def main():
parser = argparse.ArgumentParser(description='Chainer example: VAE')
parser.add_argument('--initmodel', '-m', default='',
help='Initialize the model from given file')
parser.add_argument('--resume', '-r', default='',
help='Resume the optimization from snapshot')
parser.add_argument('--gpu', '-g', default=-1, type=int,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='results',
help='Directory to output the result')
parser.add_argument('--epoch', '-e', default=100, type=int,
help='number of epochs to learn')
parser.add_argument('--dim-z', '-z', default=20, type=int,
help='dimention of encoded vector')
parser.add_argument('--dim-h', default=500, type=int,
help='dimention of hidden layer')
parser.add_argument('--beta', default=1.0, type=float,
help='Regularization coefficient for '
'the second term of ELBO bound')
parser.add_argument('--k', '-k', default=1, type=int,
help='Number of Monte Carlo samples used in '
'encoded vector')
parser.add_argument('--binary', action='store_true',
help='Use binarized MNIST')
parser.add_argument('--batch-size', '-b', type=int, default=100,
help='learning minibatch size')
parser.add_argument('--test', action='store_true',
help='Use tiny datasets for quick tests')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# dim z: {}'.format(args.dim_z))
print('# Minibatch-size: {}'.format(args.batch_size))
print('# epoch: {}'.format(args.epoch))
print('')
# Prepare VAE model, defined in net.py
encoder = net.make_encoder(784, args.dim_z, args.dim_h)
decoder = net.make_decoder(784, args.dim_z, args.dim_h,
binary_check=args.binary)
prior = net.make_prior(args.dim_z)
avg_elbo_loss = net.AvgELBOLoss(encoder, decoder, prior,
beta=args.beta, k=args.k)
if args.gpu >= 0:
avg_elbo_loss.to_gpu(args.gpu)
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(avg_elbo_loss)
# Initialize
if args.initmodel:
chainer.serializers.load_npz(args.initmodel, avg_elbo_loss)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist(withlabel=False)
if args.binary:
# Binarize dataset
train = (train >= 0.5).astype(np.float32)
test = (test >= 0.5).astype(np.float32)
if args.test:
train, _ = chainer.datasets.split_dataset(train, 100)
test, _ = chainer.datasets.split_dataset(test, 100)
train_iter = chainer.iterators.SerialIterator(train, args.batch_size)
test_iter = chainer.iterators.SerialIterator(test, args.batch_size,
repeat=False, shuffle=False)
# Set up an updater. StandardUpdater can explicitly specify a loss function
# used in the training with 'loss_func' option
updater = training.updaters.StandardUpdater(
train_iter, optimizer,
device=args.gpu, loss_func=avg_elbo_loss)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(extensions.Evaluator(
test_iter, avg_elbo_loss, device=args.gpu))
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/reconstr', 'main/kl_penalty', 'elapsed_time']))
trainer.extend(extensions.ProgressBar())
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
# Visualize the results
def save_images(x, filename):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(3, 3, figsize=(9, 9), dpi=100)
for ai, xi in zip(ax.flatten(), x):
ai.imshow(xi.reshape(28, 28))
fig.savefig(filename)
avg_elbo_loss.to_cpu()
train_ind = [1, 3, 5, 10, 2, 0, 13, 15, 17]
x = chainer.Variable(np.asarray(train[train_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = decoder(encoder(x).mean, inference=True).mean
save_images(x.array, os.path.join(args.out, 'train'))
save_images(x1.array, os.path.join(args.out, 'train_reconstructed'))
test_ind = [3, 2, 1, 18, 4, 8, 11, 17, 61]
x = chainer.Variable(np.asarray(test[test_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = decoder(encoder(x).mean, inference=True).mean
save_images(x.array, os.path.join(args.out, 'test'))
save_images(x1.array, os.path.join(args.out, 'test_reconstructed'))
# draw images from randomly sampled z
z = prior().sample(9)
x = decoder(z, inference=True).mean
save_images(x.array, os.path.join(args.out, 'sampled'))
