Deep Learning with TensorFlow

Building a denoising autoencoder

The network architecture is very simple. An input image, of size 784 pixels, is stochastically corrupted, and then it is dimensionally reduced by an encoding network layer. The reduction step is from 784 to 256 pixels.

In the decoding phase, we prepare the network for output, re-changing the original image size from 256 to 784 pixels.

As usual, we start loading all the necessary libraries to our implementation:

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt     from tensorflow.examples.tutorials.mnist import input_data 

Set the basic network parameters:

n_input    = 784  n_hidden_1 = 256  n_hidden_2 = 256  n_output   = 784 

We also set the session's parameters:

epochs     = 110 batch_size = ...