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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). | from transformers import OpenAIGPTTokenizer
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt") | _____no_output_____ | Apache-2.0 | 16_nlp_with_rnns_and_attention.ipynb | otamilocintra/ml2gh |
Now let's use the tokenizer to tokenize and encode the prompt text: | prompt_text = "This royal throne of kings, this sceptred isle"
encoded_prompt = tokenizer.encode(prompt_text,
add_special_tokens=False,
return_tensors="tf")
encoded_prompt | _____no_output_____ | Apache-2.0 | 16_nlp_with_rnns_and_attention.ipynb | otamilocintra/ml2gh |
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. | num_sequences = 5
length = 40
generated_sequences = model.generate(
input_ids=encoded_prompt,
do_sample=True,
max_length=length + len(encoded_prompt[0]),
temperature=1.0,
top_k=0,
top_p=0.9,
repetition_penalty=1.0,
num_return_sequences=num_sequences,
)
generated_sequences | _____no_output_____ | Apache-2.0 | 16_nlp_with_rnns_and_attention.ipynb | otamilocintra/ml2gh |
Now let's decode the generated sequences and print them: | for sequence in generated_sequences:
text = tokenizer.decode(sequence, clean_up_tokenization_spaces=True)
print(text)
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.'
'they couldn't have known that.
--------------------------------------------------------------------------------
this royal throne of kings, this sceptred isle and these people are royalty.'
then the mute prince and prince edward broke off and went to their rooms.
the talk passed again between the princes and the guards and the princess was of great
--------------------------------------------------------------------------------
this 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
--------------------------------------------------------------------------------
this royal throne of kings, this sceptred isle belongs to me. "
" the great throne of penvynne? "
" indeed, " said the king with a nod of his head. " this world was once composed of a magical
--------------------------------------------------------------------------------
this 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
--------------------------------------------------------------------------------
| Apache-2.0 | 16_nlp_with_rnns_and_attention.ipynb | otamilocintra/ml2gh |
Notebook to verify the calculations of our simulator Importing required libraries | # importaing standard libraries
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from scipy.signal import freqs,periodogram,cheby1
import numpy as np
# import quantum libraries
import qutip
from itertools import product
from numpy import array, kron
from 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
| MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Step 1: Create a simulatorWe 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 | dimension = 2
evolution_time = 1
num_time_steps = 1024
omega = 12
dynamic_operators = [0.5*pauli_operators[1]]
static_operators = [0.5*pauli_operators[3]*omega]
noise_operators = [0.5*pauli_operators[3]]
measurement_operators = pauli_operators[1:]
initial_states = [
np.array([[0.5, 0.5], [0.5, 0.5]]), np.array([[0.5, -0.5], [-0.5, 0.5]]),
np.array([[0.5, -0.5j], [0.5j, 0.5]]), np.array([[0.5, 0.5j], [-0.5j, 0.5]]),
np.array([[1, 0], [0, 0]]), np.array([[0, 0], [0, 1]])
]
num_realizations = 200
num_pulses = 5
noise_profile = ['Type 1']
distortion = True
simulator_with_distortion = create_custom_simulator(
evolution_time=evolution_time,
num_time_steps=num_time_steps,
dimension=dimension,
dynamic_operators=dynamic_operators,
static_operators=static_operators,
noise_operators=noise_operators,
measurement_operators=measurement_operators,
initial_states=initial_states,
num_realizations=num_realizations,
num_pulses=num_pulses,
noise_profile=noise_profile,
distortion=distortion,
pulse_shape="Square"
) | 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
2021-09-26 16:34:05.689143: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-09-26 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
2021-09-26 16:34:05.738543: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:0e:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.43GHz coreCount: 6 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 104.43GiB/s
2021-09-26 16:34:05.738610: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-09-26 16:34:05.741933: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-09-26 16:34:05.742095: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-09-26 16:34:05.743695: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-09-26 16:34:05.744247: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-09-26 16:34:05.746528: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-09-26 16:34:05.747524: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-09-26 16:34:05.747858: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-09-26 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
2021-09-26 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
2021-09-26 16:34:05.748866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-09-26 16:34:05.749988: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-09-26 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
2021-09-26 16:34:05.750682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:0e:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.43GHz coreCount: 6 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 104.43GiB/s
2021-09-26 16:34:05.750765: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-09-26 16:34:05.750804: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-09-26 16:34:05.750829: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-09-26 16:34:05.750852: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-09-26 16:34:05.750874: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-09-26 16:34:05.750896: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-09-26 16:34:05.750918: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-09-26 16:34:05.750941: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-09-26 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
2021-09-26 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
2021-09-26 16:34:05.751834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-09-26 16:34:05.751902: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-09-26 16:34:06.554192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-09-26 16:34:06.554242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-09-26 16:34:06.554252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-09-26 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
2021-09-26 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
2021-09-26 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
2021-09-26 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)
| MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Now we run a single experimentThe experiment will produce a result by simulating `num_realizations` number of noise realizations. | 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)
2021-09-26 16:34:09.761987: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3094175000 Hz
2021-09-26 16:34:13.227801: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-09-26 16:34:13.612214: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-09-26 16:34:13.642600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-09-26 16:34:13.917894: I tensorflow/core/util/cuda_solvers.cc:180] Creating CudaSolver handles for stream 0x5646df642640
2021-09-26 16:34:13.918105: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-09-26 16:34:14.231919: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
| MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Once run, let us read the experiment outcome | # plot the pulse
plt.figure()
num_controls = len(experiment_result["sim_parameters"]["dynamic_operators"])
for idx in range(num_controls):
plt.subplot(num_controls , 1, idx+1 )
plt.plot(experiment_result["time_range"], experiment_result["pulses"][:,0,idx], label="undistorted")
plt.plot(experiment_result["time_range"], experiment_result["distorted_pulses"][:,0,idx], label="distorted")
plt.xlabel('t')
plt.ylabel('f(t)')
plt.grid()
plt.legend()
print(experiment_result["pulse_parameters"]) | [[-20.345783 0.12233578 0.1 ]
[ 58.95591 0.27380085 0.1 ]
[ 38.14025 0.4457677 0.1 ]
[ 29.669308 0.61551726 0.1 ]
[-74.14498 0.7660476 0.1 ]]
| MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Display the distortion if exists | if distortion:
# display distortion filter if exists
distortion = cheby1(4,0.1,2*np.pi*20, analog=True)
# evaluate frequency response of the filter
w, Hw = freqs(distortion[0], distortion[1])
plt.figure(figsize=[15,4])
plt.subplot(1,2,1)
plt.semilogx(w, 20*np.log(np.abs(Hw)))
plt.xlabel(r'$\Omega$')
plt.ylabel(r'$|H(\Omega)|$')
plt.grid()
plt.subplot(1,2,2)
plt.semilogx(w, np.angle(Hw))
plt.xlabel(r'$\Omega$')
plt.ylabel(r'arg $H(\Omega)$')
plt.grid() | _____no_output_____ | MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Display the noise | # display noise if exists
for idx_profile,profile in enumerate(experiment_result["sim_parameters"]["noise_profile"]):
if profile in ['Type 2','Type 3','Type 4'] or (profile=='Type 6' and p==0):
# estimate the correlation matrix of the noise
correlation = 0
for k in range(experiment_result["sim_parameters"]["num_realizations"]):
correlation = correlation + experiment_result["noise"][:,k:k+1,idx_profile]@experiment_result["noise"][:,k:k+1,idx_profile].T
correlation = correlation/data["sim_parameters"]["num_realizations"]
# plot correlation matrix
plt.figure()
plt.matshow(correlation,0)
plt.colorbar()
p = 0
elif profile in ['Type 1','Type 5']:
# estimate the PSD of the noise
psd = 0
for k in range(experiment_result["sim_parameters"]["num_realizations"]):
f, Pxx = periodogram(experiment_result["noise"][:,k,idx_profile], experiment_result["sim_parameters"]["num_time_steps"]/experiment_result["sim_parameters"]["evolution_time"])
psd = psd + Pxx
psd = psd/experiment_result["sim_parameters"]["num_realizations"]
plt.figure()
plt.plot(f[f>0], psd[1:])
plt.xlabel('f')
plt.ylabel('psd')
plt.grid()
p = 1 | _____no_output_____ | MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Comparing the output with `qutip`Hint: They should be same !! | # load initial states, measurement operators, and control Hamilotonian
initial_states = [qutip.Qobj(state) for state in experiment_result["sim_parameters"]["initial_states"] ]
measurements = [qutip.Qobj(op) for op in experiment_result["sim_parameters"]["measurement_operators"] ]
H0 = [ [qutip.Qobj(op), np.ones((len(experiment_result["sim_parameters"]["time_range"])))]
for op in experiment_result["sim_parameters"]["static_operators"] ] + [
[qutip.Qobj(op), experiment_result["distorted_pulses"][:,0,idx]]
for idx, op in enumerate(experiment_result["sim_parameters"]["dynamic_operators"])]
expectations = np.zeros(
(1,experiment_result["sim_parameters"]["num_realizations"],
len(initial_states)*len(measurements)))
for idx_K in range(experiment_result["sim_parameters"]["num_realizations"]):
H1 = [
[qutip.Qobj(op), experiment_result["noise"][:, idx_K, idx]]
for idx, op in enumerate(experiment_result["sim_parameters"]["noise_operators"]) ]
results = [ qutip.mesolve(H0 + H1, rho, np.array(experiment_result["sim_parameters"]["time_range"]),
e_ops=measurements).expect for rho in initial_states]
expectations [0, idx_K, :] = np.concatenate(
[np.array([results[idx_rho][idx_M][-1]
for idx_M in range(len(measurements))]) for idx_rho in range(len(initial_states))])
print(idx_K+1, end="\r")
# plot the average expectation over all noise realizations for every observable
plt.figure()
plt.plot(np.average(expectations, 1)[0], label="qutip")
plt.plot(experiment_result["average_expectation"][0], label = "tf")
plt.ylabel("Average observable value")
plt.xlabel("observable Index")
plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
plt.legend()
plt.grid()
# plot all possible observables for a particular noise realization
idx_K = 10
plt.figure()
plt.plot(expectations[0, idx_K,:], label="qutip")
plt.plot(experiment_result["expectations"][idx_K,:], label = "tf")
plt.ylabel("Observable Value for realization %d"%idx_K)
plt.xlabel("Observable Index")
plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
plt.legend()
plt.grid() | _____no_output_____ | MIT | simulation/verification.ipynb | rajibchakravorty/QDataSet |
Continuación clase método de la transformada inversa | # Librería de optimización
from scipy import optimize
from scipy.stats import beta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# %matplotlib notebook
%matplotlib inline | _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
Función para crear histograma de distribuciones discretas | def Gen_distr_discreta(p_acum: 'P.Acumulada de la distribución a generar',
indices: 'valores reales a generar aleatoriamente',
N: 'cantidad de números aleatorios a generar'):
U =np.random.rand(N)
# Diccionario de valores aleatorios
rand2reales = {i: idx for i, idx in enumerate(indices)}
# Series de los valores aletorios
y = pd.Series([sum([1 for p in p_acum if p < ui]) for ui in U]).map(rand2reales)
return y
def plot_histogram_discrete(distribucion:'señal de varibles aleatorias de un distribución DISCRETA dada',
label:'label del legend a aparecer en el gráfica',
densidad:'por defecto regresa el histograma en densidad'=True):
# len(set(distribucion)) cuenta la cantidad de elementos distintos de la variable 'distribucion'
plt.figure(figsize=[10,4])
y, x = np.histogram(distribucion, bins=len(set(distribucion)), density=densidad)
plt.bar(x[1:], y, label=label)
plt.legend()
plt.show() | _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
Ejemplo binomial: La distribución binomial modela el número de éxitos de n ensayos independientes donde hay una probabilidad p de éxito en cada ensayo.Generar una variable aletoria binomial con parámetros $n=10$ y $p=0.7$. Recordar que$$X\sim binomial(n,p) \longrightarrow p_i=P(X=i)=\frac{n!}{i!(n-i)!}p^i(1-p)^{n-i},\quad i=0,1,\cdots,n$$> Tarea: Demostrar la validez de la siguiente ecuación>$$p_{i+1}=\frac{n-i}{i+1}\frac{p}{1-p} p_i \longrightarrow \text{Hablar de las ventajas que sea recursiva}$$ **El Algoritmo que debemos realizar:** 1. Generar $U$. 2. Si $U<p_0$, poner $X=0$ y detenerse. 3. Si $p_0<U<p_0+p_1$, poner $X=1$ y detenerse. $$ \vdots$$ 4. Si $p_0+\cdots+p_{n-1}<U<p_0+\cdots+p_{n}$, poner $X=n$ y detenerse. | # Función que calcula la probabilidad acumulada optimizada
def P_acum_Binomial_o(n,p):
Pr = np.zeros(n)
Pr[0] = (1-p)**n
def pr(i):
nonlocal Pr
c = p/(1-p)
Pr[i+1]=(c*(n-i)/(i+1))*Pr[i]
# Lleno el vector Pr usando compresión de listas
[pr(i) for i in range(n-1)]
return np.cumsum(Pr)
# def D_binomial_intermedia(n,p,N):
n = 10; p = 0.7; N = 10**5
p_acum = P_acum_Binomial_o(n,p)
# Usando el método de la transformada inversa
d_binomial = Gen_distr_discreta(p_acum, np.arange(0, n+1), N)
plot_histogram_discrete(d_binomial, 'función creada con tran. Inversa')
# Usando numpy
d_bino_numpy = np.random.binomial(n,p,N)
plot_histogram_discrete(d_bino_numpy, 'función creada con numpy')
