I have recently trained a model with NLLLoss that looks like this:
  (0): Linear(in_features=22761, out_features=300, bias=True)
  (1): ReLU()
  (2): Linear(in_features=300, out_features=300, bias=True)
  (3): ReLU()
  (4): Linear(in_features=300, out_features=300, bias=True)
  (5): ReLU()
  (6): Linear(in_features=300, out_features=2, bias=True)
  (7): Softmax()

I want to get the probabilities from the output of the model after testing it with one sample. I use this code to test:
 for batch_idx, (x, y) in enumerate(dataloader): #comprised of one sample
        x = Variable(x.cuda()) #sample of size 22761
        y = Variable(y.cuda()) #label, which is 1

        # forward pass
        y_model = model(x)

        print(torch.log(y_model))

And get the following output:
Variable containing:
  0.0000 -16.9570
[torch.cuda.FloatTensor of size 1x2 (GPU 0)]

I don’t know what these values mean or why they are the same for every input. Could someone please explain what is happening?
Thanks