# Adding L1/L2 regularization in a Convolutional Networks in PyTorch?

**URL:** <https://discuss.pytorch.org/t/adding-l1-l2-regularization-in-a-convolutional-networks-in-pytorch/7734>\
**Category:** vision\
**Created:** [September 22, 2017, 6:24pm UTC](https://discuss.pytorch.org/t/adding-l1-l2-regularization-in-a-convolutional-networks-in-pytorch/7734 "2017-09-22T18:24:01Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**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:** [March 2, 2018, 12:33pm UTC](https://discuss.pytorch.org/t/adding-l1-l2-regularization-in-a-convolutional-networks-in-pytorch/7734/3 "2018-03-02T12:33:48Z")

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You could implement L! regularization using something like [example of L2 regularization](https://discuss.pytorch.org/t/how-does-one-implement-weight-regularization-l1-or-l2-manually-without-optimum/7951/2?u=ptrblck).  
For L1 regularization, you should change `W.norm(2)` to `W.norm(p=1)`.

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