<p><code>Recurrent Dropout</code> is a regularization method for recurrent neural networks. <code>Dropout</code> is applied to the updates to LSTM memory cells, i.e. it drops out the input/update gate in LSTM. For more information you can refer <a href="https://arxiv.org/abs/1603.05118v2" rel="nofollow noreferrer">here</a>.</p>
What's the mean of “ recurrent dropout” of LSTM?
<p>Recently，I try to use the “<em>tf.contrib.rnn.LayerNormBasicLSTMCell</em>” ， but I don't know what's the mean of the argument “dropout_keep_prob”.</p>
<p>Then I look at the Document given by Google. Their explanation is “unit Tensor or float between 0 and 1 representing the <em><strong>recurrent dropout</strong></em> probability value. If float and 1.0, no dropout will be applied.”</p>
<p>But I don't know the difference between “<strong>recurrent dropout</strong>” and“<strong>dropout</strong>”.</p>