Training an autoencoder is basically a simple process. It is a neural network whose output is same as the input. The basic architecture of the autoencoder is as follows.
There is an input layer, which is followed by a few hidden layers, and then, after a certain depth, the hidden layers follow the reverse architecture until we reach a point where the final layer is the same as the input layer. We pass data into the network whose embedding we wish to learn.
In this example, we use the images input by the MNIST dataset. We begin our implementation by importing all the main libraries:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import mnist_data
We then prepare the MNIST dataset. We use the input_data function to load and set up the data:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot...
