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Sep 25, 2021 · PyTorch's negative log-likelihood loss, nn.NLLLoss is defined as: So, if the loss is calculated with the standard weight of one in a single batch the formula for the loss is always: -1 * (prediction of model for correct class)

In pytorch, how to use the weight parameter in F.cross_entropy()? – jakub May 21 ... Softmax, CrossEntropyLoss and NLLLoss,Categorical data, ...

17.08.2020 · Passing weights to NLLLoss (and CrossEntropyLoss) gives, with reduction = 'mean', a weighted average where the sum of weighted values is then divided by the sum of the weights. In your `reduction = ‘none’ version: F.nll_loss(mi, target, weight=w, reduction=‘none’).mean() by the time you get to the call to pytorch’s tensor .mean(), the ...

Mar 08, 2019 · log_prob = torch.tensor([[-0.0141, -4.2669]]) target = torch.tensor([0]) weight = torch.tensor([0.00009, 0.99991]) criterion = nn.NLLLoss(weight=weight, reduction='sum') criterion(log_prob, target) out:tensor(1.2690e-06) So in the second case, how to know that my network is well trained since the loss value is already very small. Thank you

NLLLoss. class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean') [source] The negative log likelihood loss. It is useful to train a classification problem with C classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes.

Some loss functions take class weights as input, eg torch NLLLoss, CrossEntropyLoss: parameter ... for further details see pytorch source:

Project: video-caption-openNMT.pytorch Author: xiadingZ File: Loss.py ... NLLLoss(weight, size_average=False) self.confidence = 1.0 - label_smoothing.

NLLLoss — PyTorch 1.9.0 documentation . new pytorch.org. NLLLoss. class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') [source] The negative log likelihood loss. It is useful to train a classification problem with C classes.

NLLLoss · weight (Tensor, optional) – a manual rescaling weight given to each class. · size_average (bool, optional) – Deprecated (see reduction ). · ignore_index ...

You may find answers to your questions as follows: Focal loss automatically handles the class imbalance, hence weights are not required for ...

class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') The negative log likelihood loss. It is useful to train a classification problem with C classes.. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set.

class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') The negative log likelihood loss. It is useful to train a classification problem with C classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set.

08.03.2019 · Hi, I have a very imbalanced data where the weight is in the range [0.0000000012,1], in order to use the weighted NLLloss, if I apply directly these extreme small value weight, I get the very small loss, so if I want to get the same indicator as the case without weight before. What is the strategy of assigning weights? In other words, 1. what should my weight look like (for …

11.01.2020 · But as far as I know, the weight in nn.CrossEntropyLoss() uses for the class-wise weight. In my case, I need to weight sample-wise manner. …

Expected behavior. The loss should be multiplied by the provided weight. Environment. PyTorch version: 0.4.1. Is debug build: No CUDA ...

Sep 25, 2021 · PyTorch's negative log-likelihood loss, nn.NLLLoss is defined as: So, if the loss is calculated with the standard weight of one in a single batch the formula for the loss is always: -1 * (prediction of model for correct class)

Aug 17, 2020 · Passing weights to NLLLoss (and CrossEntropyLoss) gives, with reduction = 'mean', a weighted average where the sum of weighted values is then divided by the sum of the weights. In your `reduction = ‘none’ version: F.nll_loss(mi, target, weight=w, reduction=‘none’).mean() by the time you get to the call to pytorch’s tensor .mean(), the weights

Like this (using PyTorch)? summed = 900 + 15000 + 800 weight = torch.tensor([900, 15000, 800]) / summed crit = nn.CrossEntropyLoss(weight ...

21.11.2018 · Ok this is basically issue #14184 i.e. nn.NLLLoss(weight=weight, reduction='elementwise_mean') ignores weights but nn.NLLLoss(weight=weight, reduction='sum') does not ignore weights. I'm not sure if this is a bug or a documentation issue, but I would expect (and want!) "elementwise_mean" to do a weighted mean.

NLLLoss. class torch.nn.NLLLoss(weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean') [source] The negative log likelihood loss. It is useful to train a classification problem with C classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes.