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) # 学习率指数衰减,每次epoch:学习率 = gamma * 学习率 # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9) total_batch = 0 # 记录进行到多少batch dev_best_loss = float('inf') last_improve = 0 # 记录上次验证集loss下降的batch数 flag = False # 记录是否很久没有效果提升 writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime())) for epoch in range(config.num_epochs): print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs)) # scheduler.step() # 学习率衰减 for i, (trains, labels) in enumerate(train_iter): outputs = model(trains) model.zero_grad() loss = F.cross_entropy(outputs, labels) loss.backward() optimizer.step() if total_batch % 100 == 0: # 每多少轮输出在训练集和验证集上的效果 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)) writer.add_scalar("loss/train", loss.item(), total_batch) writer.add_scalar("loss/dev", dev_loss, total_batch) writer.add_scalar("acc/train", train_acc, total_batch) writer.add_scalar("acc/dev", dev_acc, total_batch) model.train() total_batch += 1 if total_batch - last_improve > config.require_improvement: # 验证集loss超过1000batch没下降,结束训练 print("No optimization for a long time, auto-stopping...") flag = True break if flag: break writer.close() test(config, model, test_iter)
def test(config, model, test_iter): # test model.load_state_dict(torch.load(config.save_path)) model.eval() start_time = time.time() test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True) msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}' print(msg.format(test_loss, test_acc)) print("Precision, Recall and F1-Score...") print(test_report) print("Confusion Matrix...") print(test_confusion) time_dif = get_time_dif(start_time) print("Time usage:", time_dif)
from utils_fasttext import build_dataset, build_iterator, get_time_dif embedding = 'random' else: from utils import build_dataset, build_iterator, get_time_dif x = import_module('models.' + model_name) config = x.Config(dataset, embedding) np.random.seed(1) torch.manual_seed(1) torch.cuda.manual_seed_all(1) torch.backends.cudnn.deterministic = True # 保证每次结果一样 start_time = time.time() print("Loading data...") vocab, train_data, dev_data, test_data = build_dataset(config, word) train_iter = build_iterator(train_data, config) dev_iter = build_iterator(dev_data, config) test_iter = build_iterator(test_data, config) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) # train config.n_vocab = len(vocab) # to 是为了去除device model = x.Model(config).to(config.device) if model_name != 'Transformer': init_network(model) print(model.parameters) train(config, model, train_iter, dev_iter, test_iter)