Deep Learning with Keras

Local receptive fields

If we want to preserve spatial information, then it is convenient to represent each image with a matrix of pixels. Then, a simple way to encode the local structure is to connect a submatrix of adjacent input neurons into one single hidden neuron belonging to the next layer. That single hidden neuron represents one local receptive field. Note that this operation is named convolution and it gives the name to this type of network.

Of course, we can encode more information by having overlapping submatrices. For instance, let's suppose that the size of each single submatrix is 5 x 5 and that those submatrices are used with MNIST images of 28 x 28 pixels. Then we will be able to generate 23 x 23 local receptive field neurons ...