Advanced Deep Learning with Keras

Autoencoders
As shown in Figure 3.4.1, if we are given a grayscale photo of a rice field on the
foreground, a volcano in the background and sky on top, we're able to add the
appropriate colors.
Figure 3.4.1: Adding color to a grayscale photo of the Mayon Volcano. A colorization network should replicate
human abilities by adding color to a grayscale photo. Left photo is grayscale. The right photo is color. Original
color photo can be found on the book GitHub repository, https://github.com/PacktPublishing/Advanced-
Deep-Learning-with-Keras/blob/master/chapter3-autoencoders/README.md.
A simple automatic colorization algorithm seems like a suitable problem for
autoencoders. If we can train the autoencoder with a sufficient number of grayscale
photos as input and the corresponding colored photos as output, it could possibly
discover the hidden structure on properly applying colors. Roughly, it is the reverse
process of denoising. The question is, can an autoencoder add color (good noise)
to the original grayscale image?
Listing 3.4.1 shows the colorization autoencoder network. The colorization
autoencoder network is a modified version of denoising autoencoder that we
used for the MNIST dataset. Firstly, we need a dataset of grayscale to colored
photos. The CIFAR10 database, which we have used before, has 50,000 training
and 10,000 testing 32 × 32 RGB photos that can be converted to grayscale. As shown
in the following listing, we're able to use the rgb2gray() function to apply weights
on R, G, and B components to convert from color to grayscale.
Listing 3.4.1, colorization-autoencoder-cifar10-3.4.1.py, shows us
a colorization autoencoder using the CIFAR10 dataset:
from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten
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