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.sample_z(batchsize) 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 # optimize z # xp = gan.xp # x_fake = gan.generate_x_from_z(mix_z, test=True, as_numpy=False) # x_fake.unchain_backward() # for n in xrange(500): # discrimination_fake, _ = gan.discriminate(x_fake, test=True) # opt = F.sum(discrimination_fake) # print opt.data # opt.backward() # # gan.backprop_generator(-F.sum(discrimination_fake)) # x_fake = gan.to_variable(xp.clip(x_fake.data + x_fake.grad * 0.01, -1, 1)) # x_negative = gan.to_numpy(x_fake) # x_negative = (x_negative + 1.0) / 2.0 plot.tile_rgb_images(x_negative.transpose(0, 2, 3, 1), dir=args.plot_dir, filename="analogy_1", row=num_generation, col=num_col)
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)
def run_method_1(): # config discriminator_config = gan.config_discriminator generator_config = gan.config_generator num_col = 20 num_generation = 20 batchsize = 2 * num_generation base_z = gan.sample_z(batchsize) 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) plot.tile_binary_images(x_negative.reshape((-1, 28, 28)), dir=args.plot_dir, filename="analogy_1", row=num_generation, col=num_col)