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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.

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 …

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)

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

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

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.

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

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

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

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)

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.

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

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.

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

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 ...

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. …

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.

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