Transfer learning low accuracy and CUDA not working

Hi there,

I am running into a few problems while trying out this code:

So basically I have just downloaded the git and the dog and cat samples and run the code without any changes, except for the paths I am using of course. The first problem is, that I only get accuracies of about 51-52% which does not go higher after 25 epochs. Do I need to set up something different to get higher accuracies?

While classifying, I only get about 15-30% accuracies which is not acceptable I guess.

Another problem I have is, when I try to train on CUDA, which runs just fine and I can classifying using CUDA but the accuracies are negative like so: -35940% for example.

The changes I did for training was this:

 module = torch::jit::load(weight, at::kCUDA);
 target =;

and for the classifying part I made these changes:

 img_tensor =;
 model = torch::jit::load(model_path, at::kCUDA);
 torch::load(model_linear, model_path_linear, at::kCUDA);

Anybody can help please?

Edit: The classifying does not work at all. While using the CPU trained version, I always get about the same results, which is about 20% cat, 30% dog, it does only change a few % whatever images I am using to classify. The results always stay about the same.