def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 100
n_l_train_data = 4000
n_train_data = 50000
n_cls = 10
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/cifar10/cifar-10.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
zca_path = os.path.join(home, "datasets/cifar10/zca_components.npz")
# DataReader, Model, Optimizer, Losses
data_reader = Cifar10DataReader(l_train_path,
u_train_path,
test_path,
zca_path=zca_path,
batch_size=batch_size,
n_cls=n_cls,
da=True,
shape=True)
exp = Experiment005(
device,
learning_rate,
act,
n_cls,
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
acc_prev = 0.
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_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 = [Variable(to_device(x, device)) \
for x in data_reader.get_test_batch()]
bs = 100
accs = []
for i in range(0, x_l.shape[0], bs):
accs.append(
float(
cuda.to_cpu(
exp.test(x_l[i:i + bs, ], y_l[i:i + bs, ]).data)))
msg = "Epoch:{},ElapsedTime:{},Acc:{}".format(
epoch,
time.time() - st, np.mean(accs))
print(msg)
acc_prev = accs[-1]
st = time.time()
epoch += 1
def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 100
n_l_train_data = 4000
n_train_data = 50000
n_cls = 10
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/cifar10/cifar-10.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
zca_path = os.path.join(home, "datasets/cifar10/zca_components.npz")
# DataReader, Model, Optimizer, Losses
data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
zca_path=zca_path,
batch_size=batch_size,
n_cls=n_cls,
da=True,
shape=True)
exp = Experiment006(
device,
learning_rate,
act,
n_cls,
)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
acc_prev = 0.
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_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 = [Variable(to_device(x, device)) \
for x in data_reader.get_test_batch()]
bs = 100
accs = []
for i in range(0, x_l.shape[0], bs):
accs.append(float(cuda.to_cpu(
exp.test(x_l[i:i+bs, ], y_l[i:i+bs, ]).data)))
msg = "Epoch:{},ElapsedTime:{},Acc:{}".format(
epoch,
time.time() - st,
np.mean(accs))
print(msg)
acc_prev = accs[-1]
st = time.time()
epoch +=1