lp9.py

#Linear regression Model
linear_model = tf.keras.Sequential([
    normalizer,
    layers.Dense(units=1)
])

linear_model.predict(train_features1[:10])

linear_model.layers[1].kernel

linear_model.compile(
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    loss='mean_absolute_error')

history = linear_model.fit(
    train_features1,
    train_labels1,
    epochs=100,
    # Suppress logging.
    verbose=0,
    # Calculate validation results on 20% of the training data.
    validation_split = 0.2)

def plot_loss(history):
  plt.plot(history.history['loss'], label='loss')
  plt.plot(history.history['val_loss'], label='val_loss')
  plt.ylim([0, 10])
  plt.xlabel('Epoch')
  plt.ylabel('Error [MPG]')
  plt.legend()
  plt.grid(True)
  
plot_loss(history)

test_results = {}
test_results['linear_model'] = linear_model.evaluate(
    test_features1, test_labels1, verbose=0)