def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
model = "mlp"
batch_size = 128
n_l_train_data = 100
n_train_data = 60000
n_cls = 10
dims = 100
learning_rate = 1. * 1e-3
n_epoch = 50
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,
shape=True)
exp = Experiment(
device,
n_cls,
dims,
learning_rate,
act,
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
for i in range(n_iter):
# Get data
#x_l, y_l = [Variable(to_device(x, device)) \
# for x in data_reader.get_l_train_batch()]
x_l, y_l = [x for x in data_reader.get_l_train_batch()]
x_l = Variable(to_device(x_l, device))
x_u, _ = [Variable(to_device(x, device)) \
for x in data_reader.get_u_train_batch()]
# Train
exp.train(x_l, y_l, x_u)
# Eval
if (i + 1) % iter_epoch == 0:
# Get data
x_l, y_l = [x for x in data_reader.get_test_batch()]
x_l = Variable(to_device(x_l, device))
d_x_gen = exp.test(x_l, y_l)
msg = "Epoch:{},ElapsedTime:{},Acc:{}".format(
epoch,
time.time() - st, d_x_gen)
print(msg)
exp.save_model(epoch)
st = time.time()
epoch += 1
def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
model = "mlp"
batch_size = 128
n_l_train_data = 100
n_train_data = 60000
n_cls = 10
dims = 100
learning_rate = 1. * 1e-3
n_epoch = 50
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,
shape=True)
exp = Experiment(
device,
n_cls,
dims,
learning_rate,
act,
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
for i in range(n_iter):
# Get data
#x_l, y_l = [Variable(to_device(x, device)) \
# for x in data_reader.get_l_train_batch()]
x_l, y_l = [x for x in data_reader.get_l_train_batch()]
x_l = Variable(to_device(x_l, device))
x_u, _ = [Variable(to_device(x, device)) \
for x in data_reader.get_u_train_batch()]
# Train
exp.train(x_l, y_l, x_u)
# Eval
if (i+1) % iter_epoch == 0:
# Get data
x_l, y_l = [x for x in data_reader.get_test_batch()]
x_l = Variable(to_device(x_l, device))
d_x_gen = exp.test(x_l, y_l)
msg = "Epoch:{},ElapsedTime:{},Acc:{}".format(
epoch,
time.time() - st,
d_x_gen)
print(msg)
exp.save_model(epoch)
st = time.time()
epoch +=1
def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
model = "mlp"
batch_size = 128
n_l_train_data = 100
n_train_data = 60000
n_cls = 10
dims = 100
learning_rate = 1. * 1e-3
learning_rate_gan = 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,
shape=True)
exp = Experiment009(
device,
n_cls,
dims,
learning_rate,
learning_rate_gan,
act,
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
for i in range(n_iter):
# Get data
#x_l, y_l = [Variable(to_device(x, device)) \
# for x in data_reader.get_l_train_batch()]
x_l, y_l = [x for x in data_reader.get_l_train_batch()]
x_l = Variable(to_device(x_l, device))
x_u, _ = [Variable(to_device(x, device)) \
for x in data_reader.get_u_train_batch()]
# Train
exp.train(x_l, y_l, x_u)
# Eval
if (i+1) % iter_epoch == 0:
# Get data
x_l, y_l = [x for x in data_reader.get_test_batch()]
x_l = Variable(to_device(x_l, device))
loss = []
pred = []
bs = 100
for i in range(0, x_l.shape[0], bs):
l, y = exp.test(x_l[i:i+bs, ], y_l[i:i+bs, ], epoch, i)
loss.append(l)
pred.append(y)
msg = "Epoch:{},ElapsedTime:{},Loss:{}".format(
epoch,
time.time() - st,
np.mean(loss))
print(msg)
np.save("./pred_{:05d}".format(epoch), np.concatenate(pred))
exp.save_model(epoch)
st = time.time()
epoch +=1
