def main():
parser = argparse.ArgumentParser(description="Vanilla_AE")
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", "-s", type=int, default=10)
parser.add_argument("--n_dimz", "-z", type=int, default=64)
args = parser.parse_args()
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print('')
batchsize = args.batchsize
gpu_id = args.gpu
max_epoch = args.epoch
train_val, test = mnist.get_mnist(withlabel=False, ndim=1)
train, valid = split_dataset_random(train_val, 50000, seed=0)
model = Network.AE(n_dimz=args.n_dimz, n_out=784)
#set iterator
train_iter = iterators.SerialIterator(train, batchsize)
valid_iter = iterators.SerialIterator(valid,
batchsize,
repeat=False,
shuffle=False)
#optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
return optimizer
opt = make_optimizer(model)
#trainer
updater = Updater.AEUpdater(model=model,
iterator=train_iter,
optimizer=opt,
device=args.gpu)
trainer = training.Trainer(updater, (max_epoch, 'epoch'), out='result')
#trainer.extend(extensions.ExponentialShift('lr', 0.5),trigger=(30, 'epoch'))
trainer.extend(extensions.LogReport(log_name='log'))
trainer.extend(
Evaluator.AEEvaluator(iterator=valid_iter,
target=model,
device=args.gpu))
trainer.extend(extensions.snapshot_object(
model, filename='model_snapshot_epoch_{.updater.epoch}.npz'),
trigger=(args.snapshot, 'epoch'))
#trainer.extend(extensions.snapshot_object(optimizer, filename='optimizer_snapshot_epoch_{.updater.epoch}'), trigger=(args.snapshot, 'epoch'))
trainer.extend(
extensions.PrintReport(['epoch', 'main/loss', 'validation/main/loss']))
trainer.extend(extensions.ProgressBar())
trainer.run()
del trainer
def main():
parser = argparse.ArgumentParser(description="Vanilla_AE")
parser.add_argument("--batchsize", "-b", type=int, default=64)
parser.add_argument("--epoch", "-e", type=int, default=100)
parser.add_argument("--gpu", "-g", type=int, default=0)
parser.add_argument("--snapshot", "-s", type=int, default=10)
parser.add_argument("--n_dimz", "-z", type=int, default=16)
parser.add_argument("--dataset", "-d", type=str, default='mnist')
parser.add_argument("--network", "-n", type=str, default='conv')
args = parser.parse_args()
def transform(in_data):
img = in_data
img = resize(img, (32, 32))
return img
def transform2(in_data):
img, label = in_data
img = resize(img, (32, 32))
return img, label
#import program
import Updater
import Visualizer
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print('')
out = os.path.join('result', args.network)
batchsize = args.batchsize
gpu_id = args.gpu
max_epoch = args.epoch
train_val, _ = mnist.get_mnist(withlabel=False, ndim=3)
train_val = TransformDataset(train_val, transform)
#for visualize
_, test = mnist.get_mnist(withlabel=True, ndim=3)
test = TransformDataset(test, transform2)
label1 = 1
label2 = 5
test1 = [i[0] for i in test if (i[1] == label1)]
test2 = [i[0] for i in test if (i[1] == label2)]
test1 = test1[0:5]
test2 = test2[5:10]
if args.network == 'conv':
import Network.mnist_conv as Network
elif args.network == 'fl':
import Network.mnist_fl as Network
else:
raise Exception('Error!')
AE = Network.AE(n_dimz=args.n_dimz, batchsize=args.batchsize)
train, valid = split_dataset_random(train_val, 50000, seed=0)
#set iterator
train_iter = iterators.SerialIterator(train, batchsize)
valid_iter = iterators.SerialIterator(valid,
batchsize,
repeat=False,
shuffle=False)
#optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
return optimizer
opt_AE = make_optimizer(AE)
#trainer
updater = Updater.AEUpdater(model=(AE),
iterator=train_iter,
optimizer={'AE': opt_AE},
device=args.gpu)
trainer = training.Trainer(updater, (max_epoch, 'epoch'), out=out)
trainer.extend(extensions.LogReport(log_name='log'))
snapshot_interval = (args.snapshot, 'epoch')
display_interval = (1, 'epoch')
trainer.extend(extensions.snapshot_object(
AE, filename='AE_snapshot_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.PrintReport(['epoch', 'AE_loss']),
trigger=display_interval)
trainer.extend(extensions.ProgressBar())
trainer.extend(Visualizer.out_generated_image(AE, test1, test2, out),
trigger=(1, 'epoch'))
trainer.run()
del trainer