title: Tensorflow: DropoutWrapper leads to different output?
body: <p>I build a LSTM like:</p>
  
  <pre><code>lstm_cell = tf.nn.rnn_cell.LSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True, activation=tf.nn.tanh)
  lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=0.5)
  lstm_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 3, state_is_tuple=True)
  </code></pre>
  
  <p>Then i train the model, and save variables.
  The next time i load saved variables and skip training, it gives me a different prediction.</p>
  
  <p>If i change the <code>output_keep_prob</code> to 1, this model can always show me the same prediction, but if the <code>output_keep_prob</code> is less than 1, like 0.5, this model shows me different prediction every time.</p>
  
  <p>So i guess if the <code>DropoutWrapper</code> leads to different output?
  If so, how can i solve this problem?</p>
  
  <p>Thanks</p>