The decoder consists of three deconvolution layers arranged in sequence. For each deconvolution operation, we reduce the number of features to obtain an image that must be the same size as the original image. In addition to reducing the number of features, deconvolution involves a shape transformation of the images:
Data flow of the decoding phase
We're ready to look at how to implement a convolutional autoencoder. The first step is to load the basic libraries:
import matplotlib.pyplot as plt import numpy as np import math import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data
Then build the training ...
