WebMay 27, 2024 · loss = torch.nn.BCELoss (reduction='none') model = torch.sigmoid weights = torch.rand (10,1) inputs = torch.rand (10,1) targets = torch.rand (10,1) intermediate_losses = loss (model (inputs), targets) final_loss = torch.mean (weights*intermediate_losses) Of course for your scenario you still would need to calculate the weights tensor. WebMay 11, 2024 · Showing the loss reduces to 0.009 instead of 0.99. For completeness, if you have multiple segmentation channels ( B X W X H X K, where B is the batch size, W and H are the dimensions of your image, and K are the different segmentations channels), the same concepts apply, but it can be implemented as follows:
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Correct Implementation of Dice Loss in Tensorflow / Keras
WebThe model that was trained using only the w-dice Loss did not converge. As seen in Figure 1, the model reached a better optima after switching from a combination of w-cel and w-dice loss to pure w-dice loss. We also confirmed the performance gain was significant by testing our trained model on MICCAI Multi-Atlas Labeling challenge test set[6]. WebMar 14, 2024 · from what I know, dice loss for multi class is the average of dice loss for each class. So it is balancing data in a way. But if you want, I think you can change how to average them. NearsightedCV: def aggregate_loss (self, loss): return loss.mean () Var loss should be a vector with shape #Classes. You can multiply it with weight vector. WebDice Loss: Variant of Dice Coefficient Add weight to False positives and False negatives. 9: Sensitivity-Specificity Loss: Variant of Tversky loss with focus on hard examples: 10: Tversky Loss: Variant of Dice Loss and inspired regression log-cosh approach for smoothing Variations can be used for skewed dataset: 11: Focal Tversky Loss porin kaupunki katujen kunnossapito