# Multi Label Classification in pytorch

**URL:** <https://discuss.pytorch.org/t/multi-label-classification-in-pytorch/905>\
**Category:** Uncategorized\
**Created:** [March 6, 2017, 8:46am UTC](https://discuss.pytorch.org/t/multi-label-classification-in-pytorch/905 "2017-03-06T08:46:45Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![bartolsthoorn](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/bartolsthoorn/32/1182_2.png) [@bartolsthoorn](https://discuss.pytorch.org/u/bartolsthoorn)\
**Post date:** [April 29, 2017, 12:15pm UTC](https://discuss.pytorch.org/t/multi-label-classification-in-pytorch/905/11 "2017-04-29T12:15:12Z")

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@AjayTalati OK I wrote a simple example here: [https://gist.github.com/bartolsthoorn/36c813a4becec1b260392f5353c8b7cc](https://gist.github.com/bartolsthoorn/36c813a4becec1b260392f5353c8b7cc)

For accuracy it is important to note that you can pass the output first through nn.Sigmoid and everything \> 0.5 is true (look at the Sigmoid function: [https://en.wikipedia.org/wiki/Sigmoid\_function](https://en.wikipedia.org/wiki/Sigmoid_function)

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