train_loader, dev_loader, test_loader = (DataLoader(
dataset=dataset,
batch_size=args.batch_size,
collate_fn=utils.collate_fn,
shuffle=dataset.train) for i, dataset in enumerate(dataests))
print("Building Model...")
if args.model == "cnn":
model = CNN.Model(vocab_size=len(vocab),
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
filter_sizes=[3, 4, 5],
dropout=args.dropout)
elif args.model == "bilstm":
model = BiLSTM.Model(vocab_size=len(vocab),
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
dropout=args.dropout)
else:
raise ValueError(
"Model should be either cnn or bilstm, {} is invalid.".format(
args.model))
if torch.cuda.is_available():
model = model.cuda()
trainer = Trainer(model)
best_acc = 0
train_list = []
dev_list = []
for i in range(args.epochs):
def run(*args, **kwargs):
parser = argparse.ArgumentParser()
parser.add_argument('-train_file', type=str, default='./data/train.csv')
parser.add_argument('-dev_file', type=str, default='./data/dev.csv')
parser.add_argument('-test_file', type=str, default='./data/test.csv')
parser.add_argument('-save_path', type=str, default='./model.pkl')
parser.add_argument('-model',
type=str,
default=kwargs['model'],
help="[cnn, bilstm]")
parser.add_argument('-batch_size', type=int, default=kwargs['batch_size'])
parser.add_argument('-embedding_size', type=int, default=128)
parser.add_argument('-hidden_size', type=int, default=128)
parser.add_argument('-learning_rate', type=float, default=1e-3)
parser.add_argument('-dropout', type=float, default=0.5)
parser.add_argument('-epochs', type=int, default=20)
parser.add_argument('-seed', type=int, default=1)
args = parser.parse_args()
print(args)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
print("Loading Data...")
datasets, vocab = utils.build_dataset(args.train_file, args.dev_file,
args.test_file)
train_loader, dev_loader, test_loader = (DataLoader(
dataset=dataset,
batch_size=args.batch_size,
collate_fn=utils.collate_fn,
shuffle=dataset.train) for i, dataset in enumerate(datasets))
print("Building Model...")
if args.model == "cnn":
model = CNN.Model(vocab_size=len(vocab),
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
filter_sizes=[3, 4, 5],
dropout=args.dropout)
elif args.model == "bilstm":
model = BiLSTM.Model(vocab_size=len(vocab),
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
dropout=args.dropout)
if torch.cuda.is_available():
model = model.cuda()
trainer = Trainer(model, args.learning_rate)
train_loss_list = list()
dev_loss_list = list()
best_acc = 0
for i in range(args.epochs):
print("Epoch: {} ################################".format(i))
train_loss, train_acc = trainer.train(train_loader)
dev_loss, dev_acc = trainer.evaluate(dev_loader)
train_loss_list.append(train_loss)
dev_loss_list.append(dev_loss)
print("Train Loss: {:.4f} Acc: {:.4f}".format(train_loss, train_acc))
print("Dev Loss: {:.4f} Acc: {:.4f}".format(dev_loss, dev_acc))
if dev_acc > best_acc:
best_acc = dev_acc
trainer.save(args.save_path)
print("###########################################")
trainer.load(args.save_path)
test_loss, test_acc = trainer.evaluate(test_loader)
print("Test Loss: {:.4f} Acc: {:.4f}".format(test_loss, test_acc))
return train_loss_list, dev_loss_list