title: How to use LSTM neural network with 3D categorical input and output?
body: <p>I'm trying to use Keras to train a LSTM recursive neural network capable of predicting the next note played by receiving a sequence of notes. Basically my dataset is something like this and I want my model to predict the fifth note played:</p>
  
  <pre><code>     Note1     Note2     Note3    Note4  |  Note5
  
  1   [A,2,#]   [B,3,b]   [C,4,#]  [A,2,b] | [B,3,b]
  
  2   [B,2,#]   [B,2,b]   [B,5,#]  [A,2,#] | [A,3,#]
  
  3   [C,2,#]   [B,5,#]   [A,4,#]  [A,1,#] | [A,8,b]
  
  ...
  </code></pre>
  
  <p>Each note is an array of 3 features and all of them are categorical. I'm struggling to understand how I can feed the neural network with this information, so any help with code about how to process the data or network architecture is welcome. Thanks in advance.</p>