| _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
Explore el funcionamiento del siguiente comando | list(set(d_binomial)) | _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
> TareaSeguir 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:>$$P(k,\lambda)=\frac{e^{-\lambda}(\lambda)^k}{k!}$$ > Demuestre matemáticamente que > $$P(k+1)=\frac{\lambda}{k+1}P(k)$$> y a partir de esta relación genere variables aletorias que distribuyen poisson usando el método de la transformada inversa.Enlace: https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_Poisson $\begin{aligned}\frac{p_{k+1}}{p_k}& = \frac{e^{-\lambda}(\lambda)^k}{k!} \\& = \frac{e^{-\lambda}(\lambda)^k}{k!}\end{aligned}$ Método de aceptación rechazoEste 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. Suponga que tenemos una función de densidad de probabilidad (PDF) de una distribución beta, la cual viene dada:$$f(x)=\frac{x^{\alpha_1-1}(1-x)^{\alpha_2-1}}{B(\alpha_1,\alpha_2)} \quad x\in[0,1] \longrightarrow B(\alpha_1,\alpha_2)\equiv \int_{0}^{1}x^{\alpha_1-1}(1-x)^{\alpha_2-1}, \ \alpha_1,\alpha_2>1$$**Hablar de las desventajas** Ahora definiremos formalmente el método:Note que $f(x)$ debe ser una **función acotada y con dominio finito** $a\leq x \leq b$ como se muestra a continuación:De acuerdo a esta función $f(x)$ el método propone los siguientes pasos. Asuma que podemos encontrar una función $t(x)$ tal que$$t(x)\geq f(x), \quad \forall x$$Note que la función $t(x)\geq 0$ no es una PDF debido a $$\int_{-\infty}^{\infty}t(x)dx\geq \int_{-\infty}^{\infty}f(x)dx =1$$Tomemos$$c=\int_{-\infty}^{\infty}t(x)\geq 1$$Definamos la función $g(x)=t(x)/c \rightarrow g(x)$ **es una densidad**. Resultando entonces $$\frac{f(x)}{g(x)}\leq c,\quad \forall x$$El siguiente algoritmo genera una variable aleatoria $X$, distribuida de acuerdo a la densidad $f(x)$ 1. Generar $R_1$ teniendo densidad $g(x)$ 2. Generar $R_2 \rightarrow U \sim U(0,1)$ independiente de $R_1$ del paso 1 . 3. Evaluar la función de probabilidad en $R_1$. 4. Determinar si la siguiente desigualdad se cumple: $$R_2\leq \frac{f(R_1)}{t(R_1)}$$ 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.> Se puede demostrar que la $P(aceptar)=1/c$ Ejemplo 1: Función beta$$f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1} (1 - x)^{\beta - 1}$$ a). Caso particular: $\alpha=\beta=3$Con estos valores la PDF es $$f(x)=30(x^2-2x^3+x^4)$$ | # Función de aceptación y rechazo usando for
def Acep_rechazo2(R2:'Variables distruidas U~U(0,1)',
R1:'Variables distribuidas como g(x)',
f:'función objetivo a generar',
t:'función que mayora a f'):
# R1 = np.random.rand(N)
f_x = f(R1)
t_x = t(R1)
condition = R2*t_x <=f_x
for i in range(len(R1)):
if condition[i]:
plt.plot(R1[i],R2[i]*t_x[i],'ob')
else:
plt.plot(R1[i],R2[i]*t_x[i],'o')
plt.show()
# Función de aceptación y rechazo usando compresión de listas
def Acep_rechazo(R2:'Variables distruidas U~U(0,1)',
R1:'Variables distribuidas como g(x)',
f:'función objetivo a generar',
t:'función que mayora a f'):
# R1 = np.random.rand(N)
f_x = f(R1)
t_x = t(R1)
condition = R2*t_x <=f_x
# [plt.plot(R1[i],R2[i]*t_x[i],'ob') if condition[i] else plt.plot(R1[i],R2[i]*t_x[i],'o') \
# for i in range(len(R1))]
# plt.show()
x = [R1[i] for i in range(len(R1)) if condition[i]]
return x
# Ilustración del método de aceptación y rechazo cuando se toma t(x) constante
N = 100
# Función objetivo
f = lambda x: 30 * (x**2 -2 * x**3 + x**4)
# Máximo de la función f
max_f = f(optimize.fmin(lambda x:-f(x), 0, disp=False))
# Función t -> Función constante
t = lambda x: max_f * np.ones([len(x)])
# Rango donde se graficará las funciones
x = np.arange(0, 1, 0.01)
print('El máximo de f es:',max_f)
# Gráficas de las funciones
plt.plot(x,f(x),label='f(x)')
plt.plot(x,t(x),label='t(x)')
plt.legend()
# Validación del método
N = 20000 # número de puntos a simular
# Como estoy tomando t(x) constante solo es necesario generar valores aleatorios U~(0,1)
R2 = np.random.rand(N)
R1 = np.random.uniform(0, 1, size=N)
x_r = Acep_rechazo(R2, R1, f, t)
y,x_n, _ = plt.hist(x_r, bins=50, density=True)
np.cumsum(y)[-1] | _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
b). Caso general: $\alpha,\beta>0$ | # Parámetros de la función beta
a =10; b=3
N = 500 # número de puntos
# Función objetivo
f = lambda x: beta.pdf(x,a,b)
x = np.arange(0,1,0.01)
plt.plot(x,f(x),'k')
# Encuentro el máximo de la función f
c = float(f(optimize.fmin(lambda x:-f(x),0,disp=False)))
print('El máximo de la función es:',c)
t = lambda x: c*np.ones(len(x))
plt.plot(x,f(x),'k')
plt.plot(x,t(x),'b')
R2 = np.random.rand(N)
R1 = np.random.rand(N)
Acep_rechazo(R2,R1,f,t)
plt.show() | El máximo de la función es: 3.5848168690361635
| MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
Tarea 6Partiendo que se desea generar variables aleatorias para la siguiente función de densidad$$f(x)=30(x^2-2x^3+x^4)$$Responda los siguientes literales:1. 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 $t(x)\geq f(x)$.2. Encontrar la función de densidad $g(x)$ según lo visto en clase. Reportar todos los cálculos realizados para encontrar dicha función usando Markdown (Latex).3. 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). 4. 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: 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: 6. Comparar el porcentaje de puntos de aceptación cuando se usa $t(x)$ constante y $t(x)$ un pulso senoidal. Concluir 7. Genere una variable aleatoria $X$ a partir de la siguiente PDF$$f(x)=20x(1-x)^3$$ usando el método de aceptación y rechazo 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:>$$P(k,\lambda)=\frac{e^{-\lambda}(\lambda)^k}{k!}$$ > Demuestre matemáticamente que > $$P(k+1)=\frac{\lambda}{k+1}P(k)$$> y a partir de esta relación genere variables aletorias que distribuyen poisson usando el método de la transformada inversa.Enlace: https://es.wikipedia.org/wiki/Distribuci%C3%B3n_de_Poisson Parámetros de entregaVoy 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. Solución Tarea | import numpy as np
import matplotlib.pyplot as plt
| _____no_output_____ | MIT | TEMA-2/Clase10_MetodoAceptacionRechazo.ipynb | AndresHdzJmz/SPF-2021-I |
xdata로 상관계수가 높은 column을 넣어서 Ridge- elasticnet으로 상관계수가 높은 feature를 넣어 모델생성 | from sklearn.metrics import mean_squared_error
# 필요 패키지 로드
#from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error
# y값인 q1-q5가 결측인 2020년 데이터 제거
a = df[0:-82]
a
# 경찰서와 연도 데이터 제거
a.drop(columns = ['jur_stn', 'year'], inplace = True)
a_ = a.drop(columns = ['q1', 'q2', 'q3', 'q4', 'q5'])
a_
a_.columns
a_1 = a_
a_2 = a_
a_3 = a_
a_4 = a_
a_5 = a_
# StandardScaling
scaler1 = StandardScaler()
scaler1.fit(a_1) # scaler에 xdata 학습
a_s1 = scaler1.transform(a_1)
a_s1
# StandardScaling
scaler2 = StandardScaler()
scaler2.fit(a_2) # scaler에 xdata 학습
a_s2 = scaler2.transform(a_2)
a_s2
# StandardScaling
scaler3 = StandardScaler()
scaler3.fit(a_3) # scaler에 xdata 학습
a_s3 = scaler3.transform(a_3)
a_s3
# StandardScaling
scaler4 = StandardScaler()
scaler4.fit(a_4) # scaler에 xdata 학습
a_s4 = scaler4.transform(a_4)
a_s4
# StandardScaling
scaler5 = StandardScaler()
scaler5.fit(a_5) # scaler에 xdata 학습
a_s5 = scaler5.transform(a_5)
a_s5
# x데이터 설정 2017, 2018 데이터를 학습용, 2019년 데이터를 검증용 데이터셋으로 설정
xtrain1 = a_s1[:-82]
xtest1 = a_s1[-82:]
xtrain2 = a_s2[:-82]
xtest2 = a_s2[-82:]
xtrain3 = a_s3[:-82]
xtest3 = a_s3[-82:]
xtrain4 = a_s4[:-82]
xtest4 = a_s4[-82:]
xtrain5 = a_s5[:-82]
xtest5 = a_s5[-82:]
# y데이터 설정 2017, 2018 데이터를 학습용, 2019년 데이터를 검증용 데이터셋으로 설정
train = a[:-82]
test = a[-82:]
ytrain1 = train['q1']
ytrain2 = train['q2']
ytrain3 = train['q3']
ytrain4 = train['q4']
ytrain5 = train['q5']
ytest1 = test['q1']
ytest2 = test['q2']
ytest3 = test['q3']
ytest4 = test['q4']
ytest5 = test['q5']
a_1.columns
# 그리드 서치 패키지
from sklearn.model_selection import GridSearchCV
# 최적 성능을 내는 lasso의 alpha값을 얻기 위해 param_grid 생성
param_grid = {'alpha' : np.linspace(0.001, 10.0, 10000)}
#그리드 서치 설정
grid_search = GridSearchCV(Ridge(), param_grid = param_grid, cv = 10, n_jobs = -1, scoring ='r2')
#grid_search = GridSearchCV(Ridge(), param_grid = param_grid, cv = 10, n_jobs = -1, scoring ='neg_mean_absolute_error') | _____no_output_____ | MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
q1 절도폭력 | # 그리드 서치 후 최고 성능의 모델을 ridge1에 저장
grid_search.fit(xtrain1, ytrain1)
ridge1 = grid_search.best_estimator_
# MAE 출력
y_pred1 = ridge1.predict(xtest1)
mean_absolute_error(ytest1, y_pred1)
# 결과
print('alpha =', ridge1.alpha)
print(ridge1.coef_) # Ridge 회귀분석으로 나온 weghit값
print('가장 강한 양의 상관관계: ',a_1.columns[ridge1.coef_.argmax()], '\n가장 강한 음의 상관관계: ', a_1.columns[ridge1.coef_.argmin()]) | alpha = 10.0
[ 0.1858718 0.35944705 -0.18654878 -0.88752003 0.12550527 -0.00879849
0.23483285 -0.25640454 0.46469126 0.50380436 0.85031836 -0.29439753
-0.17669475 0.36080588 0.12274043 -0.78509441 -0.35994638 0.30965558
0.10691769 0.51217057 -0.14159903 0.05857899 0.07225416 -0.33213259
-0.30612817 -0.28521751 -0.02244433 0.04295547 0.23212191 -0.43095751
0.99679932 0.67407008 0.04719795 0.02266245 -0.26957157 0.1635311
0.27777131 0.18326995 -0.26235237 -0.253049 0.18470932 -0.32227054
-0.11586935 0.20246951 0.00619761 -0.08666845 -0.39903773 0.31214314
0.26027898 -0.34774913 -0.043771 0.18120353 0.03499807 -0.3080387
-0.20039405 0.26735254 -0.12913279 0.02064122 -0.08359852 0.24854472]
가장 강한 양의 상관관계: 외국인인구수대비검거수
가장 강한 음의 상관관계: vio_cnt
| MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
q2 강도살인 | # 그리드 서치 후 최고 성능의 모델을 ela2에 저장
grid_search.fit(xtrain2, ytrain2)
ridge2 = grid_search.best_estimator_
# MAE 출력
y_pred2 = ridge2.predict(xtest2)
mean_absolute_error(ytest2, y_pred2)
# 결과
print('alpha =', ridge2.alpha)
print(ridge2.coef_) # Ridge 회귀분석으로 나온 weghit값
print('가장 강한 양의 상관관계: ',a_2.columns[ridge2.coef_.argmax()], '\n가장 강한 음의 상관관계: ', a_2.columns[ridge2.coef_.argmin()]) | alpha = 4.566000000000001
[ 0.0860792 0.4554509 -0.42882456 -1.39474717 -0.04082773 0.67245513
0.44065367 -0.35757788 0.36512942 1.13101206 1.47174792 -0.50713043
-0.21119051 0.64726755 0.26600496 -1.10801628 -0.61302412 0.37013122
-0.29569089 0.61392221 -0.10098044 0.05371732 0.21285102 -0.44627423
-0.21074038 -0.11088239 -0.02215864 0.05477171 0.20860851 -0.79054475
0.7222386 1.1018597 -0.29250057 0.16025184 -0.52723943 0.31317932
0.42472581 0.10930088 -0.19559189 -0.25294844 0.02556162 -0.55658869
-0.10951789 0.24595558 0.02282998 -0.02243589 -0.49783467 0.394488
0.30491444 -0.03552448 -0.15389 0.13197577 0.11347984 -0.57586571
0.02578398 0.08091628 -0.29398064 0.16571328 -0.32548941 0.66159982]
가장 강한 양의 상관관계: for_u20
가장 강한 음의 상관관계: vio_cnt
| MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
q3 교통안전 | # 그리드 서치 후 최고 성능의 모델을 lasso3에 저장
grid_search.fit(xtrain3, ytrain3)
ridge3 = grid_search.best_estimator_
ridge3 = Ridge(alpha = 23)
ridge3.fit(xtrain3, ytrain3)
# MAE 출력
y_pred3 = ridge3.predict(xtest3)
mean_absolute_error(ytest3, y_pred3)
# 결과
print('alpha =', ridge3.alpha)
print(ridge3.coef_) # Ridge 회귀분석으로 나온 weghit값
print('가장 강한 양의 상관관계: ',a_3.columns[ridge3.coef_.argmax()], '\n가장 강한 음의 상관관계: ', a_3.columns[ridge3.coef_.argmin()]) | alpha = 23
[ 0.60462781 -0.01836216 0.33480332 0.07597766 0.05094331 -0.01801049
0.12955529 -0.03098228 -0.02389251 0.32231312 0.36155864 -0.06357061
-0.17338605 0.10758736 -0.13071408 -0.23385295 -0.16651811 0.02234594
0.22738254 0.10056028 -0.08911714 -0.08948507 0.10180638 -0.05404081
0.32308556 -0.05319068 -0.00210497 0.03723783 0.19267206 0.11423388
0.02766457 0.52296002 -0.01345592 0.01568679 0.13478935 -0.21232877
-0.05575848 0.2075885 -0.04617672 -0.14361172 0.12973644 -0.55315098
-0.43058753 0.13637724 0.26248718 0.09187102 -0.28458975 0.21837938
0.20221084 0.26698087 -0.06346565 -0.14705863 0.1048014 -0.05481377
-0.00895966 0.02854603 -0.05046488 -0.08276508 0.03386703 0.16616376]
가장 강한 양의 상관관계: child
가장 강한 음의 상관관계: ofn_10
| MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
q4 법질서 준수도 | # 그리드 서치 후 최고 성능의 모델을 lasso4에 저장
grid_search.fit(xtrain4, ytrain4)
ridge4 = grid_search.best_estimator_
# MAE 출력
y_pred4 = ridge4.predict(xtest4)
mean_absolute_error(ytest4, y_pred4)
# 결과
print('alpha =', ridge4.alpha)
print(ridge4.coef_) # Ridge 회귀분석으로 나온 weghit값
print('가장 강한 양의 상관관계: ',a_4.columns[ridge4.coef_.argmax()], '\n가장 강한 음의 상관관계: ', a_4.columns[ridge4.coef_.argmin()]) | alpha = 10.0
[ 0.08422014 0.47496439 0.14834264 -0.31512474 0.90924745 0.72152184
-0.16558919 -0.19975831 -0.11015924 0.08660646 0.65342416 -0.33030785
-0.23131614 0.15442808 -0.02441021 -0.79282277 -0.16042672 0.17084599
0.28093741 0.34898235 -0.48400998 0.13322148 -0.25607675 -0.2081495
0.36480096 -0.54529326 -0.28543694 0.0490413 0.16311427 -0.4195897
0.58055968 -0.99569428 -0.04666948 0.45819622 -0.02352386 0.81593753
0.04091217 0.1235867 0.20385689 -0.3854837 0.20093322 -0.4193185
-0.48252598 0.45797532 0.03029416 0.08614095 -0.38301169 0.23971484
0.48417109 0.48881207 -0.05618829 -0.22196673 0.16491527 -0.3411742
0.12608131 -0.01440109 -0.33302747 -0.25247363 0.13361622 0.38994271]
가장 강한 양의 상관관계: mur_rob_cnt
가장 강한 음의 상관관계: 인구수대비경찰수
| MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
q5 전반적 안전도 | # 그리드 서치 후 최고 성능의 모델을 lasso4에 저장
grid_search.fit(xtrain5, ytrain5)
ridge5 = grid_search.best_estimator_
ridge5 = Ridge(alpha = 6.25)
ridge5.fit(xtrain5, ytrain5)
# MAE 출력
y_pred5 = ridge5.predict(xtest5)
mean_absolute_error(ytest5, y_pred5)
# 결과
print('alpha =', ridge5.alpha)
print(ridge5.coef_) # Ridge 회귀분석으로 나온 weghit값
print('가장 강한 양의 상관관계: ',a_5.columns[ridge5.coef_.argmax()], '\n가장 강한 음의 상관관계: ', a_5.columns[ridge5.coef_.argmin()]) | alpha = 6.25
[ 0.06421938 0.4187727 0.01456397 -0.98143189 0.27599427 0.34433021
0.25962214 -0.1884639 0.36799516 0.6268941 0.78630458 -0.33307267
-0.33674456 0.32468536 -0.07203347 -0.87313847 -0.42267696 0.30238462
0.04100536 0.52735428 -0.07934469 -0.02891441 0.096653 -0.20399747
0.23988935 -0.51472116 -0.10890164 -0.01168908 0.09871749 -0.28613236
0.67575661 0.2864437 -0.0625666 0.13997026 -0.18723721 0.13303888
0.25390993 0.1604691 -0.01088797 -0.19593194 0.06767935 -0.38309175
-0.24771206 0.3046604 0.00549987 0.06127319 -0.37182862 0.27782696
0.29352346 -0.01387247 -0.08819971 -0.00617579 0.13260857 -0.30891844
0.00401649 0.11547854 -0.32287934 -0.00472153 -0.06880729 0.2824365 ]
가장 강한 양의 상관관계: for_u20
가장 강한 음의 상관관계: vio_cnt
| MIT | 2.Model_code/Linear/ridge_grid_search.ipynb | PpangPpang93/Main_project_police |
Optional: Dropout**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.Dropout [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.[1] Geoffrey E. Hinton et al, "Improving neural networks by preventing co-adaptation of feature detectors", arXiv 2012 | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from exercise_code.classifiers.fc_net import *
from exercise_code.data_utils import get_CIFAR10_data
from exercise_code.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from exercise_code.solver import Solver
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
# supress cluttering warnings in solutions
import warnings
warnings.filterwarnings('ignore')
def rel_error(x, y):
""" returns relative error """
return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))
