使用BERT模型进行文本分类的代码示例：训练过程可视化

%matplotlib+inline%0Aimport+matplotlib.pyplot+as+plt%0Adef+train(config%2C+model%2C+train_iter%2C+dev_iter%2C+test_iter):%0A%09start_time+%3D+time.time()%0A%09model.train()%0A%09param_optimizer+%3D+list(model.named_parameters())%0A%09no_decay+%3D+['bias'%2C+'LayerNorm.bias'%2C+'LayerNorm.weight']%0A%09optimizer_grouped_parameters+%3D+%5B%0A%09%09{'params':+[%20p+for+n%2C+p+in+param_optimizer+if+not+any(nd+in+n+for+nd+in+no_decay)]%2C+'weight_decay':+0.01}%2C%0A%09%09{'params':+[%20p+for+n%2C+p+in+param_optimizer+if+any(nd+in+n+for+nd+in+no_decay)]%2C+'weight_decay':+0.0}%5D%0A%09%2F%2F+optimizer+%3D+torch.optim.Adam(model.parameters()%2C+lr%3Dconfig.learning_rate)%0A%09optimizer+%3D+BertAdam(optimizer_grouped_parameters%2C%0A%09%09%09%09%09lr%3Dconfig.learning_rate%2C%0A%09%09%09%09%09warmup%3D0.05%2C%0A%09%09%09%09%09t_total%3Dlen(train_iter)+config.num_epochs)%0A%0A%09total_batch+%3D+0+%20%2F%2F+记录进行到多少batch%0A%09dev_best_loss+%3D+float('inf')%0A%09last_improve+%3D+0+%20%2F%2F+记录上次验证集loss下降的batch数%0A%09flag+%3D+False+%20%2F%2F+记录是否很久没有效果提升%0A%09model.train()%0A%0A%09dev_acc_list+%3D+%5B%5D%0A%09dev_loss_list+%3D+%5B%5D+%20%2F%2F+保存每个验证集损失值%0A%0A%09for+epoch+in+range(config.num_epochs):%0A%09%09print('Epoch+%5B{}/{}%5D'.format(epoch+%2B+1%2C+config.num_epochs))%0A%09%09for+i%2C+(trains%2C+labels)+in+enumerate(train_iter):%0A%09%09%09outputs+%3D+model(trains)%0A%09%09%09model.zero_grad()%0A%09%09%09loss+%3D+F.cross_entropy(outputs%2C+labels)%0A%09%09%09loss.backward()%0A%09%09%09optimizer.step()%0A%09%09%09if+total_batch+%25+100+%3D%3D+0:%0A%09%09%09%09%2F%2F+每多少轮输出在训练集和验证集上的效果%0A%0A%0A%09%09%09%09true+%3D+labels.data.cpu()%0A%09%09%09%09predic+%3D+torch.max(outputs.data%2C+1)%5B1%5D.cpu()%0A%09%09%09%09train_acc+%3D+metrics.accuracy_score(true%2C+predic)%0A%09%09%09%09dev_acc%2C+dev_loss+%3D+evaluate(config%2C+model%2C+dev_iter)%0A%0A%09%09%09%09dev_acc_list.append(dev_acc.item())%0A%09%09%09%09dev_loss_list.append(dev_loss.item())+%20%2F%2F+保存当前验证集损失值%0A%09%09%09%09if+dev_loss+%3C+dev_best_loss:%0A%09%09%09%09%09dev_best_loss+%3D+dev_loss%0A%09%09%09%09%09torch.save(model.state_dict()%2C+config.save_path)%0A%09%09%09%09%09improve+%3D+''%0A%09%09%09%09%09last_improve+%3D+total_batch%0A%09%09%09%09else:%0A%09%09%09%09%09improve+%3D+''%0A%09%09%09%09time_dif+%3D+get_time_dif(start_time)%0A%09%09%09%09msg+%3D+'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, '+%0A%09%09%09%09%09'Val Acc: {4:>6.2%}, Time: {5} {6}'%0A%09%09%09%09print(msg.format(total_batch%2C+loss.item()%2C+train_acc%2C+dev_loss%2C+dev_acc%2C+time_dif%2C+improve))%0A%09%09%09%09model.train()%0A%09%09%09total_batch+%2B%3D+1%0A%09%09%09if+total_batch+-+last_improve+%3E+config.require_improvement:%0A%09%09%09%09%2F%2F+验证集loss超过1000batch没下降，结束训练%0A%09%09%09%09print('No+optimization+for+a+long+time%2C+auto-stopping...')%0A%09%09%09%09flag+%3D+True%0A%09%09%09%09break%0A%09%09if+flag:%0A%09%09%09break%0A%09%0A%09%2F%2F+将损失列表转换为NumPy数组%0A%09dev_acc_array+%3D+np.array(dev_acc_list)%0A%09dev_loss_array+%3D+np.array(dev_loss_list)%0A%0A%09%2F%2F+绘制准确率曲线图%0A%09plt.figure()+%20%2F%2F+创建新的图像窗口%0A%09plt.plot(dev_acc_array%2C+label%3D'pred_acc'%2C+color%3D'darkorange')%0A%09plt.xlabel('Iters')%0A%09plt.ylabel('Accuracy')%0A%09plt.legend()%0A%09plt.show() %2F%2F+添加plt.show()使图像显示%0A%0A%09%2F%2F+绘制误差曲线图%0A%09plt.figure()+%20%2F%2F+创建新的图像窗口%0A%09%2F%2F+plt.plot(train_loss_array%2C+label%3D'train_loss'%2C+color%3D'limegreen')%0A%09plt.plot(dev_loss_array%2C+label%3D'pred_loss'%2C+color%3D'darkorange')%0A%09plt.xlabel('Iters')%0A%09plt.ylabel('Loss')%0A%09plt.legend()%0A%09plt.show() %2F%2F+添加plt.show()使图像显示%0A%0A%09test(config%2C+model%2C+test_iter)%0A