Function train

train_eval.py:32–82

(config, model, train_iter, dev_iter, test_iter)

def train(config, model, train_iter, dev_iter, test_iter):

    start_time = time.time()
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)

    # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
    total_batch = 0  
    dev_best_loss = float('inf')
    last_improve = 0  
    flag = False 

    for epoch in range(config.num_epochs):
        print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
        # scheduler.step()
        for i, (trains, labels) in enumerate(train_iter):
            s = time.time()
            outputs = model(trains)
            model.zero_grad()
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()
            e = time.time()
            #print(e-s)
            if total_batch % 200 == 0:
                true = labels.data.cpu()
                predic = torch.max(outputs.data, 1)[1].cpu()
                train_acc = metrics.accuracy_score(true, predic)
                dev_acc, dev_loss = evaluate(config, model, dev_iter)
                if dev_loss < dev_best_loss:
                    dev_best_loss = dev_loss
                    torch.save(model.state_dict(), config.save_path)
                    improve = '*'
                    last_improve = total_batch
                else:
                    improve = ''
                time_dif = get_time_dif(start_time)
                msg = 'Iter: {0:>6},  Train Loss: {1:>5.2},  Train Acc: {2:>6.2%},  Val Loss: {3:>5.2},  Val Acc: {4:>6.2%},  Time: {5} {6}'
                print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
                model.train()
              

            total_batch += 1
            if total_batch - last_improve > config.require_improvement:
                print("No optimization for a long time, auto-stopping...")
                flag = True
                break
        if flag:
            break

    test(config, model, test_iter)