The documentation specifies that CrossEntropyLoss combines both LogSoftmax and NLLLoss, which means that I don’t have to implement nn.LogSoftmax into my model; i can just return the output of my last dense layer. However, since I’m not actually running the loss function for the validation stage, would I need to call nn.LogSoftmax on my model output? This is what the main part of my validation loop looks like:
# model returns nn.Linear(..., num_outputs) probs = model(data) label = torch.argmax(label, dim = 1) preds = torch.argmax(probs, dim = 1) running_correct += (preds == label).sum().item()
Basically, since im not running the loss function, there is no softmax being run on the model outputs; should i call LogSoftmax on
probs before calculating the predictions? If this is the case then I assume it would be better to use NLLLoss and throw a softmax in my model.