def train():
train_loader = DataLoader(dataset=dataset,
batch_size=config.batch,
shuffle=True,
collate_fn=collate_fn,
num_workers=0)
model = SVM(config.embedding, config.strmaxlen, dataset.get_vocab_size(), config.output_size)
optimizer = optim.SGD(model.parameters(), lr=config.lr)
model.train()
for epoch in range(config.epoch):
sum_loss = 0
for i, (data, labels) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data).squeeze()
weight = model.weight.squeeze()
weight = weight.reshape((weight.shape[0],1))
loss = model.loss(output, labels)
tmp = weight.t() @ weight
loss += config.c * tmp[0][0] / 2.0
loss.backward()
optimizer.step()
sum_loss += float(loss)
print("Epoch: {:4d}\tloss: {}".format(epoch, sum_loss /len(dataset)))
if args.gpu:
model = model.cuda()
dataset = torch.from_numpy(np.load("../output/data/dataset_train.npy"))
targets = torch.from_numpy(
np.int64(np.load("../output/data/target_train.npy")))
dataset_test = np.load(dataset_path)
targets_test = np.int64(np.load(target_path))
if args.L2norm:
log_test = setup_logger(
0, 'test_log_norm',
os.path.join(args.log_dir, 'test_log_norm.txt'))
log = setup_logger(
0, 'train_log_norm',
os.path.join(args.log_dir, 'train_log_norm.txt'))
optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=10)
else:
log_test = setup_logger(0, 'test_log',
os.path.join(args.log_dir, 'test_log.txt'))
log = setup_logger(0, 'train_log',
os.path.join(args.log_dir, 'train_log.txt'))
optimizer = Adam(model.parameters(), lr=args.lr)
max_accuracy = 0.0
overfitting_cnt = 0
f_accuracy_train = open(os.path.join(args.log_dir, 'acc_train.txt'),
'w')
f_accuracy_test = open(os.path.join(args.log_dir, 'acc_test.txt'), 'w')
f_loss = open(os.path.join(args.log_dir, 'loss.txt'), 'w')
# # code for batch training
# torch_dataset = data.TensorDataset(data_tensor=dataset, target_tensor=targets)
