Myujth

class Net(nn.Module):
 def __init__(self):
 super(Net,self).__init__()
 self.conv1=nn.Conv2d(3,10,kernel_size=3,padding=1)
 self.conv2=nn.Conv2d(10,20,kernel_size=3,padding=1)
 self.conv3=nn.Conv2d(20,40,kernel_size=3,padding=1)
 self.conv4=nn.Conv2d(40,80,kernel_size=3,padding=1)
 self.pool=nn.MaxPool2d(2)
 self.fc1=nn.Linear(1024,512)
 self.fc2=nn.Linear(512,10)
 def forward(self,x):
 x=F.relu(self.conv1(x))
 x=self.pool(F.relu(self.conv2(x)))
 x=F.dropout()
 x=F.relu(self.conv3(x))
 x=self.pool(F.relu(self.conv4(x)))
 x=x.view(-1,1024)
 x=self.fc1
 x=self.fc2
 return F.Logsoftmax(x, dim=1)

net=Net()
net=net.cuda()


for epoch in range(num_epochs):
 print(epoch)
 X_train,Y_train=shuffle(X_train,Y_train)
 for i in range(no_of_batches):
 start=i*batch_size
 end=(i+1)*batch_size
 X_var=Variable(torch.cuda.FloatTensor(X_train[start:end]))
 Y_var=Variable(torch.cuda.FloatTensor(Y_train[start:end]))
 optimizer.zero_grad()
 Y_pred=net(X_var)
 loss=Criterion(Y_pred,Y_var)
 loss.backward()
 optimizer.step()

i think the problem here is that my image is 32,32,3 and i need to input image of 3,32,32 pls confirm if it is the problem and if yes pls tell me how can i solve this problem