train

Author: FreeRotate

train.py

@@ -9,57 +9,71 @@
 from utils import batch_variable
 from seqeval.metrics import accuracy_score, classification_report, f1_score
 
+
 def train(model, train_loader, dev_loader, config, vocab):
 
-    loss_all = np.array([], dtype=float)
-    label_all = np.array([], dtype=float)
-    predict_all = np.array([], dtype=float)
     dev_best_f1 = float('-inf')
-
+    avg_loss = []
     optimizer = optim.AdamW(params=model.parameters(), lr=config.lr)
     for epoch in range(0, config.epochs):
+        train_right, train_total = 0, 0
         for batch_idx, batch_data in enumerate(train_loader):
             model.train()   #训练模型
             word_ids, label_ids, label_mask = batch_variable(batch_data, vocab, config)
-            loss, label_predict = model(word_ids, label_ids, label_mask)
+            loss, predicts = model(word_ids, label_ids, label_mask)
 
-            loss_all = np.append(loss_all, loss.data.item())
-            label_all = np.append(label_all, label_ids.data.cpu().numpy())
-            predict_all = np.append(predict_all, label_predict.data.cpu().numpy())
-            acc = accuracy_score(predict_all, label_all)
+            avg_loss.append(loss.data.item())
+
+            batch_right = ((predicts == label_ids) * label_mask).sum().item()
+            batch_total = label_mask.sum().item()
+            train_right += batch_right
+            train_total += batch_total
 
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
 
+
             if batch_idx % 10 == 0:
-                print("Epoch:{}--------Iter:{}--------train_loss:{:.3f}--------train_acc:{:.3f}".format(epoch+1, batch_idx+1, loss_all.mean(), acc))
+                print("Epoch:{}--------Iter:{}--------train_loss:{:.3f}--------train_acc:{:.3f}".format(epoch+1, batch_idx+1, np.array(avg_loss).mean(), train_right/train_total))
         dev_loss, dev_acc, dev_f1, dev_report = evaluate(model, dev_loader, config, vocab)
-        msg = "Dev Loss:{}--------Dev Acc:{}--------Dev F1:{}"
+        msg = "Dev Loss:{:.3f}--------Dev Acc:{:.3f}--------Dev F1:{:.3f}"
         print(msg.format(dev_loss, dev_acc, dev_f1))
-        print("Dev Report")
         print(dev_report)
 
         if dev_best_f1 < dev_f1:
             dev_best_f1 = dev_f1
             torch.save(model.state_dict(), config.save_path)
             print("***************************** Save Model *****************************")
 
-def evaluate(config, model, dev_loader, vocab, output_dict=False):
+def evaluate(model, one_loader, config, vocab, output_dict=False):
     model.eval()    #评价模式
-    loss_all = np.array([], dtype=float)
+    loss_total = 0
     predict_all = []
     label_all = []
     with torch.no_grad():
-        for batch_idx, batch_data in enumerate(dev_loader):
+        for batch_idx, batch_data in enumerate(one_loader):
             word_ids, label_ids, label_mask = batch_variable(batch_data, vocab, config)
-            loss, label_predict = model(word_ids, label_ids, label_mask)
+            loss, predicts = model(word_ids, label_ids, label_mask)
+
+            loss_total = loss_total + loss
+
+            for i, sen_mask in enumerate(label_mask):
+                for j, word_mask in enumerate(sen_mask):
+                    if word_mask.item() == False:
+                        predicts[i][j] = 0
+            labels_list = []
+            for index_i, ids in enumerate(label_ids):
+                labels_list.append([config.id2label[id.cpu().item()] for index_j, id in enumerate(ids)])
+            predicts_list = []
+            for index_i, pres in enumerate(predicts):
+                predicts_list.append([config.id2label[pre.cpu().item()] for index_j, pre in enumerate(pres)])
+
+            label_all += labels_list
+            predict_all += predicts_list
 
-            loss_all = np.append(loss_all, loss.data.item())
-            predict_all.append(label_predict.data)
-            label_all.append(label_ids.data)
     acc = accuracy_score(label_all, predict_all)
-    f1 = f1_score(label_all, predict_all, average='macro')
+    f1 = f1_score(label_all, predict_all, average='micro')
     report = classification_report(label_all, predict_all, digits=3, output_dict=output_dict)
 
-    return loss.mean(), acc, f1, report
+    return loss_total/len(one_loader), acc, f1, report