def experiment(variant):
if variant["use_gpu"]:
gpu_id = variant["gpu_id"]
ptu.set_gpu_mode(True)
ptu.set_device(gpu_id)
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data = get_data(10000)
m = ConvVAE(representation_size, input_channels=3)
t = ConvVAETrainer(train_data, test_data, m, beta=beta, use_cuda=True)
for epoch in range(50):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
def experiment(variant):
if variant["use_gpu"]:
gpu_id = variant["gpu_id"]
ptu.set_gpu_mode(True)
ptu.set_device(gpu_id)
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data = get_data(10000)
m = ConvVAE(representation_size, input_channels=3)
t = ConvVAETrainer(train_data,
test_data,
m,
beta_schedule=PiecewiseLinearSchedule([0, 400, 800],
[0.5, 0.5, beta]))
for epoch in range(1001):
t.train_epoch(epoch)
t.test_epoch(epoch)
t.dump_samples(epoch)
ptu.set_device(gpu_id)
beta = variant["beta"]
representation_size = variant["representation_size"]
train_data, test_data = get_data(10000)
m = ConvVAE(train_data,
test_data,
representation_size,
beta=beta,
use_cuda=True,
input_channels=3)
for epoch in range(50):
m.train_epoch(epoch)
m.test_epoch(epoch)
m.dump_samples(epoch)
if __name__ == "__main__":
variants = []
train_data, test_data = get_data(100)
import ipdb
ipdb.set_trace()
for representation_size in [2, 4, 8, 16]:
for beta in [0.5, 5.0, 50]:
variant = dict(
beta=beta,
representation_size=representation_size,
)
variants.append(variant)
run_variants(experiment, variants, run_id=0)