# Dynamically weighing Binary Cross Entropy with Logits based on batch

**URL:** <https://discuss.pytorch.org/t/dynamically-weighing-binary-cross-entropy-with-logits-based-on-batch/59764>\
**Category:** Uncategorized\
**Created:** [November 1, 2019, 9:54am UTC](https://discuss.pytorch.org/t/dynamically-weighing-binary-cross-entropy-with-logits-based-on-batch/59764 "2019-11-01T09:54:21Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![dhecloud](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/dhecloud/32/6367_2.png) [@dhecloud](https://discuss.pytorch.org/u/dhecloud)\
**Post date:** [November 1, 2019, 9:54am UTC](https://discuss.pytorch.org/t/dynamically-weighing-binary-cross-entropy-with-logits-based-on-batch/59764/1 "2019-11-01T09:54:21Z")

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is it possible to pass your own weights into the loss function during each batch such that it affects how the loss is calculated? Note: I don’t want to use the same weights for every batch (which is the `weight` argument), I want to weigh the loss from each output neuron dynamically based on a rule from the ground truth labels.

So for example, a 2 neuron final layer can have loss weighing `[[1,0.5],[0,0.5]]` for the first batch of 2, and the second batch of 2 can have `[[0,0],[1,1]]`… and so on.  
and the code might look something like this in the training loop:

```auto
target = torch.randn([2, 2], dtype=torch.float32) #placeholder, will be taken from data loader
weights = create_weights_from_target(target)
output = model(inputs)
criterion = torch.nn.BCEWithLogitsLoss()
criterion(output, target, weights=weights) #extra arg to specify weights for each batch

```

Short of reimplementing the whole loss function, is there a better way to do this?  
Please let me know if you need me to clarify anything. Thank you very much!!!

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [November 1, 2019, 10:59am UTC](https://discuss.pytorch.org/t/dynamically-weighing-binary-cross-entropy-with-logits-based-on-batch/59764/2 "2019-11-01T10:59:33Z")

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If you use the functional API, you would avoid recreating the instance of the loss function.  
Alternatively, you could pass `reduction='none'` to the criterion and multiply the loss output manually with your weights. After the multiplication you could take the mean or normalize the loss values as you wish.