532 96\n95.85284217 97\n93.97853495 98\n97.21315663 99\n107.02874163 100\n102.17642112 101\n96.74630281 102\n95.93799169 103\n102.62384733 104\n105.07475277 105\n97.59572169 106\n106.57364584 107\n95.65982034 108\n107.22482426 109\n107.19119932 110\n102.93039474 111\n85.98839623 112\n95.19184343 113\n91.32093303 114\n102.35313953 115\n100.39303522 116\n100.39303522 117\n92.0108226 118\n97.75887636 119\n93.18884302 120\n100.44940274 121\n108.09423367 122\n96.50342927 123\n99.58664719 124\n95.19184343 125\n103.1521596 126\n109.40523174 127\n93.83969256 128\n99.95827854 129\n101.83462816 130\n99.69982772 131\n103.05289628 132\n103.93383957 133\n104.15899829 134\n106.11454989 135\n88.80221141 136\n94.5081787 137\n94.59300658 138\n101.08830521 139\n96.34622848 140\n96.89244283 141\n98.07122664 142\n100.28690912 143\n96.78266211 144\n99.84251605 145\n104.03478031 146\n106.57052697 147\n105.13668343 148\n105.37011896 149\n99.07551254 150\n104.15899829 151\n98.75108352 152\n101.86186193 153\n103.61720152 154\n99.57859892 155\n99.4889538 156\n103.05541444 157\n98.65912661 158\n98.72774132 159\n104.70526438 160\n110.44484313 161\n97.49594839 162\n96.59385486 163\n104.63817694 164\n102.55198606 165\n105.86078488 166\n96.5937781 167\n93.04610867 168\n99.92159953 169\n100.96781394 170\n96.76814836 171\n91.6779221 172\n101.79132774 173\n101.20773355 174\n98.29243952 175\n101.83845792 176\n97.94046856 177\n102.20618501 178\n91.37294597 179\n106.89005002 180\n106.57364584 181\n102.26648279 182\n107.40064604 183\n99.94318168 184\n103.40412146 185\n106.38276709 186\n98.00253006 187\n97.10439252 188\n99.80873105 189\n101.63973121 190\n106.46476468 191\n110.43976681 192\n100.69156231 193\n99.99579473 194\n101.32113654 195\n94.76253572 196\n97.24130034 197\n96.10020311 198\n94.57421727 199\n100.80409326 200\n105.02389857 201\n98.61325194 202\n95.62359311 203\n97.99762409 204\n103.83852459 205\n101.2226037 206\n94.11176915 207\n99.62387832 208\n104.51786419 209\n97.62787811 210\n93.97853495 211\n98.75108352 212\n106.05042487 213\n100.07721494 214\n106.89005002 215\n" ], [ "# Iterate over the whole dataset using ndenumerate\nfor index, value in np.ndenumerate(dataset):\n print(index, value)", "(0, 0) 99.14931546\n(0, 1) 104.03852715\n(0, 2) 107.43534677\n(0, 3) 97.85230675\n(0, 4) 98.74986914\n(0, 5) 98.80833412\n(0, 6) 96.81964892\n(0, 7) 98.56783189\n(0, 8) 101.34745901\n(1, 0) 92.02628776\n(1, 1) 97.10439252\n(1, 2) 99.32066924\n(1, 3) 97.24584816\n(1, 4) 92.9267508\n(1, 5) 92.65657752\n(1, 6) 105.7197853\n(1, 7) 101.23162942\n(1, 8) 93.87155456\n(2, 0) 95.66253664\n(2, 1) 95.17750125\n(2, 2) 90.93318132\n(2, 3) 110.18889465\n(2, 4) 98.80084371\n(2, 5) 105.95297652\n(2, 6) 98.37481387\n(2, 7) 106.54654286\n(2, 8) 107.22482426\n(3, 0) 91.37294597\n(3, 1) 100.96781394\n(3, 2) 100.40118279\n(3, 3) 113.42090475\n(3, 4) 105.48508838\n(3, 5) 91.6604946\n(3, 6) 106.1472841\n(3, 7) 95.08715803\n(3, 8) 103.40412146\n(4, 0) 101.20862522\n(4, 1) 103.5730309\n(4, 2) 100.28690912\n(4, 3) 105.85269352\n(4, 4) 93.37126331\n(4, 5) 108.57980357\n(4, 6) 100.79478953\n(4, 7) 94.20019732\n(4, 8) 96.10020311\n(5, 0) 102.80387079\n(5, 1) 98.29687616\n(5, 2) 93.24376389\n(5, 3) 97.24130034\n(5, 4) 89.03452725\n(5, 5) 96.2832753\n(5, 6) 104.60344836\n(5, 7) 101.13442416\n(5, 8) 97.62787811\n(6, 0) 106.71751618\n(6, 1) 102.97585605\n(6, 2) 98.45723272\n(6, 3) 100.72418901\n(6, 4) 106.39798503\n(6, 5) 95.46493436\n(6, 6) 94.35373179\n(6, 7) 106.83273763\n(6, 8) 100.07721494\n(7, 0) 96.02548256\n(7, 1) 102.82360856\n(7, 2) 106.47551845\n(7, 3) 101.34745901\n(7, 4) 102.45651798\n(7, 5) 98.74767493\n(7, 6) 97.57544275\n(7, 7) 92.5748759\n(7, 8) 91.37294597\n(8, 0) 105.30350449\n(8, 1) 92.87730812\n(8, 2) 103.19258339\n(8, 3) 104.40518318\n(8, 4) 101.29326772\n(8, 5) 100.85447132\n(8, 6) 101.2226037\n(8, 7) 106.03868807\n(8, 8) 97.85230675\n(9, 0) 110.44484313\n(9, 1) 93.87155456\n(9, 2) 101.5363647\n(9, 3) 97.65393524\n(9, 4) 92.75048583\n(9, 5) 101.72074646\n(9, 6) 96.96851209\n(9, 7) 103.29147111\n(9, 8) 99.14931546\n(10, 0) 101.3514185\n(10, 1) 100.37372248\n(10, 2) 106.6471081\n(10, 3) 100.61742813\n(10, 4) 105.0320535\n(10, 5) 99.35999981\n(10, 6) 98.87007532\n(10, 7) 95.85284217\n(10, 8) 93.97853495\n(11, 0) 97.21315663\n(11, 1) 107.02874163\n(11, 2) 102.17642112\n(11, 3) 96.74630281\n(11, 4) 95.93799169\n(11, 5) 102.62384733\n(11, 6) 105.07475277\n(11, 7) 97.59572169\n(11, 8) 106.57364584\n(12, 0) 95.65982034\n(12, 1) 107.22482426\n(12, 2) 107.19119932\n(12, 3) 102.93039474\n(12, 4) 85.98839623\n(12, 5) 95.19184343\n(12, 6) 91.32093303\n(12, 7) 102.35313953\n(12, 8) 100.39303522\n(13, 0) 100.39303522\n(13, 1) 92.0108226\n(13, 2) 97.75887636\n(13, 3) 93.18884302\n(13, 4) 100.44940274\n(13, 5) 108.09423367\n(13, 6) 96.50342927\n(13, 7) 99.58664719\n(13, 8) 95.19184343\n(14, 0) 103.1521596\n(14, 1) 109.40523174\n(14, 2) 93.83969256\n(14, 3) 99.95827854\n(14, 4) 101.83462816\n(14, 5) 99.69982772\n(14, 6) 103.05289628\n(14, 7) 103.93383957\n(14, 8) 104.15899829\n(15, 0) 106.11454989\n(15, 1) 88.80221141\n(15, 2) 94.5081787\n(15, 3) 94.59300658\n(15, 4) 101.08830521\n(15, 5) 96.34622848\n(15, 6) 96.89244283\n(15, 7) 98.07122664\n(15, 8) 100.28690912\n(16, 0) 96.78266211\n(16, 1) 99.84251605\n(16, 2) 104.03478031\n(16, 3) 106.57052697\n(16, 4) 105.13668343\n(16, 5) 105.37011896\n(16, 6) 99.07551254\n(16, 7) 104.15899829\n(16, 8) 98.75108352\n(17, 0) 101.86186193\n(17, 1) 103.61720152\n(17, 2) 99.57859892\n(17, 3) 99.4889538\n(17, 4) 103.05541444\n(17, 5) 98.65912661\n(17, 6) 98.72774132\n(17, 7) 104.70526438\n(17, 8) 110.44484313\n(18, 0) 97.49594839\n(18, 1) 96.59385486\n(18, 2) 104.63817694\n(18, 3) 102.55198606\n(18, 4) 105.86078488\n(18, 5) 96.5937781\n(18, 6) 93.04610867\n(18, 7) 99.92159953\n(18, 8) 100.96781394\n(19, 0) 96.76814836\n(19, 1) 91.6779221\n(19, 2) 101.79132774\n(19, 3) 101.20773355\n(19, 4) 98.29243952\n(19, 5) 101.83845792\n(19, 6) 97.94046856\n(19, 7) 102.20618501\n(19, 8) 91.37294597\n(20, 0) 106.89005002\n(20, 1) 106.57364584\n(20, 2) 102.26648279\n(20, 3) 107.40064604\n(20, 4) 99.94318168\n(20, 5) 103.40412146\n(20, 6) 106.38276709\n(20, 7) 98.00253006\n(20, 8) 97.10439252\n(21, 0) 99.80873105\n(21, 1) 101.63973121\n(21, 2) 106.46476468\n(21, 3) 110.43976681\n(21, 4) 100.69156231\n(21, 5) 99.99579473\n(21, 6) 101.32113654\n(21, 7) 94.76253572\n(21, 8) 97.24130034\n(22, 0) 96.10020311\n(22, 1) 94.57421727\n(22, 2) 100.80409326\n(22, 3) 105.02389857\n(22, 4) 98.61325194\n(22, 5) 95.62359311\n(22, 6) 97.99762409\n(22, 7) 103.83852459\n(22, 8) 101.2226037\n(23, 0) 94.11176915\n(23, 1) 99.62387832\n(23, 2) 104.51786419\n(23, 3) 97.62787811\n(23, 4) 93.97853495\n(23, 5) 98.75108352\n(23, 6) 106.05042487\n(23, 7) 100.07721494\n(23, 8) 106.89005002\n" ] ], [ [ "#### Filtering", "_____no_output_____" ] ], [ [ "vals_greater_five = dataset[dataset > 105]\nvals_greater_five", "_____no_output_____" ], [ "vals_between_90_95 = np.extract((dataset > 90) & (dataset < 95), dataset)\nvals_between_90_95", "_____no_output_____" ], [ "rows, cols = np.where(abs(dataset - 100) < 1)\n# Create a list comprehension\none_away_indices = [[rows[index], cols[index]] for (index, _) in np.ndenumerate(rows)]\none_away_indices", "_____no_output_____" ] ], [ [ "#### Sorting", "_____no_output_____" ] ], [ [ "# Each row will be sorted\nrow_sorted = np.sort(dataset)\nrow_sorted", "_____no_output_____" ], [ "# Sort each column\ncol_sorted = np.sort(dataset, axis=0)\ncol_sorted", "_____no_output_____" ], [ "# create a sorted index list using a fancy indexing to keep the order of the dataset and only obtain the values of index\nindex_sorted = np.argsort(dataset[0])\ndataset[0][index_sorted]", "_____no_output_____" ] ], [ [ "#### Combining", "_____no_output_____" ] ], [ [ "# Dividimos horizontalmente en 3 partes nuestro dataset es decir si son 12 columnas serian 3 bloques de 4 columnas\nthirds = np.hsplit(dataset, (3))\nprint(dataset.shape)\nprint(thirds[0].shape)\n#Dividimos verticalmente el primer bloque de los 3, en 2 partes , es decir si son 10 filas serian 2 bloques de 5 filas c/u\nhalfed_first = np.vsplit(thirds[0], (2))\nprint(halfed_first[0].shape)\n# Imprimimos el primer bloque de esta mitad\nhalfed_first[0]", "(24, 9)\n(24, 3)\n(12, 3)\n" ], [ "# Apilamos verticalmente las 2 mitades, esto nos deberia devolver el primer tercio thirds[0]\nfirst_col = np.vstack([halfed_first[0], halfed_first[1]])\nprint(thirds[0] == first_col)", "[[ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]\n [ True True True]]\n" ], [ "# Combinamos los 3 tercios de nuestros datos que serian igual a dataset\nfirst_second_col = np.hstack([first_col, thirds[1]])\nfull_data = np.hstack([first_second_col, thirds[2]])", "_____no_output_____" ] ], [ [ "#### Reshaping", "_____no_output_____" ] ], [ [ "# Reshape the dataset in to a single list\nsingle_list = np.reshape(dataset, (1, -1))\nprint(dataset.shape)\nprint(single_list.shape)", "(24, 9)\n(1, 216)\n" ], [ "# reshaping to a matrix with two columns\n# -1 Tells python to figure oyt the dimension out itself\ntwo_col_dataset = dataset.reshape(-1, 2)\nprint(two_col_dataset.shape)", "(108, 2)\n" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Colombian Identity Codes", "_____no_output_____" ], [ "## Introduction", "_____no_output_____" ], [ "The function `clean_co_nit()` cleans a column containing Colombian identity code (NIT) strings, and standardizes them in a given format. The function `validate_co_nit()` validates either a single NIT strings, a column of NIT strings or a DataFrame of NIT strings, returning `True` if the value is valid, and `False` otherwise.", "_____no_output_____" ], [ "NIT strings can be converted to the following formats via the `output_format` parameter:\n\n* `compact`: only number strings without any seperators or whitespace, like \"2131234321\"\n* `standard`: NIT strings with proper whitespace in the proper places, like \"213.123.432-1\"\n\nInvalid parsing is handled with the `errors` parameter:\n\n* `coerce` (default): invalid parsing will be set to NaN\n* `ignore`: invalid parsing will return the input\n* `raise`: invalid parsing will raise an exception\n\nThe following sections demonstrate the functionality of `clean_co_nit()` and `validate_co_nit()`. ", "_____no_output_____" ], [ "### An example dataset containing NIT strings", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\ndf = pd.DataFrame(\n {\n \"nit\": [\n \"2131234321\",\n \"2131234325\",\n \"51824753556\",\n \"51 824 753 556\",\n \"hello\",\n np.nan,\n \"NULL\"\n ], \n \"address\": [\n \"123 Pine Ave.\",\n \"main st\",\n \"1234 west main heights 57033\",\n \"apt 1 789 s maple rd manhattan\",\n \"robie house, 789 north main street\",\n \"(staples center) 1111 S Figueroa St, Los Angeles\",\n \"hello\",\n ]\n }\n)\ndf", "_____no_output_____" ] ], [ [ "## 1. Default `clean_co_nit`\n\nBy default, `clean_co_nit` will clean nit strings and output them in the standard format with proper separators.", "_____no_output_____" ] ], [ [ "from dataprep.clean import clean_co_nit\nclean_co_nit(df, column = \"nit\")", "_____no_output_____" ] ], [ [ "## 2. Output formats", "_____no_output_____" ], [ "This section demonstrates the output parameter.", "_____no_output_____" ], [ "### `standard` (default)", "_____no_output_____" ] ], [ [ "clean_co_nit(df, column = \"nit\", output_format=\"standard\")", "_____no_output_____" ] ], [ [ "### `compact`", "_____no_output_____" ] ], [ [ "clean_co_nit(df, column = \"nit\", output_format=\"compact\")", "_____no_output_____" ] ], [ [ "## 3. `inplace` parameter\n\nThis deletes the given column from the returned DataFrame. \nA new column containing cleaned NIT strings is added with a title in the format `\"{original title}_clean\"`.", "_____no_output_____" ] ], [ [ "clean_co_nit(df, column=\"nit\", inplace=True)", "_____no_output_____" ] ], [ [ "## 4. `errors` parameter", "_____no_output_____" ], [ "### `coerce` (default)", "_____no_output_____" ] ], [ [ "clean_co_nit(df, \"nit\", errors=\"coerce\")", "_____no_output_____" ] ], [ [ "### `ignore`", "_____no_output_____" ] ], [ [ "clean_co_nit(df, \"nit\", errors=\"ignore\")", "_____no_output_____" ] ], [ [ "## 4. `validate_co_nit()`", "_____no_output_____" ], [ "`validate_co_nit()` returns `True` when the input is a valid NIT. Otherwise it returns `False`.\n\nThe input of `validate_co_nit()` can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.\n\nWhen the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn't need to specify a column name to be validated. \n\nWhen the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, `validate_co_nit()` only returns the validation result for the specified column. If user doesn't specify the column name, `validate_co_nit()` returns the validation result for the whole DataFrame.", "_____no_output_____" ] ], [ [ "from dataprep.clean import validate_co_nit\nprint(validate_co_nit(\"2131234321\"))\nprint(validate_co_nit(\"2131234325\"))\nprint(validate_co_nit(\"51824753556\"))\nprint(validate_co_nit(\"51 824 753 556\"))\nprint(validate_co_nit(\"hello\"))\nprint(validate_co_nit(np.nan))\nprint(validate_co_nit(\"NULL\"))", "_____no_output_____" ] ], [ [ "### Series", "_____no_output_____" ] ], [ [ "validate_co_nit(df[\"nit\"])", "_____no_output_____" ] ], [ [ "### DataFrame + Specify Column", "_____no_output_____" ] ], [ [ "validate_co_nit(df, column=\"nit\")", "_____no_output_____" ] ], [ [ "### Only DataFrame", "_____no_output_____" ] ], [ [ "validate_co_nit(df)", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "### Setup", "_____no_output_____" ] ], [ [ "import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = \"3\"", "_____no_output_____" ] ], [ [ "To prevent elements such as Tensorflow import logs, perform these tasks.", "_____no_output_____" ] ], [ [ "import glob\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "try:\n tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n print(\"Device:\", tpu.master())\n tf.config.experimental_connect_to_cluster(tpu)\n tf.tpu.experimental.initialize_tpu_system(tpu)\n strategy = tf.distribute.experimental.TPUStrategy(tpu)\nexcept:\n strategy = tf.distribute.get_strategy()\nprint(\"Number of replicas:\", strategy.num_replicas_in_sync)", "Number of replicas: 1\n" ], [ "AUTOTUNE = tf.data.experimental.AUTOTUNE\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\nIMAGE_SIZE = [176, 208]\nEPOCHS = 100", "_____no_output_____" ] ], [ [ "### Convert the data", "_____no_output_____" ] ], [ [ "def _bytes_feature(value: [str, bytes]) -> tf.train.Feature:\n \"\"\"string / byte를 byte_list로 반환합니다.\"\"\"\n if isinstance(value, type(tf.constant(0))):\n value = value.numpy() # BytesList는 EagerTensor에서 문자열을 풀지 않습니다.\n \n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))", "_____no_output_____" ], [ "def _float_feature(value: float) -> tf.train.Feature:\n \"\"\"float / double를 float_list로 반환합니다.\"\"\"\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))", "_____no_output_____" ], [ "def _int64_feature(value: [bool, int]) -> tf.train.Feature:\n \"\"\"bool / enum / int / uint를 int64_list로 반환합니다.\"\"\"\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))", "_____no_output_____" ], [ "def serialize_example(image: bytes, label: int) -> tf.train.Example.SerializeToString:\n \"\"\"\n 파일을 만들기 위해서 tf.train.Example 메시지를 만듭니다.\n \"\"\"\n feature = {\n \"raw_image\": _bytes_feature(image),\n \"label\": _int64_feature(label),\n }\n \n return tf.train.Example(features=tf.train.Features(feature=feature))", "_____no_output_____" ], [ "def write_tfrecord(main_path: str) -> None:\n \"\"\"\n datset의 위치를 입력 받아, 이미지와 라벨 등을 구하여 반환한다.\n \"\"\"\n train_paths = glob.glob(main_path + \"/train/*/*.jpg\")\n test_paths = glob.glob(main_path + \"/test/*/*.jpg\")\n image_labels = {\"NonDemented\": 0, \"VeryMildDemented\": 1, \"MildDemented\": 2, \"ModerateDemented\": 3}\n train_file = \"./tfrecord/train.tfrecord\"\n test_file = \"./tfrecord/test.tfrecord\"\n \n # train TFRecord file\n with tf.io.TFRecordWriter(train_file) as writer:\n for path in train_paths:\n image_string = open(path, \"rb\").read()\n \n label_str = path.split(\"\\\\\")[1]\n label = image_labels[label_str]\n \n tf_example = serialize_example(image_string, label)\n writer.write(tf_example.SerializeToString())\n \n print(\"Train TFRecord Converting Done!\")\n \n # test TFRecord file\n with tf.io.TFRecordWriter(test_file) as writer:\n for path in test_paths:\n image_string = open(path, \"rb\").read()\n \n label_str = path.split(\"\\\\\")[1]\n label = image_labels[label_str]\n \n tf_example = serialize_example(image_string, label)\n writer.write(tf_example.SerializeToString())\n \n print(\"Test TFRecord Converting Done!\")", "_____no_output_____" ], [ "dataset_path = \"./dataset\"\nwrite_tfrecord(dataset_path)", "_____no_output_____" ] ], [ [ "### Load the data", "_____no_output_____" ] ], [ [ "train_dataset = tf.data.TFRecordDataset(\"./tfrecord/train.tfrecord\")\ntest_dataset = tf.data.TFRecordDataset(\"./tfrecord/test.tfrecord\")", "_____no_output_____" ], [ "TRAIN_DATA_SIZE = len(list(train_dataset))\ntrain_size = int(0.75 * TRAIN_DATA_SIZE)\n\ntrain_dataset = train_dataset.shuffle(1000)\ntest_dataset = test_dataset.shuffle(1000)\n\nvalidation_dataset = train_dataset.skip(train_size)\ntrain_dataset = train_dataset.take(train_size)", "_____no_output_____" ], [ "train_len = len(list(train_dataset))\nvalidation_len = len(list(validation_dataset))\ntest_len = len(list(test_dataset))\n\nprint(\"Train dataset:\", train_len)\nprint(\"Validation dataset:\", validation_len)\nprint(\"Test dataset:\", test_len)", "_____no_output_____" ], [ "image_feature_description = {\n \"raw_image\": tf.io.FixedLenFeature([], tf.string),\n \"label\": tf.io.FixedLenFeature([], tf.int64),\n}", "_____no_output_____" ], [ "@tf.autograph.experimental.do_not_convert\ndef _parse_image_function(example_proto):\n features = tf.io.parse_single_example(example_proto, image_feature_description)\n \n for feature in features: \n image = tf.io.decode_raw(feature['image'], tf.uint8)\n image.set_shape([3 * 176 * 208])\n image = tf.reshape(image, [176, 208, 3])\n\n label = tf.cast(feature[\"label\"].numpy(), tf.int64)\n label = tf.one_hot(label, 4)\n\n return image, label", "_____no_output_____" ], [ "def read_dataset(epochs, batch_size, dataset):\n dataset = dataset.map(_parse_image_function)\n dataset = dataset.prefetch(10)\n dataset = dataset.repeat(epochs)\n dataset = dataset.shuffle(buffer_size=10 * batch_size)\n dataset = dataset.batch(batch_size, drop_remainder=True)\n\n return dataset", "_____no_output_____" ], [ "train_dataset = read_dataset(EPOCHS, BATCH_SIZE, train_dataset)\nvalidation_dataset = read_dataset(EPOCHS, BATCH_SIZE, validation_dataset)\ntest_dataset = read_dataset(EPOCHS, BATCH_SIZE, test_dataset)", "_____no_output_____" ], [ "parsed_train_dataset.take(train_len)", "_____no_output_____" ] ], [ [ "### Visualize dataset", "_____no_output_____" ] ], [ [ "# train TFRecord\nfor image_features in parsed_train_dataset.take(1):\n image_raw = image_features[\"raw_image\"].numpy()\n image_label = image_features[\"label\"].numpy()\n display.display(display.Image(data=image_raw))\n print(\"Label:\", image_label)", "_____no_output_____" ], [ "# test TFRecord\nfor image_features in parsed_test_dataset.take(1):\n image_raw = image_features[\"raw_image\"].numpy()\n image_label = image_features[\"label\"].numpy()\n display.display(display.Image(data=image_raw))\n print(\"Label:\", image_label)", "_____no_output_____" ] ], [ [ "### Build Model", "_____no_output_____" ] ], [ [ "# 경증 치매, 중증도 치매, 비 치매, 매우 경미한 치매\nCLASS_NAMES = ['MildDementia', 'ModerateDementia', 'NonDementia', 'VeryMildDementia']\nNUM_CLASSES = len(CLASS_NAMES)", "_____no_output_____" ], [ "TRAIN_DATA_SIZE = len(list(parsed_train_dataset))\ntrain_size = int(0.75 * TRAIN_DATA_SIZE)\n# val_size = int(0.25 * TRAIN_DATA_SIZE)\n# 테스트용 데이터셋은 따로 존재하기에 분할하지 않는다.