Update 3_SimpleRegression.py

3_SimpleRegression.py

@@ -8,16 +8,15 @@ from matplotlib import pyplot as plt
 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),
+    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]
+    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)
@@ -40,7 +39,7 @@ def plot_the_model(trained_weight, trained_bias, feature, label):
     y1 = trained_bias + (trained_weight * x1)
     plt.plot([x0, x1], [y0, y1], c='r')
 
-    plt.show()
+    st.pyplot(plt)
 
 # Function to plot the loss curve
 def plot_the_loss_curve(epochs, rmse):
@@ -51,11 +50,11 @@ def plot_the_loss_curve(epochs, rmse):
     plt.plot(epochs, rmse, label='Loss')
     plt.legend()
     plt.ylim([rmse.min()*0.97, rmse.max()])
-    plt.show()
+    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])
-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])
+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).reshape(-1, 1)
 
 # Streamlit interface
 st.title("Simple Linear Regression with Synthetic Data")
@@ -70,8 +69,6 @@ if st.sidebar.button('Run'):
 
     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)