Advanced Deep Learning with Keras

Chapter 8
Figure 8.2.6: Digits 6 to 9 generated as a function of latent vector mean values and one-hot label
(CVAE CNN). For ease of interpretation, the range of values for the mean is similar to Figure 8.2.4.
In Figure 8.2.4, the distribution of mean per label is shown after 30 epochs. Unlike in
both Figures 8.1.6 and 8.1.11 in the previous sections, each label is not concentrated
on a region but distributed across the plot. This is expected since every sampling
in the latent space should generate a specific digit. Navigating the latent space
changes the attribute of that specific digit. For example, if the digit specified is 0,
then navigating the latent space will still produce a 0 but the attributes, such as tilt
angle, thickness, and other writing style aspects will be different.
These changes are more clearly shown in Figures 8.2.5 and 8.2.6. For ease of
comparison, the range of values for the latent vector is the same as in Figure 8.2.4.
Using the pretrained weights, a digit (for example, 0) can be generated by executing
the command:
$ python3 cvae-cnn-mnist-8.2.1.py --weights=cvae_cnn_mnist.h5 --digit=0
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