def main():
parser = argparse.ArgumentParser(description="DCGAN")
parser.add_argument("--batchsize", "-b", type=int, default=1)
parser.add_argument("--epoch", "-e", type=int, default=5)
parser.add_argument("--gpu", "-g", type=int, default=0)
parser.add_argument("--snapshot_interval", "-s", type=int, default=1)
parser.add_argument("--display_interval", "-d", type=int, default=1)
parser.add_argument("--n_dimz", "-z", type=int, default=100)
parser.add_argument("--dataset", "-ds", type=str, default="mnist")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--out", "-o", type=str, default="result")
parser.add_argument("--resume", '-r', default='')
args = parser.parse_args()
#import .py
import Updater
import Visualize
import Network.mnist_net as Network
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print("Dataset:{}".format(args.dataset))
print('')
out = os.path.join(args.out, args.dataset)
#Set up NN
gen = Network.Generator(n_hidden=args.n_dimz)
dis = Network.Discriminator()
ser = Network.Searcher(n_hidden=args.n_dimz)
load_path = 'DCGAN/result/mnist/gen_epoch_100.npz'
chainer.serializers.load_npz(load_path, gen)
load_path = 'DCGAN/result/mnist/dis_epoch_100.npz'
chainer.serializers.load_npz(load_path, dis)
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
gen.to_gpu()
dis.to_gpu()
ser.to_gpu()
#Make optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1) #init_lr = alpha
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001), 'hook_dec')
return optimizer
opt_gen = make_optimizer(gen)
opt_dis = make_optimizer(dis)
opt_ser = make_optimizer(ser)
#Get dataset
train_valid, test = mnist.get_mnist(withlabel=True, ndim=3)
train, valid = split_dataset_random(train_valid, 50000, seed=0)
#valid = [i[0] for i in valid if(i[1]==9)] #ラベル1のみを選択
valid = [i[0] for i in test if(i[1]==8)]
#ひとつに対して潜在空間座標を探索する．
valid = valid[0:1]
xp = gen.xp
z_noise = Variable(xp.asarray(gen.make_hidden(args.batchsize)))
#Setup iterator
train_iter = iterators.SerialIterator(valid, args.batchsize)
#Setup updater
updater = Updater.ADGANUpdater(
models=(gen, dis, ser),
iterator=train_iter,
optimizer={'gen':opt_gen, 'dis':opt_dis, 'ser':opt_ser},
z_noise=z_noise,
device=args.gpu)
#Setup trainer
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=out)
snapshot_interval = (args.snapshot_interval, 'epoch')
display_interval = (args.display_interval, 'epoch')
trainer.extend(
extensions.snapshot(
filename='snapshot_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
gen, 'gen_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
dis, 'dis_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
ser, 'ser_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.LogReport(
trigger=display_interval))
trainer.extend(extensions.PrintReport([
'epoch', 'ser/loss', 'gen/loss', 'dis/loss', 'elapsed_time'
]), trigger=display_interval)
trainer.extend(extensions.ProgressBar())
trainer.extend(Visualize.out_generated_image(
gen, dis, ser, valid, args.out, args.dataset, z_noise),
trigger=snapshot_interval)
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
parser = argparse.ArgumentParser(description="DCGAN")
parser.add_argument("--batchsize", "-b", type=int, default=128)
parser.add_argument("--epoch", "-e", type=int, default=100)
parser.add_argument("--gpu", "-g", type=int, default=0)
parser.add_argument("--snapshot_interval", "-s", type=int, default=10)
parser.add_argument("--display_interval", "-d", type=int, default=1)
parser.add_argument("--n_dimz", "-z", type=int, default=100)
parser.add_argument("--dataset", "-ds", type=str, default="mnist")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--out", "-o", type=str, default="result")
parser.add_argument("--resume", '-r', default='')
args = parser.parse_args()
#import .py
import Updater
import Visualize
import Network.mnist_net as Network
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print("Dataset:{}".format(args.dataset))
print('')
out = os.path.join(args.out, args.dataset)
#Set up NN
gen = Network.Generator(n_hidden=args.n_dimz)
dis = Network.Discriminator()
enc = Network.Encoder(n_hidden=args.n_dimz)
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
gen.to_gpu()
dis.to_gpu()
#Make optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1) #init_lr = alpha
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001), 'hook_dec')
return optimizer
opt_gen = make_optimizer(gen)
opt_dis = make_optimizer(dis)
opt_enc = make_optimizer(enc)
#Get dataset
train_valid, test = mnist.get_mnist(withlabel=True, ndim=3)
train, valid = split_dataset_random(train_valid, 50000, seed=0)
train = [i[0] for i in train if(i[1]==1)] #ラベル1のみを選択
#Setup iterator
train_iter = iterators.SerialIterator(train, args.batchsize)
#Setup updater
updater = Updater.DCGANUpdater(
models=(gen, dis, enc),
iterator=train_iter,
optimizer={'gen':opt_gen, 'dis':opt_dis, 'enc':opt_enc},
device=args.gpu)
#Setup trainer
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=out)
snapshot_interval = (args.snapshot_interval, 'epoch')
display_interval = (args.display_interval, 'epoch')
trainer.extend(extensions.snapshot_object(
gen, 'gen_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
dis, 'dis_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
enc, 'enc_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.LogReport(
trigger=display_interval))
trainer.extend(extensions.PrintReport([
'epoch', 'gen/loss', 'dis/loss', 'enc/loss', 'elapsed_time'
]), trigger=display_interval)
trainer.extend(extensions.ProgressBar())
trainer.extend(Visualize.out_generated_image(
gen, dis, enc,
10, 10, args.seed, args.out, args.dataset),
trigger=snapshot_interval)
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
from chainer.datasets import mnist
parser = argparse.ArgumentParser(description="DCGAN")
parser.add_argument("--n_dimz", "-z", type=int, default=100)
parser.add_argument("--dataset", "-ds", type=str, default="mnist")
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
import Network.mnist_net as Network
train_valid, test = mnist.get_mnist(withlabel=True, ndim=3, scale=255.)
test = [i[0] for i in test if (i[1] == 7)] #ラベル1のみを選択
test = test[0:1]
gen = Network.Generator(n_hidden=args.n_dimz)
enc = Network.Encoder(n_hidden=args.n_dimz)
dis = Network.Discriminator()
gen.to_cpu()
enc.to_cpu()
dis.to_cpu()
load_path = 'result/{}/gen_epoch_100.npz'.format(args.dataset)
chainer.serializers.load_npz(load_path, gen)
load_path = 'result/{}/enc_epoch_100.npz'.format(args.dataset)
chainer.serializers.load_npz(load_path, enc)
load_path = 'result/{}/dis_epoch_100.npz'.format(args.dataset)
chainer.serializers.load_npz(load_path, dis)
x_real = np.reshape(test[0], (1, 1, 28, 28)) / 255.
with chainer.using_config('train', False):
enc_z = enc(x_real)
y_rl, y_real = dis(x_real, enc_z)