# Load the (preprocessed) CIFAR10 data.
data = get_CIFAR10_data()
for k, v in data.items():
print('%s: ' % k, v.shape) | _____no_output_____ | RSA-MD | exercise_2/3_Dropout-optional.ipynb | nazmicancalik/i2dl |
Dropout forward passIn 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.Once you have done so, run the cell below to test your implementation. | x = np.random.randn(500, 500) + 10
for p in [0.3, 0.6, 0.75]:
out, _ = dropout_forward(x, {'mode': 'train', 'p': p})
out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})
print('Running tests with p = ', p)
print('Mean of input: ', x.mean())
print('Mean of train-time output: ', out.mean())
print('Mean of test-time output: ', out_test.mean())
print('Fraction of train-time output set to zero: ', (out == 0).mean())
print('Fraction of test-time output set to zero: ', (out_test == 0).mean())
print() | _____no_output_____ | RSA-MD | exercise_2/3_Dropout-optional.ipynb | nazmicancalik/i2dl |
Dropout backward passIn 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. | x = np.random.randn(10, 10) + 10
dout = np.random.randn(*x.shape)
dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}
out, cache = dropout_forward(x, dropout_param)
dx = dropout_backward(dout, cache)
dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)
print('dx relative error: ', rel_error(dx, dx_num)) | _____no_output_____ | RSA-MD | exercise_2/3_Dropout-optional.ipynb | nazmicancalik/i2dl |
Fully-connected nets with DropoutIn 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. | N, D, H1, H2, C = 2, 15, 20, 30, 10
X = np.random.randn(N, D)
y = np.random.randint(C, size=(N,))
for dropout in [0, 0.25, 0.5]:
print('Running check with dropout = ', dropout)
model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,
weight_scale=5e-2, dtype=np.float64,
dropout=dropout, seed=123)
loss, grads = model.loss(X, y)
print('Initial loss: ', loss)
for name in sorted(grads):
f = lambda _: model.loss(X, y)[0]
grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)
print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))
print() | _____no_output_____ | RSA-MD | exercise_2/3_Dropout-optional.ipynb | nazmicancalik/i2dl |
Regularization experimentAs 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. | # Train two identical nets, one with dropout and one without
num_train = 500
small_data = {
'X_train': data['X_train'][:num_train],
'y_train': data['y_train'][:num_train],
'X_val': data['X_val'],
'y_val': data['y_val'],
}
solvers = {}
dropout_choices = [0, 0.75]
for dropout in dropout_choices:
model = FullyConnectedNet([500], dropout=dropout)
print("dropout = ", dropout)
solver = Solver(model, small_data,
num_epochs=25, batch_size=100,
update_rule='adam',
optim_config={
'learning_rate': 5e-4,
},
verbose=True, print_every=100)
solver.train()
solvers[dropout] = solver
# Plot train and validation accuracies of the two models
train_accs = []
val_accs = []
for dropout in dropout_choices:
solver = solvers[dropout]
train_accs.append(solver.train_acc_history[-1])
val_accs.append(solver.val_acc_history[-1])
plt.subplot(3, 1, 1)
for dropout in dropout_choices:
plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)
plt.title('Train accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(ncol=2, loc='lower right')
plt.subplot(3, 1, 2)
for dropout in dropout_choices:
plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)
plt.title('Val accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(ncol=2, loc='lower right')
plt.gcf().set_size_inches(15, 15)
plt.show() | _____no_output_____ | RSA-MD | exercise_2/3_Dropout-optional.ipynb | nazmicancalik/i2dl |
QDA | load("PCA.rda")
load("DP.rda")
suppressMessages(library(caret))
set.seed(201703)
options(warn=-1)
# QDA
pca_qda_s = train(response~., data = pca_train, method = "qda", trControl = trainControl(method = "LOOCV"))
pca_qda_te = predict(pca_qda_s, data.frame(pca_test_s))
pca_qda_ac = mean(pca_qda_te == golub_test_r)
pca_qda_re = c(LOOCV = pca_qda_s$results$Accuracy, Test = pca_qda_ac)
pca_qda_re | _____no_output_____ | MIT | ReproducingMLpipelines/Paper6/ModelQDAPCA.ipynb | CompareML/AIM-Manuscript |
!pwd | /content
| MIT | Udacity Course.ipynb | jtkrohm/jt |
|
print("JT") | JT
| MIT | Udacity Course.ipynb | jtkrohm/jt |
|
Dependencies | from openvaccine_scripts import *
import warnings, json
from sklearn.model_selection import KFold, StratifiedKFold
import tensorflow.keras.layers as L
import tensorflow.keras.backend as K
from tensorflow.keras import optimizers, losses, Model
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
SEED = 0
seed_everything(SEED)
warnings.filterwarnings('ignore') | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Model parameters | config = {
"BATCH_SIZE": 64,
"EPOCHS": 120,
"LEARNING_RATE": 1e-3,
"ES_PATIENCE": 10,
"N_FOLDS": 5,
"N_USED_FOLDS": 5,
"PB_SEQ_LEN": 107,
"PV_SEQ_LEN": 130,
}
with open('config.json', 'w') as json_file:
json.dump(json.loads(json.dumps(config)), json_file)
config | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Load data | database_base_path = '/kaggle/input/stanford-covid-vaccine/'
train = pd.read_json(database_base_path + 'train.json', lines=True)
test = pd.read_json(database_base_path + 'test.json', lines=True)
print('Train samples: %d' % len(train))
display(train.head())
print(f'Test samples: {len(test)}')
display(test.head()) | Train samples: 2400
| MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Auxiliary functions | def get_dataset(x, y=None, sample_weights=None, labeled=True, shuffled=True, batch_size=32, buffer_size=-1, seed=0):
input_map = {'inputs_seq': x['sequence'],
'inputs_struct': x['structure'],
'inputs_loop': x['predicted_loop_type'],
'inputs_bpps_max': x['bpps_max'],
'inputs_bpps_sum': x['bpps_sum'],
'inputs_bpps_mean': x['bpps_mean'],
'inputs_bpps_scaled': x['bpps_scaled']}
if labeled:
output_map = {'output_react': y['reactivity'],
'output_bg_ph': y['deg_Mg_pH10'],
'output_ph': y['deg_pH10'],
'output_mg_c': y['deg_Mg_50C'],
'output_c': y['deg_50C']}
if sample_weights is not None:
dataset = tf.data.Dataset.from_tensor_slices((input_map, output_map, sample_weights))
else:
dataset = tf.data.Dataset.from_tensor_slices((input_map, output_map))
else:
dataset = tf.data.Dataset.from_tensor_slices((input_map))
if shuffled:
dataset = dataset.shuffle(2048, seed=seed)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size)
return dataset | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Model | def model_fn(hidden_dim=384, dropout=.5, pred_len=68, n_outputs=5):
inputs_seq = L.Input(shape=(None, 1), name='inputs_seq')
inputs_struct = L.Input(shape=(None, 1), name='inputs_struct')
inputs_loop = L.Input(shape=(None, 1), name='inputs_loop')
inputs_bpps_max = L.Input(shape=(None, 1), name='inputs_bpps_max')
inputs_bpps_sum = L.Input(shape=(None, 1), name='inputs_bpps_sum')
inputs_bpps_mean = L.Input(shape=(None, 1), name='inputs_bpps_mean')
inputs_bpps_scaled = L.Input(shape=(None, 1), name='inputs_bpps_scaled')
def _one_hot(x, num_classes):
return K.squeeze(K.one_hot(K.cast(x, 'uint8'), num_classes=num_classes), axis=2)
ohe_seq = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_seq)}, input_shape=(None, 1))(inputs_seq)
ohe_struct = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_struct)}, input_shape=(None, 1))(inputs_struct)
ohe_loop = L.Lambda(_one_hot, arguments={'num_classes': len(token2int_loop)}, input_shape=(None, 1))(inputs_loop)
# Conv block
conv_seq = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(ohe_seq)
conv_struct = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(ohe_struct)
conv_loop = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(ohe_loop)
conv_bpps_max = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(inputs_bpps_max)
conv_bpps_sum = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(inputs_bpps_sum)
conv_bpps_mean = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(inputs_bpps_mean)
conv_bpps_scaled = L.Conv1D(filters=64,
kernel_size=5,
strides=1,
padding='same')(inputs_bpps_scaled)
x_concat = L.concatenate([conv_seq, conv_struct, conv_loop, conv_bpps_max,
conv_bpps_sum, conv_bpps_mean, conv_bpps_scaled], axis=-1, name='conv_concatenate')
# Recurrent block
x = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x_concat)
x_rec = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x)
x = L.Add()([x_rec, x])
x_rec = L.Bidirectional(L.GRU(hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer='orthogonal'))(x)
x = L.Add()([x_rec, x])
# Since we are only making predictions on the first part of each sequence, we have to truncate it
x_truncated = x[:, :pred_len]
output_react = L.Dense(1, activation='linear', name='output_react')(x_truncated)
output_bg_ph = L.Dense(1, activation='linear', name='output_bg_ph')(x_truncated)
output_ph = L.Dense(1, activation='linear', name='output_ph')(x_truncated)
output_mg_c = L.Dense(1, activation='linear', name='output_mg_c')(x_truncated)
output_c = L.Dense(1, activation='linear', name='output_c')(x_truncated)
model = Model(inputs=[inputs_seq, inputs_struct, inputs_loop, inputs_bpps_max,
inputs_bpps_sum, inputs_bpps_mean, inputs_bpps_scaled],
outputs=[output_react, output_bg_ph, output_ph, output_mg_c, output_c])
opt = optimizers.Adam(learning_rate=config['LEARNING_RATE'])
model.compile(optimizer=opt, loss={'output_react': MCRMSE,
'output_bg_ph': MCRMSE,
'output_ph': MCRMSE,
'output_mg_c': MCRMSE,
'output_c': MCRMSE},
loss_weights={'output_react': 2.,
'output_bg_ph': 2.,
'output_ph': 1.,
'output_mg_c': 2.,
'output_c': 1.})
return model
model = model_fn()
model.summary() | Model: "functional_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
inputs_seq (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
inputs_struct (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
inputs_loop (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
lambda (Lambda) (None, None, 4) 0 inputs_seq[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, None, 3) 0 inputs_struct[0][0]
__________________________________________________________________________________________________
lambda_2 (Lambda) (None, None, 7) 0 inputs_loop[0][0]
__________________________________________________________________________________________________
inputs_bpps_max (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
inputs_bpps_sum (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
inputs_bpps_mean (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
inputs_bpps_scaled (InputLayer) [(None, None, 1)] 0
__________________________________________________________________________________________________
conv1d (Conv1D) (None, None, 64) 1344 lambda[0][0]
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, None, 64) 1024 lambda_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, None, 64) 2304 lambda_2[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, None, 64) 384 inputs_bpps_max[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, None, 64) 384 inputs_bpps_sum[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, None, 64) 384 inputs_bpps_mean[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, None, 64) 384 inputs_bpps_scaled[0][0]
__________________________________________________________________________________________________
conv_concatenate (Concatenate) (None, None, 448) 0 conv1d[0][0]
conv1d_1[0][0]
conv1d_2[0][0]
conv1d_3[0][0]
conv1d_4[0][0]
conv1d_5[0][0]
conv1d_6[0][0]
__________________________________________________________________________________________________
bidirectional (Bidirectional) (None, None, 768) 1921536 conv_concatenate[0][0]
__________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, None, 768) 2658816 bidirectional[0][0]
__________________________________________________________________________________________________
add (Add) (None, None, 768) 0 bidirectional_1[0][0]
bidirectional[0][0]
__________________________________________________________________________________________________
bidirectional_2 (Bidirectional) (None, None, 768) 2658816 add[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, None, 768) 0 bidirectional_2[0][0]
add[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice (Tens [(None, None, 768)] 0 add_1[0][0]
__________________________________________________________________________________________________
output_react (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0]
__________________________________________________________________________________________________
output_bg_ph (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0]
__________________________________________________________________________________________________
output_ph (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0]
__________________________________________________________________________________________________
output_mg_c (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0]
__________________________________________________________________________________________________
output_c (Dense) (None, None, 1) 769 tf_op_layer_strided_slice[0][0]
==================================================================================================
Total params: 7,249,221
Trainable params: 7,249,221
Non-trainable params: 0
__________________________________________________________________________________________________
| MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Pre-process | # Add bpps as features
bpps_max = []
bpps_sum = []
bpps_mean = []
bpps_scaled = []
bpps_nb_mean = 0.077522 # mean of bpps_nb across all training data
bpps_nb_std = 0.08914 # std of bpps_nb across all training data
for row in train.itertuples():
probability = np.load(f'{database_base_path}/bpps/{row.id}.npy')
bpps_max.append(probability.max(-1).tolist())
bpps_sum.append((1-probability.sum(-1)).tolist())
bpps_mean.append((1-probability.mean(-1)).tolist())
# bpps nb
bpps_nb = (probability > 0).sum(axis=0) / probability.shape[0]
bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std
bpps_scaled.append(bpps_nb)
train = train.assign(bpps_max=bpps_max, bpps_sum=bpps_sum, bpps_mean=bpps_mean, bpps_scaled=bpps_scaled)
bpps_max = []
bpps_sum = []
bpps_mean = []
bpps_scaled = []
for row in test.itertuples():
probability = np.load(f'{database_base_path}/bpps/{row.id}.npy')
bpps_max.append(probability.max(-1).tolist())
bpps_sum.append((1-probability.sum(-1)).tolist())
bpps_mean.append((1-probability.mean(-1)).tolist())
# bpps nb
bpps_nb = (probability > 0).sum(axis=0) / probability.shape[0]
bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std
bpps_scaled.append(bpps_nb)
test = test.assign(bpps_max=bpps_max, bpps_sum=bpps_sum, bpps_mean=bpps_mean, bpps_scaled=bpps_scaled)
feature_cols = ['sequence', 'structure', 'predicted_loop_type', 'bpps_max', 'bpps_sum', 'bpps_mean', 'bpps_scaled']
pred_cols = ['reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C', 'deg_50C']
encoder_list = [token2int_seq, token2int_struct, token2int_loop, None, None, None, None]
