![]() I am not sure about the “sparse” part though, but it shouldn’t affect the results. The disadvantage of using softmax and the NLL loss separately is that it’s numerical less stable than using the derivative of the NLL loss with respect to the activation function directly. Loss = F.nll_loss(F.softmax(input), target) I have found this implementation of sparse categorical cross-entropy. Both categorical cross entropy and sparse categorical cross-entropy have the same loss function as defined in Equation 2. Categorical Cross-Entropy and Sparse Categorical Cross-Entropy. The convergence difference you mentioned can have many different reasons including the random seed for the weight initialization and the optimizer parameterization. It seems that Keras Sparse Categorical Crossentropy doesnt work with class weights. Keras provides the following cross-entropy loss functions: binary, categorical, sparse categorical cross-entropy loss functions. The definition may be formulated using the KullbackLeibler divergence, divergence of from (also known as the relative entropy of with respect to ). You could combine nn.Softmax and nn.LLLOSS if you like and see if that replicates the TensorFlow outputs. The cross-entropy of the distribution relative to a distribution over a given set is defined as follows:, where is the expected value operator with respect to the distribution. Use this crossentropy loss function when there are two. There’s NLLLoss, i.e., negative log-likelihood, (operates on softmax output) and CrossEntropyLoss, which already combines the softmax plus the negative-likelihood for you:Īlso note that CrossEntropyLoss uses nn.LogSoftmax, not nn.Softmax – the log version is numerically more stable (not sure how TensorFlow implements their negative log likelihood cost func though, i.e., whether they use log softmax or softmax on the logits). Computes the crossentropy loss between the labels and predictions. The difference is simple: For sparsesoftmaxcrossentropywithlogits, labels must have the shape batchsize and the dtype int32 or int64.Each label is an int in range 0, numclasses-1. Note that BCELoss and BCEWithLogitsLoss is for binary labels. Having two different functions is a convenience, as they produce the same result. ![]()
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