def _main():
args = _parase_command_line_args()
initialize_logger(args.debug)
batch_size = 32
data_format = luchador.get_nn_conv_format()
autoencoder = _build_model(args.model, data_format, batch_size)
dataset = load_mnist(args.dataset, data_format=data_format, mock=args.mock)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_ae():
batch = dataset.train.next_batch(batch_size).data
return sess.run(
inputs={autoencoder.input: batch},
outputs=autoencoder.output['error'],
updates=autoencoder.get_update_operations(),
name='train_autoencoder',
)
def _plot_reconstruction(epoch):
if not args.output:
return
orig = dataset.test.next_batch(batch_size).data
recon = sess.run(
inputs={autoencoder.input: orig},
outputs=autoencoder.output['reconstruction'],
name='reconstruct_images',
)
axis = 3 if data_format == 'NHWC' else 1
orig = np.squeeze(orig, axis=axis)
recon = np.squeeze(recon, axis=axis)
base_path = os.path.join(args.output, '{:03}_'.format(epoch))
plot_images(orig, base_path + 'orign.png')
plot_images(recon, base_path + 'recon.png')
_train(
_train_ae, _plot_reconstruction,
n_iterations=args.n_iterations, n_epochs=args.n_epochs
)
def _main():
args = _parase_command_line_args()
initialize_logger(args.debug)
batch_size = 32
data_format = luchador.get_nn_conv_format()
autoencoder = _build_model(args.model, data_format, batch_size)
dataset = load_mnist(args.dataset, data_format=data_format, mock=args.mock)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_ae():
batch = dataset.train.next_batch(batch_size).data
return sess.run(
inputs={autoencoder.input: batch},
outputs=autoencoder.output['error'],
updates=autoencoder.get_update_operations(),
name='train_autoencoder',
)
def _plot_reconstruction(epoch):
if not args.output:
return
orig = dataset.test.next_batch(batch_size).data
recon = sess.run(
inputs={autoencoder.input: orig},
outputs=autoencoder.output['reconstruction'],
name='reconstruct_images',
)
axis = 3 if data_format == 'NHWC' else 1
orig = np.squeeze(orig, axis=axis)
recon = np.squeeze(recon, axis=axis)
base_path = os.path.join(args.output, '{:03}_'.format(epoch))
plot_images(orig, base_path + 'orign.png')
plot_images(recon, base_path + 'recon.png')
_train(_train_ae,
_plot_reconstruction,
n_iterations=args.n_iterations,
n_epochs=args.n_epochs)
def _main():
args = _parase_command_line_args()
initialize_logger(args.debug)
batch_size = 32
data_format = luchador.get_nn_conv_format()
classifier = _build_model(args.model, data_format)
dataset = load_mnist(args.dataset, data_format=data_format, mock=args.mock)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_classifier():
batch = dataset.train.next_batch(batch_size)
return sess.run(
inputs={
classifier.input['data']: batch.data,
classifier.input['label']: batch.label,
},
outputs=classifier.output['error'],
updates=classifier.get_update_operations(),
name='train_classifier',
)
def _test_classifier():
batch = dataset.test.next_batch(batch_size)
return sess.run(
inputs={
classifier.input['data']: batch.data,
classifier.input['label']: batch.label,
},
outputs=classifier.output['error'],
name='test_classifier',
)
_train(_train_classifier,
_test_classifier,
n_iterations=args.n_iterations,
n_epochs=args.n_epochs)
def _main():
args = _parase_command_line_args()
initialize_logger(args.debug)
batch_size = 32
data_format = luchador.get_nn_conv_format()
classifier = _build_model(args.model, data_format)
dataset = load_mnist(args.dataset, data_format=data_format, mock=args.mock)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_classifier():
batch = dataset.train.next_batch(batch_size)
return sess.run(
inputs={
classifier.input['data']: batch.data,
classifier.input['label']: batch.label,
},
outputs=classifier.output['error'],
updates=classifier.get_update_operations(),
name='train_classifier',
)
def _test_classifier():
batch = dataset.test.next_batch(batch_size)
return sess.run(
inputs={
classifier.input['data']: batch.data,
classifier.input['label']: batch.label,
},
outputs=classifier.output['error'],
name='test_classifier',
)
_train(
_train_classifier, _test_classifier,
n_iterations=args.n_iterations, n_epochs=args.n_epochs
)
def _main():
args = _parse_command_line_args()
initialize_logger(args.debug)
batch_size = 32
dataset = load_mnist(args.dataset, flatten=True, mock=args.mock)
model = _build_models(args.model)
discriminator, generator = model['discriminator'], model['generator']
input_gen = nn.Input(shape=(None, args.n_seeds), name='GeneratorInput')
data_real = nn.Input(shape=dataset.train.shape, name='InputData')
data_fake = generator(input_gen)
logit_fake = discriminator(data_fake)