def main():
parser = argparse.ArgumentParser(description='Chainer example: VAE')
parser.add_argument('--initmodel',
'-m',
type=str,
help='Initialize the model from given file')
parser.add_argument('--resume',
'-r',
type=str,
help='Resume the optimization from snapshot')
parser.add_argument('--gpu',
'-g',
default=-1,
type=int,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out',
'-o',
default='results',
help='Directory to output the result')
parser.add_argument('--epoch',
'-e',
default=100,
type=int,
help='number of epochs to learn')
parser.add_argument('--dim-z',
'-z',
default=20,
type=int,
help='dimention of encoded vector')
parser.add_argument('--dim-h',
default=500,
type=int,
help='dimention of hidden layer')
parser.add_argument('--beta',
default=1.0,
type=float,
help='Regularization coefficient for '
'the second term of ELBO bound')
parser.add_argument('--k',
'-k',
default=1,
type=int,
help='Number of Monte Carlo samples used in '
'encoded vector')
parser.add_argument('--binary',
action='store_true',
help='Use binarized MNIST')
parser.add_argument('--batch-size',
'-b',
type=int,
default=100,
help='learning minibatch size')
parser.add_argument('--test',
action='store_true',
help='Use tiny datasets for quick tests')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# dim z: {}'.format(args.dim_z))
print('# Minibatch-size: {}'.format(args.batch_size))
print('# epoch: {}'.format(args.epoch))
print('')
# Prepare VAE model, defined in net.py
encoder = net.make_encoder(784, args.dim_z, args.dim_h)
decoder = net.make_decoder(784,
args.dim_z,
args.dim_h,
binary_check=args.binary)
prior = net.make_prior(args.dim_z)
avg_elbo_loss = net.AvgELBOLoss(encoder,
decoder,
prior,
beta=args.beta,
k=args.k)
if args.gpu >= 0:
avg_elbo_loss.to_gpu(args.gpu)
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(avg_elbo_loss)
# Initialize
if args.initmodel is not None:
chainer.serializers.load_npz(args.initmodel, avg_elbo_loss)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist(withlabel=False)
if args.binary:
# Binarize dataset
train = (train >= 0.5).astype(np.float32)
test = (test >= 0.5).astype(np.float32)
if args.test:
train, _ = chainer.datasets.split_dataset(train, 100)
test, _ = chainer.datasets.split_dataset(test, 100)
train_iter = chainer.iterators.SerialIterator(train, args.batch_size)
test_iter = chainer.iterators.SerialIterator(test,
args.batch_size,
repeat=False,
shuffle=False)
# Set up an updater. StandardUpdater can explicitly specify a loss function
# used in the training with 'loss_func' option
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
loss_func=avg_elbo_loss)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
extensions.Evaluator(test_iter, avg_elbo_loss, device=args.gpu))
trainer.extend(extensions.DumpGraph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/reconstr',
'main/kl_penalty', 'elapsed_time'
]))
trainer.extend(extensions.ProgressBar())
if args.resume is not None:
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
# Visualize the results
def save_images(x, filename):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(3, 3, figsize=(9, 9), dpi=100)
for ai, xi in zip(ax.flatten(), x):
ai.imshow(xi.reshape(28, 28))
fig.savefig(filename)
avg_elbo_loss.to_cpu()
train_ind = [1, 3, 5, 10, 2, 0, 13, 15, 17]
x = chainer.Variable(np.asarray(train[train_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = decoder(encoder(x).mean, inference=True).mean
save_images(x.array, os.path.join(args.out, 'train'))
save_images(x1.array, os.path.join(args.out, 'train_reconstructed'))
test_ind = [3, 2, 1, 18, 4, 8, 11, 17, 61]
x = chainer.Variable(np.asarray(test[test_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = decoder(encoder(x).mean, inference=True).mean
save_images(x.array, os.path.join(args.out, 'test'))
save_images(x1.array, os.path.join(args.out, 'test_reconstructed'))
# draw images from randomly sampled z
z = prior().sample(9)