\n# test_size = ", "_____no_output_____" ], [ "# train / validation data split\ntrain_dataset = parsed_train_dataset.shuffle(100)\ntrain_dataset = train_dataset.take(train_size)\nvalidation_dataset = train_dataset.skip(train_size)\n\ntrain_dataset = train_dataset.batch(BATCH_SIZE)\nvalidation_dataset = validation_dataset.batch(BATCH_SIZE)", "_____no_output_____" ], [ "def conv_block(filters):\n block = tf.keras.Sequential([\n tf.keras.layers.SeparableConv2D(filters, 3, activation='relu', padding='same'),\n tf.keras.layers.SeparableConv2D(filters, 3, activation='relu', padding='same'),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.MaxPool2D()\n ])\n \n return block", "_____no_output_____" ], [ "def dense_block(units, dropout_rate):\n block = tf.keras.Sequential([\n tf.keras.layers.Dense(units, activation='relu'),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Dropout(dropout_rate)\n ])\n \n return block", "_____no_output_____" ], [ "def build_model():\n model = tf.keras.Sequential([\n tf.keras.Input(shape=(*IMAGE_SIZE, 3)),\n \n tf.keras.layers.Conv2D(16, 3, activation='relu', padding='same'),\n tf.keras.layers.Conv2D(16, 3, activation='relu', padding='same'),\n tf.keras.layers.MaxPool2D(),\n \n conv_block(32),\n conv_block(64),\n \n conv_block(128),\n tf.keras.layers.Dropout(0.2),\n \n conv_block(256),\n tf.keras.layers.Dropout(0.2),\n \n tf.keras.layers.Flatten(),\n dense_block(512, 0.7),\n dense_block(128, 0.5),\n dense_block(64, 0.3),\n \n tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n ])\n \n return model", "_____no_output_____" ], [ "with strategy.scope():\n model = build_model()\n\n METRICS = [tf.keras.metrics.AUC(name='auc')]\n \n model.compile(\n optimizer='adam',\n loss=tf.losses.CategoricalCrossentropy(),\n metrics=METRICS\n )\n \n model.summary()", "_____no_output_____" ] ], [ [ "### Train Model", "_____no_output_____" ] ], [ [ "@tf.autograph.experimental.do_not_convert\ndef exponential_decay(lr0, s):\n def exponential_decay_fn(epoch):\n return lr0 * 0.1 **(epoch / s)\n return exponential_decay_fn\n\nexponential_decay_fn = exponential_decay(0.01, 20)\n\nlr_scheduler = tf.keras.callbacks.LearningRateScheduler(exponential_decay_fn)\n\ncheckpoint_cb = tf.keras.callbacks.ModelCheckpoint(\"AICAv2.h5\",\n save_best_only=True)\n\nearly_stopping_cb = tf.keras.callbacks.EarlyStopping(patience=10,\n restore_best_weights=True)", "_____no_output_____" ], [ "history = model.fit(\n train_dataset,\n validation_data=validation_dataset,\n callbacks=[checkpoint_cb, early_stopping_cb, lr_scheduler],\n epochs=EPOCHS\n)", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import sys\nfrom pyspark import SparkContext, SparkConf\nif __name__ == \"__main__\":\n sc = SparkContext(\"local\",\"PySpark Exemplo - Desafio Dataproc\")\n words = sc.textFile(\"gs://{SEU_BUCKET}/livro.txt\").flatMap(lambda line: line.split(\" \"))\n wordCounts = words.map(lambda word: (word, 1)).reduceByKey(lambda a,b:a +b).sortBy(lambda a:a[1], ascending=False)\n wordCounts.saveAsTextFile(\"gs://{SEU_BUCKET}/resultado\")", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# **SONAR_ISSUES**\n\nThis notebook the selection of the rellevant attributes of the table `SONAR_ISSUES`.\n\nFirst, we import the libraries we need and, then, we read the corresponding csv.", "_____no_output_____" ] ], [ [ "import pandas as pd", "_____no_output_____" ], [ "sonarIssues = pd.read_csv(\"../../../data/raw/SONAR_ISSUES.csv\")\nprint(sonarIssues.shape)\nlist(sonarIssues)", "(1941508, 18)\n" ] ], [ [ "We select the desired attributes of the table.", "_____no_output_____" ] ], [ [ "attributes = ['projectID', 'creationDate', 'closeDate', 'creationCommitHash', 'closeCommitHash', 'type', 'severity',\n 'debt', 'author']\nsonarIssues = sonarIssues[attributes]", "_____no_output_____" ], [ "print(sonarIssues.shape)\nsonarIssues.head()", "(1941508, 9)\n" ] ], [ [ "We save this new table into a csv.", "_____no_output_____" ] ], [ [ "sonarIssues.to_csv('../../../data/interim/DataPreparation/SelectData/SONAR_ISSUES_select.csv', header=True)", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "Unlicense" ]

[ "Unlicense" ]

[ "Unlicense" ]

[ [ [ "# R - Week 2 (exercises)", "_____no_output_____" ], [ "## R-code on solving equations with inverse matrix", "_____no_output_____" ], [ "Solve the following system of equations:", "_____no_output_____" ], [ "1. $2x+y+2z=3$\n2. $x-3z=-5$\n3. $2y+5z=4$", "_____no_output_____" ], [ "$$ \\begin{bmatrix} 2 & 1 & 2 \\\\ 1 & 6 & -3 \\\\ 0 & 2 & 5 \\end{bmatrix} \\cdot \\begin{bmatrix} x \\\\ y \\\\ z \\end{bmatrix} = \\begin{bmatrix} 3 & -5 & 4 \\end{bmatrix} $$", "_____no_output_____" ], [ "$ A \\vec{x} = \\vec{b}$\n\n$A^{-1}\\cdot A \\vec{x} = A^{-1} \\vec{b}$\n\n$I \\vec{x} = A^{-1} \\vec{b}$", "_____no_output_____" ], [ "Define matrix $A$:", "_____no_output_____" ] ], [ [ "A = matrix(c(2,1,2,1,6,-3,0,2,5), nrow=3)\nA", "_____no_output_____" ] ], [ [ "The inverse $A^{-1}$ is:", "_____no_output_____" ] ], [ [ "solve(A)", "_____no_output_____" ] ], [ [ "Define vector $\\vec{b}$:", "_____no_output_____" ] ], [ [ "b = c(3, -5, 4)", "_____no_output_____" ] ], [ [ "Solve the system with R functions `solve(A,b)`:", "_____no_output_____" ] ], [ [ "solve(A,b)", "_____no_output_____" ] ], [ [ "Solve the system with $\\vec{x}=A^{-1}\\vec{b}$:", "_____no_output_____" ] ], [ [ "solve(A) %*% b", "_____no_output_____" ] ], [ [ "## R-code on least square method", "_____no_output_____" ], [ "$y=ax+b$", "_____no_output_____" ], [ "$A\\cdot \\vec{x} = \\vec{b}$\n\n$A^T\\cdot A \\vec{x} = A^T \\vec{b}$\n\n$(A^T A)^{-1}A^T A \\vec{x} = A^T \\vec{b}$\n\n$(A^T A)^{-1} ...$", "_____no_output_____" ], [ "Define $\\vec{x}=\\begin{bmatrix}12 & 2 & 3 & 5 & 10 & 9 & 8 \\end{bmatrix}$:", "_____no_output_____" ] ], [ [ "x = c(12, 2, 3, 5, 10, 9, 8)", "_____no_output_____" ], [ "x", "_____no_output_____" ] ], [ [ "Define $\\vec{y} = \\begin{bmatrix}125 & 30 & 43 & 62 & 108 & 102 & 90 \\end{bmatrix}$:", "_____no_output_____" ] ], [ [ "y = c(125, 30, 43, 62, 108, 102, 90)", "_____no_output_____" ], [ "y", "_____no_output_____" ], [ "length(x)==length(y)", "_____no_output_____" ], [ "A = matrix(union(x,y), nrow=length(x))", "_____no_output_____" ], [ "A", "_____no_output_____" ], [ "lm(y~x)", "_____no_output_____" ], [ "fit <- function(x) 9.488*x+13.583", "_____no_output_____" ], [ "fit(5)", "_____no_output_____" ], [ "plot(x,sapply(x, fit), 'l', col='blue')\npoints(x,y)", "_____no_output_____" ], [ "x", "_____no_output_____" ], [ "y", "_____no_output_____" ] ], [ [ "$y=ax+b$", "_____no_output_____" ] ], [ [ "X = matrix(c(y, rep(1, length(y))), ncol=2)", "_____no_output_____" ], [ "X", "_____no_output_____" ], [ "b = matrix(y)", "_____no_output_____" ], [ "b", "_____no_output_____" ] ], [ [ "Dit is de vorm $A\\cdot\\vec{x} = \\vec{b}$. Wat we op willen lossen met $A^{-1}A\\vec{x}=A^{-1}\\vec{b}$, maar dit werkt niet omdat $A$ geen vierkante matrix is en daardoor geen inverse kan bepalen voor $A$.\n\nDoor gebruik te maken van de getransponeerde $A^T$ kunnen we een vierkante matrix krijgen. Dus matrix-vermenigvuldigen van $A\\vec{x}=\\vec{b}$ met $A^T$ geeft $A^T\\cdot A\\vec{x} = A^T\\cdot\\vec{b}$.", "_____no_output_____" ] ], [ [ "t(A) %*% A", "_____no_output_____" ] ], [ [ "Wat inderdaad een vierkant matrix geeft. Vervolgens is deze op te lossen door de inverse te bepalen. De hele formule wordt dan:\n\n$$ (A^T \\cdot A)^{-1}\\cdot(A^T \\cdot A)\\cdot\\vec{x} = (A^T \\cdot A)^{-1} \\cdot A^T \\cdot \\vec{b} $$\n\nLaat $B = (A^T \\cdot A)^{-1}$ zijn. Substitueren en vereenvoudigen geeft: \n\n$$ I\\cdot\\vec{x} = B \\cdot A^T \\cdot \\vec{b} $$", "_____no_output_____" ] ], [ [ "B = solve(t(A) %*% A)\nB", "_____no_output_____" ], [ "B %*% t(A) %*% b", "_____no_output_____" ], [ "lm(y~x)", "_____no_output_____" ] ], [ [ "**Versimpeld voorbeeld lreg**", "_____no_output_____" ] ], [ [ "A = matrix(c(-1,0,2,3,1,1,1,1),ncol=2)", "_____no_output_____" ], [ "A", "_____no_output_____" ], [ "b = c(-1,2,1,2)", "_____no_output_____" ], [ "b", "_____no_output_____" ], [ "x = A[0:4]", "_____no_output_____" ], [ "solve(t(A) %*% A) %*% t(A) %*% b", "_____no_output_____" ], [ "lm(b~x)", "_____no_output_____" ], [ "fit <- function(x) 0.5*x+0.5", "_____no_output_____" ], [ "plot(-5:5, sapply(-5:5, fit), 'l')\npoints(x,b)", "_____no_output_____" ], [ "lreg <- function(x, y) {\n A = cbind(x, rep(1, length(x)))\n s = solve(t(A) %*% A) %*% t(A) %*% y\n function(x) s[1] * x + s[2] # Return f(x)=ax+b\n}", "_____no_output_____" ], [ "x = c(12,2,3,5,10,9,8)\ny = c(125,30,43,62,108,102,90)", "_____no_output_____" ], [ "fit <- lreg(x, y)", "_____no_output_____" ], [ "plot(0:15, sapply(0:15, fit), 'l')\npoints(x,y)", "_____no_output_____" ] ], [ [ "With other functionality:", "_____no_output_____" ], [ "### Least squares regression model", "_____no_output_____" ], [ "Find the best fitting line $y=ax+b$ for the following data points:", "_____no_output_____" ] ], [ [ "x <- c(12,2,3,5,10,9,8)\nb <- c(125,30,43,62,108,102,90)", "_____no_output_____" ] ], [ [ "We can do this by solving the equation $A\\vec{x}=\\vec{b}$. Constructing the equation with the matrices for our data points yields:\n\n$$ \\begin{bmatrix} 12 & 1 \\\\ 2 & 1 \\\\ 3 & 1 \\\\ 5 & 1 \\\\ 10 & 1 \\\\ 9 & 1 \\\\ 8 & 1 \\end{bmatrix} \\cdot \\begin{bmatrix}a \\\\ b \\end{bmatrix} = \\begin{bmatrix} 125 \\\\ 30 \\\\ 43 \\\\ 62 \\\\ 108 \\\\ 102 \\\\ 90 \\end{bmatrix} $$", "_____no_output_____" ], [ "First we will construct our matrix $A$:", "_____no_output_____" ] ], [ [ "ones <- rep(1, length(x))", "_____no_output_____" ], [ "A <- cbind(x, ones)", "_____no_output_____" ], [ "A", "_____no_output_____" ] ], [ [ "If we want to solve the equation $A\\vec{x}=\\vec{b}$ we can multiply both sides $A^{-1}$ to get:\n\n$$ \\begin{align} A\\vec{x}&=\\vec{b} \\\\ (A^{-1}\\cdot A)\\vec{x}&=A^{-1}\\vec{b} \\\\ I\\vec{x}&=A^{-1}\\vec{b} \\end{align} $$", "_____no_output_____" ], [ "However, we need to calculate the inverse of $A$, but $A$ is not a square matrix. To solve this problem we multiply $A$ with $A^T$ to get a square matrix.", "_____no_output_____" ], [ "$$ \\begin{align} A\\vec{x}&=\\vec{b} \\\\ (A^T \\cdot A) \\cdot \\vec{x} &= A^T \\cdot \\vec{b} \\\\ (A^T \\cdot A)^{-1} \\cdot (A^T \\cdot A) \\cdot \\vec{x} &= (A^T \\cdot A)^{-1} \\cdot A^T \\cdot \\vec{b} \\\\ I \\cdot \\vec{x} &= (A^T \\cdot A)^{-1} \\cdot A^T \\cdot \\vec{b} \\end{align} $$", "_____no_output_____" ] ], [ [ "S = solve(t(A) %*% A) %*% t(A) %*% b", "_____no_output_____" ] ], [ [ "The resulting matrix $S$ will have our coefficients $a$ and $b$ to construct the line:", "_____no_output_____" ] ], [ [ "lsm = c(S[2], S[1])", "_____no_output_____" ], [ "lsm", "_____no_output_____" ] ], [ [ "If we verify the coefficients with built-in R functionality for least-squares regression, we can see that our solution is correct.", "_____no_output_____" ] ], [ [ "lm(b~x)", "_____no_output_____" ] ], [ [ "Plotting our values yields:", "_____no_output_____" ] ], [ [ "plot(x, b)\nabline(lsm)", "_____no_output_____" ] ], [ [ "Tada.", "_____no_output_____" ] ] ]

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[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ [ [ "##### Copyright 2018 The TensorFlow Authors.", "_____no_output_____" ] ], [ [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// https://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.", "_____no_output_____" ] ], [ [ "# Custom differentiation\n\nThis tutorial will show you how to define your own custom derivatives, perform derivative surgery, and implement your own gradient checkpointing API in just 5 lines of Swift.", "_____no_output_____" ], [ "## Declaring custom derivatives", "_____no_output_____" ], [ "You can define custom derivatives for any Swift function that has differentiable parameters and results. By doing that, you can even import a C function and make it differentiable.", "_____no_output_____" ] ], [ [ "import Glibc\n\nfunc sillyExp(_ x: Float) -> Float {\n let 𝑒 = Float(M_E)\n print(\"Taking 𝑒(\\(𝑒)) to the power of \\(x)!\")\n return pow(𝑒, x)\n}\n\n@differentiating(sillyExp)\nfunc sillyDerivative(_ x: Float) -> (value: Float, pullback: (Float) -> Float) {\n let y = sillyExp(x)\n return (value: y, pullback: { v in v * y })\n}\n\nprint(\"exp(3) =\", sillyExp(3))\nprint(\"𝛁exp(3) =\", gradient(of: sillyExp)(3))", "Taking 𝑒(2.7182817) to the power of 3.0!\r\nexp(3) = 20.085535\r\nTaking 𝑒(2.7182817) to the power of 3.0!\r\n𝛁exp(3) = 20.085535\r\n" ] ], [ [ "## Stop derivatives from propagating\n\nCommonly known as \"stop gradient\" in machine learning use cases, method [`withoutDerivative()`](https://www.tensorflow.org/swift/api_docs/Protocols/Differentiable#/s:10TensorFlow14DifferentiablePAAE17withoutDerivativexyF) stops derivatives from propagating.\n\nPlus, `withoutDerivative()` can sometimes help the Swift compiler with identifying what not to differentiate and producing more efficient derivaitves. When it is detectable that the derivative of a function will always be zero, the Swift compiler will produce a warning. Explicitly using `.withoutDerivative()` silences that warning.", "_____no_output_____" ] ], [ [ "let x: Float = 2.0\nlet y: Float = 3.0\ngradient(at: x, y) { x, y in\n sin(sin(sin(x))) + cos(cos(cos(y))).withoutDerivative()\n}", "_____no_output_____" ] ], [ [ "## Derivative surgery\n\nMethod [`withGradient(_:)`](https://www.tensorflow.org/swift/api_docs/Protocols/Differentiable#/s:10TensorFlow14DifferentiablePAAE12withGradientyxy15CotangentVectorQzzcF) makes arbitrary operations (including mutation) run on the gradient at a value during the enclosing function’s backpropagation. \n\nUse this to debug or make experimental tweaks to backpropagation.", "_____no_output_____" ], [ "### It works anywhere", "_____no_output_____" ], [ "All differentiation APIs provided by the standard library are defined generically over all types that conform to the `Differentiable` protocol: `Float`, `Double`, `Float80`, SIMD vectors, and even your own types!\n\nRead technical document [Differentiable Types](https://github.com/tensorflow/swift/blob/master/docs/DifferentiableTypes.md) for more insights on the `Differentiable` protocol.", "_____no_output_____" ] ], [ [ "var x: Float = 30\nx.gradient { x -> Float in\n // Print the partial derivative with respect to the result of `sin(x)`.\n let a = sin(x).withGradient { print(\"∂+/∂sin = \\($0)\") } \n // Force the partial derivative with respect to `x` to be `0.5`.\n let b = log(x.withGradient { (dx: inout Float) in\n print(\"∂log/∂x = \\(dx), but rewritten to 0.5\");\n dx = 0.5\n })\n return a + b\n}", "∂log/∂x = 0.033333335, but rewritten to 0.5\r\n∂+/∂sin = 1.0\r\n" ] ], [ [ "### Use it in a neural network module", "_____no_output_____" ], [ "Just like how we used it in a simple `Float` function, we can use it in any numerical application, like the following neural network built using the [Swift for TensorFlow Deep Learning Library](https://github.com/tensorflow/swift-apis).", "_____no_output_____" ] ], [ [ "import TensorFlow\n\nstruct MLP: Layer {\n var layer1 = Dense<Float>(inputSize: 2, outputSize: 10, activation: relu)\n var layer2 = Dense<Float>(inputSize: 10, outputSize: 1, activation: relu)\n \n @differentiable\n func applied(to input: Tensor<Float>, in context: Context) -> Tensor<Float> {\n let h0 = layer1.applied(to: input, in: context).withGradient { print(\"∂L/∂layer1 =\", $0) }\n return layer2.applied(to: h0, in: context)\n }\n}\n\nlet optimizer = SGD<MLP, Float>(learningRate: 0.02)\nvar classifier = MLP()\nlet context = Context(learningPhase: .training)\n\nlet x: Tensor<Float> = [[0, 0], [0, 1], [1, 0], [1, 1]]\nlet y: Tensor<Float> = [0, 1, 1, 0]\n\nfor _ in 0..