text: from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
  from keras.models import Model
  from keras import backend as K
  from keras.datasets import mnist
  import numpy as np
  import matplotlib.pyplot as plt
  from keras.callbacks import TensorBoard
  
  input_img = Input(shape=(1, 28, 28))  # adapt this if using `channels_first` image data format
  
  x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
  x = MaxPooling2D((2, 2), padding='same')(x)
  x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
  x = MaxPooling2D((2, 2), padding='same')(x)
  x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
  encoded = MaxPooling2D((2, 2), padding='same')(x)
  
  # at this point the representation is (4, 4, 8) i.e. 128-dimensional
  
  x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
  x = UpSampling2D((2, 2))(x)
  x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
  x = UpSampling2D((2, 2))(x)
  x = Conv2D(16, (3, 3), activation='relu')(x)
  x = UpSampling2D((2, 2))(x)
  decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
  
  autoencoder = Model(input_img, decoded)
  autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
  
  (x_train, _), (x_test, _) = mnist.load_data()
  
  x_train = x_train.astype('float32') / 255.
  x_test = x_test.astype('float32') / 255.
  x_train = np.reshape(x_train, (len(x_train), 1, 28, 28))  # adapt this if using `channels_first` image data format
  x_test = np.reshape(x_test, (len(x_test), 1, 28, 28))  # adapt this if using `channels_first` image data format
  
  autoencoder.fit(x_train, x_train,
                  epochs=1,
                  batch_size=128,
                  shuffle=True,
                  validation_data=(x_test, x_test),
                  callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
  
  decoded_imgs = autoencoder.predict(x_test)
  
  # encode and decode some digits
  # note that we take them from the *test* set
  decoded_imgs = autoencoder.predict(x_test)
  
  n = 10
  plt.figure(figsize=(20, 4))
  for i in range(1,n):
      # display original
      ax = plt.subplot(2, n, i)
      plt.imshow(x_test[i].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)
  
      # display reconstruction
      ax = plt.subplot(2, n, i + 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()
  