title: AttributeError: 'Node' object has no attribute 'output_masks'
body: <p>I use Keras pretrained model VGG16. The problem is that after configuring tensorflow to use the GPU I get an error that I didn't have before when using the CPU.</p>
  
  <p>The error is the following one:</p>
  
  <pre><code>    Traceback (most recent call last):
    File "/home/guillaume/Documents/Allianz/ConstatOrNotConstatv3/train_network.py",      line 109, in &lt;module&gt;
      model = LeNet.build(width=100, height=100, depth=3, classes=5)
    File "/home/guillaume/Documents/Allianz/ConstatOrNotConstatv3/lenet.py", line 39,    in build
      output = model(pretrainedOutput)
    File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 443, in __call__
      previous_mask = _collect_previous_mask(inputs)
    File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 1311, in _collect_previous_mask
  mask = node.output_masks[tensor_index]
    AttributeError: 'Node' object has no attribute 'output_masks'
  </code></pre>
  
  <p>I get it after executing this code : </p>
  
  <pre><code>    pretrained_model = VGG16(
          include_top=False,
          input_shape=(height, width, depth),
          weights='imagenet'
      )
      for layer in pretrained_model.layers:
          layer.trainable = False
  
      model = Sequential()
      # first (and only) set of FC =&gt; RELU layers
      model.add(Flatten())
      model.add(Dense(200, activation='relu'))
      model.add(Dropout(0.5))
      model.add(BatchNormalization())
      model.add(Dense(400, activation='relu'))
      model.add(Dropout(0.5))
      model.add(BatchNormalization())
  
      # softmax classifier
      model.add(Dense(classes,activation='softmax'))
  
      pretrainedInput = pretrained_model.input
      pretrainedOutput = pretrained_model.output
      output = model(pretrainedOutput)
      model = Model(pretrainedInput, output)
  </code></pre>
  
  <p>EDIT1 : I've got keras (2.2.2) and tensorflow(1.10.0rc1). I've also tried on keras 2.2.0 and same error. The thing is that the python environment I use works on others non-pretrained NN.</p>
  
  <p>EDIT2 : I'm able to connect two homemade models. It's only whith the pretrained ones there is a problem and not only VGG16.</p>