def main():
num_samples = 10000
samples_true = sampler.gaussian_mixture_circle(
num_samples,
num_cluster=generator_params["config"]["num_mixture"],
scale=2,
std=0.2)
plot_scatter(samples_true, args.plot_dir, "scatter_true")
plot_kde(samples_true, args.plot_dir, "kde_true")
samples_fake = gan.to_numpy(gan.generate_x(num_samples, test=True))
plot_scatter(samples_fake, args.plot_dir, "scatter_gen")
plot_kde(samples_fake, args.plot_dir, "kde_gen")
def plot_samples(epoch, progress):
samples_fale = gan.generate_x(10000, from_gaussian=True)
samples_fale.unchain_backward()
samples_fale = gan.to_numpy(samples_fale)
try:
plot_scatter(samples_fale,
dir=args.plot_dir,
filename="scatter_epoch_{}_time_{}min".format(
epoch, progress.get_total_time()))
plot_kde(samples_fale,
dir=args.plot_dir,
filename="kde_epoch_{}_time_{}min".format(
epoch, progress.get_total_time()))
except:
pass
def run_method_1():
# config
discriminator_config = gan.config_discriminator
generator_config = gan.config_generator
num_col = 10
num_generation = 20
batchsize = 2 * num_generation
base_z = gan.to_variable(gan.sample_z(batchsize))
# optimize z
class_true = gan.to_variable(np.zeros(batchsize, dtype=np.int32))
for n in xrange(5):
x_fake = gan.generate_x_from_z(base_z, test=True, as_numpy=False)
discrimination_fake, _ = gan.discriminate(x_fake,
apply_softmax=False,
test=True)
cross_entropy = F.softmax_cross_entropy(discrimination_fake,
class_true)
gan.backprop_generator(cross_entropy)
base_z = gan.to_variable(base_z.data + base_z.grad * 0.01)
base_z = gan.to_numpy(base_z)
sum_z = np.sum(base_z)
if sum_z != sum_z:
raise Exception("NaN")
mix_z = np.zeros((num_col * num_generation, generator_config.ndim_input),
dtype=np.float32)
for g in xrange(num_generation):
for i in xrange(num_col):
mix_z[g * num_col +
i] = base_z[2 * g] * (i / float(num_col)) + base_z[
2 * g + 1] * (1 - i / float(num_col))
x_negative = gan.generate_x_from_z(mix_z, test=True, as_numpy=True)
x_negative = (x_negative + 1.0) / 2.0
visualizer.tile_rgb_images(x_negative.transpose(0, 2, 3, 1),
dir=args.plot_dir,
filename="analogy_1",
row=num_generation,
col=num_col)