title: How to specify padding with keras in Conv2D layer?
body: <p>I am trying to implement <a href="https://en.wikipedia.org/wiki/AlexNet" rel="noreferrer">AlexNet</a> with Keras and was inspecting the network design in MATLAB which is given as follows</p>
  
  <p><a href="https://i.stack.imgur.com/coq7C.png" rel="noreferrer"><img src="https://i.stack.imgur.com/coq7C.png" alt="enter image description here"></a></p>
  
  <p>As could be seen, the second convolution layer has 256 filters of size 5x5,  48 channels and a padding of [ 2 2 2 2 ]. How could I specify <code>padding</code> of [ 2 2 2 2] with Keras? I went through the <a href="https://keras.io/layers/convolutional/#conv2d" rel="noreferrer">documentation of Conv2D</a>. It accepts only 2 values for padding namely <code>valid</code> and <code>same</code>. I could not understand this. For what I know, <code>valid</code> would mean zero padding. How could I specify [2 2 2 2] padding with the second convolution layer? I created the first layer as:</p>
  
  <pre><code>model.add(keras.layers.Conv2D(filters = 96, kernel_size = (11,11), 
   strides = (4,4), padding = "valid", input_shape=(227,227,3)))
  </code></pre>
  
  <p>Also, since in the second layer there are 48 channels, do I need to be explicit about it?</p>