Create 3_SyntheticRegression.py

pages/3_SyntheticRegression.py

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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(lr=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]
+    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]
+    y1 = trained_bias + (trained_weight * x1)
+    plt.plot([x0, x1], [y0, y1], c='r')
+
+    plt.show()
+
+# 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()])
+    plt.show()
+
+# 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])
+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])
+
+# Streamlit interface
+st.title("Simple Linear Regression with Synthetic Data")
+
+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.pyplot(plt)
+
+    st.subheader('Loss Curve')
+    plot_the_loss_curve(epochs, rmse)
+    st.pyplot(plt)