Hey, I did some minor modifications to the CNN tutorial, and it trains fine but when testing the images and labels aren’t being send to the GPU? Thanks for any help:

    def forward(self, x):
        x = self.pool1(F.leaky_relu(self.conv1(x)))
        x = self.pool2(F.leaky_relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.leaky_relu(self.fc1(x))
        x = F.leaky_relu(self.fc2(x))
        x = self.fc3(x)
        return x



net = Net()
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=.01, momentum=0.9)

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()


   # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 500 == 499:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
            (epoch + 1, i + 1, running_loss / 500))
            running_loss = 0.0

print('Finished Training')


correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        print(images.type())
        images.to(device), labels.to(device)
        print(images.type())
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))

torch.FloatTensor
torch.FloatTensor
Traceback (most recent call last):
File “trainNet.py”, line 111, in 
outputs = net(images)
File “/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 491, in call
result = self.forward(*input, **kwargs)
File “trainNet.py”, line 61, in forward
x = self.pool1(F.leaky_relu(self.conv1(x)))
File “/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 491, in call
result = self.forward(*input, **kwargs)
File “/asugar/anaconda3/lib/python3.6/site-packages/torch/nn/modules/conv.py”, line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 ‘weight’