title: Keras LSTM go_backwards usage
body: <p>I have a question regarding the usage of the go_backwards argument in the Keras LSTM model layer.  The documentation for this layer can be found here:  <a href="https://keras.io/layers/recurrent/#lstm" rel="nofollow noreferrer">https://keras.io/layers/recurrent/#lstm</a>.  </p>
  
  <p>Question1:  If I set the "go_backwards" flag to True, do I still feed the input data "forwards" during the training process.  For example, if an input sentence in English normally reads "I fell", and it's German translation reads "Ich fiel", would I feed it forwards ("I fell", "Ich fiel"), or backwards ("fell I", "fiel Ich") during the training process.</p>
  
  <p>Question 2: Same question for making model predictions, is the data fed forward ("I fell"), or reverse ("fell I")?</p>
  
  <p>Question 3: If I was trying to use the model below and wanted to reverse the corpus data, would I set the go_backwards flag to true in both LSTM layers or just one?</p>
  
  <pre><code>model = Sequential()
  model.add(Embedding(src_vocab, embedding_dim, input_length=source_steps, mask_zero=True))
  model.add(LSTM(embedding_dim,go_backwards=True))
  model.add(RepeatVector(target_steps))
  model.add(LSTM(embedding_dim, return_sequences=True))
  model.add(TimeDistributed(Dense(tar_vocab, activation='softmax')))
  # compile model
  model.compile(optimizer='adam', loss='categorical_crossentropy')
  # summarize defined model
  model.summary()
  </code></pre>
  
  <p>I think I should feed all data forward but I can't find any documentation that convinces me that this is correct.</p>