x = decoder(z, inference=True).mean
save_images(x.array, os.path.join(args.out, 'sampled'))
def main():
parser = argparse.ArgumentParser(description='Chainer example: VAE')
parser.add_argument('--initmodel',
'-m',
type=str,
help='Initialize the model from given file')
parser.add_argument('--resume',
'-r',
type=str,
help='Resume the optimization from snapshot')
parser.add_argument('--device',
'-d',
type=str,
default='-1',
help='Device specifier. Either ChainerX device '
'specifier or an integer. If non-negative integer, '
'CuPy arrays with specified device id are used. If '
'negative integer, NumPy arrays are used')
parser.add_argument('--out',
'-o',
default='results',
help='Directory to output the result')
parser.add_argument('--epoch',
'-e',
default=100,
type=int,
help='number of epochs to learn')
parser.add_argument('--dim-z',
'-z',
default=20,
type=int,
help='dimension of encoded vector')
parser.add_argument('--dim-h',
default=500,
type=int,
help='dimension of hidden layer')
parser.add_argument('--beta',
default=1.0,
type=float,
help='Regularization coefficient for '
'the second term of ELBO bound')
parser.add_argument('--k',
'-k',
default=1,
type=int,
help='Number of Monte Carlo samples used in '
'encoded vector')
parser.add_argument('--binary',
action='store_true',
help='Use binarized MNIST')
parser.add_argument('--batch-size',
'-b',
type=int,
default=100,
help='learning minibatch size')
parser.add_argument('--test',
action='store_true',
help='Use tiny datasets for quick tests')
group = parser.add_argument_group('deprecated arguments')
group.add_argument('--gpu',
'-g',
dest='device',
type=int,
nargs='?',
const=0,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
if chainer.get_dtype() == np.float16:
warnings.warn('This example may cause NaN in FP16 mode.',
RuntimeWarning)
device = chainer.get_device(args.device)
device.use()
print('Device: {}'.format(device))
print('# dim z: {}'.format(args.dim_z))
print('# Minibatch-size: {}'.format(args.batch_size))
print('# epoch: {}'.format(args.epoch))
print('')
# Prepare VAE model, defined in net.py
encoder = net.make_encoder(784, args.dim_z, args.dim_h)
decoder = net.make_decoder(784,
args.dim_z,
args.dim_h,
binary_check=args.binary)
prior = net.make_prior(args.dim_z)
avg_elbo_loss = net.AvgELBOLoss(encoder,
decoder,
prior,
beta=args.beta,
k=args.k)
avg_elbo_loss.to_device(device)
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(avg_elbo_loss)
# If initial parameters are given, initialize the model with them.
if args.initmodel is not None:
chainer.serializers.load_npz(args.initmodel, avg_elbo_loss)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist(withlabel=False)
if args.binary:
# Binarize dataset
train = (train >= 0.5).astype(np.float32)
test = (test >= 0.5).astype(np.float32)
if args.test:
train, _ = chainer.datasets.split_dataset(train, 100)
test, _ = chainer.datasets.split_dataset(test, 100)
train_iter = chainer.iterators.SerialIterator(train, args.batch_size)
test_iter = chainer.iterators.SerialIterator(test,
args.batch_size,
repeat=False,
shuffle=False)
# Set up an updater. StandardUpdater can explicitly specify a loss function
# used in the training with 'loss_func' option
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=device,
loss_func=avg_elbo_loss)
# Set up the trainer and extensions.
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
extensions.Evaluator(test_iter, avg_elbo_loss, device=device))
# TODO(niboshi): Temporarily disabled for chainerx. Fix it.
if device.xp is not chainerx:
trainer.extend(extensions.DumpGraph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/reconstr',
'main/kl_penalty', 'elapsed_time'
]))
trainer.extend(extensions.ProgressBar())
# If snapshot file is given, resume the training.
if args.resume is not None:
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
# Save images for demonstration
save_images(device, encoder, decoder, train, test, prior, args.out)
