April 2020
Intermediate to advanced
438 pages
12h 2m
English
Content preview from Python Image Processing Cookbook
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If you have the latent variable dimension set to2 (instead of 32), what you get is a 2D VAE. You can use the VAE to predict the test dataset and visualize the predictions (with color codes) for the 10 Fashion MNIST products in the 2D latent space, as in the following figure:
You can see how the same products cluster together. The code is left to you as an exercise.
Also, if you use the decoder to visualize the images from the latent space as you change the latent variables, you will obtain a smoothly varying space where each product transforms into another, as in the following diagram (the code is again left to you ...
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