title: Bidirectional LSTM Merge Mode explanation
body: <p>I would like to know more details about the merge mode when using Bidirectional LSTM for sequence classification, and especially for the &quot;Concat&quot; merge mode which is still quite unclear to me.</p>
  <p>From what I understood with this scheme:</p>
  <p><a href="https://i.stack.imgur.com/GmKNd.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/GmKNd.png" alt="enter image description here" /></a></p>
  <p>The output y_t is computed after passing the merged results of the forward and backward layers into the sigmoid function. It seems rather intuitive for the &quot;add&quot;,&quot;mul&quot; and &quot;average&quot; merge modes but I don't understand how the output y_t is computed when 'concat' merge mode is chosen. Indeed, with this merge mode we now have a vector instead of a single value before the sidmoid function.</p>