mnist_autoencoder_simple3.py

# flatten the 28x28 images into vectors of size 784.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
x_train_noisy = x_train_noisy.reshape((len(x_train_noisy), np.prod(x_train_noisy.shape[1:])))
x_test_noisy = x_test_noisy.reshape((len(x_test_noisy), np.prod(x_test_noisy.shape[1:])))

#training
history = autoencoder.fit(x_train_noisy, x_train,
                          epochs=100,
                          batch_size=128,
                          shuffle=True,
                          validation_data=(x_test_noisy, x_test))
                          
# plot training performance
def plot_training_loss(history):

  loss = history.history['loss']
  val_loss = history.history['val_loss']

  epochs = range(1, len(loss) + 1)

  plt.plot(epochs, loss, 'bo', label='Training loss')
  plt.plot(epochs, val_loss, 'r', label='Validation loss')
  plt.title('Training and validation loss')
  plt.xlabel('Epochs')
  plt.ylabel('Loss')
  plt.legend()

  plt.show()

plot_training_loss(history)