title: visualize the output of convolutional layer in autoencoder
body: <p>I have a simple autoencoder and I want to see the output of each layer especially the latent space. I know that after each layer some features like edges achieved but I want to show the output of each layer. I want to know what is the output of each layer and precisely I want to visualize the output of each layer in my autoencoder. what should I do for this and how can I do this?
  because I do not have a true imagination of my network and I want to know what happens in each layer of my network. I really need it. please help me with this issue. I am waiting to hear from you.</p>
  
  <pre><code>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
  input_img = Input(shape=(28, 28, 1))  # 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')
  
  #train part
  
  
  (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), 28, 28, 1))  # adapt this if using `channels_first` image data format
  x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format
  from keras.callbacks import TensorBoard
  
  autoencoder.fit(x_train, x_train,
                  epochs=50,
                  batch_size=128,
                  shuffle=True,
                  validation_data=(x_test, x_test),
                  callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
  </code></pre>