<10 {\n let 𝛁model = classifier.gradient { classifier -> Tensor<Float> in\n let ŷ = classifier.applied(to: x, in: context).withGradient { print(\"∂L/∂ŷ =\", $0) }\n let loss = (ŷ - y).squared().mean()\n print(\"Loss: \\(loss)\")\n return loss\n }\n optimizer.update(&classifier.allDifferentiableVariables, along: 𝛁model)\n}", "Loss: 0.33426732\n∂L/∂ŷ = [[-0.25], [-0.078446716], [-0.12092987], [0.031454742]]\n∂L/∂layer1 = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.03357383, -0.027463656, 0.037523113, -0.002631738, -0.030937709, -0.014981618, -0.02623924, -0.026290288, 0.027446445, 0.01046889], [-0.051755875, -0.042336714, 0.057843916, -0.004056967, -0.047692157, -0.023094976, -0.040449213, -0.040527906, 0.042310182, 0.016138362], [0.013462082, 0.0110120885, -0.015045625, 0.0010552468, 0.012405078, 0.006007172, 0.010521135, 0.010541604, -0.011005187, -0.0041977055]]\nLoss: 0.33176333\n∂L/∂ŷ = [[-0.24746439], [-0.07523262], [-0.11674469], [0.03514868]]\n∂L/∂layer1 = [[-0.10602461, -0.08665162, 0.11829134, -0.008301959, -0.09804342, -0.04726032, -0.08280819, -0.082981184, 0.08658129, 0.032860693], [-0.032232955, -0.0263433, 0.035962217, -0.0025239112, -0.029806564, -0.014367796, -0.025174841, -0.025227433, 0.026321916, 0.009990108], [-0.050018553, -0.040879082, 0.05580555, -0.003916562, -0.046253316, -0.022295699, -0.039065886, -0.0391475, 0.0408459, 0.015502479], [0.015059238, 0.01230759, -0.016801547, 0.0011791714, 0.013925628, 0.006712634, 0.011761686, 0.011786258, -0.0122976, -0.0046673785]]\nLoss: 0.3263967\n∂L/∂ŷ = [[-0.24090183], [-0.068522125], [-0.10922298], [0.04169026]]\n∂L/∂layer1 = [[-0.10332261, -0.08436972, 0.115082964, -0.008081798, -0.09583634, -0.046007015, -0.08064418, -0.08082552, 0.08428522, 0.031835504], [-0.029389087, -0.023998126, 0.032734204, -0.002298787, -0.027259693, -0.013086237, -0.022938434, -0.022990014, 0.02397409, 0.009055292], [-0.046845652, -0.038252562, 0.052177705, -0.0036642232, -0.043451436, -0.020859215, -0.036563434, -0.03664565, 0.03821425, 0.014433966], [0.01788092, 0.01460095, -0.019916158, 0.0013986289, 0.016585354, 0.0079619335, 0.013956212, 0.013987594, -0.014586327, -0.005509425]]\nLoss: 0.32171223\n∂L/∂ŷ = [[-0.23473385], [-0.062339008], [-0.102276154], [0.047599167]]\n∂L/∂layer1 = [[-0.10078285, -0.08222727, 0.11207248, -0.007874874, -0.09368697, -0.044829067, -0.078606784, -0.07879931, 0.082127206, 0.030880556], [-0.026765218, -0.021837354, 0.029763442, -0.0020913552, -0.024880743, -0.011905396, -0.02087585, -0.02092698, 0.021810781, 0.008201047], [-0.04391221, -0.035827335, 0.04883123, -0.0034311705, -0.040820457, -0.019532524, -0.03424985, -0.034333736, 0.035783738, 0.013455003], [0.020436676, 0.016673988, -0.02272598, 0.0015968615, 0.018997777, 0.009090407, 0.015939828, 0.015978869, -0.016653698, -0.006261938]]\nLoss: 0.31760892\n∂L/∂ŷ = [[-0.22893232], [-0.056644887], [-0.0958622], [0.0529218]]\n∂L/∂layer1 = [[-0.09839373, -0.080213994, 0.109245166, -0.007680244, -0.0915977, -0.0437211, -0.0766867, -0.076893255, 0.0800974, 0.02998989], [-0.024345629, -0.019847406, 0.02703061, -0.0019003282, -0.022664083, -0.010817943, -0.018974645, -0.019025752, 0.019818557, 0.00742042], [-0.041200995, -0.033588488, 0.045744885, -0.0032159945, -0.038355254, -0.018307598, -0.03211148, -0.03219797, 0.033539664, 0.0125578465], [0.02274547, 0.0185429, -0.025253974, 0.0017754257, 0.021174446, 0.010106915, 0.017727504, 0.01777525, -0.018515948, -0.0069327]]\nLoss: 0.3140006\n∂L/∂ŷ = [[-0.22347087], [-0.051403634], [-0.08994151], [0.057702184]]\n∂L/∂layer1 = [[-0.09614439, -0.07832037, 0.106587306, -0.0074970224, -0.08956989, -0.04267808, -0.07487536, -0.07509866, 0.07818659, 0.029158076], [-0.022115506, -0.018015554, 0.024517624, -0.0017244942, -0.020603213, -0.009816977, -0.017223122, -0.017274486, 0.017984781, 0.0067070536], [-0.038695745, -0.031522017, 0.04289876, -0.0030173666, -0.03604967, -0.017176874, -0.030135486, -0.030225359, 0.03146817, 0.011735407], [0.024825346, 0.020223022, -0.027521798, 0.0019357986, 0.02312775, 0.011019863, 0.019333491, 0.01939115, -0.020188477, -0.0075288774]]\nLoss: 0.3108136\n∂L/∂ŷ = [[-0.21832475], [-0.046581082], [-0.084476836], [0.061981946]]\n∂L/∂layer1 = [[-0.094024695, -0.07653748, 0.10408614, -0.0073243803, -0.08760406, -0.041695286, -0.07316483, -0.07340741, 0.076386094, 0.02838015], [-0.020060813, -0.016329797, 0.022207491, -0.0015627067, -0.018690927, -0.008895975, -0.015610217, -0.015661974, 0.016297497, 0.0060551], [-0.036381166, -0.029614802, 0.04027426, -0.0028340372, -0.033896815, -0.01613324, -0.028309815, -0.028403677, 0.029556224, 0.010981189], [0.026693417, 0.021728832, -0.02954984, 0.0020793765, 0.024870614, 0.011837205, 0.020771343, 0.020840213, -0.021685854, -0.008057066]]\nLoss: 0.30798542\n∂L/∂ŷ = [[-0.2134709], [-0.042145163], [-0.07943327], [0.06580055]]\n∂L/∂layer1 = [[-0.092025176, -0.07485708, 0.10172984, -0.007161543, -0.08570006, -0.040768307, -0.07154774, -0.07181193, 0.07468786, 0.02765159], [-0.018168358, -0.0147788925, 0.020084333, -0.00141389, -0.016919604, -0.00804881, -0.014125537, -0.014177696, 0.014745485, 0.0054592015], [-0.034242887, -0.027854579, 0.03785403, -0.0026648352, -0.031889293, -0.015170028, -0.026623163, -0.02672147, 0.027791614, 0.010289253], [0.028365958, 0.023074042, -0.031357337, 0.0022074832, 0.026416298, 0.012566475, 0.022053968, 0.022135403, -0.023021882, -0.008523362]]\nLoss: 0.30546278\n∂L/∂ŷ = [[-0.20888776], [-0.03806588], [-0.07477814], [0.069194704]]\n∂L/∂layer1 = [[-0.09013698, -0.07327145, 0.099507414, -0.007007787, -0.08385716, -0.039893024, -0.07001727, -0.070305154, 0.07308434, 0.026968256], [-0.016425777, -0.013352349, 0.018133363, -0.0012770379, -0.015281396, -0.007269756, -0.012759335, -0.012811797, 0.013318251, 0.0049144593], [-0.03226745, -0.02622989, 0.035621904, -0.0025086643, -0.030019386, -0.014281, -0.025064949, -0.025168007, 0.026162906, 0.009654161], [0.02985815, 0.024271391, -0.032962132, 0.0023213506, 0.027777938, 0.013214685, 0.023193432, 0.023288796, -0.024209408, -0.008933316]]\nLoss: 0.30320063\n∂L/∂ŷ = [[-0.20455518], [-0.0343152], [-0.07048094], [0.07219905]]\n∂L/∂layer1 = [[-0.08835188, -0.07177346, 0.09740865, -0.0068624374, -0.082074195, -0.039065596, -0.068567075, -0.06888049, 0.07156848, 0.02632637], [-0.014821488, -0.012040373, 0.016340809, -0.0011512097, -0.013768374, -0.0065534576, -0.011502485, -0.011555062, 0.012005987, 0.0044163857], [-0.03044227, -0.024730057, 0.033562843, -0.0023645016, -0.028279249, -0.013460329, -0.023625273, -0.023733262, 0.02465943, 0.0090709375], [0.031184357, 0.025332898, -0.034381, 0.0024221407, 0.028968606, 0.01378845, 0.024201185, 0.024311805, -0.025260549, -0.009292059]]\n" ] ], [ [ "## Recomputing activations during backpropagation to save memory (checkpointing)\n\nCheckpointing is a traditional technique in reverse-mode automatic differentiation to save memory when computing derivatives by making large intermediate values in the original computation not be saved in memory for backpropagation, but instead recomputed as needed during backpropagation. This technique has been realized in modern deep learning libraries as well. In Swift, API [`withComputationInPullbacks(_:)`](https://www.tensorflow.org/swift/api_docs/Protocols/Differentiable#/s:10TensorFlow14DifferentiablePAAE28withRecomputationInPullbacksyqd__qd__xcAaBRd__lF) makes you able to control what to recompute during backpropagation, and it is available on all `Differentiable` types.\n\nBut today, let us learn how to define our own gradient checkpointing APIs from scratch, in just a few lines of code.", "_____no_output_____" ], [ "### My gradient checkpointing API", "_____no_output_____" ], [ "We can define our own gradient checkpointing API, `makeRecomputedInGradient(_:)`, in terms of standard library function [`differentiableFunction(from:)`](https://www.tensorflow.org/swift/api_docs/Functions#/s:10TensorFlow22differentiableFunction4fromq0_x_q_tcq0_5value_15CotangentVectorQz_AEQy_tAEQy0_c8pullbacktx_q_tc_tAA14DifferentiableRzAaJR_AaJR0_r1_lF), which is a shorthand for creating a differentiable function directly from a derivative function (also called a \"vector-Jacobian products (VJP) function\").\n\nAs we have seen before, the derivative function returns a tuple of the original function's result and a pullback closure. We return `original(x)` in `value:`, and call `pullback(at:in:)` on `original` to evaluate the original function again and get a pullback.", "_____no_output_____" ] ], [ [ "/// Given a differentiable function, returns the same differentiable function except when\n/// derivatives of this function is being computed, values in the original function that are needed\n/// for computing the derivatives will be recomputed, instead of being captured by the differnetial\n/// or pullback.\n///\n/// - Parameter body: The body of the differentiable function.\n/// - Returns: The same differentiable function whose derivatives, when computed, will recompute\n/// some values from the original function.\nfunc makeRecomputedInGradient<T: Differentiable, U: Differentiable>(\n _ original: @escaping @differentiable (T) -> U\n) -> @differentiable (T) -> U {\n return differentiableFunction { x in\n (value: original(x), pullback: { v in pullback(at: x, in: original)(v) })\n }\n}", "_____no_output_____" ] ], [ [ "### Verify it works", "_____no_output_____" ] ], [ [ "let input: Float = 10.0\nprint(\"Running original computation...\")\n\n// Differentiable multiplication with checkpointing.\nlet square = makeRecomputedInGradient { (x: Float) -> Float in\n print(\" Computing square...\")\n return x * x\n}\n\n// Differentiate `f(x) = (cos(x))^2`.\nlet (output, backprop) = input.valueWithPullback { input -> Float in\n return square(cos(input))\n}\nprint(\"Running backpropagation...\")\nlet grad = backprop(1)\nprint(\"Gradient = \\(grad)\")", "Running original computation...\r\n Computing square...\r\nRunning backpropagation...\r\n Computing square...\r\nGradient = -0.9129453\r\n" ] ], [ [ "### Extend it to neural network modules\n\nIn this example, we define a simple convolutional neural network.\n\n```swift\nstruct Model: Layer {\n var conv = Conv2D<Float>(filterShape: (5, 5, 3, 6))\n var maxPool = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))\n var flatten = Flatten<Float>()\n var dense = Dense<Float>(inputSize: 36 * 6, outputSize: 10)\n\n @differentiable\n func applied(to input: Tensor<Float>, in context: Context) -> Tensor<Float> {\n return input.sequenced(in: context, through: conv, maxPool, flatten, dense)\n }\n}\n```\n\nWe want to make activations in the convolution layer (`conv`) be recomputed during backpropagation. However, using `makeRecomputedInGradient(_:)` could make the resulting code look cumbersome, especially when we want to apply layers sequentially using [`sequenced(in:through:_:_:_:_:)`](https://www.tensorflow.org/swift/api_docs/Protocols/Differentiable#/s:10TensorFlow14DifferentiablePAAE9sequenced2in7through____6OutputQyd_3_AA7ContextC_qd__qd_0_qd_1_qd_2_qd_3_t5InputQyd__RszAA5LayerRd__AaMRd_0_AaMRd_1_AaMRd_2_AaMRd_3_AKQyd_0_AGRtd__AKQyd_1_AGRtd_0_AKQyd_2_AGRtd_1_AKQyd_3_AGRtd_2_r3_lF).\n\n```swift\ninput.sequenced(in: context, through: conv, maxPool, flatten, dense)\n```\n\nSo, why don't we define a **special layer type** that wraps a layer and makes its activations be recomputed during backpropagation? Let's do it.", "_____no_output_____" ], [ "First, we define a `makeRecomputedInGradient(_:)` function that takes a binary function.", "_____no_output_____" ] ], [ [ "// Same as the previous `makeRecomputedInGradient(_:)`, except it's for binary functions.\nfunc makeRecomputedInGradient<T: Differentiable, U: Differentiable, V: Differentiable>(\n _ original: @escaping @differentiable (T, U) -> V\n) -> @differentiable (T, U) -> V {\n return differentiableFunction { x, y in\n (value: original(x, y), pullback: { v in pullback(at: x, y, in: original)(v) })\n }\n}", "_____no_output_____" ] ], [ [ "Then, we define a generic layer `ActivationRecomputing<Wrapped>`.", "_____no_output_____" ] ], [ [ "/// A layer wrapper that makes the underlying layer's activations be discarded during application\n/// and recomputed during backpropagation.\nstruct ActivationDiscarding<Wrapped: Layer>: Layer \n where Wrapped.AllDifferentiableVariables == Wrapped.CotangentVector {\n /// The wrapped layer.\n var wrapped: Wrapped\n\n @differentiable\n func applied(to input: Wrapped.Input, in context: Context) -> Wrapped.Output {\n let apply = makeRecomputedInGradient { (layer: Wrapped, input: Input) -> Wrapped.Output in\n print(\" Applying \\(Wrapped.self) layer...\")\n return layer.applied(to: input, in: context)\n }\n return apply(wrapped, input)\n }\n}", "_____no_output_____" ] ], [ [ "Finally, we can add a method on all layers that returns the same layer except its activations are discarded during application and recomputeed during backpropagation.", "_____no_output_____" ] ], [ [ "extension Layer where AllDifferentiableVariables == CotangentVector {\n func discardingActivations() -> ActivationDiscarding<Self> {\n return ActivationDiscarding(wrapped: self)\n }\n}", "_____no_output_____" ] ], [ [ "Back in the model, all we have to change is to wrap the convolution layer into the activation-recomputing layer.\n\n```swift\nvar conv = Conv2D<Float>(filterShape: (5, 5, 3, 6)).discardingActivations()\n```", "_____no_output_____" ], [ "Now, simply use it in the model!", "_____no_output_____" ] ], [ [ "struct Model: Layer {\n var conv = Conv2D<Float>(filterShape: (5, 5, 3, 6)).discardingActivations()\n var maxPool = MaxPool2D<Float>(poolSize: (2, 2), strides: (2, 2))\n var flatten = Flatten<Float>()\n var dense = Dense<Float>(inputSize: 36 * 6, outputSize: 10)\n\n @differentiable\n func applied(to input: Tensor<Float>, in context: Context) -> Tensor<Float> {\n return input.sequenced(in: context, through: conv, maxPool, flatten, dense)\n }\n}", "_____no_output_____" ] ], [ [ "When we run a training loop, we can see that the convolution layer's activations are computed twice: once during layer application, and once during backpropagation.", "_____no_output_____" ] ], [ [ "// Use random training data.\nlet x = Tensor<Float>(randomNormal: [10, 16, 16, 3])\nlet y = Tensor<Int32>(rangeFrom: 0, to: 10, stride: 1)\n\nvar model = Model()\nlet opt = SGD<Model, Float>()\nlet context = Context(learningPhase: .training)\n\nfor i in 1...5 {\n print(\"Starting training step \\(i)\")\n print(\" Running original computation...\")\n let (logits, backprop) = model.appliedForBackpropagation(to: x, in: context)\n let (loss, dL_dŷ) = logits.valueWithGradient { logits in\n softmaxCrossEntropy(logits: logits, labels: y)\n }\n print(\" Loss: \\(loss)\")\n print(\" Running backpropagation...\")\n let (dL_dθ, _) = backprop(dL_dŷ)\n \n opt.update(&model.allDifferentiableVariables, along: dL_dθ)\n}", "Starting training step 1\r\n Running original computation...\r\n Applying Conv2D<Float> layer...\n Loss: 3.6660562\n Running backpropagation...\n Applying Conv2D<Float> layer...\nStarting training step 2\n Running original computation...\n Applying Conv2D<Float> layer...\n Loss: 3.1203392\n Running backpropagation...\n Applying Conv2D<Float> layer...\nStarting training step 3\n Running original computation...\n Applying Conv2D<Float> layer...\n Loss: 2.7324893\n Running backpropagation...\n Applying Conv2D<Float> layer...\nStarting training step 4\n Running original computation...\n Applying Conv2D<Float> layer...\n Loss: 2.4246051\n Running backpropagation...\n Applying Conv2D<Float> layer...\nStarting training step 5\n Running original computation...\n Applying Conv2D<Float> layer...\n Loss: 2.1656146\n Running backpropagation...\n Applying Conv2D<Float> layer...\n" ] ], [ [ "Just like that, it is super easy to define generic differentiable programming libraries for different domains.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# 케라스로 AlexNet 만들기", "_____no_output_____" ], [ "이 노트북에서 [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)과 비슷한 심층 합성곱 신경망으로 [Oxford Flowers](http://www.robots.ox.ac.uk/~vgg/data/flowers/17/) 데이터셋의 꽃을 17개의 카테고리로 분류하겠습니다.", "_____no_output_____" ], [ "[](https://colab.research.google.com/github/rickiepark/dl-illustrated/blob/master/notebooks/10-2.alexnet_in_keras.ipynb)", "_____no_output_____" ], [ "#### 라이브러리를 적재합니다.", "_____no_output_____" ] ], [ [ "from tensorflow import keras\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\nfrom tensorflow.keras.layers import BatchNormalization", "_____no_output_____" ] ], [ [ "#### 데이터를 적재하고 전처리합니다.", "_____no_output_____" ], [ "원서 노트북은 tflearn을 사용해 oxflower17 데이터셋을 다운로드합니다. 이 라이브러리는 텐서플로 2와 호환되지 않습니다. 여기에서는 사전에 tflearn으로 다운받은 데이터를 다운로드하여 사용합니다.\n\n이 데이터셋에 대한 자세한 내용은 http://www.robots.ox.ac.uk/~vgg/data/flowers/17/ 을 참고하세요.", "_____no_output_____" ] ], [ [ "!rm oxflower17*\n!wget https://bit.ly/31IvwtD -O oxflower17.npz", "rm: cannot remove 'oxflower17*': No such file or directory\n--2021-02-01 15:40:25-- https://bit.ly/31IvwtD\nResolving bit.ly (bit.ly)... 67.199.248.11, 67.199.248.10\nConnecting to bit.ly (bit.ly)|67.199.248.11|:443... connected.\nHTTP request sent, awaiting response... 301 Moved Permanently\nLocation: https://onedrive.live.com/download?cid=822579D69D2DC3B5&resid=822579D69D2DC3B5!597497&authkey=AGGo9IgYC0QZfXo [following]\n--2021-02-01 15:40:25-- https://onedrive.live.com/download?cid=822579D69D2DC3B5&resid=822579D69D2DC3B5!597497&authkey=AGGo9IgYC0QZfXo\nResolving onedrive.live.com (onedrive.live.com)... 13.107.42.13\nConnecting to onedrive.live.com (onedrive.live.com)|13.107.42.13|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://evtwow.bl.files.1drv.com/y4mj6Q18QJugmPu4IyZM_bNTnaH6izWk_AI1kCIBS6QBpEg3m0m9RrFhbHGSHL7rOWZ491KhaXuB7gP3DSXh5eOWt2xNgUk7kcNyzhYIqhiOu3XiukEKWY1RxFqmFNbXsqnccyxPI_YKjALX3b2qxZ5so9LeW39Atyi_EvwuTjDj0fj7h6L3J1JwDFBVXUQcvoXYW4_wy_htpogQocLCErOKg/oxflower17.npz?download&psid=1 [following]\n--2021-02-01 15:40:26-- https://evtwow.bl.files.1drv.com/y4mj6Q18QJugmPu4IyZM_bNTnaH6izWk_AI1kCIBS6QBpEg3m0m9RrFhbHGSHL7rOWZ491KhaXuB7gP3DSXh5eOWt2xNgUk7kcNyzhYIqhiOu3XiukEKWY1RxFqmFNbXsqnccyxPI_YKjALX3b2qxZ5so9LeW39Atyi_EvwuTjDj0fj7h6L3J1JwDFBVXUQcvoXYW4_wy_htpogQocLCErOKg/oxflower17.npz?download&psid=1\nResolving evtwow.bl.files.1drv.com (evtwow.bl.files.1drv.com)... 13.107.42.12\nConnecting to evtwow.bl.files.1drv.com (evtwow.bl.files.1drv.com)|13.107.42.12|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 252415092 (241M) [application/zip]\nSaving to: ‘oxflower17.npz’\n\noxflower17.npz 100%[===================>] 240.72M 18.9MB/s in 12s \n\n2021-02-01 15:40:39 (19.4 MB/s) - ‘oxflower17.npz’ saved [252415092/252415092]\n\n" ], [ "import numpy as np\n\ndata = np.load('oxflower17.npz')\nX = data['X']\nY = data['Y']", "_____no_output_____" ], [ "X.shape, Y.shape", "_____no_output_____" ] ], [ [ "#### 신경망 모델을 만듭니다.", "_____no_output_____" ] ], [ [ "model = Sequential()\n\nmodel.add(Conv2D(96, kernel_size=(11, 11), strides=(4, 4), activation='relu', input_shape=(224, 224, 3)))\nmodel.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(256, kernel_size=(5, 5), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\nmodel.add(BatchNormalization())\n\nmodel.