title: Python Neural Network with (apparently) 3 input layers and a matrix output
body: <p>I am trying to make a NN (topic I don't know anything about, and reading didn't make it clear) that does the following:</p>
  
  <p>takes an m x n matrix as first input, then a second and a third matrix and finally outs the resulting matrix. E.g:</p>
  
  <pre><code>[0,0,0,1,0,0]    [0,0,0,2,0,0]    [0,0,0,0,0,0]        [0,0,0,0,1,0]   
  [0,0,0,1,0,0] =&gt; [0,0,1,0,1,0] =&gt; [0,0,0,1,0,0] yields [2,0,1,0,0,0] 
  [1,0,1,0,1,0]    [1,0,0,0,0,0]    [0,1,0,0,1,0]        [0,1,0,1,0,0]
  </code></pre>
  
  <p>Note that I they are 1 matrix and not 3 vectors (I don't know how to do a giant squere bracket).</p>
  
  <p>Other than having no clue how to do that (if it can be done), other concerns I have are:</p>
  
  <ul>
  <li>As seen in the first matrix the first and second row are the same, but the second matrix do not yields the same results. This can be due that the overall first matrix has an impact on that or not (I am working with data that is supposed to be "random", but that's exactly what I want to figure: how random it is).</li>
  <li>Most examples I've seen out just a single number, not a matrix. The numbers are between 0 and 1 (if I understand correctly because the sigmoid function is used), but I need them to be ints >= 0.</li>
  </ul>
  
  <p>I hope I made it clearly enough.</p>
  
  <p>Thank you very much!</p>
  
  <p>P.S: I am sure this won't impact the general answer, but for the sake of completeness I will add that the matrix is 100 x 6.</p>