public_test = test.query("seq_length == 107").copy()
private_test = test.query("seq_length == 130").copy()
x_test_public = get_features_dict(public_test, feature_cols, encoder_list, public_test.index)
x_test_private = get_features_dict(private_test, feature_cols, encoder_list, private_test.index)
# To use as stratified col
train['signal_to_noise_int'] = train['signal_to_noise'].astype(int) | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Training | AUTO = tf.data.experimental.AUTOTUNE
skf = KFold(n_splits=config['N_USED_FOLDS'], shuffle=True, random_state=SEED)
history_list = []
oof = train[['id', 'SN_filter', 'signal_to_noise'] + pred_cols].copy()
oof_preds = np.zeros((len(train), 68, len(pred_cols)))
test_public_preds = np.zeros((len(public_test), config['PB_SEQ_LEN'], len(pred_cols)))
test_private_preds = np.zeros((len(private_test), config['PV_SEQ_LEN'], len(pred_cols)))
for fold,(train_idx, valid_idx) in enumerate(skf.split(train['signal_to_noise_int'])):
if fold >= config['N_USED_FOLDS']:
break
print(f'\nFOLD: {fold+1}')
### Create datasets
x_train = get_features_dict(train, feature_cols, encoder_list, train_idx)
x_valid = get_features_dict(train, feature_cols, encoder_list, valid_idx)
y_train = get_targets_dict(train, pred_cols, train_idx)
y_valid = get_targets_dict(train, pred_cols, valid_idx)
w_train = np.log(train.iloc[train_idx]['signal_to_noise'].values+1.1)/2
w_valid = np.log(train.iloc[valid_idx]['signal_to_noise'].values+1.1)/2
train_ds = get_dataset(x_train, y_train, w_train, labeled=True, shuffled=True, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)
valid_ds = get_dataset(x_valid, y_valid, w_valid, labeled=True, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)
oof_ds = get_dataset(x_valid, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)
test_public_ds = get_dataset(x_test_public, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)
test_private_ds = get_dataset(x_test_private, labeled=False, shuffled=False, batch_size=config['BATCH_SIZE'], buffer_size=AUTO, seed=SEED)
### Model
K.clear_session()
model = model_fn()
model_path = f'model_{fold}.h5'
es = EarlyStopping(monitor='val_loss', mode='min', patience=config['ES_PATIENCE'], restore_best_weights=True, verbose=1)
rlrp = ReduceLROnPlateau(monitor='val_loss', mode='min', factor=0.1, patience=5, verbose=1)
### Train
history = model.fit(train_ds,
validation_data=valid_ds,
callbacks=[es, rlrp],
epochs=config['EPOCHS'],
batch_size=config['BATCH_SIZE'],
verbose=2).history
history_list.append(history)
# Save last model weights
model.save_weights(model_path)
### Inference
oof_ds_preds = np.array(model.predict(oof_ds)).reshape((len(pred_cols), len(valid_idx), 68)).transpose((1, 2, 0))
oof_preds[valid_idx] = oof_ds_preds
# Short sequence (public test)
model = model_fn(pred_len=config['PB_SEQ_LEN'])
model.load_weights(model_path)
test_public_ds_preds = np.array(model.predict(test_public_ds)).reshape((len(pred_cols), len(public_test),
config['PB_SEQ_LEN'])).transpose((1, 2, 0))
test_public_preds += test_public_ds_preds * (1 / config['N_USED_FOLDS'])
# Long sequence (private test)
model = model_fn(pred_len=config['PV_SEQ_LEN'])
model.load_weights(model_path)
test_private_ds_preds = np.array(model.predict(test_private_ds)).reshape((len(pred_cols), len(private_test),
config['PV_SEQ_LEN'])).transpose((1, 2, 0))
test_private_preds += test_private_ds_preds * (1 / config['N_USED_FOLDS']) |
FOLD: 1
Epoch 1/120
30/30 - 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
Epoch 2/120
30/30 - 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
Epoch 3/120
30/30 - 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
Epoch 4/120
30/30 - 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
Epoch 5/120
30/30 - 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
Epoch 6/120
30/30 - 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
Epoch 7/120
30/30 - 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
Epoch 8/120
30/30 - 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
Epoch 9/120
30/30 - 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
Epoch 10/120
30/30 - 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
Epoch 11/120
30/30 - 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
Epoch 12/120
30/30 - 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
Epoch 13/120
30/30 - 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
Epoch 14/120
30/30 - 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
Epoch 15/120
30/30 - 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
Epoch 16/120
30/30 - 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
Epoch 17/120
30/30 - 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
Epoch 18/120
30/30 - 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
Epoch 19/120
30/30 - 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
Epoch 20/120
30/30 - 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
Epoch 21/120
30/30 - 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
Epoch 22/120
30/30 - 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
Epoch 23/120
30/30 - 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
Epoch 24/120
30/30 - 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
Epoch 25/120
30/30 - 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
Epoch 26/120
30/30 - 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
Epoch 27/120
30/30 - 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
Epoch 28/120
30/30 - 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
Epoch 29/120
30/30 - 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
Epoch 30/120
30/30 - 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
Epoch 31/120
30/30 - 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
Epoch 32/120
30/30 - 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
Epoch 33/120
30/30 - 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
Epoch 34/120
30/30 - 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
Epoch 35/120
30/30 - 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
Epoch 36/120
30/30 - 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
Epoch 37/120
30/30 - 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
Epoch 38/120
30/30 - 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
Epoch 39/120
30/30 - 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
Epoch 40/120
30/30 - 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
Epoch 41/120
30/30 - 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
Epoch 42/120
30/30 - 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
Epoch 43/120
30/30 - 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
Epoch 44/120
30/30 - 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
Epoch 45/120
30/30 - 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
Epoch 46/120
30/30 - 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
Epoch 47/120
30/30 - 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
Epoch 48/120
30/30 - 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
Epoch 49/120
30/30 - 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
Epoch 50/120
30/30 - 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
Epoch 51/120
30/30 - 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
Epoch 52/120
30/30 - 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
Epoch 53/120
30/30 - 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
Epoch 54/120
30/30 - 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
Epoch 55/120
30/30 - 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
Epoch 56/120
30/30 - 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
Epoch 57/120
30/30 - 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
Epoch 58/120
30/30 - 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
Epoch 59/120
Epoch 00059: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
30/30 - 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
Epoch 60/120
30/30 - 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
Epoch 61/120
30/30 - 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
Epoch 62/120
30/30 - 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
Epoch 63/120
30/30 - 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
Epoch 64/120
30/30 - 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
Epoch 65/120
30/30 - 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
Epoch 66/120
30/30 - 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
Epoch 67/120
30/30 - 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
Epoch 68/120
30/30 - 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
Epoch 69/120
30/30 - 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
Epoch 70/120
30/30 - 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
Epoch 71/120
Epoch 00071: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.
30/30 - 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
Epoch 72/120
30/30 - 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
Epoch 73/120
30/30 - 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
Epoch 74/120
30/30 - 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
Epoch 75/120
30/30 - 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
Epoch 76/120
Restoring model weights from the end of the best epoch.
Epoch 00076: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.
30/30 - 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
Epoch 00076: early stopping
FOLD: 2
Epoch 1/120
30/30 - 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
Epoch 2/120
30/30 - 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
Epoch 3/120
30/30 - 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
Epoch 4/120
30/30 - 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
Epoch 5/120
30/30 - 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
Epoch 6/120
30/30 - 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
Epoch 7/120
30/30 - 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
Epoch 8/120
30/30 - 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
Epoch 9/120
30/30 - 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
Epoch 10/120
30/30 - 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
Epoch 11/120
30/30 - 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
Epoch 12/120
30/30 - 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
Epoch 13/120
30/30 - 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
Epoch 14/120
30/30 - 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
Epoch 15/120
30/30 - 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
Epoch 16/120
30/30 - 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
Epoch 17/120
30/30 - 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
Epoch 18/120
30/30 - 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
Epoch 19/120
30/30 - 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
Epoch 20/120
30/30 - 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
Epoch 21/120
30/30 - 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
Epoch 22/120
30/30 - 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
Epoch 23/120
30/30 - 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
Epoch 24/120
30/30 - 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
Epoch 25/120
30/30 - 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
Epoch 26/120
30/30 - 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
Epoch 27/120
30/30 - 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
Epoch 28/120
30/30 - 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
Epoch 29/120
30/30 - 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
Epoch 30/120
30/30 - 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
Epoch 31/120
30/30 - 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
Epoch 32/120
30/30 - 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
Epoch 33/120
30/30 - 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
Epoch 34/120
30/30 - 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
Epoch 35/120
30/30 - 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
Epoch 36/120
30/30 - 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
Epoch 37/120
30/30 - 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
Epoch 38/120
30/30 - 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
Epoch 39/120
30/30 - 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
Epoch 40/120
30/30 - 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
Epoch 41/120
30/30 - 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
Epoch 42/120
30/30 - 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
Epoch 43/120
30/30 - 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
Epoch 44/120
30/30 - 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
Epoch 45/120
30/30 - 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
Epoch 46/120
30/30 - 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
Epoch 47/120
30/30 - 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
Epoch 48/120
30/30 - 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
Epoch 49/120
30/30 - 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
Epoch 50/120
30/30 - 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
Epoch 51/120
30/30 - 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
Epoch 52/120
30/30 - 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
Epoch 53/120
30/30 - 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
Epoch 54/120
30/30 - 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
Epoch 55/120
30/30 - 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
Epoch 56/120
30/30 - 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
Epoch 57/120
30/30 - 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
Epoch 58/120
Epoch 00058: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
30/30 - 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
Epoch 59/120
30/30 - 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
Epoch 60/120
30/30 - 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
Epoch 61/120
30/30 - 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
Epoch 62/120
30/30 - 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
Epoch 63/120
30/30 - 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
Epoch 64/120
30/30 - 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
Epoch 65/120
30/30 - 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
Epoch 66/120
Epoch 00066: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.
30/30 - 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
Epoch 67/120
30/30 - 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
Epoch 68/120
30/30 - 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
Epoch 69/120
30/30 - 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
Epoch 70/120
30/30 - 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
Epoch 71/120
Restoring model weights from the end of the best epoch.
Epoch 00071: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.