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))\nmodel.add(Conv2D(384, kernel_size=(3, 3), activation='relu'))\nmodel.add(Conv2D(384, kernel_size=(3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\nmodel.add(BatchNormalization())\n\nmodel.add(Flatten())\nmodel.add(Dense(4096, activation='tanh'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(4096, activation='tanh'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(17, activation='softmax'))", "_____no_output_____" ], [ "model.summary()", "Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv2d (Conv2D) (None, 54, 54, 96) 34944 \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 26, 26, 96) 0 \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 26, 26, 96) 384 \n_________________________________________________________________\nconv2d_1 (Conv2D) (None, 22, 22, 256) 614656 \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 10, 10, 256) 0 \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 10, 10, 256) 1024 \n_________________________________________________________________\nconv2d_2 (Conv2D) (None, 8, 8, 256) 590080 \n_________________________________________________________________\nconv2d_3 (Conv2D) (None, 6, 6, 384) 885120 \n_________________________________________________________________\nconv2d_4 (Conv2D) (None, 4, 4, 384) 1327488 \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 1, 1, 384) 0 \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 1, 1, 384) 1536 \n_________________________________________________________________\nflatten (Flatten) (None, 384) 0 \n_________________________________________________________________\ndense (Dense) (None, 4096) 1576960 \n_________________________________________________________________\ndropout (Dropout) (None, 4096) 0 \n_________________________________________________________________\ndense_1 (Dense) (None, 4096) 16781312 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 4096) 0 \n_________________________________________________________________\ndense_2 (Dense) (None, 17) 69649 \n=================================================================\nTotal params: 21,883,153\nTrainable params: 21,881,681\nNon-trainable params: 1,472\n_________________________________________________________________\n" ] ], [ [ "#### 모델을 설정합니다.", "_____no_output_____" ] ], [ [ "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])", "_____no_output_____" ] ], [ [ "#### 훈련!", "_____no_output_____" ] ], [ [ "model.fit(X, Y, batch_size=64, epochs=100, verbose=1, validation_split=0.1, shuffle=True)", "Epoch 1/100\n20/20 [==============================] - 9s 78ms/step - loss: 4.6429 - accuracy: 0.1772 - val_loss: 6.9113 - val_accuracy: 0.0662\nEpoch 2/100\n20/20 [==============================] - 1s 50ms/step - loss: 3.2046 - accuracy: 0.2823 - val_loss: 3.8402 - val_accuracy: 0.1838\nEpoch 3/100\n20/20 [==============================] - 1s 50ms/step - loss: 2.3982 - accuracy: 0.3301 - val_loss: 5.0392 - val_accuracy: 0.1250\nEpoch 4/100\n20/20 [==============================] - 1s 50ms/step - loss: 2.4482 - accuracy: 0.3819 - val_loss: 4.1104 - val_accuracy: 0.2279\nEpoch 5/100\n20/20 [==============================] - 1s 51ms/step - loss: 2.2152 - accuracy: 0.4310 - val_loss: 4.6752 - val_accuracy: 0.2206\nEpoch 6/100\n20/20 [==============================] - 1s 50ms/step - loss: 2.2077 - accuracy: 0.4286 - val_loss: 4.6224 - val_accuracy: 0.2279\nEpoch 7/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.9991 - accuracy: 0.4783 - val_loss: 3.2980 - val_accuracy: 0.3015\nEpoch 8/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.8836 - accuracy: 0.4758 - val_loss: 4.4707 - val_accuracy: 0.2721\nEpoch 9/100\n20/20 [==============================] - 1s 50ms/step - loss: 2.0022 - accuracy: 0.4745 - val_loss: 6.2244 - val_accuracy: 0.2059\nEpoch 10/100\n20/20 [==============================] - 1s 50ms/step - loss: 1.7743 - accuracy: 0.5284 - val_loss: 4.5157 - val_accuracy: 0.2574\nEpoch 11/100\n20/20 [==============================] - 1s 50ms/step - loss: 1.8367 - accuracy: 0.5051 - val_loss: 3.1568 - val_accuracy: 0.3750\nEpoch 12/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.9101 - accuracy: 0.4916 - val_loss: 2.5058 - val_accuracy: 0.3971\nEpoch 13/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.7524 - accuracy: 0.5305 - val_loss: 2.6367 - val_accuracy: 0.3824\nEpoch 14/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.6340 - accuracy: 0.5884 - val_loss: 3.8476 - val_accuracy: 0.3382\nEpoch 15/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.7423 - accuracy: 0.5187 - val_loss: 2.8836 - val_accuracy: 0.4412\nEpoch 16/100\n20/20 [==============================] - 1s 50ms/step - loss: 1.4916 - accuracy: 0.5700 - val_loss: 5.7071 - val_accuracy: 0.3382\nEpoch 17/100\n20/20 [==============================] - 1s 49ms/step - loss: 1.2675 - accuracy: 0.6554 - val_loss: 3.1568 - val_accuracy: 0.4118\nEpoch 18/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.1675 - accuracy: 0.6507 - val_loss: 4.5942 - val_accuracy: 0.4191\nEpoch 19/100\n20/20 [==============================] - 1s 50ms/step - loss: 1.3940 - accuracy: 0.6306 - val_loss: 2.8359 - val_accuracy: 0.4706\nEpoch 20/100\n20/20 [==============================] - 1s 49ms/step - loss: 1.2050 - accuracy: 0.6855 - val_loss: 2.7855 - val_accuracy: 0.5515\nEpoch 21/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.1311 - accuracy: 0.6832 - val_loss: 3.0645 - val_accuracy: 0.4853\nEpoch 22/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.2334 - accuracy: 0.6764 - val_loss: 3.5505 - val_accuracy: 0.4706\nEpoch 23/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.0275 - accuracy: 0.7142 - val_loss: 3.0626 - val_accuracy: 0.4706\nEpoch 24/100\n20/20 [==============================] - 1s 50ms/step - loss: 0.9573 - accuracy: 0.7388 - val_loss: 2.9081 - val_accuracy: 0.5221\nEpoch 25/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.7289 - accuracy: 0.7712 - val_loss: 2.2599 - val_accuracy: 0.5809\nEpoch 26/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.8660 - accuracy: 0.7556 - val_loss: 2.5860 - val_accuracy: 0.5809\nEpoch 27/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.7782 - accuracy: 0.7686 - val_loss: 4.9205 - val_accuracy: 0.3676\nEpoch 28/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.7287 - accuracy: 0.7853 - val_loss: 2.6654 - val_accuracy: 0.5368\nEpoch 29/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.6768 - accuracy: 0.7977 - val_loss: 3.3202 - val_accuracy: 0.5294\nEpoch 30/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.7499 - accuracy: 0.7763 - val_loss: 2.9776 - val_accuracy: 0.5368\nEpoch 31/100\n20/20 [==============================] - 1s 50ms/step - loss: 1.0794 - accuracy: 0.7134 - val_loss: 4.4612 - val_accuracy: 0.4559\nEpoch 32/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.7277 - accuracy: 0.7920 - val_loss: 3.6071 - val_accuracy: 0.4632\nEpoch 33/100\n20/20 [==============================] - 1s 55ms/step - loss: 0.6720 - accuracy: 0.8274 - val_loss: 5.9109 - val_accuracy: 0.3309\nEpoch 34/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.5504 - accuracy: 0.8461 - val_loss: 4.8567 - val_accuracy: 0.4338\nEpoch 35/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.6322 - accuracy: 0.8097 - val_loss: 5.7461 - val_accuracy: 0.4485\nEpoch 36/100\n20/20 [==============================] - 1s 51ms/step - loss: 1.0962 - accuracy: 0.7786 - val_loss: 4.8283 - val_accuracy: 0.4338\nEpoch 37/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.7069 - accuracy: 0.7959 - val_loss: 3.1211 - val_accuracy: 0.5441\nEpoch 38/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.5671 - accuracy: 0.8275 - val_loss: 3.0753 - val_accuracy: 0.5809\nEpoch 39/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.7584 - accuracy: 0.8193 - val_loss: 3.6496 - val_accuracy: 0.4412\nEpoch 40/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.7445 - accuracy: 0.8087 - val_loss: 4.3113 - val_accuracy: 0.5000\nEpoch 41/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.6313 - accuracy: 0.8254 - val_loss: 3.3609 - val_accuracy: 0.5515\nEpoch 42/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.3299 - accuracy: 0.7039 - val_loss: 5.8714 - val_accuracy: 0.4118\nEpoch 43/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.2052 - accuracy: 0.7081 - val_loss: 6.4298 - val_accuracy: 0.3382\nEpoch 44/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.8349 - accuracy: 0.7821 - val_loss: 3.2248 - val_accuracy: 0.5368\nEpoch 45/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.6766 - accuracy: 0.8154 - val_loss: 2.9413 - val_accuracy: 0.5735\nEpoch 46/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.8999 - accuracy: 0.7800 - val_loss: 5.3587 - val_accuracy: 0.3529\nEpoch 47/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.7444 - accuracy: 0.7849 - val_loss: 3.3938 - val_accuracy: 0.5147\nEpoch 48/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.5099 - accuracy: 0.8597 - val_loss: 3.2823 - val_accuracy: 0.5882\nEpoch 49/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.6309 - accuracy: 0.8320 - val_loss: 3.0612 - val_accuracy: 0.6471\nEpoch 50/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.4649 - accuracy: 0.8590 - val_loss: 3.9522 - val_accuracy: 0.5441\nEpoch 51/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.5488 - accuracy: 0.8589 - val_loss: 3.7538 - val_accuracy: 0.5662\nEpoch 52/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.4251 - accuracy: 0.8835 - val_loss: 2.6496 - val_accuracy: 0.6544\nEpoch 53/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2903 - accuracy: 0.9169 - val_loss: 2.7501 - val_accuracy: 0.6618\nEpoch 54/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.4575 - accuracy: 0.8965 - val_loss: 3.5671 - val_accuracy: 0.6324\nEpoch 55/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.2682 - accuracy: 0.9198 - val_loss: 2.7003 - val_accuracy: 0.6765\nEpoch 56/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2174 - accuracy: 0.9196 - val_loss: 3.3102 - val_accuracy: 0.6029\nEpoch 57/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.4567 - accuracy: 0.8849 - val_loss: 5.5328 - val_accuracy: 0.4632\nEpoch 58/100\n20/20 [==============================] - 1s 52ms/step - loss: 1.0789 - accuracy: 0.7738 - val_loss: 3.0945 - val_accuracy: 0.5515\nEpoch 59/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.5780 - accuracy: 0.8461 - val_loss: 4.2850 - val_accuracy: 0.4926\nEpoch 60/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.4107 - accuracy: 0.8884 - val_loss: 4.2642 - val_accuracy: 0.4853\nEpoch 61/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.2438 - accuracy: 0.9170 - val_loss: 2.4405 - val_accuracy: 0.6691\nEpoch 62/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1929 - accuracy: 0.9441 - val_loss: 2.9831 - val_accuracy: 0.6912\nEpoch 63/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1585 - accuracy: 0.9387 - val_loss: 3.8142 - val_accuracy: 0.5956\nEpoch 64/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2168 - accuracy: 0.9435 - val_loss: 3.9020 - val_accuracy: 0.5735\nEpoch 65/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1878 - accuracy: 0.9484 - val_loss: 3.6544 - val_accuracy: 0.6029\nEpoch 66/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.5521 - accuracy: 0.9019 - val_loss: 4.1064 - val_accuracy: 0.5294\nEpoch 67/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.4251 - accuracy: 0.8947 - val_loss: 3.4000 - val_accuracy: 0.5956\nEpoch 68/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.3610 - accuracy: 0.8991 - val_loss: 3.0546 - val_accuracy: 0.6324\nEpoch 69/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2694 - accuracy: 0.9378 - val_loss: 3.6128 - val_accuracy: 0.6544\nEpoch 70/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1124 - accuracy: 0.9633 - val_loss: 2.7719 - val_accuracy: 0.7132\nEpoch 71/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1700 - accuracy: 0.9529 - val_loss: 3.3141 - val_accuracy: 0.6912\nEpoch 72/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.1228 - accuracy: 0.9611 - val_loss: 3.3079 - val_accuracy: 0.6838\nEpoch 73/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1909 - accuracy: 0.9535 - val_loss: 4.6782 - val_accuracy: 0.5662\nEpoch 74/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.3665 - accuracy: 0.9193 - val_loss: 6.2701 - val_accuracy: 0.4779\nEpoch 75/100\n20/20 [==============================] - 1s 55ms/step - loss: 0.1703 - accuracy: 0.9522 - val_loss: 4.0180 - val_accuracy: 0.5809\nEpoch 76/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1623 - accuracy: 0.9538 - val_loss: 4.4584 - val_accuracy: 0.5956\nEpoch 77/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1211 - accuracy: 0.9786 - val_loss: 3.2625 - val_accuracy: 0.6618\nEpoch 78/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.0457 - accuracy: 0.9852 - val_loss: 2.9800 - val_accuracy: 0.6691\nEpoch 79/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2802 - accuracy: 0.9531 - val_loss: 3.1548 - val_accuracy: 0.6838\nEpoch 80/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1678 - accuracy: 0.9532 - val_loss: 3.5188 - val_accuracy: 0.6544\nEpoch 81/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.3620 - accuracy: 0.9322 - val_loss: 6.0728 - val_accuracy: 0.4338\nEpoch 82/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.6820 - accuracy: 0.8755 - val_loss: 3.5640 - val_accuracy: 0.5809\nEpoch 83/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.4729 - accuracy: 0.8956 - val_loss: 3.7106 - val_accuracy: 0.5882\nEpoch 84/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.3984 - accuracy: 0.9084 - val_loss: 3.6485 - val_accuracy: 0.6324\nEpoch 85/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1942 - accuracy: 0.9470 - val_loss: 4.6554 - val_accuracy: 0.5441\nEpoch 86/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.0973 - accuracy: 0.9677 - val_loss: 3.3203 - val_accuracy: 0.6691\nEpoch 87/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.0920 - accuracy: 0.9700 - val_loss: 2.9472 - val_accuracy: 0.6765\nEpoch 88/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1521 - accuracy: 0.9595 - val_loss: 2.9696 - val_accuracy: 0.6985\nEpoch 89/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.2279 - accuracy: 0.9486 - val_loss: 3.7965 - val_accuracy: 0.6324\nEpoch 90/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2021 - accuracy: 0.9517 - val_loss: 3.2243 - val_accuracy: 0.6691\nEpoch 91/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1544 - accuracy: 0.9627 - val_loss: 3.6171 - val_accuracy: 0.6985\nEpoch 92/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2457 - accuracy: 0.9491 - val_loss: 4.3328 - val_accuracy: 0.6103\nEpoch 93/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.1223 - accuracy: 0.9587 - val_loss: 3.0888 - val_accuracy: 0.7132\nEpoch 94/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1074 - accuracy: 0.9683 - val_loss: 3.4378 - val_accuracy: 0.6765\nEpoch 95/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.4015 - accuracy: 0.9229 - val_loss: 4.6498 - val_accuracy: 0.5882\nEpoch 96/100\n20/20 [==============================] - 1s 53ms/step - loss: 0.5772 - accuracy: 0.8908 - val_loss: 4.6537 - val_accuracy: 0.5735\nEpoch 97/100\n20/20 [==============================] - 1s 50ms/step - loss: 0.4412 - accuracy: 0.8848 - val_loss: 6.5095 - val_accuracy: 0.4632\nEpoch 98/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.1980 - accuracy: 0.9405 - val_loss: 3.0124 - val_accuracy: 0.6618\nEpoch 99/100\n20/20 [==============================] - 1s 52ms/step - loss: 0.2180 - accuracy: 0.9522 - val_loss: 3.4620 - val_accuracy: 0.6250\nEpoch 100/100\n20/20 [==============================] - 1s 51ms/step - loss: 0.2533 - accuracy: 0.9323 - val_loss: 5.3905 - val_accuracy: 0.5294\n" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# ATTENTION: Please do not alter any of the provided code in the exercise. Only add your own code where indicated\n# ATTENTION: Please do not add or remove any cells in the exercise. The grader will check specific cells based on the cell position.\n# ATTENTION: Please use the provided epoch values when training.