Thanks I saved it as PNG using ‘L’.
I used unsqueeze(0) in this part
iter=0
for epoch in range(num_epochs):
    for img, labels in train_loader:
        **img=img.unsqueeze(0)**
        print(img.size())
        img=Variable(img)
        labels=Variable(labels)
        optimizer.zero_grad()
        outputs=model(img)
        loss=criterion(outputs,labels)
        loss.backward()
        optimizer.step()
        iter+=1
        if(iter%100==0):
            correct=0
            total=0
            for Images,labels in test_loader:
                Images=Variable(Images)
                outputs=model(Images)
                _,predicted=torch.max(outputs.data,1)
                total+=labels.size(0)
                correct+=(predicted==labels).sum()
            accuracy=100*correct/total
            print('Iterations: {}. Loss: {}, Accuracy: {}'.format(iter,loss.data[0],accuracy))

It shows,
torch.Size([1, 100, 3, 64, 64])
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-90-59c079bda59c> in <module>
      7         labels=Variable(labels)
      8         optimizer.zero_grad()
----> 9         outputs=model(img)
     10         loss=criterion(outputs,labels)
     11         loss.backward()

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    489             result = self._slow_forward(*input, **kwargs)
    490         else:
--> 491             result = self.forward(*input, **kwargs)
    492         for hook in self._forward_hooks.values():
    493             hook_result = hook(self, input, result)

<ipython-input-82-0291a955352f> in forward(self, x)
     14     def forward(self,x):
     15         #Convo_1
---> 16         out=self.cnn1(x)
     17         out=self.relu1(out)
     18         #Max_pool1

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    489             result = self._slow_forward(*input, **kwargs)
    490         else:
--> 491             result = self.forward(*input, **kwargs)
    492         for hook in self._forward_hooks.values():
    493             hook_result = hook(self, input, result)

~\Anaconda3\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
    299     def forward(self, input):
    300         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 301                         self.padding, self.dilation, self.groups)
    302 
    303 

RuntimeError: expected stride to be a single integer value or a list of 3 values to match the convolution dimensions, but got stride=[1, 1]