30/30 - 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
Epoch 00071: early stopping
FOLD: 3
Epoch 1/120
30/30 - 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
Epoch 2/120
30/30 - 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
Epoch 3/120
30/30 - 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
Epoch 4/120
30/30 - 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
Epoch 5/120
30/30 - 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
Epoch 6/120
30/30 - 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
Epoch 7/120
30/30 - 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
Epoch 8/120
30/30 - 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
Epoch 9/120
30/30 - 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
Epoch 10/120
30/30 - 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
Epoch 11/120
30/30 - 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
Epoch 12/120
30/30 - 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
Epoch 13/120
30/30 - 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
Epoch 14/120
30/30 - 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
Epoch 15/120
30/30 - 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
Epoch 16/120
30/30 - 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
Epoch 17/120
30/30 - 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
Epoch 18/120
30/30 - 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
Epoch 19/120
30/30 - 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
Epoch 20/120
30/30 - 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
Epoch 21/120
30/30 - 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
Epoch 22/120
30/30 - 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
Epoch 23/120
30/30 - 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
Epoch 24/120
30/30 - 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
Epoch 25/120
30/30 - 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
Epoch 26/120
30/30 - 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
Epoch 27/120
30/30 - 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
Epoch 28/120
30/30 - 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
Epoch 29/120
30/30 - 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
Epoch 30/120
30/30 - 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
Epoch 31/120
30/30 - 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
Epoch 32/120
30/30 - 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
Epoch 33/120
30/30 - 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
Epoch 34/120
30/30 - 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
Epoch 35/120
30/30 - 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
Epoch 36/120
30/30 - 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
Epoch 37/120
30/30 - 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
Epoch 38/120
30/30 - 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
Epoch 39/120
30/30 - 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
Epoch 40/120
30/30 - 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
Epoch 41/120
30/30 - 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
Epoch 42/120
30/30 - 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
Epoch 43/120
30/30 - 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
Epoch 44/120
30/30 - 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
Epoch 45/120
30/30 - 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
Epoch 46/120
30/30 - 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
Epoch 47/120
30/30 - 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
Epoch 48/120
30/30 - 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
Epoch 49/120
30/30 - 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
Epoch 50/120
30/30 - 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
Epoch 51/120
30/30 - 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
Epoch 52/120
30/30 - 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
Epoch 53/120
30/30 - 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
Epoch 54/120
30/30 - 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
Epoch 55/120
30/30 - 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
Epoch 56/120
30/30 - 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
Epoch 57/120
30/30 - 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
Epoch 58/120
30/30 - 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
Epoch 59/120
30/30 - 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
Epoch 60/120
30/30 - 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
Epoch 61/120
Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
30/30 - 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
Epoch 62/120
30/30 - 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
Epoch 63/120
30/30 - 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
Epoch 64/120
30/30 - 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
Epoch 65/120
30/30 - 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
Epoch 66/120
30/30 - 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
Epoch 67/120
30/30 - 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
Epoch 68/120
30/30 - 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
Epoch 69/120
Epoch 00069: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.
30/30 - 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
Epoch 70/120
30/30 - 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
Epoch 71/120
30/30 - 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
Epoch 72/120
30/30 - 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
Epoch 73/120
30/30 - 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
Epoch 74/120
Restoring model weights from the end of the best epoch.
Epoch 00074: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.
30/30 - 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
Epoch 00074: early stopping
FOLD: 4
Epoch 1/120
30/30 - 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
Epoch 2/120
30/30 - 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
Epoch 3/120
30/30 - 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
Epoch 4/120
30/30 - 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
Epoch 5/120
30/30 - 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
Epoch 6/120
30/30 - 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
Epoch 7/120
30/30 - 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
Epoch 8/120
30/30 - 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
Epoch 9/120
30/30 - 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
Epoch 10/120
30/30 - 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
Epoch 11/120
30/30 - 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
Epoch 12/120
30/30 - 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
Epoch 13/120
30/30 - 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
Epoch 14/120
30/30 - 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
Epoch 15/120
30/30 - 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
Epoch 16/120
30/30 - 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
Epoch 17/120
30/30 - 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
Epoch 18/120
30/30 - 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
Epoch 19/120
30/30 - 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
Epoch 20/120
30/30 - 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
Epoch 21/120
30/30 - 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
Epoch 22/120
30/30 - 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
Epoch 23/120
30/30 - 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
Epoch 24/120
30/30 - 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
Epoch 25/120
30/30 - 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
Epoch 26/120
30/30 - 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
Epoch 27/120
30/30 - 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
Epoch 28/120
30/30 - 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
Epoch 29/120
30/30 - 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
Epoch 30/120
30/30 - 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
Epoch 31/120
30/30 - 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
Epoch 32/120
30/30 - 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
Epoch 33/120
30/30 - 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
Epoch 34/120
30/30 - 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
Epoch 35/120
30/30 - 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
Epoch 36/120
30/30 - 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
Epoch 37/120
30/30 - 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
Epoch 38/120
30/30 - 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
Epoch 39/120
30/30 - 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
Epoch 40/120
30/30 - 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
Epoch 41/120
30/30 - 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
Epoch 42/120
30/30 - 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
Epoch 43/120
30/30 - 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
Epoch 44/120
30/30 - 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
Epoch 45/120
30/30 - 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
Epoch 46/120
30/30 - 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
Epoch 47/120
30/30 - 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
Epoch 48/120
30/30 - 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
Epoch 49/120
30/30 - 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
Epoch 50/120
30/30 - 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
Epoch 51/120
30/30 - 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
Epoch 52/120
30/30 - 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
Epoch 53/120
30/30 - 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
Epoch 54/120
30/30 - 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
Epoch 55/120
30/30 - 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
Epoch 56/120
30/30 - 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
Epoch 57/120
30/30 - 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
Epoch 58/120
30/30 - 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
Epoch 59/120
30/30 - 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
Epoch 60/120
30/30 - 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
Epoch 61/120
30/30 - 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
Epoch 62/120
30/30 - 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
Epoch 63/120
Epoch 00063: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
30/30 - 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
Epoch 64/120
30/30 - 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
Epoch 65/120
30/30 - 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
Epoch 66/120
30/30 - 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
Epoch 67/120
30/30 - 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
Epoch 68/120
30/30 - 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
Epoch 69/120
30/30 - 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
Epoch 70/120
30/30 - 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
Epoch 71/120
30/30 - 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
Epoch 72/120
30/30 - 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
Epoch 73/120
30/30 - 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
Epoch 74/120
30/30 - 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
Epoch 75/120
30/30 - 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
Epoch 76/120
Epoch 00076: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.
30/30 - 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
Epoch 77/120
30/30 - 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
Epoch 78/120
30/30 - 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
Epoch 79/120
30/30 - 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
Epoch 80/120
30/30 - 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
Epoch 81/120
Restoring model weights from the end of the best epoch.
Epoch 00081: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.
30/30 - 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
Epoch 00081: early stopping
FOLD: 5
Epoch 1/120
30/30 - 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
Epoch 2/120
30/30 - 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
Epoch 3/120
30/30 - 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
Epoch 4/120
30/30 - 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
Epoch 5/120
30/30 - 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
Epoch 6/120
30/30 - 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
Epoch 7/120
30/30 - 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
Epoch 8/120
30/30 - 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
Epoch 9/120
30/30 - 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
Epoch 10/120
30/30 - 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
Epoch 11/120
30/30 - 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
Epoch 12/120
30/30 - 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
Epoch 13/120
30/30 - 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
Epoch 14/120
30/30 - 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
Epoch 15/120
30/30 - 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
Epoch 16/120
30/30 - 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
Epoch 17/120
30/30 - 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
Epoch 18/120
30/30 - 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
Epoch 19/120
30/30 - 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
Epoch 20/120
30/30 - 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
Epoch 21/120
30/30 - 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
Epoch 22/120
30/30 - 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
Epoch 23/120
30/30 - 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
Epoch 24/120
30/30 - 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
Epoch 25/120
30/30 - 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
Epoch 26/120
30/30 - 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
Epoch 27/120
30/30 - 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
Epoch 28/120
30/30 - 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
Epoch 29/120
30/30 - 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
Epoch 30/120
30/30 - 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
Epoch 31/120
30/30 - 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
Epoch 32/120
30/30 - 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
Epoch 33/120
30/30 - 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
Epoch 34/120
30/30 - 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
Epoch 35/120
30/30 - 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
Epoch 36/120
30/30 - 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
Epoch 37/120
30/30 - 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
Epoch 38/120
30/30 - 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
Epoch 39/120
30/30 - 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
Epoch 40/120
30/30 - 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
Epoch 41/120
30/30 - 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
Epoch 42/120
30/30 - 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
Epoch 43/120
30/30 - 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
Epoch 44/120
30/30 - 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
Epoch 45/120
30/30 - 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
Epoch 46/120
30/30 - 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
Epoch 47/120
30/30 - 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
Epoch 48/120
30/30 - 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
Epoch 49/120
30/30 - 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
Epoch 50/120
30/30 - 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
Epoch 51/120
30/30 - 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
Epoch 52/120
30/30 - 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
Epoch 53/120
30/30 - 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
Epoch 54/120
30/30 - 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
Epoch 55/120
30/30 - 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
Epoch 56/120
30/30 - 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
Epoch 57/120
30/30 - 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
Epoch 58/120
30/30 - 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
Epoch 59/120
30/30 - 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
Epoch 60/120
30/30 - 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
Epoch 61/120
30/30 - 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
Epoch 62/120
30/30 - 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
Epoch 63/120
30/30 - 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
Epoch 64/120
30/30 - 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
Epoch 65/120
30/30 - 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
Epoch 66/120
30/30 - 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
Epoch 67/120
30/30 - 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
Epoch 68/120
30/30 - 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
Epoch 69/120
30/30 - 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
Epoch 70/120
30/30 - 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
Epoch 71/120
30/30 - 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
Epoch 72/120
30/30 - 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
Epoch 73/120
30/30 - 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
Epoch 74/120
30/30 - 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
Epoch 75/120
30/30 - 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
Epoch 76/120
30/30 - 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
Epoch 77/120
30/30 - 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
Epoch 78/120
30/30 - 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
Epoch 79/120
30/30 - 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
Epoch 80/120
30/30 - 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
Epoch 81/120
30/30 - 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
Epoch 82/120
Epoch 00082: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.
30/30 - 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
Epoch 83/120
30/30 - 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
Epoch 84/120
30/30 - 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
Epoch 85/120
30/30 - 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
Epoch 86/120
30/30 - 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
Epoch 87/120
30/30 - 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
Epoch 88/120
30/30 - 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
Epoch 89/120
30/30 - 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
Epoch 90/120
30/30 - 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
Epoch 91/120
30/30 - 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
Epoch 92/120
30/30 - 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
Epoch 93/120
30/30 - 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
Epoch 94/120
30/30 - 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
Epoch 95/120
30/30 - 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
Epoch 96/120
Epoch 00096: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.
30/30 - 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
Epoch 97/120
30/30 - 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
Epoch 98/120
30/30 - 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
Epoch 99/120
30/30 - 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
Epoch 100/120
30/30 - 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
Epoch 101/120
Restoring model weights from the end of the best epoch.
Epoch 00101: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.