logit_real = discriminator(data_real)
gen_loss, disc_loss = _build_loss(logit_real, logit_fake)
opt_gen, opt_disc = _build_optimization(
generator, gen_loss, discriminator, disc_loss)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_disc():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
data_real: dataset.train.next_batch(batch_size).data
},
outputs=disc_loss,
updates=opt_disc,
name='train_discriminator',
)
def _train_gen():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
},
outputs=gen_loss,
updates=opt_gen,
name='train_generator',
)
def _plot_samples(epoch):
if not args.output:
return
images = sess.run(
inputs={
input_gen: _sample_seed(16, args.n_seeds),
},
outputs=data_fake,
name='generate_samples',
).reshape(-1, 28, 28)
path = os.path.join(args.output, '{:03d}.png'.format(epoch))
plot_images(images, path)
_train(
_train_disc, _train_gen, _plot_samples,
args.n_iterations, args.n_epochs
)
def _main():
args = _parse_command_line_args()
initialize_logger(args.debug)
batch_size = 32
dataset = load_mnist(args.dataset, flatten=True, mock=args.mock)
model = _build_models(args.model)
discriminator, generator = model['discriminator'], model['generator']
input_gen = nn.Input(shape=(None, args.n_seeds), name='GeneratorInput')
data_real = nn.Input(shape=dataset.train.shape, name='InputData')
data_fake = generator(input_gen)
logit_fake = discriminator(data_fake)
logit_real = discriminator(data_real)
gen_loss, disc_loss = _build_loss(logit_real, logit_fake)
opt_gen, opt_disc = _build_optimization(generator, gen_loss, discriminator,
disc_loss)
sess = nn.Session()
sess.initialize()
if args.output:
summary = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
summary.add_graph(sess.graph)
def _train_disc():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
data_real: dataset.train.next_batch(batch_size).data
},
outputs=disc_loss,
updates=opt_disc,
name='train_discriminator',
)
def _train_gen():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
},
outputs=gen_loss,
updates=opt_gen,
name='train_generator',
)
def _plot_samples(epoch):
if not args.output:
return
images = sess.run(
inputs={
input_gen: _sample_seed(16, args.n_seeds),
},
outputs=data_fake,
name='generate_samples',
).reshape(-1, 28, 28)
path = os.path.join(args.output, '{:03d}.png'.format(epoch))
plot_images(images, path)
_train(_train_disc, _train_gen, _plot_samples, args.n_iterations,
args.n_epochs)
def _main():
args = _parse_command_line_args()
initialize_logger(args.debug)
batch_size = 32
format_ = luchador.get_nn_conv_format()
dataset = load_celeba_face(args.dataset,
data_format=format_,
mock=args.mock)
model = _build_models(args.model)
discriminator, generator = model['discriminator'], model['generator']
input_gen = nn.Input(shape=(None, args.n_seeds), name='GeneratorInput')
data_shape = (None, ) + dataset.train.shape[1:]
data_real = nn.Input(shape=data_shape, name='InputData')
_LG.info('Building Generator')
data_fake = generator(input_gen)
_LG.info('Building fake discriminator')
logit_fake = discriminator(data_fake)
_LG.info('Building real discriminator')
logit_real = discriminator(data_real)
gen_loss, disc_loss = _build_loss(logit_real, logit_fake)
opt_gen, opt_disc = _build_optimization(generator, gen_loss, discriminator,
disc_loss)
sess = nn.Session()
sess.initialize()
_summary_writer = None
if args.output:
_summary_writer = nn.SummaryWriter(output_dir=args.output)
if sess.graph is not None:
_summary_writer.add_graph(sess.graph)
def _train_disc():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
data_real: dataset.train.next_batch(batch_size).data
},
outputs=disc_loss,
updates=discriminator.get_update_operations() + [opt_disc],
name='train_discriminator',
)
def _train_gen():
return sess.run(
inputs={
input_gen: _sample_seed(batch_size, args.n_seeds),
},
outputs=gen_loss,
updates=generator.get_update_operations() + [opt_gen],
name='train_generator',
)
random_seed = _sample_seed(batch_size, args.n_seeds)
def _summarize(epoch, losses=None):
if not args.output:
return
if losses:
_summary_writer.summarize(
summary_type='scalar',
global_step=epoch,
dataset={
'Generator/Loss': losses[0],
'Discriminator/Loss': losses[1],
},
)
images = sess.run(
inputs={
input_gen: random_seed,
},
outputs=data_fake,
name='generate_samples',
)
if format_ == 'NCHW':
images = images.transpose(0, 2, 3, 1)
images = (255 * images).astype(np.uint8)
_summary_writer.summarize(
summary_type='image',
global_step=epoch,
dataset={'Genearated/epoch_{:02d}'.format(epoch): images},
max_outputs=10,
)
_train(
_train_disc,
_train_gen,
_summarize,
args.n_iterations,
args.n_epochs,
)