\n\n# In this exercise you will train a CNN on the FULL Cats-v-dogs dataset\n# This will require you doing a lot of data preprocessing because\n# the dataset isn't split into training and validation for you\n# This code block has all the required inputs\nimport os\nimport zipfile\nimport random\nimport tensorflow as tf\nimport shutil\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom shutil import copyfile\nfrom os import getcwd", "_____no_output_____" ], [ "path_cats_and_dogs = f\"{getcwd()}/../tmp2/cats-and-dogs.zip\"\nshutil.rmtree('/tmp')\n\nlocal_zip = path_cats_and_dogs\nzip_ref = zipfile.ZipFile(local_zip, 'r')\nzip_ref.extractall('/tmp')\nzip_ref.close()\n", "_____no_output_____" ], [ "print(len(os.listdir('/tmp/PetImages/Cat/')))\nprint(len(os.listdir('/tmp/PetImages/Dog/')))\n\n# Expected Output:\n# 1500\n# 1500", "1500\n1500\n" ], [ "# Use os.mkdir to create your directories\n# You will need a directory for cats-v-dogs, and subdirectories for training\n# and testing. These in turn will need subdirectories for 'cats' and 'dogs'\ntry:\n print(len(os.listdir(\"/tmp/PetImages/\")))\n print(\"CAT_SOURCE_DIR = \", len(os.listdir('/tmp/PetImages/Cat/')))\n print(\"DOG_SOURCE_DIR = \", len(os.listdir('/tmp/PetImages/Dog/')))\n \n os.mkdir('/tmp/cats-v-dogs/')\n os.mkdir('/tmp/cats-v-dogs/training/'); os.mkdir('/tmp/cats-v-dogs/training/cats'); os.mkdir('/tmp/cats-v-dogs/training/dogs')\n os.mkdir('/tmp/cats-v-dogs/testing/'); os.mkdir('/tmp/cats-v-dogs/testing/cats'); os.mkdir('/tmp/cats-v-dogs/testing/dogs')\nexcept OSError:\n print(\"\\nDone\")\n print(\"/tmp/cats-v-dogs/ -> \", len(os.listdir(\"/tmp/cats-v-dogs/\")))\n print(\"TRAINING_CATS_DIR = \", len(os.listdir('/tmp/cats-v-dogs/training/cats')))\n print(\"TESTING_CATS_DIR = \", len(os.listdir('/tmp/cats-v-dogs/testing/cats')))\n \n print(\"TRAINING_DOGS_DIR = \", len(os.listdir('/tmp/cats-v-dogs/training/dogs')))\n print(\"TESTING_DOGS_DIR = \", len(os.listdir('/tmp/cats-v-dogs/testing/dogs')))\n \n pass", "3\nCAT_SOURCE_DIR = 1500\nDOG_SOURCE_DIR = 1500\nDone\n/tmp/cats-v-dogs/ -> 2\nTRAINING_CATS_DIR = 0\nTESTING_CATS_DIR = 0\nTRAINING_DOGS_DIR = 0\nTESTING_DOGS_DIR = 0\n" ], [ "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\nTRAINING_CATS_DIR = \"/tmp/cats-v-dogs/training/cats/\"\nTESTING_CATS_DIR = \"/tmp/cats-v-dogs/testing/cats/\"\n\nDOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\nTRAINING_DOGS_DIR = \"/tmp/cats-v-dogs/training/dogs/\"\nTESTING_DOGS_DIR = \"/tmp/cats-v-dogs/testing/dogs/\"\n# Write a python function called split_data which takes\n# a SOURCE directory containing the files\n# a TRAINING directory that a portion of the files will be copied to\n# a TESTING directory that a portion of the files will be copie to\n# a SPLIT SIZE to determine the portion\n# The files should also be randomized, so that the training set is a random\n# X% of the files, and the test set is the remaining files\n# SO, for example, if SOURCE is PetImages/Cat, and SPLIT SIZE is .9\n# Then 90% of the images in PetImages/Cat will be copied to the TRAINING dir\n# and 10% of the images will be copied to the TESTING dir\n# Also -- All images should be checked, and if they have a zero file length,\n# they will not be copied over\n#\n# os.listdir(DIRECTORY) gives you a listing of the contents of that directory\n# os.path.getsize(PATH) gives you the size of the file\n# copyfile(source, destination) copies a file from source to destination\n# random.sample(list, len(list)) shuffles a list\n\ndef split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n good_images = []\n for filename in os.listdir(SOURCE):\n if os.path.getsize(SOURCE + filename) > 0:\n good_images.append(filename)\n\n num_train = int(len(good_images) * SPLIT_SIZE)\n num_test = int(len(good_images) - num_train)\n \n good_images = random.sample(good_images, len(good_images))\n \n train_list = good_images[0:num_train]\n test_list = good_images[ :num_test ]\n\n for filename in train_list:\n copyfile(SOURCE + filename, TRAINING + filename)\n\n for filename in test_list:\n copyfile(SOURCE + filename, TESTING + filename)\n\n\n\nsplit_size = .9\nsplit_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)\nsplit_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)", "_____no_output_____" ], [ "print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))\nprint(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))\nprint(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))\nprint(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))\n\n# Expected output:\n# 1350\n# 1350\n# 150\n# 150", "1350\n1350\n150\n150\n" ], [ "# DEFINE A KERAS MODEL TO CLASSIFY CATS V DOGS\n# USE AT LEAST 3 CONVOLUTION LAYERS\nmodel = tf.keras.models.Sequential([\n # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n tf.keras.layers.MaxPooling2D(2,2),\n \n tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2), \n \n tf.keras.layers.Conv2D(64, (3,3), activation='relu'), \n tf.keras.layers.MaxPooling2D(2,2),\n \n # Flatten the results to feed into a DNN\n tf.keras.layers.Flatten(), \n # 512 neuron hidden layer\n tf.keras.layers.Dense(512, activation='relu'), \n # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('cats') and 1 for the other ('dogs')\n tf.keras.layers.Dense(1, activation='sigmoid') \n \n])\n\nmodel.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['acc'])\nmodel.summary()", "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv2d_3 (Conv2D) (None, 148, 148, 16) 448 \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 74, 74, 16) 0 \n_________________________________________________________________\nconv2d_4 (Conv2D) (None, 72, 72, 32) 4640 \n_________________________________________________________________\nmax_pooling2d_4 (MaxPooling2 (None, 36, 36, 32) 0 \n_________________________________________________________________\nconv2d_5 (Conv2D) (None, 34, 34, 64) 18496 \n_________________________________________________________________\nmax_pooling2d_5 (MaxPooling2 (None, 17, 17, 64) 0 \n_________________________________________________________________\nflatten_1 (Flatten) (None, 18496) 0 \n_________________________________________________________________\ndense_2 (Dense) (None, 512) 9470464 \n_________________________________________________________________\ndense_3 (Dense) (None, 1) 513 \n=================================================================\nTotal params: 9,494,561\nTrainable params: 9,494,561\nNon-trainable params: 0\n_________________________________________________________________\n" ] ], [ [ "# NOTE:\n\nIn the cell below you **MUST** use a batch size of 10 (`batch_size=10`) for the `train_generator` and the `validation_generator`. Using a batch size greater than 10 will exceed memory limits on the Coursera platform.", "_____no_output_____" ] ], [ [ "TRAINING_DIR = '/tmp/cats-v-dogs/training/'\ntrain_datagen = ImageDataGenerator( rescale = 1.0/255. )\n\n# NOTE: YOU MUST USE A BATCH SIZE OF 10 (batch_size=10) FOR THE \n# TRAIN GENERATOR.\ntrain_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n batch_size=10,\n class_mode='binary',\n target_size=(150, 150)) \n\nVALIDATION_DIR = '/tmp/cats-v-dogs/testing/'\nvalidation_datagen = ImageDataGenerator( rescale = 1.0/255. )\n\n# NOTE: YOU MUST USE A BACTH SIZE OF 10 (batch_size=10) FOR THE \n# VALIDATION GENERATOR.\nvalidation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n batch_size=10,\n class_mode='binary',\n target_size=(150, 150)) \n\n\n\n# Expected Output:\n# Found 2700 images belonging to 2 classes.\n# Found 300 images belonging to 2 classes.", "Found 2700 images belonging to 2 classes.\nFound 300 images belonging to 2 classes.\n" ], [ "history = model.fit_generator(train_generator,\n epochs=2,\n verbose=1,\n validation_data=validation_generator)\n", "Epoch 1/2\n270/270 [==============================] - 37s 136ms/step - loss: 3.9129 - acc: 0.5211 - val_loss: 0.7052 - val_acc: 0.5433\nEpoch 2/2\n270/270 [==============================] - 33s 123ms/step - loss: 0.6467 - acc: 0.6430 - val_loss: 0.5377 - val_acc: 0.7300\n" ], [ "# PLOT LOSS AND ACCURACY\n%matplotlib inline\n\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\n\n#-----------------------------------------------------------\n# Retrieve a list of list results on training and test data\n# sets for each training epoch\n#-----------------------------------------------------------\nacc=history.history['acc']\nval_acc=history.history['val_acc']\nloss=history.history['loss']\nval_loss=history.history['val_loss']\n\nepochs=range(len(acc)) # Get number of epochs\n\n#------------------------------------------------\n# Plot training and validation accuracy per epoch\n#------------------------------------------------\nplt.plot(epochs, acc, 'r', \"Training Accuracy\")\nplt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\nplt.title('Training and validation accuracy')\nplt.figure()\n\n#------------------------------------------------\n# Plot training and validation loss per epoch\n#------------------------------------------------\nplt.plot(epochs, loss, 'r', \"Training Loss\")\nplt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n\n\nplt.title('Training and validation loss')\n\n# Desired output. Charts with training and validation metrics. No crash :)", "_____no_output_____" ] ], [ [ "# Submission Instructions", "_____no_output_____" ] ], [ [ "# Now click the 'Submit Assignment' button above.", "_____no_output_____" ] ], [ [ "# When you're done or would like to take a break, please run the two cells below to save your work and close the Notebook. This will free up resources for your fellow learners. ", "_____no_output_____" ] ], [ [ "%%javascript\n<!-- Save the notebook -->\nIPython.notebook.save_checkpoint();", "_____no_output_____" ], [ "%%javascript\nIPython.notebook.session.delete();\nwindow.onbeforeunload = null\nsetTimeout(function() { window.close(); }, 1000);", "_____no_output_____" ] ] ]

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[ [ "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from silx.io.dictdump import h5todict\nimport os\nimport numpy as np\n#vox = h5todict(name + \".h5\", path='.')\n#vox = h5todict('/Users/prguser/Documents/cnns4qspr_trial/notebooks/test-output/1a4r.h5')\n", "_____no_output_____" ], [ "channels = ['all_C', 'all_O', 'all_N', 'acidic', 'basic', 'polar', 'nonpolar',\\\n 'charged', 'amphipathic','hydrophobic', 'aromatic', 'acceptor', 'donor',\\\n 'ring', 'hyb', 'heavyvalence', 'heterovalence', 'partialcharge','protein', 'ligand']", "_____no_output_____" ], [ "path = '/Users/prguser/Documents/cnns4qspr_trial/notebooks/small_set/'\n\ndef convert_h5_data(file_path, channels_considered):\n\n file_names = os.listdir(file_path)\n data_for_cnn = []\n target = []\n\n for file in file_names:\n input_file = os.path.join(file_path, file)\n vox = h5todict(input_file)\n data_structure = []\n for channel in channels_considered:\n data_structure.append(vox[channel])\n data_for_cnn.append(data_structure)\n target.append(vox['affinity'])\n\n return np.array(data_for_cnn), np.array(target)", "_____no_output_____" ], [ "input_data, affinity = convert_h5_data(path, channels)\n", "_____no_output_____" ], [ "from sys import getsizeof\ngetsizeof(input_data)", "_____no_output_____" ], [ "affinity", "_____no_output_____" ], [ "input_data.shape[1]", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator\nimport os", "_____no_output_____" ] ], [ [ "# Helper function to plot", "_____no_output_____" ] ], [ [ "def plot_graph(axis_title, x, y_train, y_val, xlabel, ylabel, xtick_range, ytick_range, save_path=None):\n\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 6))\n\n line1 = ax.plot(x, y_train, color=\"blue\", label=\"train\")\n line2 = ax.plot(x, y_val, color=\"red\", label=\"val\")\n\n # Nicer visuals.\n ax.set_title(axis_title)\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n ax.grid(b=True, which=\"major\", axis=\"both\", color=\"#d3d3d3\", linestyle=\"-\")\n ax.grid(b=True, which=\"minor\", axis=\"both\", color=\"#e7e7e7\", linestyle=\"dashed\")\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n ax.xaxis.set_ticks(np.arange(xtick_range[0], xtick_range[1], xtick_range[2]))\n ax.xaxis.set_minor_locator(MultipleLocator(5))\n ax.set_xlim(left=0)\n ax.yaxis.set_ticks(np.arange(ytick_range[0], ytick_range[1], ytick_range[2]))\n ax.yaxis.set_minor_locator(MultipleLocator(0.01))\n ax.patch.set_alpha(0)\n \n handles, labels = ax.get_legend_handles_labels()\n ax.legend(handles=handles, labels=labels, loc=\"best\")\n \n # Save graph\n plt.tight_layout()\n if save_path:\n plt.savefig(fname=save_path, dpi=300)", "_____no_output_____" ] ], [ [ "# Batch-size = 64", "_____no_output_____" ] ], [ [ "df_64 = pd.read_csv(\"../results/fit/20201203_040325___INSTANCE/csv_logger/csv_logger.csv\")", "_____no_output_____" ], [ "df_64.head()", "_____no_output_____" ], [ "plot_graph(axis_title=\"Batch Size = 64\",\n x=df_64[\"epoch\"],\n y_train=df_64[\"loss\"],\n y_val=df_64[\"val_loss\"],\n xlabel=\"Epoch\",\n ylabel=\"Binary crossentropy loss\",\n xtick_range=(0, 50, 10),\n ytick_range=(0, 0.16, 0.02),\n save_path=\"../results/fit/20201203_040325___INSTANCE/csv_logger/loss.png\")", "_____no_output_____" ], [ "plot_graph(axis_title=\"Batch Size = 64\",\n x=df_64[\"epoch\"],\n y_train=df_64[\"iouMetric\"],\n y_val=df_64[\"val_iouMetric\"],\n xlabel=\"Epoch\",\n ylabel=\"iouMetric\",\n xtick_range=(0, 50, 10),\n ytick_range=(0, 0.26, 0.02),\n save_path=\"../results/fit/20201203_040325___INSTANCE/csv_logger/iouMetric.png\")", "_____no_output_____" ] ], [ [ "# Batch-size = 10", "_____no_output_____" ] ], [ [ "df_10 = pd.read_csv(\"../results/fit/20201203_013807___INSTANCE/csv_logger/csv_logger.csv\")", "_____no_output_____" ], [ "df_10.head()", "_____no_output_____" ], [ "plot_graph(axis_title=\"Batch Size = 10\",\n x=df_10[\"epoch\"],\n y_train=df_10[\"loss\"],\n y_val=df_10[\"val_loss\"],\n xlabel=\"Epoch\",\n ylabel=\"Binary crossentropy loss\",\n xtick_range=(0, 50, 10),\n ytick_range=(0, 0.16, 0.02),\n save_path=\"../results/fit/20201203_013807___INSTANCE/csv_logger/loss.png\")", "_____no_output_____" ], [ "plot_graph(axis_title=\"Batch Size = 10\",\n x=df_10[\"epoch\"],\n y_train=df_10[\"iouMetric\"],\n y_val=df_10[\"val_iouMetric\"],\n xlabel=\"Epoch\",\n ylabel=\"iouMetric\",\n xtick_range=(0, 50, 10),\n ytick_range=(0, 0.26, 0.02),\n save_path=\"../results/fit/20201203_013807___INSTANCE/csv_logger/iouMetric.png\")", "_____no_output_____" ] ], [ [ "---\n\n# Combine .csv", "_____no_output_____" ] ], [ [ "mass_train_df = pd.read_csv(\"../data/raw_data/csv-description-updated/Mass-Training-Description-UPDATED.csv\")\nmass_test_df = pd.read_csv(\"../data/raw_data/csv-description-updated/Mass-Test-Description-UPDATED.csv\")\n\nmass_df = pd.concat([mass_train_df, mass_test_df])\n\nmass_df", "_____no_output_____" ], [ "# Create identifier column.\nmass_df.insert(loc=0, column=\"identifier\", value=np.nan)\nmass_df[\"identifier\"] = mass_df.apply(lambda x: \"_\".join([x[\"patient_id\"], x[\"left_or_right_breast\"], x[\"image_view\"]]), axis=1)\n\n# Drop filepath columns, they are useless because the filepaths always change.\nmass_df.drop([\"image_file_path\", \"cropped_image_file_path\", \"ROI_mask_file_path\", \"full_path\", \"mask_path\", \"crop_path\"], axis=1, inplace=True)\n\n# Sort by identifier column.\nmass_df.sort_values(by=[\"identifier\"])", "_____no_output_____" ], [ "mass_df.to_csv(\"../data/csv/Mass_all.csv\", index=False)", "_____no_output_____" ], [ "# Get list of test and train image identifiers.\ntrain_identifiers = []\ntest_identifiers = []\n\ntrain_path = \"../data/preprocessed/Mass/Train_FULL\"\ntest_path = \"../data/preprocessed/Mass/Test_FULL\"\n\n# Train images.\nfor curdir, dirs, files in os.walk(train_path):\n \n files.sort()\n \n for f in files:\n if f.endswith(\".png\"):\n f = f.replace(\"_FULL___PRE.png\", \"\")\n train_identifiers.append(f)\n \n# Test images.\nfor curdir, dirs, files in os.walk(test_path):\n \n files.sort()\n \n for f in files:\n if f.endswith(\".png\"):\n f = f.replace(\"_FULL___PRE.png\", \"\")\n test_identifiers.append(f)\n \nprint(len(train_identifiers))\nprint(train_identifiers[:5])\nprint(len(test_identifiers))\nprint(test_identifiers[:5])\n\n# Create dataframe for train images.\nmass_train_df_new = mass_df[mass_df[\"identifier\"].isin(train_identifiers)]\n\n# Create dataframe for test images.\nmass_test_df_new = mass_df[mass_df[\"identifier\"].isin(test_identifiers)]", "1433\n['P_00001_LEFT_CC', 'P_00004_LEFT_CC', 'P_00004_LEFT_MLO', 'P_00009_RIGHT_CC', 'P_00009_RIGHT_MLO']\n159\n['P_00001_LEFT_MLO', 'P_00004_RIGHT_MLO', 'P_00018_RIGHT_CC', 'P_00021_RIGHT_CC', 'P_00023_RIGHT_CC']\n" ], [ "print(mass_train_df_new.shape)\nprint(mass_test_df_new.shape)", "(1528, 12)\n(168, 12)\n" ], [ "mass_train_df_new.to_csv(\"../data/csv/Mass_train.csv\", index=False)\nmass_test_df_new.to_csv(\"../data/csv/Mass_test.csv\", index=False)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import matplotlib.pyplot as plt\nfrom PIL import Image\nimport numpy as np\nimport json\nimport os\nimport random\nimport glob\n#from detectron_pro import detectron_mask_img,detectron_mask_img_composite\nimport shutil\nimport cv2", "_____no_output_____" ], [ "\n# to get location that stickimg will sticked on jpg_dir center or random\ndef center_location(jpg_dir,stickimg_dir):\n im = np.array(Image.open(jpg_dir), dtype=np.uint8)\n x_center,y_center = im.shape[1]/2,im.shape[0]/2\n im_stick = np.array(Image.open(stickimg_dir), dtype=np.uint8)\n im_stick_shape = im_stick.shape\n bd_box_x,bd_box_y = x_center-(im_stick_shape[1]/2),y_center-(im_stick_shape[0]/2)\n bd_box_length,bd_box_height =im_stick_shape[1],im_stick_shape[0]\n return bd_box_x,bd_box_y,bd_box_length,bd_box_height\n\ndef random_location(jpg_dir,stickimg_dir):\n im = np.array(Image.open(jpg_dir), dtype=np.uint8)\n # x boundary\n rangeX = 128\n x_left_bound,x_right_bound = rangeX, im.shape[1]-rangeX\n x_center = random.randrange(x_left_bound, x_right_bound)\n seq = [350,300,250,200]\n y_center = random.choice(seq)\n i_shape1 = [128,64]\n i_shape2 = [128*7/8,64*7/8]\n i_shape3 = [128*3/4,64*3/4]\n i_shape4 = [128*2/4,64*2/4]\n dict = {350:i_shape1,300:i_shape2 ,250:i_shape3,200:i_shape4}\n image_shape = dict[y_center]\n bd_box_x,bd_box_y = x_center-(image_shape[1]/2),y_center-(image_shape[0]/2)\n bd_box_length,bd_box_height =image_shape[1],image_shape[0]\n return bd_box_x,bd_box_y,bd_box_length,bd_box_height", "_____no_output_____" ], [ "#it can generate json file defined by your street_jpg and people_jpg \n\ndef create_json_file(jpg_dir,street_json,stickimg_dir,results_dir,function='center'):\n if function == 'center':\n bd_box_x,bd_box_y,bd_box_length,bd_box_height = center_location(jpg_dir,stickimg_dir)\n elif function == 'random':\n bd_box_x,bd_box_y,bd_box_length,bd_box_height = random_location(jpg_dir,stickimg_dir)\n \n input_file = open (street_json)\n json_array = json.load(input_file)\n \n data = []\n data.append({\n 'end':0,\n 'hide':0,\n 'id':0,\n 'init':0,\n 'lbl':\"person\",\n 'lock':0,\n 'occl':0,\n 'pos':[\n bd_box_x,\n bd_box_y,\n bd_box_length,\n bd_box_height], \n 'posv':[\n 0,\n 0,\n 0,\n 0],\n 'str':0\n })\n \n if json_array != []:\n for item in json_array:\n data.append(item)\n \n with open(results_dir, 'w') as outfile:\n json.dump(data, outfile)", "_____no_output_____" ] ], [ [ "# Start create", "_____no_output_____" ] ], [ [ "# Image read dir\nstreet_dir = '/root/notebooks/0858611-2/final_project/caltech_pedestrian_extractor/video_extractor/*'\n\npeople_dir = '/root/notebooks/0858611-2/final_project/caltech_pedestrian_extractor/js_on_image/people_img/Market-1501-v15.09.15'\n\n# Image save dir\nsave_dir = '/root/notebooks/0858611-2/final_project/caltech_pedestrian_extractor/0603_result'\n\nnum_imgs = 10", "_____no_output_____" ], [ "# Check dir folder exit\n# If not, create one\nif os.path.exists(save_dir) == False:\n os.makedirs(save_dir)\n\nfor s in ['people', 'mask', 'street', 'street_json','json']:\n if os.path.exists(os.path.join(save_dir, s)) == False:\n os.makedirs(os.path.join(save_dir, s))", "_____no_output_____" ], [ "street_imgs = glob.glob(street_dir+'/**/*.jpg', recursive=True)\n\n#street_imgs = random.shuffle(random.sample(street_imgs, 5000))\nstreet_imgs = random.sample(street_imgs, num_imgs)\n\nrandom.shuffle(street_imgs)", "_____no_output_____" ], [ "people_imgs = glob.glob(people_dir+'/bounding_box_train/*.jpg', recursive=True)\n\npeople_imgs = random.sample(people_imgs, num_imgs)\n\nrandom.shuffle(people_imgs)", "_____no_output_____" ], [ "for i in range(num_imgs):\n \n if (i%100==0):\n print(\"Process (\",i,\"/\",num_imgs,\") \",\"{:.2f}\".format(100*i/num_imgs),\" %\")\n \n # create mask and save\n try:\n mask_img = detectron_mask_img(people_imgs[i],(64,128))\n mask_img = Image.fromarray(mask_img)\n except Exception as e:\n print(\"Skip image :\",i)\n continue\n \n mask_img.save(save_dir+'/mask/'+str('{0:06}'.format(i))+'.jpg')\n \n # save street img\n street_img = cv2.imread(street_imgs[i])\n street_img = cv2.resize(street_img,(640,480))\n cv2.imwrite(save_dir+'/street/'+str('{0:06}'.format(i))+'.jpg', street_img)\n \n ################################################################\n img_path = street_imgs[i]\n json_dir = img_path.replace('images', 'annotations')\n json_dir = json_dir.replace('jpg', 'json')\n shutil.copyfile(json_dir, save_dir+'/street_json/'+str('{0:06}'.format(count))+'.json')\n ################################################################\n \n # save poeple img\n people_img = cv2.imread(people_imgs[i])\n people_img = cv2.resize(people_img,(64,128))\n cv2.imwrite(save_dir+'/people/'+str('{0:06}'.format(i))+'.jpg', people_img)\n \n # create json file and save\n create_json_file(save_dir+'/street/'+str('{0:06}'.format(i))+'.jpg',\n save_dir+'/street_json/'+str('{0:06}'.format(i))+'.json',\n save_dir+'/people/'+str('{0:06}'.format(i))+'.jpg',\n save_dir+'/json/'+str('{0:06}'.format(i))+'.json',\n function=\"random\")", "_____no_output_____" ], [ "import json\n\njson_path = '/root/notebooks/0858611-2/final_project/caltech_pedestrian_extractor/video_extractor/set00/V009/annotations/I00018.json'\n\ninput_file = open (json_path)\njson_array = json.load(input_file)", "_____no_output_____" ], [ "json_array\nprint(type(json_array[0]))", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "cd \"/content/drive/My Drive/Colab Notebook/dw_matrix/matrix_2/dw_matrix_car\"", "/content/drive/My Drive/Colab Notebook/dw_matrix/matrix_2/dw_matrix_car\n" ], [ "ls data/car.h5", "data/car.h5\n" ], [ "!pip install --upgrade tables #instalacja tables", "Collecting tables\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/ed/c3/8fd9e3bb21872f9d69eb93b3014c86479864cca94e625fd03713ccacec80/tables-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (4.3MB)\n\u001b[K |████████████████████████████████| 4.3MB 4.7MB/s \n\u001b[?25hRequirement already satisfied, skipping upgrade: numexpr>=2.6.2 in /usr/local/lib/python3.6/dist-packages (from tables) (2.7.1)\nRequirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from tables) (1.17.5)\nInstalling collected packages: tables\n Found existing installation: tables 3.4.4\n Uninstalling tables-3.4.4:\n Successfully uninstalled tables-3.4.4\nSuccessfully installed tables-3.6.1\n" ], [ "df = pd.read_hdf('data/car.h5')\ndf.shape\n", "_____no_output_____" ], [ "df.columns.values", "_____no_output_____" ], [ "df['price_value'].hist(bins=100);", "_____no_output_____" ], [ "df['price_value'].max() #cena max samochodu", "_____no_output_____" ], [ "df['price_value'].describe()", "_____no_output_____" ], [ "df['param_marka-pojazdu'].unique() #unikalne wartości", "_____no_output_____" ], [ "df.groupby('param_marka-pojazdu')['price_value'].mean()", "_____no_output_____" ], [ "df.groupby('param_marka-pojazdu')['price_value'].agg(np.mean).plot(kind='bar')", "_____no_output_____" ], [ "def group_and_barplot(feat_groupby, feat_agg='price_value', agg_funcs=[np.mean, np.median, np.size], feat_sort='mean', top=50, subplots=True):\n return(\n df\n .groupby(feat_groupby)[feat_agg]\n .agg(agg_funcs)\n .sort_values(by = feat_sort, ascending=False) #sortowanie wartości (odwrócenie początku)\n .head(top) #pozostawienie 50 początkowych\n\n ).plot(kind='bar',figsize=(15,5), subplots=subplots)", "_____no_output_____" ], [ "group_and_barplot('param_marka-pojazdu');", "_____no_output_____" ], [ "group_and_barplot('param_kraj-pochodzenia', feat_sort='size');", "_____no_output_____" ], [ "group_and_barplot('param_kolor', feat_sort='mean');", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ [ [ "### Import Packages", "_____no_output_____" ] ], [ [ "# Import packages\n\nimport glob\nimport csv\nimport pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\nimport psycopg2\n", "_____no_output_____" ] ], [ [ "### Append each .txt file into a DataFrame\nEach txt file is a row", "_____no_output_____" ] ], [ [ "# Iterate through each file name\n\nmain = pd.DataFrame()\n\nfor filename in glob.iglob('./training_set_a/*.txt'):\n \n # Open each file as data\n with open(filename) as inputfile:\n \n data = list(csv.reader(inputfile)) # list of list\n data = pd.DataFrame(data[1:],columns=data[0]) # Convert list of list to DataFrame\n data.Value = data.Value.astype(float) # Change Value to float\n \n \n \n # Pivot_table to convert from long to wide dataset\n\n # Creation of new features - aggregate across the time series to find mean, min, max values\n # mean is chosen rather than median because we want to take into the account of 'outlier values'\n\n wide_data = pd.pivot_table(data,values=['Value'],columns='Parameter',aggfunc=[np.mean,np.min,np.max])\n wide_data.columns = wide_data.columns.droplevel(level=0)\n \n \n \n # rename new columns & lower capitalise\n new_columns = []\n\n for ind, col in enumerate(wide_data.columns):\n \n if ind < wide_data.columns.shape[0]/3:\n col = 'mean_'+col \n new_columns.append(col)\n\n elif ind >= wide_data.columns.shape[0]/3 and ind < 2*wide_data.columns.shape[0]/3:\n col = 'min_'+col\n new_columns.append(col)\n\n else:\n col = 'max_'+col\n new_columns.append(col)\n \n wide_data.columns = new_columns\n wide_data.columns = wide_data.columns.str.lower()\n \n \n # rename descriptor row\n wide_data.rename(columns={'mean_age':'age','mean_gender':'gender','mean_height':'height',\n 'mean_icutype':'icutype','mean_recordid':'recordid'},inplace=True)\n \n # drop min/max descriptor rows\n wide_data.drop(['min_age','max_age','min_gender','max_gender','min_height','max_height',\n 'min_icutype','max_icutype','min_recordid','max_recordid'],axis=1,inplace=True)\n \n # set recordid as index\n wide_data.set_index(['recordid'],inplace = True)\n \n main = main.append(wide_data)", "_____no_output_____" ], [ "# Open set a outcomes file as dataframe\nwith open('training_outcomes_a.txt') as outcomesfile:\n \n label = list(csv.reader(outcomesfile)) # list of list\n label = pd.DataFrame(label[1:],columns=label[0]) # Convert list of list to DataFrame\n \n label = label.astype(float) # Change all values to float\n label.columns = label.columns.str.lower() # Change all column to lowercase\n \n \n label.set_index(['recordid'],inplace = True) # set recordid as index", "_____no_output_____" ], [ "# merge main data and label data\nmortality = main.merge(label,how='outer',left_index=True,right_index=True)", "_____no_output_____" ], [ "mortality.head(5)", "_____no_output_____" ] ], [ [ "RecordID (a unique integer for each ICU stay)\n\nAge (years)<br>\nGender (0: female, or 1: male)<br>\nHeight (cm)<br>\nICUType (1: Coronary Care Unit, 2: Cardiac Surgery Recovery Unit, 3: Medical ICU, or 4: Surgical ICU)<br>\nWeight (kg)", "_____no_output_____" ], [ "Variables Description \n\nALB Albumin (g/dL) <br>\nALP Alkaline phosphatase (IU/L) <br>\nALT Alanine transaminase (IU/L) <br>\nAST Aspartate transaminase (IU/L) <br>\nBIL Bilirubin (mg/dL) <br>\nBUN Blood urea nitrogen (mg/dL) <br>\nCHO Cholesterol (mg/dL) <br>\nCREA Serum creatinine (mg/dL) <br>\nDBP Invasive diastolic arterial blood pressure (mmHg) <br>\nFIO Fractional inspired O2 (0-1) <br>\nGCS Glasgow Coma Score (3-15) <br>\nGLU Serum glucose (mg/dL) <br>\nHCO Serum bicarbonate (mmol/L) <br> \nHCT Hematocrit (%) <br>\nHR Heart rate (bpm) <br>\nK Serum potassium (mEq/L) <br>\nLAC Lactate (mmol/L) <br>\nMG Serum magnesium (mmol/L) <br>\nMAP Invasive mean arterial blood pressure (mmHg) <br>\nMEVE Mechanical ventilation respiration <br>\nNA Serum sodium (mEq/L) <br>\nNBP Non-invasive diastolic arterial blood pressure (mmHg) <br>\nNAP Non-invasive mean arterial blood pressure (mmHg) <br>\nNSP Non-invasive systolic arterial blood pressure (mmHg) <br>\nPCO partial pressure of arterial <br>\nCO2 (mmHg) <br>\nPO2 Partial pressure of arterial <br>\nO2 (mmHg) <br>\nPH Arterial pH (0-14) <br>\nPLA cells/nL RRA Respiration rate (bpm) <br>\nSO2 O2 saturation in hemoglobin (%) <br>\nSBP Invasive systolic arterial blood pressure (mmHg) <br>\nTEM Temperature (°C) <br>\nTRI Troponin-I (μg/L) <br>\nTRT Troponin-T (μg/L) <br>\nURI Urine output (mL) <br>\nWBC White blood cell count (cells/nL) <br>\nWEI kg <br>", "_____no_output_____" ] ], [ [ "# Open file\n\nwith open('./training_set_a/132539.txt') as inputfile:\n \n results = list(csv.reader(inputfile)) # Open file in list of list\n results = pd.DataFrame(results[1:],columns=results[0]) # Convert list of list to DataFrame\n results.Value = results.Value.astype(float) # Change Value to float\n \nresults.head(8)", "_____no_output_____" ], [ "# Pivot_table to convert from long to wide dataset\n\n# Creation of new features - aggregate across the time series to find mean, min, max values\n# mean is chosen rather than median because we want to take into the account of 'outlier values'\n\nwide_result = pd.pivot_table(results,values=['Value'],columns='Parameter',aggfunc=[np.mean,np.min,np.max])\n\nwide_result.columns = wide_result.columns.droplevel(level=0)", "_____no_output_____" ], [ "new_columns = []\n\nfor ind, col in enumerate(wide_result.columns):\n \n if ind < wide_result.columns.shape[0]/3:\n col = 'mean_'+col \n new_columns.append(col)\n\n elif ind >= wide_result.columns.shape[0]/3 and ind < 2*wide_result.columns.shape[0]/3:\n col = 'min_'+col\n new_columns.append(col)\n\n else:\n col = 'max_'+col\n new_columns.append(col)\n \nprint new_columns", "['mean_Age', 'mean_BUN', 'mean_Creatinine', 'mean_GCS', 'mean_Gender', 'mean_Glucose', 'mean_HCO3', 'mean_HCT', 'mean_HR', 'mean_Height', 'mean_ICUType', 'mean_K', 'mean_Mg', 'mean_NIDiasABP', 'mean_NIMAP', 'mean_NISysABP', 'mean_Na', 'mean_Platelets', 'mean_RecordID', 'mean_RespRate', 'mean_Temp', 'mean_Urine', 'mean_WBC', 'mean_Weight', 'min_Age', 'min_BUN', 'min_Creatinine', 'min_GCS', 'min_Gender', 'min_Glucose', 'min_HCO3', 'min_HCT', 'min_HR', 'min_Height', 'min_ICUType', 'min_K', 'min_Mg', 'min_NIDiasABP', 'min_NIMAP', 'min_NISysABP', 'min_Na', 'min_Platelets', 'min_RecordID', 'min_RespRate', 'min_Temp', 'min_Urine', 'min_WBC', 'min_Weight', 'max_Age', 'max_BUN', 'max_Creatinine', 'max_GCS', 'max_Gender', 'max_Glucose', 'max_HCO3', 'max_HCT', 'max_HR', 'max_Height', 'max_ICUType', 'max_K', 'max_Mg', 'max_NIDiasABP', 'max_NIMAP', 'max_NISysABP', 'max_Na', 'max_Platelets', 'max_RecordID', 'max_RespRate', 'max_Temp', 'max_Urine', 'max_WBC', 'max_Weight']\n" ], [ "# rename the columns and lower capitalise\n\n#new_columns = [u'Age', u'mean_BUN', u'mean_Creatinine', u'mean_GCS', u'Gender', u'mean_Glucose', u'mean_HCO3',\n # u'mean_HCT', u'mean_HR', u'Height', u'ICUType', u'mean_K', u'mean_Mg', u'mean_NIDiasABP',\n # u'mean_NIMAP', u'mean_NISysABP', u'mean_Na', u'mean_Platelets', u'RecordID', u'mean_RespRate',\n # u'mean_Temp', u'mean_Urine', u'mean_WBC', u'mean_Weight', u'min_Age', u'min_BUN', u'min_Creatinine',\n # u'min_GCS', u'min_Gender', u'min_Glucose', u'min_HCO3', u'min_HCT', u'min_HR', u'min_Height',\n # u'min_ICUType', u'min_K', u'min_Mg', u'min_NIDiasABP', u'min_NIMAP', u'min_NISysABP', u'min_Na',\n # u'min_Platelets', u'min_RecordID', u'min_RespRate', u'min_Temp', u'min_Urine', u'min_WBC',\n #u'min_Weight', u'max_Age', u'max_BUN', u'max_Creatinine', u'max_GCS', u'max_Gender', u'max_Glucose',\n #u'max_HCO3', u'max_HCT', u'max_HR', u'max_Height', u'max_ICUType', u'max_K', u'max_Mg',\n #u'max_NIDiasABP', u'max_NIMAP', u'max_NISysABP', u'max_Na', u'max_Platelets', u'max_RecordID',\n #u'max_RespRate', u'max_Temp', u'max_Urine', u'max_WBC', u'max_Weight']\nwide_result.columns = new_columns\nwide_result.columns = wide_result.columns.str.lower()", "_____no_output_____" ], [ "wide_result.head()", "_____no_output_____" ], [ "# rename descriptor row\nwide_result.rename(columns={'mean_age':'age','mean_gender':'gender','mean_height':'height',\n 'mean_icutype':'icutype','mean_recordid':'recordid'},inplace=True)", "_____no_output_____" ], [ "wide_result.columns", "_____no_output_____" ], [ "# drop descriptor rows\n\nwide_result.drop(['min_age','max_age','min_gender','max_gender','min_height','max_height'\n ,'min_icutype','max_icutype','min_recordid','max_recordid'],axis=1,inplace=True)", "_____no_output_____" ], [ "wide_result.set_index(['recordid'],inplace = True)", "_____no_output_____" ], [ "wide_result", "_____no_output_____" ], [ "main = pd.DataFrame()", "_____no_output_____" ], [ "main = main.append(wide_result)", "_____no_output_____" ], [ "main", "_____no_output_____" ], [ "# Open each file as result\nwith open('./training_set_a/132599.txt') as inputfile:\n \n data = list(csv.reader(inputfile)) # list of list\n data = pd.DataFrame(data[1:],columns=data[0]) # Convert list of list to DataFrame\n data.Value = data.Value.astype(float) # Change Value to float\n \n \n \n # Pivot_table to convert from long to wide dataset\n\n # Creation of new features - aggregate across the time series to find mean, min, max values\n # mean is chosen rather than median because we want to take into the account of 'outlier values'\n\n wide_data = pd.pivot_table(data,values=['Value'],columns='Parameter',aggfunc=[np.mean,np.min,np.max])\n wide_data.columns = wide_data.columns.droplevel(level=0)\n \n \n \n # rename new columns & lower capitalise\n new_columns = []\n\n for ind, col in enumerate(wide_data.columns):\n \n if ind < wide_data.columns.shape[0]/3: \n col = 'mean_'+col \n new_columns.append(col)\n\n elif ind >= wide_data.columns.shape[0]/3 and ind < 2*wide_data.columns.shape[0]/3:\n col = 'min_'+col\n new_columns.append(col)\n\n else:\n col = 'max_'+col\n new_columns.append(col)\n \n wide_data.columns = new_columns\n wide_data.columns = wide_data.columns.str.lower()\n \n \n # rename descriptor row\n wide_data.rename(columns={'mean_age':'age','mean_gender':'gender','mean_height':'height',\n 'mean_icutype':'icutype','mean_recordid':'recordid'},inplace=True)\n \n # drop min/max descriptor rows\n wide_data.drop(['min_age','max_age','min_gender','max_gender','min_height','max_height',\n 'min_icutype','max_icutype','min_recordid','max_recordid'],axis=1,inplace=True)\n \n # set recordid as index\n wide_data.set_index(['recordid'],inplace = True)", "_____no_output_____" ], [ "main = main.append(wide_data)", "_____no_output_____" ], [ "main", "_____no_output_____" ], [ "for col in main.columns:\n print col", "age\ngender\nheight\nicutype\nmax_albumin\nmax_alp\nmax_alt\nmax_ast\nmax_bilirubin\nmax_bun\nmax_creatinine\nmax_diasabp\nmax_fio2\nmax_gcs\nmax_glucose\nmax_hco3\nmax_hct\nmax_hr\nmax_k\nmax_lactate\nmax_map\nmax_mechvent\nmax_mg\nmax_na\nmax_nidiasabp\nmax_nimap\nmax_nisysabp\nmax_paco2\nmax_pao2\nmax_ph\nmax_platelets\nmax_resprate\nmax_sao2\nmax_sysabp\nmax_temp\nmax_troponint\nmax_urine\nmax_wbc\nmax_weight\nmean_albumin\nmean_alp\nmean_alt\nmean_ast\nmean_bilirubin\nmean_bun\nmean_creatinine\nmean_diasabp\nmean_fio2\nmean_gcs\nmean_glucose\nmean_hco3\nmean_hct\nmean_hr\nmean_k\nmean_lactate\nmean_map\nmean_mechvent\nmean_mg\nmean_na\nmean_nidiasabp\nmean_nimap\nmean_nisysabp\nmean_paco2\nmean_pao2\nmean_ph\nmean_platelets\nmean_resprate\nmean_sao2\nmean_sysabp\nmean_temp\nmean_troponint\nmean_urine\nmean_wbc\nmean_weight\nmin_albumin\nmin_alp\nmin_alt\nmin_ast\nmin_bilirubin\nmin_bun\nmin_creatinine\nmin_diasabp\nmin_fio2\nmin_gcs\nmin_glucose\nmin_hco3\nmin_hct\nmin_hr\nmin_k\nmin_lactate\nmin_map\nmin_mechvent\nmin_mg\nmin_na\nmin_nidiasabp\nmin_nimap\nmin_nisysabp\nmin_paco2\nmin_pao2\nmin_ph\nmin_platelets\nmin_resprate\nmin_sao2\nmin_sysabp\nmin_temp\nmin_troponint\nmin_urine\nmin_wbc\nmin_weight\n" ], [ "#wide_result.reset_index(inplace=True)\n#wide_result.drop('index',axis=1,inplace=True)", "_____no_output_____" ], [ "# Pivot_table to convert from long to wide dataset\n\n#wide_result = pd.pivot_table(results,values=['Value'],columns='Parameter',index=['Time'])\n\n#wide_result.columns = wide_result.columns.droplevel(level=0)\n#wide_result.reset_index(inplace=True)", "_____no_output_____" ], [ "# Trying to convert time to an 'aggreable' data type\n\n#def str_time2(time):\n # hours, minutes = map(int, time.split(':'))\n # time = (hours,minutes)\n \n # return time\n\n#def str_time(time):\n # hours, minutes = map(int, time.split(':'))\n # time = time.format(int(hours),int(minutes))\n # return time\n\n#for time in wide_result.index:\n # hours, minutes = map(int, time.split(':'))\n # time = (hours,minutes)\n # print time\n \n#wide_result.Time = wide_result.Time.apply(str_time)", "_____no_output_____" ], [ "#class patient_details(object):\n # \"\"\"Run description of the patient when admitted on the 48th hour\"\"\"\n \n # def __init__(self,df = wide_result):\n \n # self.record_id = df[df.Time == '00:00']['RecordID'][0]\n # self.age = df[df.Time == '00:00']['Age'][0]\n # self.gender = df[df.Time == '00:00']['Gender'][0]\n # self.height = df[df.Time == '00:00']['Height'][0]\n # self.ICUtype = df[df.Time == '00:00']['ICUType'][0]\n\n \n \n # def fill(self,df = wide_result,details='RecordID'):\n # \"\"\"Filling of the NaN values with patient's details can be automated \n # by specifying the descriptor(column) in **kwargs \"\"\"\n # \"\"\"Default set as RecordID\"\"\"\n \n # wide_result[details].fillna(value=df[df.Time == '00:00'][details][0],inplace=True)\n ", "_____no_output_____" ], [ "# Initiate the class patient_details\n#patient = patient_details()\n\n# Fill NaN values in respective descriptor columns\n#patient.fill()\n#patient.fill(details='Age')\n#patient.fill(details='Gender')\n#patient.fill(details='Height')\n#patient.fill(details='ICUType')", "_____no_output_____" ], [ "# change all column names to lower key\n#wide_result.columns = wide_result.columns.str.lower()", "_____no_output_____" ], [ "# Connect to database\n\nconn = psycopg2.connect(host=\"localhost\",dbname=\"mortality\")\ncur = conn.cursor()", "_____no_output_____" ] ], [ [ "## EDA\n\n### 1. Check if the data is unbalanced", "_____no_output_____" ] ], [ [ "# Open outcomes file\n\nwith open('./training_outcomes_a.txt') as outcomefile:\n \n # Open file in list of list\n \n outcome = list(csv.reader(outcomefile))\n \noutcome = pd.DataFrame(outcome[1:],columns=outcome[0]) # Convert list of list to DataFrame\noutcome = outcome.astype(float,'ignore') # Change values to float", "_____no_output_____" ], [ "# Count the number of positives in dataset\n# Positives = 1 = Death, Negative = 0 = Survived\n\ndef imbalance_check(column,labels):\n \"\"\"labels can be a list or a tuple.