30/30 - 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
Epoch 00101: early stopping
| MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Model loss graph | for fold, history in enumerate(history_list):
print(f'\nFOLD: {fold+1}')
print(f"Train {np.array(history['loss']).min():.5f} Validation {np.array(history['val_loss']).min():.5f}")
plot_metrics_agg(history_list) |
FOLD: 1
Train 1.05189 Validation 1.37240
FOLD: 2
Train 1.07609 Validation 1.38107
FOLD: 3
Train 1.05777 Validation 1.33757
FOLD: 4
Train 1.02478 Validation 1.38429
FOLD: 5
Train 0.91044 Validation 1.34764
| MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Post-processing | # Assign preds to OOF set
for idx, col in enumerate(pred_cols):
val = oof_preds[:, :, idx]
oof = oof.assign(**{f'{col}_pred': list(val)})
oof.to_csv('oof.csv', index=False)
oof_preds_dict = {}
for col in pred_cols:
oof_preds_dict[col] = oof_preds[:, :, idx]
# Assign values to test set
preds_ls = []
for df, preds in [(public_test, test_public_preds), (private_test, test_private_preds)]:
for i, uid in enumerate(df.id):
single_pred = preds[i]
single_df = pd.DataFrame(single_pred, columns=pred_cols)
single_df['id_seqpos'] = [f'{uid}_{x}' for x in range(single_df.shape[0])]
preds_ls.append(single_df)
preds_df = pd.concat(preds_ls) | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Model evaluation | y_true_dict = get_targets_dict(train, pred_cols, train.index)
y_true = np.array([y_true_dict[col] for col in pred_cols]).transpose((1, 2, 0, 3)).reshape(oof_preds.shape)
display(evaluate_model(train, y_true, oof_preds, pred_cols)) | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Visualize test predictions | submission = pd.read_csv(database_base_path + 'sample_submission.csv')
submission = submission[['id_seqpos']].merge(preds_df, on=['id_seqpos']) | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Test set predictions | display(submission.head(10))
display(submission.describe())
submission.to_csv('submission.csv', index=False) | _____no_output_____ | MIT | Model backlog/Models/41-openvaccine-weighted-samples.ipynb | dimitreOliveira/COVID-19-Vaccine-Degradation-Prediction |
Inspecting trained model | seed = 600
system = 'chaotic-rnn'
if os.path.exists('./synth_data/%s_%s'%(system, seed)):
data_dict = read_data('./synth_data/%s_%s'%(system, seed))
else:
from synthetic_data import generate_chaotic_rnn_data
param_dict = yaml.load(open('./synth_data/%s_params.yaml'%system, 'r'), Loader=yaml.FullLoader)
data_dict = generate_chaotic_rnn_data(Ncells=param_dict['cells'],
Ninits=param_dict['inits'],
Ntrial=param_dict['trials'],
Nsteps=param_dict['steps'],
# Nstepsinbin=param_dict['steps_in_bin'],
dt_rnn=param_dict['dt_sys'],
dt_spike = param_dict['dt_spike'],
maxRate= param_dict['rate_scale'],
save=False,
seed=seed)
# For spike data
train_data = torch.Tensor(data_dict['train_spikes']).to(device)
valid_data = torch.Tensor(data_dict['valid_spikes']).to(device)
train_truth = {'rates' : data_dict['train_rates']}
valid_truth = {'rates' : data_dict['valid_rates']}
train_ds = torch.utils.data.TensorDataset(train_data)
valid_ds = torch.utils.data.TensorDataset(valid_data)
num_trials, num_steps, num_cells = train_data.shape;
print(train_data.shape);
print('Number of datapoints = %s'%train_data.numel())
hyperparams = load_parameters('parameters/parameters_%s_spikes.yaml'%system)
hyperparams['run_name'] = 'poisson_%s%i_f20_g1200_eg1128_u1_c1128_ec1128_191125_localtest'%(system, seed)
model = LFADS(inputs_dim = num_cells, T = num_steps, dt = float(data_dict['dt']), device=device,
model_hyperparams=hyperparams).to(device)
# model.load_checkpoint('best')
# model.epochs
model.gru_generator.fc_h_ru.weight.std()
1/(np.sqrt(400))
total_params = 0
for ix, (name, param) in enumerate(model.named_parameters()):
print(ix, name, list(param.shape), param.numel(), param.requires_grad)
total_params += param.numel()
print('Total parameters: %i'%total_params)
model.fit(train_ds, valid_ds, train_truth=train_truth, valid_truth=valid_truth,
max_epochs=2000, batch_size=128, use_tensorboard=True, health_check=True)
model.load_checkpoint('best')
model.plot_summary(valid_data, valid_truth)
results_dict = model.plot_recon_rsquared(valid_data, valid_truth, train_data, train_truth) | _____no_output_____ | MIT | deprecated/.ipynb_checkpoints/lfads_demo-checkpoint.ipynb | lyprince/hierarchical_lfads |
Analyze a large dataset with Google BigQuery**Learning Objectives**1. Access an ecommerce dataset1. Look at the dataset metadata1. Remove duplicate entries1. Write and execute queries Introduction BigQuery 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.We 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.BigQuery 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`.The 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. Set up the notebook environment__VERY IMPORTANT__: In the cell below you must replace the text `` with you GCP project id. | import os
import pandas as pd
PROJECT = "<YOUR PROJECT>" #TODO Replace with your project id
os.environ["PROJECT"] = PROJECT
pd.options.display.max_columns = 50 | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
Explore eCommerce data and identify duplicate recordsScenario: 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.First, 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.BigQuery supports [two flavors](https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sqlcomparison_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`.Our 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. Tip: To run the current cell you can click the cell and hit **shift enter** TODO 2 | %%bigquery --project $PROJECT
#standardsql
SELECT *
EXCEPT
(table_catalog, table_schema, is_generated, generation_expression, is_stored,
is_updatable, is_hidden, is_system_defined, is_partitioning_column, clustering_ordinal_position)
FROM `data-to-insights.ecommerce.INFORMATION_SCHEMA.COLUMNS`
WHERE table_name="all_sessions_raw" | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
Next examine how many rows are in the table. TODO 1 | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*)
FROM `data-to-insights.ecommerce.all_sessions_raw` | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
Now take a quick at few rows of data in the table. | %%bigquery --project $PROJECT
#standardSQL
SELECT *
FROM `data-to-insights.ecommerce.all_sessions_raw`
LIMIT 7 | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
Identify duplicate rowsSeeing 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.The 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.If 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.If 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.Run the following query to find duplicate records across all columns. TODO 3 | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*) AS num_duplicate_rows,
*
FROM `data-to-insights.ecommerce.all_sessions_raw`
GROUP BY fullvisitorid,
channelgrouping,
time,
country,
city,
totaltransactionrevenue,
transactions,
timeonsite,
pageviews,
sessionqualitydim,
date,
visitid,
type,
productrefundamount,
productquantity,
productprice,
productrevenue,
productsku,
v2productname,
v2productcategory,
productvariant,
currencycode,
itemquantity,
itemrevenue,
transactionrevenue,
transactionid,
pagetitle,
searchkeyword,
pagepathlevel1,
ecommerceaction_type,
ecommerceaction_step,
ecommerceaction_option
HAVING num_duplicate_rows > 1; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
As you can see there are quite a few "duplicate" records (615) when analyzed with these parameters.In 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. Analyze the new all_sessions tableIn this section you use a deduplicated table called all_sessions.Scenario: 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.Run the query to confirm that no duplicates exist, this time against the "all_sessions" table: | %%bigquery --project $PROJECT
#standardSQL
SELECT fullvisitorid, # the unique visitor ID
visitid, # a visitor can have multiple visits
date, # session date stored as string YYYYMMDD
time, # time of the individual site hit (can be 0 or more)
v2productname, # not unique since a product can have variants like Color
productsku, # unique for each product
type, # visit and/or event trigger
ecommerceaction_type, # maps to ‘add to cart', ‘completed checkout'
ecommerceaction_step,
ecommerceaction_option,
transactionrevenue, # revenue of the order
transactionid, # unique identifier for revenue bearing transaction
count(*) AS row_count
FROM `data-to-insights.ecommerce.all_sessions`
GROUP BY 1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
HAVING row_count > 1 # find duplicates
| _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
The query returns zero records indicating no duplicates exist. Write basic SQL against the eCommerce data (TODO 4)In this section, you query for insights on the ecommerce dataset.A good first path of analysis is to find the total unique visitorsThe query below determines the total views by counting product_views and the number of unique visitors by counting fullVisitorID. | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*) AS product_views,
count(DISTINCT fullvisitorid) AS unique_visitors
FROM `data-to-insights.ecommerce.all_sessions`; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
The next query shows total unique visitors(fullVisitorID) by the referring site (channelGrouping): | %%bigquery --project $PROJECT
#standardSQL
SELECT count(DISTINCT fullvisitorid) AS unique_visitors,
channelgrouping
FROM `data-to-insights.ecommerce.all_sessions`
GROUP BY 2
ORDER BY 2 DESC; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
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: | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*) AS product_views,
( v2productname ) AS ProductName
FROM `data-to-insights.ecommerce.all_sessions`
WHERE type = 'PAGE'
GROUP BY v2productname
ORDER BY product_views DESC
LIMIT 5; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
Now expand your previous query to include the total number of distinct products ordered and the total number of total units ordered (productQuantity): | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*) AS product_views,
count(productquantity) AS orders,
sum(productquantity) AS quantity_product_ordered,
v2productname
FROM `data-to-insights.ecommerce.all_sessions`
WHERE type = 'PAGE'
GROUP BY v2productname
ORDER BY product_views DESC
LIMIT 5; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
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)`). | %%bigquery --project $PROJECT
#standardSQL
SELECT count(*) AS product_views,
count(productquantity) AS orders,
sum(productquantity) AS quantity_product_ordered,
sum(productquantity) / Count(productquantity) AS avg_per_order,
v2productname AS productName
FROM `data-to-insights.ecommerce.all_sessions`
WHERE type = 'PAGE'
GROUP BY v2productname
ORDER BY product_views DESC
LIMIT 5; | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | Glairly/introduction_to_tensorflow |
download the dataset from https://www.ncdc.noaa.gov/cag/global/time-series Data wrangling Normalize the column name :change the `value` to `Surface Temperature in Africa` in each dataframe | Africa1 <- read_csv(file = "Africa.csv")
Africa <- Africa1 %>% rename("SurfaceTemperature" = "Value")
Africa2 <- Africa %>% rename("Surface Temperature in Africa" = "SurfaceTemperature")
North_America1 <- read_csv(file = "North America.csv")
North_America <- North_America1 %>% rename("SurfaceTemperature" = "Value")
North_America2 <- North_America %>% rename("Surface Temperature in North America" = "SurfaceTemperature")
South_America1 <- read_csv(file = "South America.csv")
South_America <- South_America1 %>% rename("SurfaceTemperature" = "Value")
South_America2 <- South_America %>% rename("Surface Temperature in South America" = "SurfaceTemperature")
Europe1 <- read_csv(file = "Europe.csv")
Europe <- Europe1 %>% rename("SurfaceTemperature" = "Value")
Europe2 <- Europe %>% rename("Surface Temperature in Europe" = "SurfaceTemperature")
Asia1 <- read_csv(file = "Asia.csv")
Asia <- Asia1 %>% rename("SurfaceTemperature" = "Value")
Asia2 <- Asia %>% rename("Surface Temperature in Asia" = "SurfaceTemperature")
Oceania1 <- read_csv(file = "Oceania.csv")
Oceania <- Oceania1 %>% rename("SurfaceTemperature" = "Value")
Oceania2 <- Oceania %>% rename("Surface Temperature in Oceania" = "SurfaceTemperature") | _____no_output_____ | Apache-2.0 | Climate.ipynb | Deyang-Li/tidy-beauty |
Join together! | climate_df <- Africa2 %>%
full_join(North_America2) %>%
full_join(South_America2) %>%
full_join(Europe2) %>%
full_join(Asia2) %>%
full_join(Oceania2) | Joining, by = "Year"
Joining, by = "Year"
Joining, by = "Year"
Joining, by = "Year"
Joining, by = "Year"
| Apache-2.0 | Climate.ipynb | Deyang-Li/tidy-beauty |
check about the types of the columns, the missing values, and output a quick summary of the dataset. | glimpse(climate_df)
summary(climate_df)
climate_df %>%
skim() %>%
kable()
write_csv(climate_df,"Climate.csv") | _____no_output_____ | Apache-2.0 | Climate.ipynb | Deyang-Li/tidy-beauty |
Data analysis choose the data from 1950 to 2018 for ploting | Africa$pos = Africa$SurfaceTemperature >= 0
Africa_climate_plot <- Africa %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in Africa") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
Africa_climate_plot
ggsave(Africa_climate_plot,filename = "Africa climate plot.jpg",width = 12,height = 9)
North_America$pos = North_America$SurfaceTemperature >= 0
North_America_climate_plot <- North_America %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in North America") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
North_America_climate_plot
ggsave(North_America_climate_plot,filename = "North America climate plot.jpg",width = 12,height = 9)
South_America$pos = South_America$SurfaceTemperature >= 0
South_America_climate_plot <- South_America %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in South America") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
South_America_climate_plot
ggsave(South_America_climate_plot,filename = "South America climate plot.jpg",width = 12,height = 9)
Europe$pos = Europe$SurfaceTemperature >= 0
Europe_climate_plot <- Europe %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in Europe") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
Europe_climate_plot
ggsave(Europe_climate_plot,filename = "Europe climate plot.jpg",width = 12,height = 9)
Asia$pos = Asia$SurfaceTemperature >= 0
Asia_climate_plot <- Asia %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in Asia") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
Asia_climate_plot
ggsave(Asia_climate_plot,filename = "Asia climate plot.jpg",width = 12,height = 9)
Oceania$pos = Oceania$SurfaceTemperature >= 0
Oceania_climate_plot <- Oceania %>%
filter( Year >= 1950) %>%
ggplot(aes(
x = Year,
y = SurfaceTemperature,
fill = pos)) +
labs(title = "Time Series of Surface Temperature Anomalies in Oceania") +
scale_x_continuous(breaks=seq(1950, 2020, 10)) +
scale_y_continuous(breaks=seq(-1, 1.8, 0.2)) +
geom_bar(stat = "identity",position = "identity", colour = "black", size = 0.05) +
xlab("Year") + ylab ("Surface Temperature ( ºC )") +
theme_light()+
theme(plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)
Oceania_climate_plot
ggsave(Oceania_climate_plot,filename = "Oceania climate plot.jpg",width = 12,height = 9) | _____no_output_____ | Apache-2.0 | Climate.ipynb | Deyang-Li/tidy-beauty |
Put all plots together | library(ggpubr)
general_plot <- ggarrange(Africa_climate_plot, Asia_climate_plot,
Europe_climate_plot, South_America_climate_plot,
North_America_climate_plot, Oceania_climate_plot, ncol = 2, nrow = 3)
general_plot
ggsave(general_plot,filename = "Climate general plot.jpg",width = 12,height = 9) | _____no_output_____ | Apache-2.0 | Climate.ipynb | Deyang-Li/tidy-beauty |
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. | mxldepth.coords['i'] = coords['i']
mxldepth.coords['j'] = coords['j'] | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
Calculate climatological mean mixed layer depth. We will be using this later to mask grid points outside of the mixed layer. | mxldepth_clim=mxldepth.mean(dim='time').load()
#mxldepth_clim=mxldepth.mean(dim='time').persist() | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
Make a mask of points outside the ocean mixed layer: | mxlpoints = np.abs(coords['Z']) <= mxldepth_clim
# Flag for low-pass filtering
lowpass=True
# Filter requirements
order = 5
fs = 1 # sample rate, (cycles per month)
Tn = 12*3.
cutoff = 1/Tn # desired cutoff frequency of the filter (cycles per month)
# Face numbers to analyze
# 0: Southern Ocean (Atlantic)
# 1: South Atlantic Ocean / Africa
# 2: East North Atlantic / Europe
# 3: Southern Ocean (Indian)
# 4: Indian Ocean
# 5: Asia
# 6: Arctic
# 7: North Pacific (central)
# 8: West South Pacific
# 9: Southern Ocean (West Pacific)
# 10: North America / West North Atlantic
# 11: East South Pacific / South America
# 12: Southern Ocean(East Pacific)
#facen = [5,7]
#Note: longitude bounds can either be 0 < bounds < 360, or -180 < bounds < 180.
#The only requirement is that the left longitude bound is less than the right bound
#(along date line must use 0 < bounds < 360).
