diff --git a/浙江电压等级电量/电压等级_输出为3.py b/浙江电压等级电量/电压等级_输出为3.py
index 125b537..7f013db 100644
--- a/浙江电压等级电量/电压等级_输出为3.py
+++ b/浙江电压等级电量/电压等级_输出为3.py
@@ -108,26 +108,28 @@ eval_y = dataset_y[train_size:]
 train_x = train_x.reshape(-1, 1, 27)
 train_y = train_y.reshape(-1, 1, 3)
 eval_x = eval_x.reshape(-1, 1, 27)
-eval_y = eval_y.reshape(-1, 1, 27)
+eval_y = eval_y.reshape(-1, 1, 3)
 
 # # 转为pytorch的tensor对象
 train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
 train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
 eval_x = torch.from_numpy(eval_x).to(device).type(torch.float32)
+eval_y = torch.from_numpy(eval_y).to(device).type(torch.float32)
 
 train_ds = TensorDataset(train_x,train_y)
-train_dl = DataLoader(train_ds,batch_size=32,drop_last=True)
+train_dl = DataLoader(train_ds,batch_size=2,drop_last=True)
 eval_ds = TensorDataset(eval_x,eval_y)
-eval_dl = DataLoader(eval_ds,batch_size=64,drop_last=True)
+eval_dl = DataLoader(eval_ds,batch_size=4,drop_last=True)
 
 
 model = LSTM(27, 16, output_size=3, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等
 
 train_loss = []
 loss_function = nn.MSELoss()
-optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+optimizer = torch.optim.Adam(model.parameters(), lr=0.005)
 min_loss = 1
 for i in range(200):
+    model.train()
     for j,(x,y) in enumerate(train_dl):
         x,y = x.to(device),y.to(device)
         out = model(x)
@@ -138,18 +140,28 @@ for i in range(200):
         train_loss.append(loss.item())
     # if (i+1) % 100 == 0:
     #     print(f'epoch {i+1}/1500 loss:{round(loss.item(),5)}')
-        if (j + 1) % 50 == 0:
+        if (j + 1) % 10 == 0:
             print(f'epoch {i+1}/200 step {j+1}/{len(train_dl)} loss:{loss}' )
+    test_running_loss = 0
     model.eval()
-    for k,(x,y) in enumerate(eval_dl):
-        pred = model(eval_x)
-
+    with torch.no_grad():
+        for x,y in eval_dl:
+            pred = model(eval_x)
+            loss = loss_function(pred,y)
+            test_running_loss += loss.item()
+    test_loss = test_running_loss/len(eval_dl)
+    if test_loss < min_loss:
+        min_loss = test_loss
+        best_model_weight = model.state_dict()
+    print(f'epoch {i+1} test_loss:{test_loss}')
 
 # 保存模型
-torch.save(model.state_dict(),'dy3.pth')
+torch.save(best_model_weight,'dy3.pth')
 
+# 读取模型
+model = LSTM(27, 16, output_size=3, num_layers=2).to(device)
+model.load_state_dict(torch.load('dy3.pth'))
 # for test
-model = model.eval()
 
 dataset_x = dataset_x.reshape(-1, 1, 27)  # (seq_size, batch_size, feature_size)
 dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)