def experiment(variant):
beta = variant["beta"]
representation_size = variant["representation_size"]
m = ConvVAE(representation_size, beta=beta)
for epoch in range(10):
m.train_epoch(epoch)
m.test_epoch(epoch)
m.dump_samples(epoch)
def experiment(variant):
c = joblib.load(
"/Users/ashvin/data/s3doodad/ashvin/vae/point2d-conv/run0/id0/params.pkl"
)
import pdb
pdb.set_trace()
beta = variant["beta"]
representation_size = variant["representation_size"]
m = ConvVAE(representation_size, beta=beta)
for epoch in range(10):
m.train_epoch(epoch)
m.test_epoch(epoch)
m.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(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)