In previous chapters, we discussed dense nets, in which each layer is fully connected to the adjacent layers. We applied those dense networks to classify the MNIST handwritten characters dataset. In that context, each pixel in the input image is assigned to a neuron for a total of 784 (28 x 28 pixels) input neurons. However, this strategy does not leverage the spatial structure and relations of each image. In particular, this piece of code transforms the bitmap representing each written digit into a flat vector, where the spatial locality is gone:
#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
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Convolutional neural networks (also called ConvNet) leverage spatial information and are therefore very well suited for classifying images. These nets use an ad hoc architecture inspired by...
