title: Tensorflow weights for kernels of convolution for colored images?
body: <p>At the moment I have some networks doing classification stuff with greyscaled images. I want to move on to colored (RGB) images.</p>
  
  <p>In the CIFAR-10 tutorial of Tensorflow I got confused by the weights for the convolution kernels. The first convolution there looks like this:</p>
  
  <pre><code>kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64],
                                           stddev=1e-4, wd=0.0)
  conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
  </code></pre>
  
  <p>So it is a <code>5x5</code> convolution with an input of 3 (one for each color channel: the red, green and blue image information) and it is generating 64 feature maps.</p>
  
  <p>However, the second convolution layer takes an input of 64 feature maps:</p>
  
  <pre><code>kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64],
                                           stddev=1e-4, wd=0.0)
  conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
  </code></pre>
  
  <p>...so, how does this process the color information? Does this mean the different color channels are somehow <em>spread</em> on the 64 feature maps of convolution layer 1?</p>
  
  <p>I thought conv layer 1 produces 64 feature maps for each color channel, therefore ending up in 3 * 64 = 196 feature maps...but obviously I were wrong.</p>
  
  <p>How is the color information <em>mixed</em> there in conv layer 1?</p>