The last step is to print the reconstructed images to give us visual proof of how the encoder is able to reconstruct the images based on the weights:
predicted_imgs = autoencoder.reconstruct(X_test[:100])# plot the reconstructed imagesplt.figure(1, figsize=(10, 10))plt.title('Autoencoded Images')for i in range(0, 100): im = predicted_imgs[i].reshape((28, 28)) ax = plt.subplot(10, 10, i + 1) for label in (ax.get_xticklabels() + ax.get_yticklabels()): label.set_fontname('Arial') label.set_fontsize(8) plt.imshow(im, cmap="gray", clim=(0.0, 1.0))plt.suptitle('Additive Gaussian Noise AutoEncoder Images', fontsize=15, y=0.95)plt.savefig('figures/additive_gaussian_images.png')plt.show()
Reconstructed images using the ...