\"\"\"\n \n for x in labels:\n label = float(column[column == x].count())\n total = float(column.count())\n \n percentage = float((label/total)*100)\n \n print 'percentage of',x,'in dataset:',percentage,'%'\n", "_____no_output_____" ], [ "imbalance_check(outcome['In-hospital_death'],[0,1]) # Conclude that this is an imbalanced dataset", "percentage of 0 in dataset: 86.15 %\npercentage of 1 in dataset: 13.85 %\n" ] ], [ [ "### 2. Create outcomes table in database", "_____no_output_____" ] ], [ [ "outcome.head(5)", "_____no_output_____" ], [ "pd.to_sql()", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "##### Copyright 2018 The TensorFlow Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\").\n\n# Neural Machine Translation with Attention\n\n<table class=\"tfo-notebook-buttons\" align=\"left\"><td>\n<a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb\">\n <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n</td><td>\n<a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>", "_____no_output_____" ], [ "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example that assumes some knowledge of sequence to sequence models.\n\nAfter training the model in this notebook, you will be able to input a Spanish sentence, such as *\"¿todavia estan en casa?\"*, and return the English translation: *\"are you still at home?\"*\n\nThe translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n\n<img src=\"https://tensorflow.org/images/spanish-english.png\" alt=\"spanish-english attention plot\">\n\nNote: This example takes approximately 10 mintues to run on a single P100 GPU.", "_____no_output_____" ] ], [ [ "from __future__ import absolute_import, division, print_function\n\n# Import TensorFlow >= 1.9 and enable eager execution\nimport tensorflow as tf\n\ntf.enable_eager_execution()\n\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\nimport unicodedata\nimport re\nimport numpy as np\nimport os\nimport time\n\nprint(tf.__version__)", "_____no_output_____" ] ], [ [ "## Download and prepare the dataset\n\nWe'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:\n\n```\nMay I borrow this book?\t¿Puedo tomar prestado este libro?\n```\n\nThere are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:\n\n1. Add a *start* and *end* token to each sentence.\n2. Clean the sentences by removing special characters.\n3. Create a word index and reverse word index (dictionaries mapping from word → id and id → word).\n4. Pad each sentence to a maximum length.", "_____no_output_____" ] ], [ [ "# Download the file\npath_to_zip = tf.keras.utils.get_file(\n 'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip', \n extract=True)\n\npath_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"", "_____no_output_____" ], [ "# Converts the unicode file to ascii\ndef unicode_to_ascii(s):\n return ''.join(c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn')\n\n\ndef preprocess_sentence(w):\n w = unicode_to_ascii(w.lower().strip())\n \n # creating a space between a word and the punctuation following it\n # eg: \"he is a boy.\" => \"he is a boy .\" \n # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n w = re.sub(r'[\" \"]+', \" \", w)\n \n # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n \n w = w.rstrip().strip()\n \n # adding a start and an end token to the sentence\n # so that the model know when to start and stop predicting.\n w = '<start> ' + w + ' <end>'\n return w", "_____no_output_____" ], [ "# 1. Remove the accents\n# 2. Clean the sentences\n# 3. Return word pairs in the format: [ENGLISH, SPANISH]\ndef create_dataset(path, num_examples):\n lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n \n word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n \n return word_pairs", "_____no_output_____" ], [ "# This class creates a word -> index mapping (e.g,. \"dad\" -> 5) and vice-versa \n# (e.g., 5 -> \"dad\") for each language,\nclass LanguageIndex():\n def __init__(self, lang):\n self.lang = lang\n self.word2idx = {}\n self.idx2word = {}\n self.vocab = set()\n \n self.create_index()\n \n def create_index(self):\n for phrase in self.lang:\n self.vocab.update(phrase.split(' '))\n \n self.vocab = sorted(self.vocab)\n \n self.word2idx['<pad>'] = 0\n for index, word in enumerate(self.vocab):\n self.word2idx[word] = index + 1\n \n for word, index in self.word2idx.items():\n self.idx2word[index] = word", "_____no_output_____" ], [ "def max_length(tensor):\n return max(len(t) for t in tensor)\n\n\ndef load_dataset(path, num_examples):\n # creating cleaned input, output pairs\n pairs = create_dataset(path, num_examples)\n\n # index language using the class defined above \n inp_lang = LanguageIndex(sp for en, sp in pairs)\n targ_lang = LanguageIndex(en for en, sp in pairs)\n \n # Vectorize the input and target languages\n \n # Spanish sentences\n input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]\n \n # English sentences\n target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]\n \n # Calculate max_length of input and output tensor\n # Here, we'll set those to the longest sentence in the dataset\n max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)\n \n # Padding the input and output tensor to the maximum length\n input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor, \n maxlen=max_length_inp,\n padding='post')\n \n target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor, \n maxlen=max_length_tar, \n padding='post')\n \n return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar", "_____no_output_____" ] ], [ [ "### Limit the size of the dataset to experiment faster (optional)\n\nTraining on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):", "_____no_output_____" ] ], [ [ "# Try experimenting with the size of that dataset\nnum_examples = 30000\ninput_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)", "_____no_output_____" ], [ "# Creating training and validation sets using an 80-20 split\ninput_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n\n# Show length\nlen(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)", "_____no_output_____" ] ], [ [ "### Create a tf.data dataset", "_____no_output_____" ] ], [ [ "BUFFER_SIZE = len(input_tensor_train)\nBATCH_SIZE = 64\nN_BATCH = BUFFER_SIZE//BATCH_SIZE\nembedding_dim = 256\nunits = 1024\nvocab_inp_size = len(inp_lang.word2idx)\nvocab_tar_size = len(targ_lang.word2idx)\n\ndataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\ndataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))", "_____no_output_____" ] ], [ [ "## Write the encoder and decoder model\n\nHere, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n\n<img src=\"https://www.tensorflow.org/images/seq2seq/attention_mechanism.jpg\" width=\"500\" alt=\"attention mechanism\">\n\nThe input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n\nHere are the equations that are implemented:\n\n<img src=\"https://www.tensorflow.org/images/seq2seq/attention_equation_0.jpg\" alt=\"attention equation 0\" width=\"800\">\n<img src=\"https://www.tensorflow.org/images/seq2seq/attention_equation_1.jpg\" alt=\"attention equation 1\" width=\"800\">\n\nWe're using *Bahdanau attention*. Lets decide on notation before writing the simplified form:\n\n* FC = Fully connected (dense) layer\n* EO = Encoder output\n* H = hidden state\n* X = input to the decoder\n\nAnd the pseudo-code:\n\n* `score = FC(tanh(FC(EO) + FC(H)))`\n* `attention weights = softmax(score, axis = 1)`. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. `Max_length` is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n* `context vector = sum(attention weights * EO, axis = 1)`. Same reason as above for choosing axis as 1.\n* `embedding output` = The input to the decoder X is passed through an embedding layer.\n* `merged vector = concat(embedding output, context vector)`\n* This merged vector is then given to the GRU\n \nThe shapes of all the vectors at each step have been specified in the comments in the code:", "_____no_output_____" ] ], [ [ "def gru(units):\n # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n # the code automatically does that.\n if tf.test.is_gpu_available():\n return tf.keras.layers.CuDNNGRU(units, \n return_sequences=True, \n return_state=True, \n recurrent_initializer='glorot_uniform')\n else:\n return tf.keras.layers.GRU(units, \n return_sequences=True, \n return_state=True, \n recurrent_activation='sigmoid', \n recurrent_initializer='glorot_uniform')", "_____no_output_____" ], [ "class Encoder(tf.keras.Model):\n def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n super(Encoder, self).__init__()\n self.batch_sz = batch_sz\n self.enc_units = enc_units\n self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n self.gru = gru(self.enc_units)\n \n def call(self, x, hidden):\n x = self.embedding(x)\n output, state = self.gru(x, initial_state = hidden) \n return output, state\n \n def initialize_hidden_state(self):\n return tf.zeros((self.batch_sz, self.enc_units))", "_____no_output_____" ], [ "class Decoder(tf.keras.Model):\n def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n super(Decoder, self).__init__()\n self.batch_sz = batch_sz\n self.dec_units = dec_units\n self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n self.gru = gru(self.dec_units)\n self.fc = tf.keras.layers.Dense(vocab_size)\n \n # used for attention\n self.W1 = tf.keras.layers.Dense(self.dec_units)\n self.W2 = tf.keras.layers.Dense(self.dec_units)\n self.V = tf.keras.layers.Dense(1)\n \n def call(self, x, hidden, enc_output):\n # enc_output shape == (batch_size, max_length, hidden_size)\n \n # hidden shape == (batch_size, hidden size)\n # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n # we are doing this to perform addition to calculate the score\n hidden_with_time_axis = tf.expand_dims(hidden, 1)\n \n # score shape == (batch_size, max_length, hidden_size)\n score = tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n \n # attention_weights shape == (batch_size, max_length, 1)\n # we get 1 at the last axis because we are applying score to self.V\n attention_weights = tf.nn.softmax(self.V(score), axis=1)\n \n # context_vector shape after sum == (batch_size, hidden_size)\n context_vector = attention_weights * enc_output\n context_vector = tf.reduce_sum(context_vector, axis=1)\n \n # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n x = self.embedding(x)\n \n # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n \n # passing the concatenated vector to the GRU\n output, state = self.gru(x)\n \n # output shape == (batch_size * max_length, hidden_size)\n output = tf.reshape(output, (-1, output.shape[2]))\n \n # output shape == (batch_size * max_length, vocab)\n x = self.fc(output)\n \n return x, state, attention_weights\n \n def initialize_hidden_state(self):\n return tf.zeros((self.batch_sz, self.dec_units))", "_____no_output_____" ], [ "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\ndecoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)", "_____no_output_____" ] ], [ [ "## Define the optimizer and the loss function", "_____no_output_____" ] ], [ [ "optimizer = tf.train.AdamOptimizer()\n\n\ndef loss_function(real, pred):\n mask = 1 - np.equal(real, 0)\n loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n return tf.reduce_mean(loss_)", "_____no_output_____" ] ], [ [ "## Training\n\n1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.\n2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.\n3. The decoder returns the *predictions* and the *decoder hidden state*.\n4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n5. Use *teacher forcing* to decide the next input to the decoder.\n6. *Teacher forcing* is the technique where the *target word* is passed as the *next input* to the decoder.\n7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate.", "_____no_output_____" ] ], [ [ "EPOCHS = 10\n\nfor epoch in range(EPOCHS):\n start = time.time()\n \n hidden = encoder.initialize_hidden_state()\n total_loss = 0\n \n for (batch, (inp, targ)) in enumerate(dataset):\n loss = 0\n \n with tf.GradientTape() as tape:\n enc_output, enc_hidden = encoder(inp, hidden)\n \n dec_hidden = enc_hidden\n \n dec_input = tf.expand_dims([targ_lang.word2idx['<start>']] * BATCH_SIZE, 1) \n \n # Teacher forcing - feeding the target as the next input\n for t in range(1, targ.shape[1]):\n # passing enc_output to the decoder\n predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n \n loss += loss_function(targ[:, t], predictions)\n \n # using teacher forcing\n dec_input = tf.expand_dims(targ[:, t], 1)\n \n batch_loss = (loss / int(targ.shape[1]))\n \n total_loss += batch_loss\n \n variables = encoder.variables + decoder.variables\n \n gradients = tape.gradient(loss, variables)\n \n optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n \n if batch % 100 == 0:\n print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n batch,\n batch_loss.numpy()))\n \n print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n total_loss / N_BATCH))\n print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))", "_____no_output_____" ] ], [ [ "## Translate\n\n* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n* Stop predicting when the model predicts the *end token*.\n* And store the *attention weights for every time step*.\n\nNote: The encoder output is calculated only once for one input.", "_____no_output_____" ] ], [ [ "def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n attention_plot = np.zeros((max_length_targ, max_length_inp))\n \n sentence = preprocess_sentence(sentence)\n\n inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]\n inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')\n inputs = tf.convert_to_tensor(inputs)\n \n result = ''\n\n hidden = [tf.zeros((1, units))]\n enc_out, enc_hidden = encoder(inputs, hidden)\n\n dec_hidden = enc_hidden\n dec_input = tf.expand_dims([targ_lang.word2idx['<start>']], 0)\n\n for t in range(max_length_targ):\n predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)\n \n # storing the attention weigths to plot later on\n attention_weights = tf.reshape(attention_weights, (-1, ))\n attention_plot[t] = attention_weights.numpy()\n\n predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n\n result += targ_lang.idx2word[predicted_id] + ' '\n\n if targ_lang.idx2word[predicted_id] == '<end>':\n return result, sentence, attention_plot\n \n # the predicted ID is fed back into the model\n dec_input = tf.expand_dims([predicted_id], 0)\n\n return result, sentence, attention_plot", "_____no_output_____" ], [ "# function for plotting the attention weights\ndef plot_attention(attention, sentence, predicted_sentence):\n fig = plt.figure(figsize=(10,10))\n ax = fig.add_subplot(1, 1, 1)\n ax.matshow(attention, cmap='viridis')\n \n fontdict = {'fontsize': 14}\n \n ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n\n plt.show()", "_____no_output_____" ], [ "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n \n print('Input: {}'.format(sentence))\n print('Predicted translation: {}'.format(result))\n \n attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n plot_attention(attention_plot, sentence.split(' '), result.split(' '))", "_____no_output_____" ], [ "translate('hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)", "_____no_output_____" ], [ "translate('esta es mi vida.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)", "_____no_output_____" ], [ "translate('¿todavia estan en casa?', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)", "_____no_output_____" ], [ "# wrong translation\ntranslate('trata de averiguarlo.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)", "_____no_output_____" ] ], [ [ "## Next steps\n\n* [Download a different dataset](http://www.manythings.org/anki/) to experiment with translations, for example, English to German, or English to French.\n* Experiment with training on a larger dataset, or using more epochs\n", "_____no_output_____" ] ] ]

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[ "MIT-0" ]

[ "MIT-0" ]

[ "MIT-0" ]

