title: How to share weights using tf.layers.conv2d
body: <p>I have built an autoencoder using tf.layers.conv2d layers and would like to train it in phases. That is to train the outer layers first then the middle layers and then the inner. I understand this is possible using tf.nn.conv2d because the weights are declared using tf.get_variable but I would think this should also be possible using tf.layers.conv2d.</p>
  
  <p>If I enter a new variable scope different from the original graph to change the inputs to the convolutional layers (i.e. skip the inner layers during phase 1) I am not able to reuse the weights. If I do not enter a new variable scope I am not able to freeze the weights that I dont want to train in this phase. </p>
  
  <p>Basically I am trying to use the training method from Aurélien Géron here <a href="https://github.com/ageron/handson-ml/blob/master/15_autoencoders.ipynb" rel="nofollow noreferrer">https://github.com/ageron/handson-ml/blob/master/15_autoencoders.ipynb</a></p>
  
  <p>Except I would like to use a cnn instead of dense layers. How to do it?</p>