In this part, we'll build a simple autoencoder that can be used to compress the MNIST dataset. So we will feed the images of this dataset to the encoder part, which will try to learn a lower compressed representation for them; then we will try to construct the input images again in the decoder part.
Compressing the MNIST dataset
The MNIST dataset
We will start the implementation by getting the MNIST dataset, using the helper functions of TensorFlow.
Let's import the necessary packages for this implementation:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist_dataset = input_data.read_data_sets('MNIST_data...