Nrthugu

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