Consider the code below num_epochs = 500 batch = 100 # Const…
Consider the code below num_epochs = 500 batch = 100 # Construct hidden layers inputs = tf.keras.layers.Dense(units=16, activation=’relu’, input_shape=]) hidden = tf.keras.layers.Dense(units=16, activation=’relu’) outputs = tf.keras.layers.Dense(units=1) # Stack the layers model = tf.keras.Sequential() # Loss function and optimizer(with learning rate) loss = ‘mse’ optimizer = tf.keras.optimizers.Adam(0.001) # Compile the model model.compile(loss=loss, optimizer=optimizer, metrics=) # Train the model history = model.fit(x_train_normalized, y_train_normalized, epochs=num_epochs, batch_size=batch, validation_split=0.1, verbose=0)After running the code above, how do you pick the new number of epochs to train your model one last time in the whole dataset?