Advanced Deep Learning with Keras

Autoencoders
plt.title('Input: 1st 2 rows, Decoded: last 2 rows')
plt.imshow(imgs, interpolation='none', cmap='gray')
plt.savefig('input_and_decoded.png')
plt.show()
Figure 3.2.1: The encoder model is a made up of Conv2D(32)-Conv2D(64)-Dense(16)
in order to generate the low dimensional latent vector
The decoder in Listing 3.2.1 decompresses the latent vector in order to recover the
MNIST digit. The decoder input stage is a Dense layer that will accept the latent
vector. The number of units is equal to the product of the saved Conv2D output
dimensions from the encoder. This is done so we can easily resize the output
of the Dense layer for Conv2DTranspose to finally recover the original MNIST
image dimensions.
The decoder is made of a stack of three Conv2DTranspose. In our case, we're going to
use a Transposed CNN (sometimes called deconvolution), which is more commonly
used in decoders. We can imagine transposed CNN (Conv2DTranspose) as the
reversed process of CNN. In a simple example, if the CNN converts an image to
feature maps, the transposed CNN will produce an image given feature maps. Figure
3.2.2 shows the decoder model.
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