title: Neural network for bidding in a card game using Encog
body: <p>I'd like to use Encog to train a neural network on the data at this <a href="https://dl.dropboxusercontent.com/u/67726/BidTraining.csv" rel="nofollow">link</a>. There are 17 input features (2 numeric, 15 categorical) and 2 output features (both categorical).</p>
  
  <p>I'd like to create a basic feedforward network that solves this problem, but my efforts so far have failed to converge. The design of my network is:</p>
  
  <ul>
  <li>Input layer: 57 nodes
  
  <ul>
  <li>2 nodes for the score in columns A-B</li>
  <li>3 nodes for the prior bids in columns C-E (using -1 for None, 0 for Pass, 2 for Two, and 3 for Three)</li>
  <li>52 nodes for the 6 cards in columns G-Q (in a "six-hot" vector)</li>
  </ul></li>
  <li>Hidden layer: 104 nodes (just a guess based on 2 * 57)</li>
  <li>Output layer: 13 nodes (3 non-pass bids * 4 suits + 1 pass bid)</li>
  </ul>
  
  <p>I'm using tanh as an activation function and have enabled bias nodes. The call to create this network (in C#) is:</p>
  
  <pre><code>Encog.Util.Simple.EncogUtility.SimpleFeedForward(nInputs, nHidden, 0, nOutputs, true)
  </code></pre>
  
  <p>I'm new to neural networks, so I'm really not sure how to approach this. So far, I've just been using trial and error, but I'm hoping there's a better way. Thanks.</p>