mnist_autoencoder_simple4.py

# plot de-noised images
def plot_mnist_predict(x_test, x_test_noisy, autoencoder, y_test, labels=[]):
  
  if len(labels)>0:
    x_test = x_test[np.isin(y_test, labels)]
    x_test_noisy = x_test_noisy[np.isin(y_test, labels)]

  decoded_imgs = autoencoder.predict(x_test)
  n = 10  
  plt.figure(figsize=(20, 4))
  for i in range(n):
      ax = plt.subplot(2, n, i + 1)
      plt.imshow(x_test_noisy[i].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)

      ax = plt.subplot(2, n, i + 1 + n)
      plt.imshow(decoded_imgs[i].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)
  plt.show()
  return decoded_imgs, x_test
 
decoded_imgs_test, x_test_new = plot_mnist_predict(x_test, x_test_noisy, autoencoder, y_test)