def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 128
inp_dim = 784
out_dim = n_cls = 10
n_l_train_data = 100
n_train_data = 60000
dims = [inp_dim, 250, 100, out_dim]
lambdas = [1., 1., 1.]
learning_rate = 1. * 1e-3
n_epoch = 100
decay = 0.5
act = F.relu
noise = False
bn = False
lateral = False
test = False
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)
exp = Experiment005(
device,
learning_rate,
lambdas,
dims,
act,
noise,
bn,
lateral,
test)
# 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_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()]
exp.test(x_l, y_l)
acc, sloss, rloss = exp.test(x_l, y_l)
msg = "Epoch:{},ElapsedTime:{},Acc:{},SupervisedLoss:{},ReconstructionLoss:{}".format(epoch, time.time() - st, acc.data, sloss.data, rloss.data)
print(msg)
st = time.time()
epoch +=1
def main():
# Settings
device = int(sys.argv[1]) if len(sys.argv) > 1 else None
batch_size = 128
inp_dim = 784
out_dim = n_cls = 10
n_l_train_data = 100
n_train_data = 60000
dims = [inp_dim, 250, 100, out_dim]
lambdas = [1., 1., 1.]
learning_rate = 1. * 1e-3
n_epoch = 100
decay = 0.5
act = F.relu
noise = True
bn = True
lateral = True
test = False
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)
exp = Experiment080(
device,
learning_rate,
lambdas,
dims,
act,
noise,
bn,
lateral,
test)
# 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_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()]
exp.test(x_l, y_l)
acc, sloss, rloss = exp.test(x_l, y_l)
msg = "Epoch:{},ElapsedTime:{},Acc:{},SupervisedLoss:{},ReconstructionLoss:{}".format(epoch, time.time() - st, acc.data, sloss.data, rloss.data)
print(msg)
st = time.time()
epoch +=1
