Used in https://towardsdatascience.com/variational-autoencoders-explained-in-detail-d585327c660a

vae generated images plot.py

def plot_samples(samples):
    IMAGE_WIDTH = 0.7
    plt.figure(figsize=(IMAGE_WIDTH * num_digits,
                        len(samples) * IMAGE_WIDTH))
    for epoch, images in enumerate(samples):
        for digit, image in enumerate(images):
            plt.subplot(len(samples),
                        num_digits,
                        epoch * num_digits + digit + 1)
            plt.imshow(image.reshape((28, 28)),
                       cmap='Greys_r')
            plt.gca().xaxis.set_visible(False)
            if digit == 0:
                plt.gca().yaxis.set_ticks([])
                plt.ylabel('epoch {}'.format(epoch + 1),
                           verticalalignment='center',
                           horizontalalignment='right',
                           rotation=0,
                           fontsize=14)
            else:
                plt.gca().yaxis.set_visible(False)
                
                
plot_samples(samples)