#(along prime meridian must use -180 < bounds < 180)
# Complete global
#facen=[0,1,2,3,4,5,6,7,8,9,10,11,12]
#bnds = [0,359.9,-90,90]
#facen=[]
#bnds = [0,359.9,-90,90]
# Global (excluding polar regions)
#facen=[1,2,4,5,7,8,10,11]
#bnds = [0,359.9,-58,70]
#Southern Ocean (Atlantic)
#facen=[0]
#bnds = [-20,20,-58,-90]
#1: South Atlantic Ocean / Africa
#facen=[1]
#bnds = [-38,30,-58,10]
#2: East North Atlantic
#facen=[2]
#bnds = [-38,30,10,70]
#3: Southern Ocean (Indian)
#facen=[3]
#bnds = [60,143,-58,-90]
#4: Indian Ocean
#facen=[4]
#bnds = [60,143,-58,10]
#7: North Pacific (central)
#facen=[7]
#bnds = [145,230,10,70]
#8: West South Pacific
#facen=[8]
#bnds = [145,230,-58,10]
#11: East South Pacific
#facen=[11]
#bnds = [-128,-38,-58,10]
#2, 10: North Atlantic
facen=[2,10]
bnds = [-80,0,10,70]
#5,7,10: North Pacific
#facen=[5,7,10]
#bnds = [100,270,10,70]
#4,5,7,8,10,11: Pacific
#facen=[4,5,7,8,10,11]
#bnds = [100,300,-70,70]
#5,7,8,10,11: Tropical Pacific
#facen=[5,7,8,10,11]
#bnds = [145,290,-15,15]
#5,7: KOE
#facen=[5,7]
#bnds = [120,180,15,60]
rho0 = 1029 #sea-water density (kg/m^3)
c_p = 3994 #sea-water heat capacity (J/kg/K)
coords=coords.isel(face=facen)
# Vertical grid spacing
drF = coords.drF
hFacC = coords.hFacC
#rA = coords.rA.isel(face=facen).load()
#vol = drF*hFacC*rA.load()
c_o = rho0*c_p*drF*hFacC
T = ds_snp.T.isel(face=facen)
adv_ConvH = ds.adv_ConvH.isel(face=facen)
dif_ConvH = ds.dif_ConvH.isel(face=facen)
forcH = ds.forcH.isel(face=facen)
dt = coords.time_snp[1:].load()
dt = dt.rename({'time_snp': 'time'})
# delta t in seconds. Note: divide by 10**9 to convert nanoseconds to seconds
dt.values = [float(t)/10**9 for t in np.diff(coords.time_snp)]
# time axis of dt should be the same as of the monthly averages
dt.time.values = coords.time[1:-1].values
lons = coords.XC
lats = coords.YC
T_anom, T_clim = st.anom(T)
C_adv_anom, C_adv_clim = st.anom(adv_ConvH)
C_dif_anom, C_dif_clim = st.anom(dif_ConvH)
C_forc_anom, C_forc_clim = st.anom(forcH)
totalH_anom = C_adv_anom + C_dif_anom + C_forc_anom
T_anom = T_anom.chunk({'time':ntchunk-1})
C_adv_anom = C_adv_anom.chunk({'time':ntchunk})
C_dif_anom = C_dif_anom.chunk({'time':ntchunk})
C_forc_anom = C_forc_anom.chunk({'time':ntchunk})
if lowpass:
T_anom = T_anom.chunk({'time':288, 'j':10, 'i':10})
C_adv_anom = C_adv_anom.chunk({'time':288, 'j':10, 'i':10})
C_dif_anom = C_dif_anom.chunk({'time':288, 'j':10, 'i':10})
C_forc_anom = C_forc_anom.chunk({'time':288, 'j':10, 'i':10})
T_anom = stats.butter_lowpass_filter_xr(T_anom, cutoff, fs, order)
C_adv_anom = stats.butter_lowpass_filter_xr(C_adv_anom, cutoff, fs, order)
C_dif_anom = stats.butter_lowpass_filter_xr(C_dif_anom, cutoff, fs, order)
C_forc_anom = stats.butter_lowpass_filter_xr(C_forc_anom, cutoff, fs, order)
totalH_anom = C_adv_anom + C_dif_anom + C_forc_anom
%time T_anom.load()
%time C_adv_anom.load()
%time C_dif_anom.load()
%time C_forc_anom.load()
tendH_perMonth = (T_anom.shift(time=-1)-T_anom)[:-1]
# Make sure time axis is the same as for the monthly variables
tendH_perMonth.time.values = coords.time[1:-1].values
# Convert tendency from 1/month to 1/s
tendH_perSec = tendH_perMonth/dt
tendH_perSec = tendH_perSec.transpose('face','time', 'k', 'j', 'i')
# Define tendH array with correct dimensions
tendH_anom = xr.DataArray(np.nan*np.zeros([len(facen),np.shape(tendH_perSec)[1]+2,50,90,90]),
coords={'face': facen, 'time': range(np.shape(tendH_perSec)[1]+2),'k': np.array(range(0,50)),
'j': np.array(range(0,90)),'i': np.array(range(0,90))},dims=['face', 'time','k', 'j','i'])
tendH_anom.time.values = coords.time.values
tendH_anom
tendH_anom.nbytes/1e9
# Add coordinates#
tendH_anom['XC'] = lons
tendH_anom['YC'] = lats
tendH_anom['Z'] = coords.Z
# Total tendency (degC/s)
tendH_anom.values[:,1:-1,:] = tendH_perSec.values
%time tendH_anom.load()
#%time tendH.persist()
# Convert from degC/s to W/m^2
tendH_anom = c_o*tendH_anom
tendH_anom = tendH_anom.transpose('time','face', 'k', 'j', 'i')
face=0
k = 0
j = 15
i = 15
plt.figure(figsize=(14,10))
plt.subplot(2, 1, 1)
plt.plot(tendH_anom.time, tendH_anom.isel(face=face,k=k,j=j,i=i), lw=4, color='K', marker='.',label='total tendency')
plt.plot(C_forc_anom.time, C_forc_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C0', marker='.',label='forcing')
plt.plot(C_adv_anom.time, C_adv_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C1', marker='.',label='advection')
plt.axhline(0,color='k',lw=1)
plt.plot(C_dif_anom.time, C_dif_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='C2',label='diffusion')
plt.setp(plt.gca(), 'xticklabels',[])
plt.legend(loc='best',frameon=False,fontsize=14)
plt.subplot(2, 1, 2)
plt.plot(totalH_anom.time, totalH_anom.isel(face=face,k=k,j=j,i=i), lw=4, color='red', marker='.',label='RHS')
plt.plot(tendH_anom.time, tendH_anom.isel(face=face,k=k,j=j,i=i), lw=2, color='blue', marker='.',label='LHS')
plt.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')
plt.legend(loc='best',frameon=False,fontsize=14)
plt.savefig(fout + 'sstbudget_anom_ts.png')
T_var = T_anom.var(dim='time')
%time T_var.load()
#%time T_var.persist()
tendH_anom = tendH_anom/c_o
#tendH_anom = tendH_anom.transpose('time','face', 'k', 'j', 'i')
cov_adv = st.cov(tendH_anom, C_adv_anom)
cov_dif = st.cov(tendH_anom, C_dif_anom)
cov_forc = st.cov(tendH_anom, C_forc_anom)
cov_adv.nbytes/1e9
%time cov_adv.load()
%time cov_dif.load()
%time cov_forc.load()
deltat = dt.mean()
deltat.compute()
r_1 = st.cor(T_anom, T_anom,lagx=1).compute()
r_1
fac = (deltat**2/(2*c_o*(1-r_1)))
fac.load()
T_var_sum = fac*(cov_adv + cov_dif + cov_forc)
%time T_var_sum.load()
#%time T_var_sum.persist()
mapper = LLCMapper(coords)
k=0
mapper(T_var.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0)
mapper(T_var_sum.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0) | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
The temperature variance budget is clearly balanced! Let's take a look at the contribution due to each term. | T_var_adv = fac*cov_adv
T_var_dif = fac*cov_dif
T_var_forc = fac*cov_forc
vmin=-1.0
vmax=1.0
sstmax=1.6
if lowpass:
sstmax=0.5
vmin=-0.5
vmax=0.5 | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
Contributions to temperature variance from advection, diffusion and surface forcing | k=0
mapper(T_var_sum.isel(k=k), bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=sstmax)
plt.title(r'temperature variance (K$^2$)')
plt.savefig(fout + 'Tvar_sum.png')
mapper(T_var_adv.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'advective contribution (K$^2$)')
plt.savefig(fout + 'Tvar_adv.png')
mapper(T_var_dif.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'diffusive contribution (K$^2$)')
plt.savefig(fout + 'Tvar_dif.png')
mapper(T_var_forc.isel(k=k), bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'surface forcing contribution (K$^2$)')
plt.savefig(fout + 'Tvar_forc.png') | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
Contributions to ocean mixed layer temperature variance from advection, diffusion and surface forcing | mxlpoints = mxlpoints.isel(face=facen)
delz = drF*hFacC
delz=delz.where(mxlpoints)
delz_sum = delz.sum(dim='k')
mxlpoints
weights = delz/delz_sum
T_var_mxl = (weights*T_var).where(mxlpoints).sum(dim='k')
T_var_adv_mxl = (weights*T_var_adv).where(mxlpoints).sum(dim='k')
T_var_dif_mxl = (weights*T_var_dif).where(mxlpoints).sum(dim='k')
T_var_forc_mxl = (weights*T_var_forc).where(mxlpoints).sum(dim='k')
T_var_sum_mxl = T_var_adv_mxl + T_var_dif_mxl + T_var_forc_mxl
#f, axes = plt.subplots(2,2,figsize=(16,12))
#f.tight_layout()
mapper(T_var_sum_mxl, bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=sstmax)
plt.title(r'temperature variance (K$^2$)')
plt.savefig(fout + 'Tmxlvar_sum.png')
mapper(T_var_adv_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'advective contribution (K$^2$)')
plt.savefig(fout + 'Tmxlvar_adv.png')
mapper(T_var_dif_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'diffusive contribution (K$^2$)')
plt.savefig(fout + 'Tmxlvar_dif.png')
mapper(T_var_forc_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'surface forcing contribution (K$^2$)')
plt.savefig(fout + 'Tmxlvar_forc.png')
#mapper(T_var_sum_mxl, bnds=bnds, cmap='cubehelix_r', vmin=0,vmax=1.0)
#plt.title(r'temperature variance (K$^2$)')
#plt.savefig(fout + 'Tmxlvar_sum.png')
mapper(T_var_adv_mxl + T_var_dif_mxl, bnds=bnds, cmap='RdBu_r', vmin=vmin,vmax=vmax)
plt.title(r'ocean dynamics (advective + diffusive) contribution (K$^2$)')
plt.savefig(fout + 'Tmxlvar_ocndyn.png')
#mapper(T_var_forc_mxl, bnds=bnds, cmap='RdBu_r', vmin=-1.0,vmax=1.0)
#plt.title(r'surface forcing contribution (K$^2$)')
#plt.savefig(fout + 'Tmxlvar_forc.png') | _____no_output_____ | BSD-3-Clause | ecco_LPsstvarbudget_load.ipynb | cpatrizio88/pangeo_binder_example |
speakers = os.listdir('./speaker_spectrograms/')speaker_pred = dict()for speaker in speakers: spects = np.load('./speaker_spectrograms/' + speaker) spects = spects.reshape(spects.shape+(1,)) pred = model.predict(spects) pred = np.argmax(pred, axis=-1) pred_labels = classes[pred] speaker_pred[speaker.split('.')[0]] = pred_labelswith open('./per_speaker_pred.pkl', 'wb') as handle: pickle.dump(speaker_pred, handle, protocol=pickle.HIGHEST_PROTOCOL) | speaker_pred = pickle.load(open('./per_speaker_pred.pkl', 'rb'))
speaker_gt = pickle.load(open('./per_speaker_gt.pkl', 'rb'))
per_speaker = dict()
for speaker in os.listdir('./speaker_spectrograms/'):
speaker = speaker.split('.')[0]
pred = np.array(speaker_pred[speaker])
gt = np.array(speaker_gt[speaker])
per_label = dict()
for label in np.unique(gt):
label_idx = np.where(gt == label)
acc = np.sum(np.core.defchararray.equal(pred[label_idx], gt[label_idx])) / len(label_idx[0])
per_label[label] = acc * 100
per_speaker[speaker] = per_label
list(per_speaker.values())[0]
per_speaker_acc = dict()
for speaker in os.listdir('./speaker_spectrograms/'):
speaker = speaker.split('.')[0]
pred = speaker_pred[speaker]
gt = speaker_gt[speaker]
acc = np.sum(np.core.defchararray.equal(pred, gt)) / len(pred)
per_speaker_acc[speaker] = acc * 100
sorted_per_speaker_acc = sorted(per_speaker_acc.items(), key=lambda x: x[1], reverse=True)
class_names = []
class_accs = []
per_class_accuracy_list = np.full((len(classes), len(per_speaker)), np.nan)
for index, item in enumerate(sorted_per_class_acc):
class_names.append(item[0])
class_accs.append(item[1])
for i, speaker in enumerate(list(per_speaker.values())):
if item[0] in speaker.keys():
per_class_accuracy_list[index, i] = speaker[item[0]]
boxprops = dict(linestyle='-', linewidth=1.0, color='k')
medianprops = dict(linestyle='-', linewidth=1.0, color='k')
whiskerprops = dict(linestyle='-', linewidth=1.0, color='k')
capprops = dict(linestyle='-', linewidth=1.0, color='k')
plt.boxplot(per_class_accuracy_list.T,
patch_artist = True,
boxprops=boxprops,
capprops=capprops,
medianprops=medianprops,
whiskerprops=whiskerprops,
whis="range")
class_names
fig, ax = plt.subplots(1, 1, sharex=True, figsize=(5, 3))
boxprops = dict(linestyle='-', linewidth=1.0, color='k')
medianprops = dict(linestyle='-', linewidth=1.0, color='k')
whiskerprops = dict(linestyle='-', linewidth=1.0, color='k')
capprops = dict(linestyle='-', linewidth=1.0, color='k')
bplot = ax.boxplot(
[100 * ,
100 * none_accs,
100 * all_accs],
patch_artist = True,
boxprops=boxprops,
capprops=capprops,
medianprops=medianprops,
whiskerprops=whiskerprops,
whis="range"); | _____no_output_____ | Apache-2.0 | Speaker_predictions.ipynb | aakaashjois/Dense-Recurrent-Net-For-Speech-Command-Classification |
Tweepy streamer Find Top tweeting user: - Find User who is tweeting a lot. - Find top 50 across the world. Since this is streaming application, we will use python logging module to log. [Further read.](https://www.webcodegeeks.com/python/python-logging-example/) | import logging # python logging module
# basic format for logging
logFormat = "%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s"
# logs will be stored in tweepy.log
logging.basicConfig(filename='tweepytopuser.log', level=logging.INFO,
format=logFormat, datefmt="%Y-%m-%d %H:%M:%S")
| _____no_output_____ | Apache-2.0 | Dalon_4_RTD_MiniPro_Tweepy_Q5.ipynb | intellect82/venkateswarlu_SVAP_Asmt_R3 |
Authentication and AuthorisationCreate an app in twitter [here](https://apps.twitter.com/). Copy the necessary keys and access tokens, which will be used here in our code. The 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/). We 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. | import tweepy # importing all the modules required
import socket # will be used to create sockets
import json # manipulate json
from httplib import IncompleteRead
# Keep these tokens secret, as anyone can have full access to your
# twitter account, using these tokens
consumerKey = "#"
consumerSecret = "#"
accessToken = "#-#"
accessTokenSecret = "#"
| _____no_output_____ | Apache-2.0 | Dalon_4_RTD_MiniPro_Tweepy_Q5.ipynb | intellect82/venkateswarlu_SVAP_Asmt_R3 |
Post this step, we will have full access to twitter api's | # Performing the authentication and authorization, post this step
# we will have full access to twitter api's
def connectToTwitter():
"""Connect to twitter."""
try:
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
api = tweepy.API(auth)
logging.info("Successfully logged in to twitter.")
return api, auth
except Exception as e:
logging.info("Something went wrong in oauth, please check your tokens.")
logging.error(e)
| _____no_output_____ | Apache-2.0 | Dalon_4_RTD_MiniPro_Tweepy_Q5.ipynb | intellect82/venkateswarlu_SVAP_Asmt_R3 |
Streaming with tweepyThe 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.In 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. But the on_data method is only a stub, so we need to implement the functionality by subclassing StreamListener. Using the streaming api has three steps.1. Create a class inheriting from StreamListener2. Using that class create a Stream object3. Connect to the Twitter API using the Stream. | # Tweet listner class which subclasses from tweepy.StreamListener
class TweetListner(tweepy.StreamListener):
"""Twitter stream listner"""
def __init__(self, csocket):
self.clientSocket = csocket
def dataProcessing(self, data):
"""Process the data, before sending to spark streaming
"""
sendData = {} # data that is sent to spark streamer
user = data.get("user", {})
name = user.get("name", "undefined").encode('utf-8')
sendData["name"] = name
#data_string = "{}:{}".format(name, followersCount)
self.clientSocket.send(json.dumps(sendData) + u"\n") # append new line character, so that spark recognizes it
logging.debug(json.dumps(sendData))
def on_data(self, raw_data):
""" Called when raw data is received from connection.
return False to stop stream and close connection.
