要跑很多次

main.py

@@ -28,7 +28,7 @@ trainData=torchvision.datasets.MNIST(path,train=True,transform=transform,downloa
 testData=torchvision.datasets.MNIST(path,train=False,transform=transform,download=False)
 #使用dataloader方法开始训练
 #设定batch大小
-BATCH_SIZE=2048
+BATCH_SIZE=1000
 #构建dataloader
 TrainDataLoader = torch.utils.data.DataLoader(dataset = trainData,batch_size=BATCH_SIZE)
 TestDataLoader = torch.utils.data.DataLoader(dataset=testData,batch_size=BATCH_SIZE)
@@ -40,7 +40,7 @@ class Net(torch.nn.Module):
         super(Net,self).__init__()
         self.model = torch.nn.Sequential(
             torch.nn.Conv2d(in_channels=1,out_channels=16,kernel_size=3,stride=1,padding=1),
-        torch.nn.ReLU(),
+            torch.nn.ReLU(),
             torch.nn.MaxPool2d(kernel_size = 2,stride = 2),
             
             #The size of the picture is 14x14
@@ -104,5 +104,4 @@ for epochs in range(0,Epochs):
             history['Test Accuracy'].append(testAccuracy.item())
             processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f, Test Loss: %.4f, Test Acc: %.4f" % 
                                    (epochs,Epochs,loss.item(),accuracy.item(),testLoss.item(),testAccuracy.item()))
-    processBar.close()
-torch.save(net,'./model.pth')
+    processBar.close()