def main(): # load MNIST images images, labels = dataset.load_test_images() # config config = aae.config num_scatter = len(images) x, _, label_ids = dataset.sample_labeled_data(images, labels, num_scatter, config.ndim_x, config.ndim_y) z = aae.to_numpy(aae.encode_x_z(x, test=True)) visualizer.plot_labeled_z(z, label_ids, dir=args.plot_dir)
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# config
config = aae.config
# settings
num_analogies = 10
pylab.gray()
# generate style vector z
x = dataset.sample_unlabeled_data(images,
num_analogies,
config.ndim_x,
binarize=False)
z = aae.to_numpy(aae.encode_x_z(x))
# plot original image on the left
for m in xrange(num_analogies):
pylab.subplot(num_analogies, config.ndim_y + 2, m * 12 + 1)
pylab.imshow(x[m].reshape((28, 28)), interpolation="none")
pylab.axis("off")
all_y = np.identity(config.ndim_y, dtype=np.float32)
for m in xrange(num_analogies):
# copy z as many as the number of classes
fixed_z = np.repeat(z[m].reshape(1, -1), config.ndim_y, axis=0)
gen_x = aae.to_numpy(aae.decode_yz_x(all_y, fixed_z))
# plot images generated from each label
for n in xrange(config.ndim_y):
pylab.subplot(num_analogies, config.ndim_y + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_analogies, config.ndim_y)
pylab.savefig("{}/analogy.png".format(args.plot_dir))
def main():
# load MNIST images
train_images, train_labels = dataset.load_train_images()
# config
config = aae.config
# settings
# _l -> labeled
# _u -> unlabeled
max_epoch = 1000
num_trains_per_epoch = 5000
batchsize = 100
alpha = 1
# seed
np.random.seed(args.seed)
if args.gpu_device != -1:
cuda.cupy.random.seed(args.seed)
# classification
# 0 -> true sample
# 1 -> generated sample
class_true = aae.to_variable(np.zeros(batchsize, dtype=np.int32))
class_fake = aae.to_variable(np.ones(batchsize, dtype=np.int32))
# training
progress = Progress()
for epoch in xrange(1, max_epoch):
progress.start_epoch(epoch, max_epoch)
sum_loss_reconstruction = 0
sum_loss_discriminator = 0
sum_loss_generator = 0
for t in xrange(num_trains_per_epoch):
# sample from data distribution
images_l, label_onehot_l, label_ids_l = dataset.sample_labeled_data(
train_images, train_labels, batchsize)
# reconstruction phase
z_l = aae.encode_x_z(images_l)
reconstruction_l = aae.decode_yz_x(label_onehot_l, z_l)
loss_reconstruction = F.mean_squared_error(
aae.to_variable(images_l), reconstruction_l)
aae.backprop_generator(loss_reconstruction)
aae.backprop_decoder(loss_reconstruction)
# adversarial phase
images_l = dataset.sample_labeled_data(train_images, train_labels,
batchsize)[0]
z_fake_l = aae.encode_x_z(images_l)
z_true_l = sampler.gaussian(batchsize,
config.ndim_z,
mean=0,
var=1)
dz_true = aae.discriminate_z(z_true_l, apply_softmax=False)
dz_fake = aae.discriminate_z(z_fake_l, apply_softmax=False)
loss_discriminator = F.softmax_cross_entropy(
dz_true, class_true) + F.softmax_cross_entropy(
dz_fake, class_fake)
aae.backprop_discriminator(loss_discriminator)
# adversarial phase
images_l = dataset.sample_labeled_data(train_images, train_labels,
batchsize)[0]
z_fake_l = aae.encode_x_z(images_l)
dz_fake = aae.discriminate_z(z_fake_l, apply_softmax=False)
loss_generator = F.softmax_cross_entropy(dz_fake, class_true)
aae.backprop_generator(loss_generator)
sum_loss_reconstruction += float(loss_reconstruction.data)
sum_loss_discriminator += float(loss_discriminator.data)
sum_loss_generator += float(loss_generator.data)
if t % 10 == 0:
progress.show(t, num_trains_per_epoch, {})
aae.save(args.model_dir)
progress.show(
num_trains_per_epoch, num_trains_per_epoch, {
"loss_r": sum_loss_reconstruction / num_trains_per_epoch,
"loss_d": sum_loss_discriminator / num_trains_per_epoch,
"loss_g": sum_loss_generator / num_trains_per_epoch,
})
