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Convolutional
Neural Networks
In the previous chapters we have discussed DenseNets, in which each layer is
fully connected to the adjacent layers. We looked at one application of these dense
networks in classifying the MNIST handwritten characters dataset. In that context,
each pixel in the input image has been assigned to a neuron with a total of 784
(28 × 28 pixels) input neurons. However, this strategy does not leverage the spatial
structure and relationships between each image. In particular, this piece of code
is a DenseNet that transforms the bitmap representing each written digit into a flat
vector where the local spatial structure is removed. Removing the spatial structure
is a problem because important information is lost:
#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)
Convolutional neural networks (in short, convnets or CNNs) leverage spatial
information, and they are therefore very well-suited for classifying images. These
nets use an ad hoc architecture inspired by biological data taken from physiological
experiments performed on the visual cortex. As we discussed in Chapter 2, TensorFlow
1.x and 2.x, our vision is based on multiple cortex levels, each one recognizing more
and more structured information. First, we see single pixels, then from that we
recognize simple geometric forms and then more and more sophisticated elements
such as objects, faces, human bodies, animals, and so on.
Convolutional neural networks are a fascinating subject. Over a short period of time,
they have shown themselves to be a disruptive technology, breaking performance
records in multiple domains from text, to video, to speech, going well beyond the
initial image processing domain where they were originally conceived.
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