Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis

 

 

 

Getting Results

We see that the generator generates images that look like MNIST numbers: \( 1, 4, 7, 9 \). Let's try to tweak it a bit more to see if we are able to generate a similar plot where we generate every MNIST number. Let us now try to 'move' a bit around in the latent space. Note: decrease the plot number if these following cells take too long to run on your computer. 

plot_number = 225

generated_images = generate_images(generate_latent_points(number=plot_number,
                                                          scale_means=5,
                                                          scale_stds=1))
plot_result(generated_images, number=plot_number)

generated_images = generate_images(generate_latent_points(number=plot_number,
                                                          scale_means=-5,
                                                          scale_stds=1))
plot_result(generated_images, number=plot_number)

generated_images = generate_images(generate_latent_points(number=plot_number,
                                                          scale_means=1,
                                                          scale_stds=5))
plot_result(generated_images, number=plot_number)

Again, we have found something interesting. Moving around using our means takes us from digit to digit, while moving around using our standard deviations seem to increase the number of different digits! In the last image above, we can barely make out every MNIST digit. Let us make on last plot using this information by upping the standard deviation of our Gaussian noises. 

plot_number = 400
generated_images = generate_images(generate_latent_points(number=plot_number,
                                                          scale_means=1,
                                                          scale_stds=10))
plot_result(generated_images, number=plot_number)

A pretty cool result! We see that our generator indeed has learned a distribution which qualitatively looks a whole lot like the MNIST dataset.