Cross entropy is one out of many possible loss functions (another popular one is SVM hinge loss). These loss functions are typically written as J (theta) and can be used within gradient descent, which …

The combination of nn.LogSoftmax and nn.NLLLoss is equivalent to using nn.CrossEntropyLoss. This terminology is a particularity of PyTorch, as the nn.NLLoss [sic] computes, in fact, the cross entropy …

Cross Entropy H (p, q) Cross-entropy is a function that compares two probability distributions. From a practical standpoint it's probably not worth getting into the formal motivation of cross-entropy, though …

Dec 8, 2020 · Why?. Because if you add a nn.LogSoftmax (or F.log_softmax) as the final layer of your model's output, you can easily get the probabilities using torch.exp(output), and in order to get cross …

Apr 24, 2017 · Although both of the above methods provide a better score for the better closeness of prediction, still cross-entropy is preferred. Is it in every case or there are some peculiar scenarios …

Mar 16, 2018 · The point is that the cross-entropy and MSE loss are the same. The modern NN learn their parameters using maximum likelihood estimation (MLE) of the parameter space.

Feb 7, 2017 · Therefore it is the product of binary cross-entropy for each single output unit. The binary cross-entropy is defined as and categorical cross-entropy is defined as where c is the index running …

Jul 23, 2019 · This is a very newbie question but I'm trying to wrap my head around cross_entropy loss in Torch so I created the following code: x = torch.FloatTensor([ [1.,0.,0.] ...

As far as I know, the calculation of cross-entropy usually used between two tensors like: Target as [0,0,0,1], where 1 is the right class Output tensor as [0.1,0.2,0.3,0.4], where the sum as 1. So based …

Oct 24, 2018 · Seems, binary cross entropy it's just a special case of the categorical cross entropy. So, when you have only two classes, you can use binary cross entropy, you don't need to do one hot …