![]() ![]() In the forward method, we first apply the log_softmax function to the predicted outputs to get the log probabilities. The forward method takes the model’s predicted outputs and the true targets as input. The constructor takes a list of class weights as input. In the above code, we define a custom loss function called RealWorldWeightCrossEntropyLoss. device ) loss = - weights * log_probs ), targets ] loss = torch. log_softmax ( inputs, dim = 1 ) weights = torch. class_weights = class_weights def forward ( self, inputs, targets ): log_probs = F. Module ): def _init_ ( self, class_weights ): super ( RealWorldWeightCrossEntropyLoss, self ). Import torch import torch.nn.functional as F class RealWorldWeightCrossEntropyLoss ( torch. To implement real-world-weight cross-entropy loss in PyTorch, we need to define a custom loss function that takes into account the class weights. How to Implement Real-World-Weight Cross-Entropy Loss in PyTorch Where w_i is the weight assigned to class i, y_i is the true distribution of class i, and y_hat_i is the predicted distribution of class i. The formula for real-world-weight cross-entropy loss is given by: In this loss function, each class is assigned a weight that is inversely proportional to its frequency in the dataset. Real-world-weight cross-entropy loss is a modified version of cross-entropy loss that takes into account the class imbalance in the dataset. What is Real-World-Weight Cross-Entropy Loss? To overcome this issue, we can use real-world-weight cross-entropy loss. In such cases, the model may become biased towards the majority class and perform poorly on the minority class. For example, in a medical diagnosis model, the number of healthy patients may be much higher than the number of diseased patients. In real-world scenarios, the distribution of classes may not be balanced. Where y is the true distribution and y_hat is the predicted distribution. The formula for cross-entropy loss is given by: It measures the dissimilarity between the predicted and actual probability distributions of classes. What is Cross-Entropy Loss?Ĭross-entropy loss is a popular loss function used in classification tasks. In this article, we will explore how to use real-world-weight cross-entropy loss in PyTorch to overcome this issue. However, in real-world scenarios, the distribution of classes may not be balanced, which can lead to biased models. It is a popular loss function used in many deep learning models, including image classification, object detection, and natural language processing. On the other hand, the results with the log of dice score doesn´t lead to great results, but I will experiment it again when my dataset will be bigger.| Miscellaneous How to Use Real-World-Weight Cross-Entropy Loss in PyTorchĪs a data scientist or software engineer, you may be familiar with the concept of cross-entropy loss. ![]() However, I truly believe that there is a “smarter” way to combine them and I am trying to finding that “smarter” way… Maybe something like DiceLoss + 0,7 * CE should be fine to “rescale” the CE But still at the end we don´t have the same range…īut as the values I obtain are close from I simply add the two losses and the result is “good” in terms of mIoU. I think that with nn.CrossEntropyLoss(), pytorch handle by default the normalization of the values (by taking the mean). CE works with the probabilities and then if you have a pixel with a very little probability to belong to a certain class (let´s say 1e-6), the value will be -log( p ) which in our case corresponds to ~14 which is far from the range of the DL. I am not sure to have understood your point. ![]()
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