In this implementation, we can use any kind of imaging dataset and see how the convolutional version of the autoencoder will make a difference. We will still be using the MNIST dataset for this, so let's start off by getting the dataset using the TensorFlow helpers:

%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', validation_size=0)

Output:
from tensorflow.examples.tutorials.mnist import input_data

mnist_dataset = input_data.read_data_sets('MNIST_data', validation_size=0)

Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

Let's show one digit from the dataset:

# Plotting one image from the training set.
image = mnist_dataset.train.images[2]
plt.imshow(image.reshape((28, 28)), cmap='Greys_r')

Output: