title: Tensorflow: Understanding LSTM output with and without Dropout Wrapper
body: <pre><code>import tensorflow as tf
  import tensorflow.contrib.eager as tfe
  
  tfe.enable_eager_execution()
  
  x = tf.range(1, 11, dtype=tf.float32)
  x = tf.reshape(x, (5, 1, 2))
  
  cell = tf.contrib.rnn.LSTMCell(10)
  initial_state = cell.zero_state(5, dtype=tf.float32)
  
  y1, _ = tf.nn.dynamic_rnn(cell, x, dtype=tf.float32, initial_state=initial_state)
  
  y2, _ = tf.nn.dynamic_rnn(
      tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=1.0, output_keep_prob=0.5, state_keep_prob=1.0),
      x,
      dtype=tf.float32,
      initial_state=initial_state)
  </code></pre>
  
  <p>I am using Tensorflow 1.8.0.</p>
  
  <p>I expected the output of <code>y2</code> to be the similar as <code>y1</code> because <code>y2</code> is using the same LSTM cell as <code>y1</code> except that it is passed through a dropout layer as well. Since the dropout is only applied on the output of the LSTM cell, I thought the values of <code>y2</code> will be the same as <code>y1</code> except for a few 0s here and there. But this is what I got for <code>y1</code>:</p>
  
  <pre><code>&lt;tf.Tensor: id=5540, shape=(5, 1, 10), dtype=float32, numpy=
  array([[[-4.2897560e-02,  1.9367093e-01, -1.1827464e-01, -1.2339889e-01,
            1.3408028e-01,  1.3082971e-02, -2.4622230e-02, -1.5669680e-01,
            1.1127964e-01, -5.3087018e-02]],
         [[-7.1379542e-02,  4.5163053e-01, -1.6180833e-01, -1.3278724e-01,
            2.2819680e-01, -4.8406985e-02, -8.2188733e-03, -2.5466946e-01,
            2.8928292e-01, -7.3916554e-02]],
         [[-5.9056517e-02,  6.1984581e-01, -1.9882108e-01, -9.6297756e-02,
            2.5009862e-01, -8.0139056e-02, -2.2850712e-03, -2.7935350e-01,
            4.4566888e-01, -7.8914449e-02]],
         [[-3.8571563e-02,  6.9930458e-01, -2.2960691e-01, -6.1545946e-02,
            2.5194761e-01, -7.9383254e-02, -5.4560765e-04, -2.7542716e-01,
            5.5587584e-01, -7.3568568e-02]],
         [[-2.2481792e-02,  7.3400390e-01, -2.5636050e-01, -3.7012421e-02,
            2.4684550e-01, -6.3926049e-02, -1.1120128e-04, -2.5999820e-01,
            6.2801009e-01, -6.3132115e-02]]], dtype=float32)&gt;
  </code></pre>
  
  <p>and for <code>y2</code>:</p>
  
  <pre><code>&lt;tf.Tensor: id=5609, shape=(5, 1, 10), dtype=float32, numpy=
  array([[[-0.08579512,  0.38734186, -0.23654927, -0.24679779,
            0.        ,  0.02616594, -0.        , -0.3133936 ,
            0.        , -0.        ]],
         [[-0.14275908,  0.        , -0.32361665, -0.26557449,
            0.        , -0.        , -0.        , -0.5093389 ,
            0.        , -0.        ]],
         [[-0.11811303,  0.        , -0.39764217, -0.        ,
            0.50019723, -0.16027811, -0.00457014, -0.        ,
            0.89133775, -0.        ]],
         [[-0.        ,  0.        , -0.45921382, -0.12309189,
            0.        , -0.        , -0.        , -0.        ,
            1.1117517 , -0.14713714]],
         [[-0.        ,  0.        , -0.        , -0.07402484,
            0.        , -0.        , -0.        , -0.5199964 ,
            1.2560202 , -0.        ]]], dtype=float32)&gt;
  </code></pre>
  
  <p>The nonzero values in <code>y2</code> is completely different than the values in the corresponding position at <code>y1</code>.</p>
  
  <p>Is this a bug or do I have the wrong idea on what applying dropout on the output of an LSTM cell mean?</p>