title: Does applying a Dropout Layer after the Embedding Layer have the same effect as applying the dropout through the LSTM dropout parameter?
body: <p>I am slightly confused on the different ways to apply dropout to my Sequential model in Keras.</p>
  
  <p>My model is the following:</p>
  
  <pre><code>model = Sequential()
  model.add(Embedding(input_dim=64,output_dim=64, input_length=498))
  model.add(LSTM(units=100,dropout=0.5, recurrent_dropout=0.5))
  model.add(Dense(units=1, activation='sigmoid'))
  model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
  </code></pre>
  
  <p>Assume that I added an extra Dropout layer after the Embedding layer in the below manner:</p>
  
  <pre><code>model = Sequential()
  model.add(Embedding(input_dim=64,output_dim=64, input_length=498))
  model.add(Dropout(0.25))
  model.add(LSTM(units=100,dropout=0.5, recurrent_dropout=0.5))
  model.add(Dense(units=1, activation='sigmoid'))
  model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
  </code></pre>
  
  <p>Will this make any difference since I then specified that the dropout should be 0.5 in the LSTM parameter specifically, or am I getting this all wrong?</p>