title: TypeError: while_loop() got an unexpected keyword argument 'maximum_iterations' In Jupyter Azure
body: <p>I am setting up my recurrent neural network in Azure:</p>
  
  <pre><code>model = Sequential()
  
  
  model.add(GRU(units=512,
                  return_sequences=True,
                  input_shape=(None, x1,)))
  
    model.add(Dense(y1, activation='sigmoid'))
  </code></pre>
  
  <p>But I am getting the error:</p>
  
  <pre><code>TypeError: while_loop() got an unexpected keyword argument 'maximum_iterations'
  </code></pre>
  
  <p>I am not cretin but I believe I may be doing something that is now depreciated in current versions of TensorFlow &amp; Keras, as in <a href="https://github.com/keras-team/keras/issues/10440" rel="nofollow noreferrer">this</a> example a similar error has occurred and such errors were pointed out. I am using Python 3.6 in the Jupyter Azure platform which also means I am unsure as to what version of Keras and TensoFlow I am using. </p>
  
  <p>My full tracback error message is: </p>
  
  <pre><code>TypeError                                 Traceback (most recent call last)
  &lt;ipython-input-7-e6bcba2d0346&gt; in &lt;module&gt;()
      205 model.add(GRU(units=512, return_sequences=True,
  --&gt; 207                 input_shape=(None,x1,)))
      208 
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/engine/sequential.py in add(self, layer)
      164                     # and create the node connecting the current layer
      165                     # to the input layer we just created.
  --&gt; 166                     layer(x)
      167                     set_inputs = True
      168                 else:
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
      498 
      499         if initial_state is None and constants is None:
  --&gt; 500             return super(RNN, self).__call__(inputs, **kwargs)
      501 
      502         # If any of `initial_state` or `constants` are specified and are Keras
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
      458             # Actually call the layer,
      459             # collecting output(s), mask(s), and shape(s).
  --&gt; 460             output = self.call(inputs, **kwargs)
      461             output_mask = self.compute_mask(inputs, previous_mask)
      462 
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/layers/recurrent.py in call(self, inputs, mask, training, initial_state)
     1587                                      mask=mask,
     1588                                      training=training,
  -&gt; 1589                                      initial_state=initial_state)
     1590 
     1591     @property
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/layers/recurrent.py in call(self, inputs, mask, training, initial_state, constants)
      607                                              mask=mask,
      608                                              unroll=self.unroll,
  --&gt; 609                                              input_length=timesteps)
      610         if self.stateful:
      611             updates = []
  
  ~/anaconda3_501/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in rnn(step_function, inputs, initial_states, go_backwards, mask, constants, unroll, input_length)
     2955             parallel_iterations=32,
     2956             swap_memory=True,
  -&gt; 2957             maximum_iterations=input_length)
     2958         last_time = final_outputs[0]
     2959         output_ta = final_outputs[1]
  
  TypeError: while_loop() got an unexpected keyword argument 'maximum_iterations'
  </code></pre>
  
  <p>I have also learned from <a href="https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb" rel="nofollow noreferrer">this</a> tutorial that <code>WARNING:tensorflow:From keep_dims is deprecated, use keepdims instead</code>. If this is valid, how could I do this? </p>
  
  <p>This maybe something quite straight forward but I am quite confused, Help with this would be really appreciated.    </p>