title: Understanding scikit neural network parameters
body: <p>I've been trying to train a neural network to recognise the three types of tags I have images of (circle, rectangle and blank). I used the example set up for recognising the digits dataset provided <a href="https://github.com/aigamedev/scikit-neuralnetwork/blob/master/examples/plot_digits.py" rel="nofollow noreferrer">here</a> and found that I got a 75% correct prediction rate with barely any tweaking (provided my images had a certain level of preprocessing with filters etc).</p>
  
  <p>What I'm interested in understanding more about is the classifier section (code below). I'm not sure what the different convolution and layer options do and what options I have for tweaking them. Does anyone have any advice for other convolution or layers that I could use to try improve my prediction accuracy and what they mean? Apologies for being vague, this is the first time I've touched a NN and am struggling to get my head around it.</p>
  
  <pre><code>nn = Classifier(
      layers=[
          Convolution('Rectifier', channels=12, kernel_shape=(3, 3), border_mode='full'),
          Convolution('Rectifier', channels=8, kernel_shape=(3, 3), border_mode='valid'),
          Layer('Rectifier', units=64),
          Layer('Softmax')],
      learning_rate=0.002,
      valid_size=0.2,
      n_stable=10,
      verbose=True)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/V8YZn.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/V8YZn.png" alt="enter image description here"></a></p>