Dataset

We are planning to use the MNIST dataset in the idx3 format as input to train our autoencoders. We will be testing the autoencoder on the first 100 images. Let's first plot the original images:

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


mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

class OriginalImages:

    def __init__(self):
        pass

    def main(self):
        X_train, X_test = self.standard_scale(mnist.train.images, mnist.test.images)

        original_imgs = X_test[:100]
        plt.figure(1, figsize=(10, 10))

        for i in range(0, 100):
            im = original_imgs[i].reshape((28, 28))
            ax = plt.subplot(10, 10, i + 1)
            for label in (ax.get_xticklabels() + ax.get_yticklabels()):
                label.set_fontsize(8)

            plt.imshow(im, cmap="gray", clim=(0.0, 1.0))
        plt.suptitle(' Original Images', fontsize=15, y=0.95)
        plt.savefig('figures/original_images...