title: Keras: implementation issue for CNN + LSTM with TimeDistributed layers for various input size
body: <p>I have implemented a CNN+LSTM auto-encoder by using Keras. The code is similar as:</p>
  
  <pre><code>......
  x = self._conv_bn_relu(filters=64, kernel_size=(3,3))(x)
  x = MaxPooling2D((2, 2), padding='same')(x)  # 16x16
  # branches (LSTM-vertical)
  x = TimeDistributed(Bidirectional(LSTM(32, input_shape=(4, 64), return_sequences=True)))(x)
  x = self._conv_bn_relu(filters=64, kernel_size=(3,3))(x)
  x = UpSampling2D((2, 2))(x) # 32x32
  ......
  </code></pre>
  
  <p>For this setup, the network can only accepts input with fixed size, like (64x64), but I really want my network can accept various input size. Is there any method to do this?</p>
  
  <p>Thanks,
  X. P</p>