lp10.py

#DNN Model
def build_and_compile_model(norm):
  model = keras.Sequential([
      norm,
      layers.Dense(64, activation='relu'),
      layers.Dense(64, activation='relu'),
      layers.Dense(1)
  ])

  model.compile(loss='mean_absolute_error',
                optimizer=tf.keras.optimizers.Adam(0.001))
  return model

dnn_model = build_and_compile_model(normalizer)
dnn_model.summary()


history = dnn_model.fit(
    train_features1,
    train_labels1,
    validation_split=0.2,
    verbose=0, epochs=100)

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['dnn_model'] = dnn_model.evaluate(test_features1, test_labels1, verbose=0)