We have successfully loaded in our latest model. Let us now play around a bit and see what kind of things we can learn about this model. Our generator takes an array of 100 numbers. One idea can be to try to systematically change our input. Let us try and see what we get
def generate_latent_points(number=100, scale_means=1, scale_stds=1):
latent_dim = 100
means = scale_means * tf.linspace(-1, 1, num=latent_dim)
stds = scale_stds * tf.linspace(-1, 1, num=latent_dim)
latent_space_value_range = tf.random.normal([number, latent_dim],
means,
stds,
dtype=tf.float64)
return latent_space_value_range
def generate_images(latent_points):
# notice we set training to false because we are making inferences
generated_images = restored_generator.predict(latent_points)
return generated_images
def plot_result(generated_images, number=100):
# obviously this assumes sqrt number is an int
fig, axs = plt.subplots(int(np.sqrt(number)), int(np.sqrt(number)),
figsize=(10, 10))
for i in range(int(np.sqrt(number))):
for j in range(int(np.sqrt(number))):
axs[i, j].imshow(generated_images[i*j], cmap='Greys')
axs[i, j].axis('off')
plt.show()
generated_images = generate_images(generate_latent_points())
plot_result(generated_images)