![]() When we use accuracy as a loss function, most of the time our gradients will actually be zero, and the model will not be able to learn from that number. This means it is not useful to use accuracy as a loss function. In other words, the gradient is zero almost everywhere.Īs a result, a very small change in the value of a weight will often not actually change the accuracy at all. ![]() So the problem is that a small change in weights from x_old to x_new isn't likely to cause any prediction to change, so (y_new - y_old) will be zero. The Connectionist Temporal Classification loss. This criterion computes the cross entropy loss between input logits and target. But accuracy only changes at all when a prediction changes from a 3 to a 7, or vice versa. Function that measures Binary Cross Entropy between target and input logits. Specifically, it is defined when x_new is very similar to x_old, meaning that their difference is very small. We can write this in maths: (y_new-y_old) / (x_new-x_old). "The gradient of a function is its slope, or its steepness, which can be defined as rise over run - that is, how much the value of function goes up or down, divided by how much you changed the input. Or the more technical explanation from fastbook: a morbid people whose precarious life spans were riven by constant loss. Rember that a loss function returns a number. Their science allowed them to cross the vast gulfs of space with only a single. That information provides you're model with a much better insight w/r/t to how well it is really doing in a single number (INF to 0), resulting in gradients that the model can actually use! It will:ġ) Penalize correct predictions that it isn't confident about more so than correct predictions it is very confident about.Ģ) And vice-versa, it will penalize incorrect predictions it is very confident about more so than incorrect predictions it isn't very confident aboutīecause accuracy simply tells you whether you got it right or wrong (a 1 or a 0), whereast NLL incorporates the confidence as well. What does this all mean? The lower the confidence it has in predicting the correct class, the higher the loss. Logit = (1-gt_tensor) * a + gt_tensor * bįocal_loss = - (1-logit) ** gamma * torch.log(logit)įocal loss is also used quite frequently so here it is.NLL loss will be higher the smaller the probability of the correct class Using the functions defined above, def manual_focal_loss(pred_tensor, gt_tensor, gamma, epsilon = 1e-8): torch.nn.functional.crossentropy(input, target, weightNone, sizeaverageNone, ignoreindex- 100, reduceNone, reductionmean, labelsmoothing0. The epsilon value will be limiting the original logit value’s minimum value. The above binary cross entropy calculation will try to avoid any NaN occurrences due to excessively small logits when calculating torch.log which should return a very large negative number which may be too big to process resulting in NaN. If you are using torch 1.6, you can change refactor the logit_sanitation function with the updated torch.max function. However, in 1.4 this feature is not yet supported and that is why I had to unsqueeze, concatenate and then apply torch.max in the above snippet. Loss = - ( (1- gt_tensor) * torch.log(a) + gt_tensor * torch.log(b))Ĭurrently, torch 1.6 is out there and according to the pytorch docs, the torch.max function can receive two tensors and return element-wise max values. ![]() Limit = torch.ones_like(unsqueezed_a) * min_valĭef manual_bce_loss(pred_tensor, gt_tensor, epsilon = 1e-8):Ī = logit_sanitation(1-pred_tensor, epsilon)ī = logit_sanitation(pred_tensor, epsilon) At the moment, the code is written for torch 1.4 binary cross entropy loss # using pytorch 1.4
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