I am pretty new to neural networks, but I understand linear algebra and the mathematics of convolution pretty decently.
I am trying to understand the example code I find in various places on the net for training a Keras convolutional NN with MNIST data to recognize digits. My expectation would be that when I create a convolutional layer, I would have to specify a filter or set of filters to apply to the input. But the three samples I have found all create a convolutional layer like this:
model.add(Convolution2D(nb_filter = 32, nb_row = 3, nb_col = 3, border_mode='valid', input_shape=input_shape))
This seems to be applying a total of 32 3x3 filters to the images processed by the CNN. But what are those filters? How would I describe them mathematically? The keras documentation is no help.
Thanks in advance,