generate_faces.py

# example of loading the generator model and generating images
from numpy import asarray
from numpy.random import randn
from numpy.random import randint
from keras.models import load_model
from matplotlib import pyplot

# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples, n_classes=10):
	# generate points in the latent space
	x_input = randn(latent_dim * n_samples)
	# reshape into a batch of inputs for the network
	z_input = x_input.reshape(n_samples, latent_dim)
	return z_input

# create a plot of generated images
def plot_generated(examples, n, filepath):
	# plot images
	for i in range(n * n):
		# define subplot
		pyplot.subplot(n, n, 1 + i)
		# turn off axis
		pyplot.axis('off')
		# plot raw pixel data
		pyplot.imshow(examples[i, :, :])
	pyplot.savefig(filepath)

# load model
model = load_model('generated_models/generator_model_030.h5')
# generate images
latent_points = generate_latent_points(100, 25)
# generate images
X  = model.predict(latent_points)
# scale from [-1,1] to [0,1]
X = (X + 1) / 2.0
# plot the result
plot_generated(X, 5, 'generated_plots/generated_faces.png')