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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, feature, label, epochs, batch_size): | |
| history = model.fit(x=feature, y=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): | |
| plt.figure(figsize=(10, 6)) | |
| plt.xlabel('Feature') | |
| plt.ylabel('Label') | |
| # Plot the feature values vs. label values | |
| plt.scatter(feature, label, c='b') | |
| # Create a red line representing the model | |
| x0 = 0 | |
| y0 = trained_bias | |
| x1 = feature[-1][0] | |
| 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) | |
| # Define the dataset | |
| my_feature = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0], dtype=float).reshape(-1, 1) | |
| my_label = np.array([5.0, 8.8, 9.6, 14.2, 18.8, 19.5, 21.4, 26.8, 28.9, 32.0, 33.8, 38.2], dtype=float) | |
| # Streamlit interface | |
| st.title("Simple Linear Regression with Synthetic Data") | |
| st.write("https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_synthetic_data.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=linear_regression_synthetic_tf2-colab&hl=en") | |
| 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=10, step=1) | |
| batch_size = st.sidebar.slider('Batch Size', min_value=1, max_value=len(my_feature), value=12, step=1) | |
| if st.sidebar.button('Run'): | |
| my_model = build_model(learning_rate) | |
| trained_weight, trained_bias, epochs, rmse = train_model(my_model, my_feature, my_label, epochs, batch_size) | |
| st.subheader('Model Plot') | |
| plot_the_model(trained_weight, trained_bias, my_feature, my_label) | |
| st.subheader('Loss Curve') | |
| plot_the_loss_curve(epochs, rmse) | |