[ [ [ "%matplotlib inline\nimport os\nfrom collections import namedtuple\nfrom collections import defaultdict\nfrom collections import Counter\nfrom datetime import datetime\nimport itertools\nimport base64\nimport glob\nimport json\nimport random\nimport time\nimport imageio\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport shutil\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom sklearn.metrics import confusion_matrix\nimport boto3\nimport botocore\nimport sagemaker\nfrom urllib.parse import urlparse", "_____no_output_____" ], [ "role = sagemaker.get_execution_role()\nregion = boto3.session.Session().region_name\ns3 = boto3.client('s3')", "_____no_output_____" ] ], [ [ "## Job parameters", "_____no_output_____" ] ], [ [ "BUCKET=\"tanmcrae-greengrass-blog\"", "_____no_output_____" ], [ "bucket_region = s3.head_bucket(Bucket=BUCKET)['ResponseMetadata']['HTTPHeaders']['x-amz-bucket-region']\nassert bucket_region == region, \"Your S3 bucket {} and this notebook need to be in the same region.\".format(BUCKET)", "_____no_output_____" ], [ "MANIFEST = \"blue_box_large_job.json\"\nJOB_NAME = \"blue-box-large-job-public\"\nEXP_NAME = 'blue-box'\nprint(JOB_NAME)\n", "blue-box-large-job-public\n" ], [ "USE_AUTO_LABELING = False\nRUN_FULL_AL_DEMO = False \nUSE_PRIVATE_WORKFORCE = False", "_____no_output_____" ] ], [ [ "## specifying categories", "_____no_output_____" ] ], [ [ "CLASS_NAME = \"storage box\"", "_____no_output_____" ], [ "CLASS_LIST = [CLASS_NAME]\nprint(\"Label space is {}\".format(CLASS_LIST))\n\njson_body = {\n 'labels': [{'label': label} for label in CLASS_LIST]\n}\nwith open('class_labels.json', 'w') as f:\n json.dump(json_body, f)\n\nLABEL_KEY = \"ground-truth/{}/class_labels.json\".format(EXP_NAME)\ns3.upload_file('class_labels.json', BUCKET, LABEL_KEY)\nprint (\"uploaded s3://{}/{}\".format(BUCKET, LABEL_KEY))", "Label space is ['storage box']\nuploaded s3://tanmcrae-greengrass-blog/ground-truth/blue-box/class_labels.json\n" ] ], [ [ "## Create the instruction template\n", "_____no_output_____" ] ], [ [ "def make_template(test_template=False, save_fname='instructions.template'):\n template = r\"\"\"<script src=\"https://assets.crowd.aws/crowd-html-elements.js\"></script>\n\n<crowd-form>\n <crowd-bounding-box\n name=\"boundingBox\"\n src=\"{{ task.input.taskObject | grant_read_access }}\"\n header=\"Draw bounding box for the storage boxes in the picture (blue). Each bounding box should fit tight around the box. Only draw one bounding box per storage box, even if part of the box may be occluded.\"\n labels=\"['storage box']\"\n >\n <full-instructions header=\"Please annotate storage boxes in the picture\">\n <ol>\n <li><strong>Inspect</strong> the image</li>\n <li><strong>Determine</strong> if there are visible blue storage box in the picture.</li>\n <li><strong>Outline</strong> the storage box in the image using the provided “Box” tool. </li>\n </ol>\n\n <h2><span style=\"color: rgb(0, 138, 0);\">Good Example</span></h2>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-arm.png \" style=\"max-width:450\"></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-occlusion.png \" style=\"max-width:450\"></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-partial.png \" style=\"max-width:450\"></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-standard.png \" style=\"max-width:450\"></p>\n <h2><span style=\"color: rgb(230, 0, 0);\">Bad Example</span></h2>\n\n <p>The bounding boxes below are bad as it didn't cover the entire box. </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-full.png\" style=\"max-width:450\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-full-2.png\" style=\"max-width:450\"></p>\n <p>The bounding boxes below are bad as it's not tight around storage box. </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-tight.png\" style=\"max-width:450\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-tight-2.png\" style=\"max-width:450\"></p>\n <p>The labeling below are bad as it didn't cover the full </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-occlusion-partial.png\" style=\"max-width:450\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-occlusion-partial-2.png\" style=\"max-width:450\"></p>\n\n </full-instructions>\n\n <short-instructions>\n <p>Label every blue storage box in the picture. Boxes should fit tight. If the target goes off the screen, label up to the edge of the image. Do not label if it completely cannot be seen. </p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-arm.png \" style=\"max-width:100%\"/></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-occlusion.png \" style=\"max-width:100%\"/></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-partial.png \" style=\"max-width:100%\"/></p>\n <p><img src=\" https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/good-exmaples/good-example-standard.png \" style=\"max-width:100%\"/></p>\n <p><br/></p>\n <h2><span style=\"color: rgb(230, 0, 0);\">Bad examples</span></h2>\n <p>The bounding boxes below are bad as it didn't cover the entire box. </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-full.png\" style=\"max-width:100%\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-full-2.png\" style=\"max-width:100%\"></p>\n <p>The bounding boxes below are bad as it's not tight around storage box. </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-tight.png\" style=\"max-width:100%\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-not-tight-2.png\" style=\"max-width:100%\"></p>\n <p>The labeling below are bad as it only labeled part of the storage box </p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-occlusion-partial.png\" style=\"max-width:100%\"></p>\n <p><img src=\"https://s3.amazonaws.com/angelaw-workshop/groundtruth/greengrass-blog/bad-examples/bad-example-occlusion-partial-2.png\" style=\"max-width:100%\"></p>\n\n </short-instructions>\n </crowd-bounding-box>\n</crowd-form>\n \"\"\"\n with open(save_fname, 'w') as f:\n f.write(template)\n \ntemplate_name = 'instructions.template'\n# make_template(test_template=True, save_fname='instructions.html')\nmake_template(test_template=False, save_fname=template_name)\ns3.upload_file(template_name, BUCKET, EXP_NAME + '/' + template_name)\n\nprint(\"uploaded template to s3://{}/ground-truth/{}/{}\".format(BUCKET, EXP_NAME, template_name))", "uploaded template to s3://tanmcrae-greengrass-blog/ground-truth/blue-box/instructions.template\n" ], [ "private_workteam_arn = \"arn:aws:sagemaker:us-east-1:854681337758:workteam/private-crowd/greengrass-blog\"\n", "_____no_output_____" ] ], [ [ "## Create job", "_____no_output_____" ] ], [ [ "task_description = 'Dear Annotator, please draw a box around the yellow or blue storage box in the picture. Thank you!'\ntask_keywords = ['image', 'object', 'detection', CLASS_NAME]\ntask_title = 'Draw a box around storage box in the picture'\n\nprint(\"task_title: {}\".format(task_title))\nprint(\"JOB_NAME: {}\".format(JOB_NAME))\ntask_keywords", "task_title: Draw a box around storage box in the picture\nJOB_NAME: blue-box-large-job-public\n" ], [ "# Specify ARNs for resources needed to run an object detection job.\nac_arn_map = {'us-west-2': '081040173940',\n 'us-east-1': '432418664414',\n 'us-east-2': '266458841044',\n 'eu-west-1': '568282634449',\n 'ap-northeast-1': '477331159723'}\n\nprehuman_arn = 'arn:aws:lambda:{}:{}:function:PRE-BoundingBox'.format(region, ac_arn_map[region])\nacs_arn = 'arn:aws:lambda:{}:{}:function:ACS-BoundingBox'.format(region, ac_arn_map[region]) \nlabeling_algorithm_specification_arn = 'arn:aws:sagemaker:{}:027400017018:labeling-job-algorithm-specification/object-detection'.format(region)\npublic_workteam_arn = 'arn:aws:sagemaker:{}:394669845002:workteam/public-crowd/default'.format(region)", "_____no_output_____" ], [ "human_task_config = {\n \"AnnotationConsolidationConfig\": {\n \"AnnotationConsolidationLambdaArn\": acs_arn,\n },\n \"PreHumanTaskLambdaArn\": prehuman_arn,\n \"MaxConcurrentTaskCount\": 300, # 200 images will be sent at a time to the workteam.\n \"NumberOfHumanWorkersPerDataObject\": 1, # We will obtain and consolidate just 1 human annotation for each image.\n \"TaskAvailabilityLifetimeInSeconds\": 43200, #864000, #43200 # Your workteam has 10 days to complete all pending tasks.\n \"TaskDescription\": task_description,\n \"TaskKeywords\": task_keywords,\n \"TaskTimeLimitInSeconds\": 600, # Each image must be labeled within 10 minutes.\n \"TaskTitle\": task_title,\n \"UiConfig\": {\n \"UiTemplateS3Uri\": 's3://{}/{}/{}'.format(BUCKET, EXP_NAME, template_name),\n }\n }", "_____no_output_____" ], [ "if not USE_PRIVATE_WORKFORCE:\n human_task_config[\"PublicWorkforceTaskPrice\"] = {\n \"AmountInUsd\": {\n \"Dollars\": 0,\n \"Cents\": 3,\n \"TenthFractionsOfACent\": 6,\n }\n } \n human_task_config[\"WorkteamArn\"] = public_workteam_arn\nelse:\n human_task_config[\"WorkteamArn\"] = private_workteam_arn", "_____no_output_____" ], [ "print(json.dumps (human_task_config, indent =2 ))", "{\n \"AnnotationConsolidationConfig\": {\n \"AnnotationConsolidationLambdaArn\": \"arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox\"\n },\n \"PreHumanTaskLambdaArn\": \"arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox\",\n \"MaxConcurrentTaskCount\": 300,\n \"NumberOfHumanWorkersPerDataObject\": 1,\n \"TaskAvailabilityLifetimeInSeconds\": 43200,\n \"TaskDescription\": \"Dear Annotator, please draw a box around the yellow or blue storage box in the picture. Thank you!\",\n \"TaskKeywords\": [\n \"image\",\n \"object\",\n \"detection\",\n \"storage box\"\n ],\n \"TaskTimeLimitInSeconds\": 600,\n \"TaskTitle\": \"Draw a box around storage box in the picture\",\n \"UiConfig\": {\n \"UiTemplateS3Uri\": \"s3://tanmcrae-greengrass-blog/blue-box/instructions.template\"\n },\n \"PublicWorkforceTaskPrice\": {\n \"AmountInUsd\": {\n \"Dollars\": 0,\n \"Cents\": 3,\n \"TenthFractionsOfACent\": 6\n }\n },\n \"WorkteamArn\": \"arn:aws:sagemaker:us-east-1:394669845002:workteam/public-crowd/default\"\n}\n" ], [ "ground_truth_request = {\n \"InputConfig\" : {\n \"DataSource\": {\n \"S3DataSource\": {\n \"ManifestS3Uri\": 's3://{}/{}/{}'.format(BUCKET, 'manifests', MANIFEST),\n }\n },\n \"DataAttributes\": {\n \"ContentClassifiers\": [\n \"FreeOfPersonallyIdentifiableInformation\",\n \"FreeOfAdultContent\"\n ]\n }, \n },\n \"OutputConfig\" : {\n \"S3OutputPath\": 's3://{}/ground-truth-output/'.format(BUCKET),\n },\n \"HumanTaskConfig\" : human_task_config,\n \"LabelingJobName\": JOB_NAME,\n \"RoleArn\": role, \n \"LabelAttributeName\": \"bb\",\n \"LabelCategoryConfigS3Uri\": 's3://{}/{}'.format(BUCKET, LABEL_KEY),\n }\n\n\nif USE_AUTO_LABELING and RUN_FULL_AL_DEMO:\n ground_truth_request[ \"LabelingJobAlgorithmsConfig\"] = {\n \"LabelingJobAlgorithmSpecificationArn\": labeling_algorithm_specification_arn\n }", "_____no_output_____" ], [ "print(json.dumps (ground_truth_request, indent =2 ))", "{\n \"InputConfig\": {\n \"DataSource\": {\n \"S3DataSource\": {\n \"ManifestS3Uri\": \"s3://tanmcrae-greengrass-blog/manifests/blue_box_large_job.json\"\n }\n },\n \"DataAttributes\": {\n \"ContentClassifiers\": [\n \"FreeOfPersonallyIdentifiableInformation\",\n \"FreeOfAdultContent\"\n ]\n }\n },\n \"OutputConfig\": {\n \"S3OutputPath\": \"s3://tanmcrae-greengrass-blog/ground-truth-output/\"\n },\n \"HumanTaskConfig\": {\n \"AnnotationConsolidationConfig\": {\n \"AnnotationConsolidationLambdaArn\": \"arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox\"\n },\n \"PreHumanTaskLambdaArn\": \"arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox\",\n \"MaxConcurrentTaskCount\": 300,\n \"NumberOfHumanWorkersPerDataObject\": 1,\n \"TaskAvailabilityLifetimeInSeconds\": 43200,\n \"TaskDescription\": \"Dear Annotator, please draw a box around the yellow or blue storage box in the picture. Thank you!\",\n \"TaskKeywords\": [\n \"image\",\n \"object\",\n \"detection\",\n \"storage box\"\n ],\n \"TaskTimeLimitInSeconds\": 600,\n \"TaskTitle\": \"Draw a box around storage box in the picture\",\n \"UiConfig\": {\n \"UiTemplateS3Uri\": \"s3://tanmcrae-greengrass-blog/blue-box/instructions.template\"\n },\n \"PublicWorkforceTaskPrice\": {\n \"AmountInUsd\": {\n \"Dollars\": 0,\n \"Cents\": 3,\n \"TenthFractionsOfACent\": 6\n }\n },\n \"WorkteamArn\": \"arn:aws:sagemaker:us-east-1:394669845002:workteam/public-crowd/default\"\n },\n \"LabelingJobName\": \"blue-box-large-job-public\",\n \"RoleArn\": \"arn:aws:iam::854681337758:role/service-role/AmazonSageMaker-ExecutionRole-20190521T132559\",\n \"LabelAttributeName\": \"bb\",\n \"LabelCategoryConfigS3Uri\": \"s3://tanmcrae-greengrass-blog/ground-truth/blue-box/class_labels.json\"\n}\n" ], [ "sagemaker_client = boto3.client('sagemaker')\nsagemaker_client.create_labeling_job(**ground_truth_request)", "_____no_output_____" ] ], [ [ "## look at output manifest", "_____no_output_____" ] ], [ [ "job_name = 'yellow-box-small-job-public'\nOUTPUT_MANIFEST = 's3://{}/ground-truth-output/{}/manifests/output/output.manifest'.format(BUCKET, job_name)\n\noutput_file = job_name+'.output.manifest'\n!aws s3 cp {OUTPUT_MANIFEST} {output_file}", "Completed 15.7 KiB/15.7 KiB (261.1 KiB/s) with 1 file(s) remaining\rdownload: s3://tanmcrae-greengrass-blog/ground-truth-output/yellow-box-small-job-public/manifests/output/output.manifest to ./yellow-box-small-job-public.output.manifest\r\n" ], [ "with open(output_file, 'r') as f:\n output = [json.loads(line.strip()) for line in f.readlines()]", "_____no_output_____" ], [ "len(output)", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause-LBNL" ]

[ "BSD-3-Clause-LBNL" ]

[ "BSD-3-Clause-LBNL" ]

[ [ [ "from maggma.stores.advanced_stores import MongograntStore\nfrom maggma.stores.advanced_stores import Sort\nfrom typing import List\nfrom pathlib import Path\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport pandas as pd\nimport numpy as np\nfrom datetime import timedelta, date, datetime\nfrom monty.json import MontyDecoder\nfrom datetime import timedelta\n# configuration stuff\nfrom sys import platform\nimport maggma\nif platform == \"linux\" or platform == \"linux2\":\n import plotly.io as pio\n pio.orca.config.use_xvfb = True\nimport plotly.graph_objs as go\nimport plotly.offline as py\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly", "_____no_output_____" ], [ "renderer = \"notebook\" # change to pdf for live viewing", "_____no_output_____" ], [ "gdrive_mongo_store = MongograntStore(mongogrant_spec=\"rw:knowhere.lbl.gov/mp_core_mwu\",\n collection_name=\"gdrive\")\ngdrive_mongo_store.connect()\n\ntasks_mongo_store = MongograntStore(mongogrant_spec=\"ro:mongodb04.nersc.gov/mp_emmet_prod\",\n collection_name=\"tasks\")\ntasks_mongo_store.connect()", "_____no_output_____" ], [ "import plotly.graph_objects as go\n\ndf = pd.DataFrame()\ndf[\"title\"] = np.array([\"Total in Gdrive\", \"Total Tasks\"])\ndf[\"count\"] = np.array([gdrive_mongo_store.count(), tasks_mongo_store.count()])\nfig = px.pie(df, values='count', names='title', title=\"Tasks & GDrive\")\nfig.show(renderer=renderer)\n\nprint(\"WARNING: This Pie chart might not reflect the actual progress since there are tasks that belong to an deprecated material\")", "_____no_output_____" ], [ "df = pd.DataFrame()\ndf[\"type\"] = np.array([\"Total in Gdrive\",\"Total in NOMAD\" ])\ndf[\"count\"] = np.array([gdrive_mongo_store.count(criteria={\"error\": {\"$eq\": None}}), \n gdrive_mongo_store.count(criteria={\"nomad_updated\": {\"$ne\": None}})])\nfig = px.bar(df,\n x=\"type\",\n y=\"count\", title=\"Num uploaded to Gdrive and NOMAD\", color=\"type\")\nfig.show(renderer=renderer)\n\n\n\n", "_____no_output_____" ], [ "all_content_gdrive = gdrive_mongo_store.query(criteria={\"error\": None},properties={\"file_size\":1})\ngdrive_size = 0\nfor c in all_content_gdrive:\n gdrive_size += c[\"file_size\"]\nprint(f\"GDrive: {gdrive_size} bytes = {gdrive_size*1e-6} mb = {gdrive_size*1e-9} gb\")\n\n\nall_content_nomad = gdrive_mongo_store.query(criteria={\"$and\": \n [{\"error\": None}, \n {\"nomad_updated\": {\"$ne\":None}}]\n },\n properties={\"file_size\":1})\nnomad_size = 0\nfor c in all_content_nomad:\n nomad_size += c[\"file_size\"]\nprint(f\"Nomad: {nomad_size} bytes = {nomad_size*1e-6} mb = {nomad_size*1e-9} gb\")\n\n\ndf = pd.DataFrame()\ndf[\"title\"] = np.array([\"GDrive Upload GB\",\"Nomad Upload GB\" ])\ndf[\"bytes\"] = np.array([gdrive_size*1e-9, nomad_size*1e-9])\nfig = px.bar(df, y='bytes', x='title', color='title', title=\"GDrive & NOMAD by bytes\")\nfig.show(renderer=renderer)", "_____no_output_____" ], [ "df = pd.DataFrame()\ndf[\"title\"] = np.array([\"Success\",\"Failed\" ])\ndf[\"count\"] = np.array([gdrive_mongo_store.count(criteria={\"error\": {\"$eq\": None}}), \n gdrive_mongo_store.count(criteria={\"error\": {\"$ne\": None}})])\nfig = px.pie(df, values='count', names='title', title=\"GDrive Upload Status\")\nfig.show(renderer=renderer)\n\n", "_____no_output_____" ], [ "def find_dates_btw(start_dt, end_dt):\n \"\"\"\n find the number of dates between start date and end date\n \"\"\"\n def daterange(date1, date2):\n if date1 is None: date1 = date2\n if date2 is None: date2 = date1\n for n in range(int((date2 - date1).days)+1):\n yield date1 + timedelta(n)\n dates = []\n for dt in daterange(start_dt, end_dt+timedelta(days=1)):\n date_format = dt.date()\n dates.append(datetime(date_format.year, date_format.month, date_format.day))\n return dates\n\ndef find_earliest_date(store, field):\n \"\"\"\n find the earliest record date\n \"\"\"\n return list(store.query(criteria={\"error\": {\"$eq\": None}}, sort={field:maggma.core.store.Sort.Ascending}, limit=1))[0][field]\n\ndef find_latest_date(store, field):\n \"\"\"\n find the latest_record date\n \"\"\"\n return list(store.query(criteria={\"error\": {\"$eq\": None}}, sort={field:maggma.core.store.Sort.Descending}, limit=1))[0][field]", "_____no_output_____" ], [ "def make_time_series_data(field_name):\n \"\"\"\n Find all time series data for that field, put them in buckets of dates.\n \"\"\"\n dates = find_dates_btw(find_earliest_date(gdrive_mongo_store, field_name), \n find_latest_date(gdrive_mongo_store, field_name))\n # last_updated \n result = dict()\n for i in range(len(dates)):\n if i == 0:\n result[dates[i]] = 0\n else:\n c = gdrive_mongo_store.count(criteria={field_name: {\"$lte\": dates[i]}})\n result[dates[i]] = c\n return result\ndef make_time_series_data_nomad(field_name=\"nomad_updated\"):\n \"\"\"\n Find all time series data for that field, put them in buckets of dates.\n \"\"\"\n start = list(gdrive_mongo_store.query(criteria={field_name: {\"$ne\": None}}, \n sort={field_name:maggma.core.store.Sort.Ascending}, limit=1))[0][field_name]\n \n end = list(gdrive_mongo_store.query(criteria={field_name: {\"$ne\": None}}, \n sort={field_name:maggma.core.store.Sort.Descending}, limit=1))[0][field_name]\n dates = find_dates_btw(start, end)\n # last_updated \n result = dict()\n for i in range(len(dates)):\n if i == 0:\n result[dates[i]] = 0\n else:\n c = gdrive_mongo_store.count(criteria={field_name: {\"$lte\": dates[i]}})\n result[dates[i]] = c\n return result", "_____no_output_____" ], [ "last_updated_data = make_time_series_data(\"last_updated\")\nnomad_updated_data = make_time_series_data_nomad()", "_____no_output_____" ], [ "Xs = set(last_updated_data.keys()).union(set(nomad_updated_data.keys()))\nfig = go.Figure()\nfig.add_trace(go.Scatter(x=list(last_updated_data.keys()), y=list(last_updated_data.values()),\n mode='lines+markers',\n name='last_updated'))\nfig.add_trace(go.Scatter(x=list(nomad_updated_data.keys()), y=list(nomad_updated_data.values()),\n mode='lines+markers',\n name='nomad_updated'))\n\n# add features\nfig.update_layout(\n title=\"GDrive Upload Status\",\n xaxis_title=\"Time\",\n yaxis_title=\"# Submission\",\n font=dict(\n family=\"Franklin Gothic\",\n size=14,\n color=\"#0d0d0d\"\n ), \n yaxis_type=\"log\",\n)\nfig.show(renderer=renderer)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]