def tune(lr=0.1, dropout=0.3, kernel_num=100, kernel_sizes='3,4,5', embed_dim=100):
parser = argparse.ArgumentParser()
parser.add_argument("--lr", type=float, default=lr)
parser.add_argument("--dropout", type=float, default=dropout)
parser.add_argument("--kernel_num", type=int, default=kernel_num)
parser.add_argument("--kernel_sizes", type=str, default=kernel_sizes)
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--early_stop", type=int, default=10)
parser.add_argument("--embed_dim", type=int, default=embed_dim)
parser.add_argument("--max_len", type=int, default=200)
parser.add_argument("--class_num", type=int, default=3)
parser.add_argument("--lr_decay", type=float, default=0.5)
args = parser.parse_args()
# print("lr", args.lr, "dropout", args.dropout, "kernel_num", args.kernel_num, "kernel_sizes",args.kernel_sizes, "batch_size", args.batch_size, "early_stop", args.early_stop, "embed_dim", args.embed_dim, "max_len", args.max_len, "class_num", args.class_num, "lr_decay", args.lr_decay)
train_loader, dev_loader, test_loader, vocab_size = get_dataloaders(args.batch_size, args.max_len)
model = WordCNN(args, vocab_size, embedding_matrix=None)
# loss function
criterion = nn.CrossEntropyLoss()
# choose optimizer
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr)
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=args.lr_decay)
model, best_acc = trainer(train_loader, dev_loader, model, optimizer, criterion, early_stop=args.early_stop)
print('best_dev_acc:{}'.format(best_acc))
predict(model, test_loader)
print("This is args", args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--lr", type=float, default=0.005)
parser.add_argument("--dropout", type=float, default=0.3)
parser.add_argument("--kernel_num", type=int, default=100)
parser.add_argument("--kernel_sizes", type=str, default='2,3,4')
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--early_stop", type=int, default=15)
parser.add_argument("--embed_dim", type=int, default=100)
parser.add_argument("--max_len", type=int, default=200)
parser.add_argument("--class_num", type=int, default=3)
parser.add_argument("--lr_decay", type=float, default=0.5)
args = parser.parse_args()
#load data
train_loader, dev_loader, test_loader, vocab_size = get_dataloaders(args.batch_size, args.max_len)
#build model
# try to use pretrained embedding here
model = WordCNN(args, vocab_size, embedding_matrix=None)
#loss function
criterion = nn.CrossEntropyLoss()
#choose optimizer
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,model.parameters()), lr=args.lr)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=args.lr_decay)
model, best_acc = trainer(train_loader, dev_loader, model, optimizer, criterion, early_stop = args.early_stop)
print('best_dev_acc:{}'.format(best_acc))
predict(model, test_loader)