Cross entropy loss12/10/2023 ![]() ![]() Sparse categorical crossentropy loss value. The crossentropy is a function of the model's weights and biases, the pixels of the training image, and its known class. By default ( ignore_class=None), all classes are considered. Problems featuring a "void" class (commonly -1 or 255) in segmentation This is useful, for example, in segmentation One such loss ListNets which measures the cross entropy between a distribution over documents obtained from scores and another from ground-truth labels. Byĭefault, we assume that y_pred encodes a probability distribution. Model As cross-entropy loss is 2.073 model Bs is 0.505. CrossEntropyLoss class torch.nn.CrossEntropyLoss(weightNone, sizeaverageNone, ignoreindex- 100, reduceNone, reductionmean, labelsmoothing0. from_logits: Whether y_pred is expected to be a logits tensor. Cross-entropy loss is the sum of the negative logarithm of predicted probabilities of each student.y_true, y_pred, ignore_class =- 1 ) > loss. ( lossA will, of course,īe zero for “perfect” predictions.> y_true =. From your code: loss lossfn(out,torch.argmax(targets, dim1)) you are using torch.argmax function which expects targets size as torch.Size(numsamples, numclasses) or torch.Size(32, 6) in your case. Will yield lossB = -17.2 rather than zero. First of all, Lets review the way you are calculating loss. Imagine optimizing with lossB = lossA - 17.2. But this is by no meansĬonsider, for example, optimizing with lossA = MSELoss. To calculate the total loss of a single discriminator, a sigmoid function is introduced to normalise the output, and a binary cross-entropy loss is used to. It is true that several common loss functions are non-negative, andīecome zero precisely when the predictions are “perfect.” Examples (Also, when the gradient is zero, plain-vanilla gradient When gradient descent drives the loss to a minimum, the gradientīecomes zero (although it can be zero at places other than a Cross entropy can be used to calculate loss. To change the model parameters to reduce the loss, and it doesn’tĬare about the overall level of the loss. The cross entropy between two probability distributions over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set. The overall level of the loss doesn’t matter asįar as the optimization goes. Optimization step uses some version of gradient descent to make Negative – means (or should mean) better predictions. Yes, it is perfectly fine to use a loss that can become negative.Ī smaller loss – algebraically less positive or algebraically more This can be best explained through an example. My current guess is that it is working fine because the optimization of loss function is to reduce the gradient of loss to zero not the loss itself. Cross entropy is a loss function that can be used to quantify the difference between two probability distributions. Thank you and I look forward to hearing from someone to answer this question soon! I used the following approach for IOU loss: How to implement soft-IoU loss? My question is, is it okay to use combination of positive and negative loss functions as what matters is just a gradient of my final loss function? The lesser the loss, the better the model for prediction. Before going into detail, however, let’s briefly discuss loss functions. Cross entropy is used to determine how the loss can be minimized to get a better prediction. It measures the variables to extract the difference in the information they contain, showcasing the results. My current guess is that it is working fine because the optimization of loss function is to reduce the gradient of loss to zero not the loss itself. Cross-entropy loss refers to the contrast between two random variables. Hence, during training, my loss values go below 0 as the training continues. This means though that my final loss will be sum of positive, positive and negative values which seem to me very odd and don’t really make sense but surprisingly working not badly. First, it seemed odd to me that it returns -loss, so i changed the function to return 1-loss, but it performed worse so I believe the negative loss is correct approach. Basically, for my loss function I am using Weighted cross entropy + Soft dice loss functions but recently I came across with a mean IOU loss which works, but the problem is that it purposely return negative loss. I am currently doing a deep learning research project and have a question regarding use of loss function. ![]()
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