def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 128
n_cls = 10
n_l_train_data = 100
n_train_data = 60000
dim = 100
learning_rate = 1. * 1e-3
n_epoch = 100
act = F.relu
iter_epoch = n_train_data / batch_size
n_iter = n_epoch * iter_epoch
# Separate dataset
home = os.environ.get("HOME")
fpath = os.path.join(home, "datasets/mnist/train.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/mnist/l_train.npz")
u_train_path = os.path.join(home, "datasets/mnist/train.npz")
test_path = os.path.join(home, "datasets/mnist/test.npz")
# DataReader, Model, Optimizer, Losses
data_reader = MNISTDataReader(l_train_path,
u_train_path,
test_path,
batch_size=batch_size,
n_cls=n_cls,
da=False,
shape=True)
exp = Experiment006(device, learning_rate, act, dim)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
filename, _ = os.path.splitext(os.path.basename(__file__))
for i in range(n_iter):
# Get data
x_u, _ = [Variable(to_device(x, device)) \
for x in data_reader.get_u_train_batch()]
# Train
exp.train(x_u)
# Eval
if (i + 1) % iter_epoch == 0:
# Get data
x_l, y_l = [Variable(to_device(x, device)) \
for x in data_reader.get_l_train_batch()]
loss_d_x_gen = exp.test(x_l, y_l, epoch, filename)
msg = "Epoch:{},ElapsedTime:{},Loss:{}".format(
epoch,
time.time() - st, loss_d_x_gen.data)
print(msg)
exp.serialize(epoch, filename)
st = time.time()
epoch += 1
def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 128
n_cls = 10
n_l_train_data = 100
n_train_data = 60000
dim = 100
learning_rate = 1. * 1e-3
n_epoch = 100
act = F.relu
iter_epoch = n_train_data / batch_size
n_iter = n_epoch * iter_epoch
# Separate dataset
home = os.environ.get("HOME")
fpath = os.path.join(home, "datasets/mnist/train.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/mnist/l_train.npz")
u_train_path = os.path.join(home, "datasets/mnist/train.npz")
test_path = os.path.join(home, "datasets/mnist/test.npz")
# DataReader, Model, Optimizer, Losses
data_reader = MNISTDataReader(l_train_path, u_train_path, test_path,
batch_size=batch_size,
n_cls=n_cls,
da=False,
shape=True)
exp = Experiment011(
device,
learning_rate,
act,
dim
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
filename, _ = os.path.splitext(os.path.basename(__file__))
for i in range(n_iter):
# Get data
x_u, _ = [Variable(to_device(x, device)) \
for x in data_reader.get_u_train_batch()]
# Train
exp.train(x_u)
# Eval
if (i+1) % iter_epoch == 0:
# Get data
x_l, y_l = [Variable(to_device(x, device)) \
for x in data_reader.get_l_train_batch()]
loss_d_x_gen = exp.test(x_l, y_l, epoch, filename)
msg = "Epoch:{},ElapsedTime:{},Loss:{}".format(
epoch,
time.time() - st,
loss_d_x_gen.data)
print(msg)
exp.serialize(epoch, filename)
st = time.time()
epoch +=1