"""
try:
data = json.loads(raw_data)
self.dataProcessing(data)
#self.clientSocket.send(json.dumps(sendData) + u"\n") # Because the connection was breaking
return True
except Exception as e:
logging.error("An unhandled exception has occured, check your data processing")
logging.error(e)
raise e
def on_error(self, status_code):
"""Called when a non-200 status code is returned"""
logging.error("A non-200 status code is returned")
return True
# Creating a proxy socket
def createProxySocket(host, port):
""" Returns a socket which can be used to connect
to spark.
"""
try:
s = socket.socket() # initialize socket instance
s.bind((host, port)) # bind to the given host and port
s.listen(5) # Enable a server to accept connections.
logging.info("Listening on the port {}".format(port))
cSocket, address = s.accept() # waiting for a connection
logging.info("Received Request from: {}".format(address))
return cSocket
except socket.error as e:
if e.errno == socket.errno.EADDRINUSE: # Address in use
logging.error("The given host:port {}:{} is already in use"\
.format(host, port))
logging.info("Trying on port: {}".format(port + 1))
return createProxySocket(host, port + 1)
| _____no_output_____ | Apache-2.0 | Dalon_4_RTD_MiniPro_Tweepy_Q5.ipynb | intellect82/venkateswarlu_SVAP_Asmt_R3 |
Drawbacks of twitter streaming APIThe 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/)So we will use a hack i.e. get the top trending topics and use that to filter data. | if __name__ == "__main__":
try:
api, auth = connectToTwitter() # connecting to twitter
# Global information is available by using 1 as the WOEID
# woeid = getWOEIDForTrendsAvailable(api, "Worldwide") # get the woeid of the worldwide
host = "localhost"
port = 8600
cSocket = createProxySocket(host, port) # Creating a socket
while True:
try:
# Connect/reconnect the stream
tweetStream = tweepy.Stream(auth, TweetListner(cSocket)) # Stream the twitter data
# DON'T run this approach async or you'll just create a ton of streams!
tweetStream.filter(track="iphone") # Filter on trending topics
except IncompleteRead:
# Oh well, reconnect and keep trucking
continue
except KeyboardInterrupt:
# Or however you want to exit this loop
tweetStream.disconnect()
break
except Exception as e:
logging.error("Unhandled exception has occured")
logging.error(e)
continue
except KeyboardInterrupt: # Keyboard interrupt called
logging.error("KeyboardInterrupt was hit")
except Exception as e:
logging.error("Unhandled exception has occured")
logging.error(e)
| _____no_output_____ | Apache-2.0 | Dalon_4_RTD_MiniPro_Tweepy_Q5.ipynb | intellect82/venkateswarlu_SVAP_Asmt_R3 |
Analizando información de IMDB con KerasYa 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. | import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 1. Cargar la información | # IMDB ya es un dataset que es parte de Keras, así que lo tenemos fácil!
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)
print(x_train.shape)
print(x_test.shape) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 2. Comprender la informaciónEsta 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 | print(x_train[0])
print(y_train[0]) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 3. Modificar la información para la red neuronal One-hot encodingTenemos 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. | # One-hot encoding the output into vector mode, each of length 1000
tokenizer = Tokenizer(num_words=1000)
x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')
x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')
print(x_train[0])
# One-hot encoding the output
num_classes = 2
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print(y_train.shape)
print(y_test.shape) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 4. Construimos Arquitectura del ModeloConstruye un modelo secuencial. Siéntete libre de explorar y experimentar. | ## TODO: Construye un modelo secuencial
## TODO: Compila el modelo con un optimizador y una función de pérdida | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 5. Entrenamos el modelo | ## TODO: Corre el modelo. Experimenta con diferentes tamaños de batch y número de epochs.
# Usa verbose=2 para ver cómo va progresando el modelo | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Paso 6. Evaluamos el modelo¿Crees poder llegar a más de 80%? ¿Qué tal arriba de 85%? | score = model.evaluate(x_test, y_test, verbose=0)
print("Accuracy: ", score[1]) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
SOLUCIONES No las veas antes de intentar tú primero Ya intentaste tú primero? Intenta primero | ## TODO: Construye un modelo secuencial
model = Sequential()
model.add(Dense(512, activation='relu', input_dim=1000))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
## TODO: Compila el modelo con un optimizador y una función de pérdida
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
## TODO: Corre el modelo. Experimenta con diferentes tamaños de batch y número de epochs.
# Usa verbose=2 para ver cómo va progresando el modelo
model.fit(x_train, y_train,
batch_size=32,
epochs=10,
validation_data=(x_test, y_test),
verbose=2) | _____no_output_____ | MIT | 2.IMDB.ipynb | Krax7/master-data-ai |
Learn with us: www.zerotodeeplearning.comCopyright © 2021: Zero to Deep Learning ® Catalit LLC. | # Licensed 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless 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_____ | Apache-2.0 | notebooks/Pre-trained_Models.ipynb | zuhairah87/ztdl-masterclasses |
Pre-trained Models | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# sports_images_path = tf.keras.utils.get_file(
# 'sports_images',
# 'https://archive.org/download/ztdl_sports_images/sports_images.tgz',
# untar=True)
![[ ! -f sports_images.tar.gz ]] && gsutil cp gs://ztdl-datasets/sports_images.tar.gz .
![[ ! -d sports_images ]] && echo "Extracting images..." && tar zxf sports_images.tar.gz
sports_images_path = './sports_images'
train_path = os.path.join(sports_images_path, 'train')
test_path = os.path.join(sports_images_path, 'test')
batch_size = 16
img_size = 224
train_datagen = ImageDataGenerator() \
.flow_from_directory(train_path,
target_size = (img_size, img_size),
batch_size = batch_size,
class_mode = 'sparse')
try:
assert(train_datagen.samples == 11414)
except:
raise Exception("Found less images than expected. Please remove the files and download again.")
classes_dict = train_datagen.class_indices
classes = list(classes_dict.keys())
classes
batch, labels = train_datagen.next()
batch.shape
labels.shape
plt.figure(figsize=(10, 10))
for i in range(len(batch)):
plt.subplot(4, 4, i+1)
plt.imshow(batch[i].astype('int'))
plt.title(classes[int(labels[i])])
plt.axis('off')
plt.tight_layout() | _____no_output_____ | Apache-2.0 | notebooks/Pre-trained_Models.ipynb | zuhairah87/ztdl-masterclasses |
Pre-trained modelLet's use a Resnet50 model to classify the images without any training. | from PIL import Image
from io import BytesIO
from IPython.display import HTML
import base64
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input as preprocess_input_resnet50
from tensorflow.keras.applications.resnet50 import decode_predictions as decode_predictions_resnet50
model = ResNet50(weights='imagenet')
batch_preprocessed = preprocess_input_resnet50(batch.copy())
predictions = model.predict(batch_preprocessed)
decoded_top_3 = decode_predictions_resnet50(predictions, top=3)
def image_formatter(a):
im = Image.fromarray(a)
im.thumbnail((28, 28), Image.LANCZOS)
with BytesIO() as buffer:
im.save(buffer, 'jpeg')
im_base64 = base64.b64encode(buffer.getvalue()).decode()
return f'<img src="data:image/jpeg;base64,{im_base64}">'
def display_batch(batch, decoded_top_3):
res = []
for i, top3 in enumerate(decoded_top_3):
im = image_formatter(batch[i].astype('uint8'))
cl = classes[int(labels[i])]
line = [im, cl]
for item in top3:
line = line + list(item[1:])
res.append(line)
res_df = pd.DataFrame(res,
columns=['image', 'ground_truth',
'top_1', 'prob_1',
'top_2', 'prob_2',
'top_3', 'prob_3'])
return res_df.style.bar(color='lightgreen', vmin=0, vmax=1)
display_batch(batch, decoded_top_3) | _____no_output_____ | Apache-2.0 | notebooks/Pre-trained_Models.ipynb | zuhairah87/ztdl-masterclasses |
Stochastic examplesThis example is designed to show how to use the stochatic optimizationalgorithms for descrete and semicontinous measures from the POT library. | # Author: Kilian Fatras <[email protected]>
#
# License: MIT License
import matplotlib.pylab as pl
import numpy as np
import ot
import ot.plot | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM | print("------------SEMI-DUAL PROBLEM------------") | ------------SEMI-DUAL PROBLEM------------
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
DISCRETE CASESample two discrete measures for the discrete case---------------------------------------------Define 2 discrete measures a and b, the points where are defined the sourceand the target measures and finally the cost matrix c. | n_source = 7
n_target = 4
reg = 1
numItermax = 1000
a = ot.utils.unif(n_source)
b = ot.utils.unif(n_target)
rng = np.random.RandomState(0)
X_source = rng.randn(n_source, 2)
Y_target = rng.randn(n_target, 2)
M = ot.dist(X_source, Y_target) | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Call the "SAG" method to find the transportation matrix in the discrete case---------------------------------------------Define the method "SAG", call ot.solve_semi_dual_entropic and plot theresults. | method = "SAG"
sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
numItermax)
print(sag_pi) | [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]
[1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]
[3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]
[2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]
[9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]
[2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]
[4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
SEMICONTINOUS CASESample one general measure a, one discrete measures b for the semicontinouscase---------------------------------------------Define one general measure a, one discrete measures b, the points whereare defined the source and the target measures and finally the cost matrix c. | n_source = 7
n_target = 4
reg = 1
numItermax = 1000
log = True
a = ot.utils.unif(n_source)
b = ot.utils.unif(n_target)
rng = np.random.RandomState(0)
X_source = rng.randn(n_source, 2)
Y_target = rng.randn(n_target, 2)
M = ot.dist(X_source, Y_target) | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Call the "ASGD" method to find the transportation matrix in the semicontinouscase---------------------------------------------Define the method "ASGD", call ot.solve_semi_dual_entropic and plot theresults. | method = "ASGD"
asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,
numItermax, log=log)
print(log_asgd['alpha'], log_asgd['beta'])
print(asgd_pi) | [3.75309361 7.63288278 3.76418767 2.53747778 1.70389504 3.53981297
2.67663944] [-2.49164966 -2.25281897 -0.77666675 5.52113539]
[[2.19699465e-02 1.03185982e-01 1.76983379e-02 2.87611188e-06]
[1.20688044e-01 1.49823131e-02 1.50635578e-03 5.68043045e-03]
[3.01194583e-03 7.75764779e-02 6.22686313e-02 8.78225379e-08]
[2.28707628e-02 3.52120795e-02 8.44977549e-02 2.76545693e-04]
[1.19721129e-02 1.10087991e-03 1.53333937e-02 1.14450756e-01]
[2.65247890e-02 1.33140544e-03 2.66861405e-03 1.12332334e-01]
[3.71512413e-02 2.86513804e-02 7.53932500e-02 1.66127118e-03]]
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Compare the results with the Sinkhorn algorithm---------------------------------------------Call the Sinkhorn algorithm from POT | sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
print(sinkhorn_pi) | [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]
[1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]
[3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]
[2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]
[9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]
[2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]
[4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
PLOT TRANSPORTATION MATRIX Plot SAG results---------------- | pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')
pl.show() | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Plot ASGD results----------------- | pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')
pl.show() | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Plot Sinkhorn results--------------------- | pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
pl.show() | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM | print("------------DUAL PROBLEM------------") | ------------DUAL PROBLEM------------
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
SEMICONTINOUS CASESample one general measure a, one discrete measures b for the semicontinouscase---------------------------------------------Define one general measure a, one discrete measures b, the points whereare defined the source and the target measures and finally the cost matrix c. | n_source = 7
n_target = 4
reg = 1
numItermax = 100000
lr = 0.1
batch_size = 3
log = True
a = ot.utils.unif(n_source)
b = ot.utils.unif(n_target)
rng = np.random.RandomState(0)
X_source = rng.randn(n_source, 2)
Y_target = rng.randn(n_target, 2)
M = ot.dist(X_source, Y_target) | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Call the "SGD" dual method to find the transportation matrix in thesemicontinous case---------------------------------------------Call ot.solve_dual_entropic and plot the results. | sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,
batch_size, numItermax,
lr, log=log)
print(log_sgd['alpha'], log_sgd['beta'])
print(sgd_dual_pi) | [ 1.67648902 5.3770004 1.70385554 0.4276547 -0.77206786 1.0474898
0.54202203] [-0.23723788 -0.20259434 1.30855788 8.06179985]
[[2.62451875e-02 1.00499531e-01 1.78515577e-02 4.57450829e-06]
[1.20510690e-01 1.21972758e-02 1.27002374e-03 7.55197481e-03]
[3.65708350e-03 7.67963231e-02 6.38381061e-02 1.41974930e-07]
[2.64286344e-02 3.31748063e-02 8.24445965e-02 4.25479786e-04]
[9.59295422e-03 7.19190875e-04 1.03739180e-02 1.22100712e-01]
[2.09087627e-02 8.55676046e-04 1.77617241e-03 1.17896019e-01]
[4.18792948e-02 2.63326297e-02 7.17598381e-02 2.49335733e-03]]
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Compare the results with the Sinkhorn algorithm---------------------------------------------Call the Sinkhorn algorithm from POT | sinkhorn_pi = ot.sinkhorn(a, b, M, reg)
print(sinkhorn_pi) | [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]
[1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]
[3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]
[2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]
[9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]
[2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]
[4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]
| MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Plot SGD results----------------- | pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')
pl.show() | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
Plot Sinkhorn results--------------------- | pl.figure(4, figsize=(5, 5))
ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')
pl.show() | _____no_output_____ | MIT | notebooks/plot_stochastic.ipynb | vfdev-5/POT |
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