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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import tensorflow as tf | |
| from matplotlib import pyplot as plt | |
| # Function to build the model | |
| def build_model(my_learning_rate): | |
| model = tf.keras.models.Sequential() | |
| model.add(tf.keras.layers.Dense(units=1, input_shape=(1,))) | |
| model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=my_learning_rate), | |
| loss='mean_squared_error', | |
| metrics=[tf.keras.metrics.RootMeanSquaredError()]) | |
| return model | |
| # Function to train the model | |
| def train_model(model, df, feature, label, epochs, batch_size): | |
| history = model.fit(x=df[feature], y=df[label], batch_size=batch_size, epochs=epochs) | |
| trained_weight = model.get_weights()[0][0] | |
| trained_bias = model.get_weights()[1] | |
| epochs = history.epoch | |
| hist = pd.DataFrame(history.history) | |
| rmse = hist["root_mean_squared_error"] | |
| return trained_weight, trained_bias, epochs, rmse | |
| # Function to plot the model | |
| def plot_the_model(trained_weight, trained_bias, feature, label, df): | |
| plt.figure(figsize=(10, 6)) | |
| plt.xlabel(feature) | |
| plt.ylabel(label) | |
| random_examples = df.sample(n=200) | |
| plt.scatter(random_examples[feature], random_examples[label]) | |
| x0 = 0 | |
| y0 = trained_bias | |
| x1 = random_examples[feature].max() | |
| y1 = trained_bias + (trained_weight * x1) | |
| plt.plot([x0, x1], [y0, y1], c='r') | |
| st.pyplot(plt) | |
| # Function to plot the loss curve | |
| def plot_the_loss_curve(epochs, rmse): | |
| plt.figure(figsize=(10, 6)) | |
| plt.xlabel("Epoch") | |
| plt.ylabel("Root Mean Squared Error") | |
| plt.plot(epochs, rmse, label="Loss") | |
| plt.legend() | |
| plt.ylim([rmse.min()*0.97, rmse.max()]) | |
| st.pyplot(plt) | |
| # Load the dataset | |
| def load_data(): | |
| url = "https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv" | |
| df = pd.read_csv(url) | |
| df["median_house_value"] /= 1000.0 | |
| return df | |
| training_df = load_data() | |
| # Streamlit interface | |
| st.title("Simple Linear Regression with Real Data") | |
| st.write("https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_a_real_dataset.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=linear_regression_real_tf2-colab&hl=en") | |
| if st.checkbox('Show raw data'): | |
| st.write(training_df.head()) | |
| learning_rate = st.sidebar.slider('Learning Rate', min_value=0.001, max_value=1.0, value=0.01, step=0.01) | |
| epochs = st.sidebar.slider('Epochs', min_value=1, max_value=1000, value=30, step=1) | |
| batch_size = st.sidebar.slider('Batch Size', min_value=1, max_value=len(training_df), value=30, step=1) | |
| feature = st.sidebar.selectbox('Select Feature', training_df.columns) | |
| label = 'median_house_value' | |
| my_model = None # Initialize the model variable | |
| if st.sidebar.button('Run'): | |
| my_model = build_model(learning_rate) | |
| weight, bias, epochs, rmse = train_model(my_model, training_df, feature, label, epochs, batch_size) | |
| st.subheader('Model Plot') | |
| plot_the_model(weight, bias, feature, label, training_df) | |
| st.subheader('Loss Curve') | |
| plot_the_loss_curve(epochs, rmse) | |
| # Function to make predictions | |
| def predict_house_values(n, feature, label): | |
| batch = training_df[feature][10000:10000 + n] | |
| predicted_values = my_model.predict_on_batch(x=batch) | |
| st.write("feature label predicted") | |
| st.write(" value value value") | |
| st.write(" in thousand$ in thousand$") | |
| st.write("--------------------------------------") | |
| for i in range(n): | |
| st.write("%5.0f %6.0f %15.0f" % (training_df[feature][10000 + i], | |
| training_df[label][10000 + i], | |
| predicted_values[i][0] )) | |
| n_predictions = st.sidebar.slider('Number of Predictions', min_value=1, max_value=100, value=10) | |
| if my_model is not None and st.sidebar.button('Predict'): | |
| st.subheader('Predictions') | |
| predict_house_values(n_predictions, feature, label) | |