def main():
# load MNIST images
images, labels = dataset.load_train_images()
# config
config = aae.config
# settings
# _l -> labeled
# _u -> unlabeled
max_epoch = 1000
num_trains_per_epoch = 5000
batchsize_l = 100
batchsize_u = 100
alpha = 1
# seed
np.random.seed(args.seed)
if args.gpu_device != -1:
cuda.cupy.random.seed(args.seed)
# create semi-supervised split
num_labeled_data = 10000
num_types_of_label = 11 # additional label corresponds to unlabeled data
training_images_l, training_labels_l, training_images_u, _, _ = dataset.create_semisupervised(
images, labels, 0, num_labeled_data, num_types_of_label)
# classification
# 0 -> true sample
# 1 -> generated sample
class_true = aae.to_variable(np.zeros(batchsize_u, dtype=np.int32))
class_fake = aae.to_variable(np.ones(batchsize_u, dtype=np.int32))
# training
progress = Progress()
for epoch in xrange(1, max_epoch):
progress.start_epoch(epoch, max_epoch)
sum_loss_reconstruction = 0
sum_loss_supervised = 0
sum_loss_discriminator = 0
sum_loss_generator = 0
for t in xrange(num_trains_per_epoch):
# sample from data distribution
images_l, label_onehot_l, label_ids_l = dataset.sample_labeled_data(
training_images_l,
training_labels_l,
batchsize_l,
ndim_y=num_types_of_label)
images_u = dataset.sample_unlabeled_data(training_images_u,
batchsize_u)
# reconstruction phase
z_u = aae.encode_x_z(images_u)
reconstruction_u = aae.decode_z_x(z_u)
loss_reconstruction = F.mean_squared_error(
aae.to_variable(images_u), reconstruction_u)
aae.backprop_generator(loss_reconstruction)
aae.backprop_decoder(loss_reconstruction)
# adversarial phase
z_fake_u = aae.encode_x_z(images_u)
z_fake_l = aae.encode_x_z(images_l)
onehot = np.zeros((1, num_types_of_label), dtype=np.float32)
onehot[0, -1] = 1 # turn on the extra class
label_onehot_u = np.repeat(onehot, batchsize_u, axis=0)
z_true_l = sampler.supervised_gaussian_mixture(
batchsize_l, config.ndim_z, label_ids_l,
num_types_of_label - 1)
z_true_u = sampler.gaussian_mixture(batchsize_u, config.ndim_z,
num_types_of_label - 1)
dz_true_l = aae.discriminate_z(label_onehot_l,
z_true_l,
apply_softmax=False)
dz_true_u = aae.discriminate_z(label_onehot_u,
z_true_u,
apply_softmax=False)
dz_fake_l = aae.discriminate_z(label_onehot_l,
z_fake_l,
apply_softmax=False)
dz_fake_u = aae.discriminate_z(label_onehot_u,
z_fake_u,
apply_softmax=False)
loss_discriminator = F.softmax_cross_entropy(
dz_true_l, class_true) + F.softmax_cross_entropy(
dz_true_u, class_true) + F.softmax_cross_entropy(
dz_fake_l, class_fake) + F.softmax_cross_entropy(
dz_fake_u, class_fake)
aae.backprop_discriminator(loss_discriminator)
# adversarial phase
z_fake_u = aae.encode_x_z(images_u)
z_fake_l = aae.encode_x_z(images_l)
dz_fake_l = aae.discriminate_z(label_onehot_l,
z_fake_l,
apply_softmax=False)
dz_fake_u = aae.discriminate_z(label_onehot_u,
z_fake_u,
apply_softmax=False)
loss_generator = F.softmax_cross_entropy(
dz_fake_l, class_true) + F.softmax_cross_entropy(
dz_fake_u, class_true)
aae.backprop_generator(loss_generator)
sum_loss_reconstruction += float(loss_reconstruction.data)
sum_loss_discriminator += float(loss_discriminator.data)
sum_loss_generator += float(loss_generator.data)
if t % 10 == 0:
progress.show(t, num_trains_per_epoch, {})
aae.save(args.model_dir)
progress.show(
num_trains_per_epoch, num_trains_per_epoch, {
"loss_r": sum_loss_reconstruction / num_trains_per_epoch,
"loss_d": sum_loss_discriminator / num_trains_per_epoch,
"loss_g": sum_loss_generator / num_trains_per_epoch,
})
def main(): images, labels = dataset.load_test_images() num_scatter = len(images) x, _, label_ids = dataset.sample_labeled_data(images, labels, num_scatter) z = aae.to_numpy(aae.encode_x_z(x, test=True)) plot.scatter_labeled_z(z, label_ids, dir=args.plot_dir)