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

 

 

 

Interpolating Between MNIST Digits

Another interesting way to explore the latent space of our generator model is by interpolating between the MNIST digits. This section is largely based on this excellent blogpost by Jason Brownlee.

So let us start by defining a function to interpolate between two points in the latent space. 

def interpolation(point_1, point_2, n_steps=10):
    ratios = np.linspace(0, 1, num=n_steps)
    vectors = []
    for i, ratio in enumerate(ratios):
        vectors.append(((1.0 - ratio) * point_1 + ratio * point_2))

    return tf.stack(vectors)

Now we have all we need to do our interpolation analysis. 

plot_number = 100
latent_points = generate_latent_points(number=plot_number)
results = None
for i in range(0, 2*np.sqrt(plot_number), 2):
    interpolated = interpolation(latent_points[i], latent_points[i+1])
    generated_images = generate_images(interpolated)

    if results is None:
        results = generated_images
    else:
        results = tf.stack((results, generated_images))

plot_results(results, plot_number)