<p>Above answer highlights one of the recurrent dropout methods but that one is NOT used by tensorflow and keras. <a href="https://www.tensorflow.org/addons/api_docs/python/tfa/rnn/LayerNormLSTMCell" rel="noreferrer">Tensorflow Doc</a>.</p>
<p>Keras/TF refers a recurrent method proposed by <a href="https://arxiv.org/abs/1603.05118" rel="noreferrer">Semeniuta et al</a>. Also, check below the image comparing different recurrent dropout methods. The <a href="https://arxiv.org/pdf/1512.05287.pdf" rel="noreferrer">Gal and Ghahramani</a> method which is mentioned in above answer is at second position and Semeniuta method is the right most.</p>
<p><a href="https://i.stack.imgur.com/CeUjV.png" rel="noreferrer"><img src="https://i.stack.imgur.com/CeUjV.png" alt="enter image description here" /></a></p>
Keras: the difference between LSTM dropout and LSTM recurrent dropout
<p>From the Keras documentation:</p>

<p>dropout: Float between 0 and 1. Fraction of the units to drop for the
 linear transformation of the inputs.</p>

<p>recurrent_dropout: Float between 0 and 1. Fraction of the units to
 drop for the linear transformation of the recurrent state.</p>

<p>Can anyone point to where on the image below each dropout happens?</p>

<p><a href="https://i.stack.imgur.com/DS97N.png" rel="noreferrer"><img src="https://i.stack.imgur.com/DS97N.png" alt="enter image description here"></a></p>