bug一堆

main.py

@@ -17,7 +17,7 @@ transform =transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[
 path ='./MNIST/raw'
 
 EPOCH =10
-Batch_Size = 64
+Batch_Size = 10
 #创建dataset
 
 class Dataset:
@@ -73,6 +73,7 @@ class DataLoader:
                 batch_images.append(image)
                 batch_labels.append(label)
             yield (batch_images, batch_labels)
+            torch.tensor(batch_labels)
 #下载数据集
 #下载训练集
 trainData=Dataset(path,train=True)
@@ -87,18 +88,17 @@ class Net(torch.nn.Module):
         #继承父类
         super(Net,self).__init__()
         self.conv1 = torch.nn.Sequential(
-            torch.nn.Conv2d(1, 10, kernel_size=5),
-            torch.nn.ReLU(),
-            torch.nn.MaxPool2d(kernel_size=2),
-        )
-        self.conv2 = torch.nn.Sequential(
             torch.nn.Conv2d(10, 20, kernel_size=5),
             torch.nn.ReLU(),
             torch.nn.MaxPool2d(kernel_size=2),
         )
+        self.conv2 = torch.nn.Sequential(
+            torch.nn.Conv2d(20, 40, kernel_size=5),
+            torch.nn.ReLU(),
+            torch.nn.MaxPool2d(kernel_size=2),
+        )
         self.fc = torch.nn.Sequential(
-            torch.nn.Linear(320, 50),
-            torch.nn.Linear(50, 10),
+            torch.nn.Linear(64,10),
         )
     def forward(self,x):
         batch_size = x.size(0)
@@ -135,22 +135,23 @@ def train(epoch):
         running_loss += loss.item()
         #准确率
         _,predicted = torch.max(outputs,dim=1)
-        running_total+=inputs.shape[0]
+        running_total+=target.size(0)
         running_correct += (predicted == target).sum().item()
-        print('[%d,%5d]:loss:%.3f,acc:%.2f',epoch+1,batch_idx+1,running_loss,running_correct/running_total)
+        print(f'[%d,%5d]:loss:%.3f,acc:%.2f',epoch+1,batch_idx+1,running_loss,running_correct/running_total)
 #测试
 def test():
     correct =0
     total = 0
     with torch.no_grad():
-        for data in test_DataLoader:
-            images,labels = data
+        for images, labels in test_DataLoader:
+            images = torch.stack([torch.tensor(img, dtype=torch.float) for img in images]).to(device)
+            labels = torch.tensor(labels, dtype=torch.long).to(device)
             outputs = model(images)
-            predicted = torch.max(outputs.data,dim=1)
+            _,predicted = torch.max(outputs.data,dim=1)
             total += labels.size(0)
             correct +=(predicted == labels).sum().item()
     accuracy = correct/total
-    print('[%d/%d]Accuracy: %.lf %%', epoch+1,EPOCH,accuracy)
+    print(f'[%d/%d]Accuracy: %.lf %%', epoch+1,EPOCH,accuracy)
     return accuracy