# Understanding transform.Normalize( )

**URL:** <https://discuss.pytorch.org/t/understanding-transform-normalize/21730>\
**Category:** vision\
**Created:** [July 25, 2018, 11:09pm UTC](https://discuss.pytorch.org/t/understanding-transform-normalize/21730 "2018-07-25T23:09:50Z")\
**Posts on this page:** 1\
**Showing post:** 8

<div class="post-metadata">

**Author:** ![bhushans23](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/bhushans23/32/10901_2.png) [@bhushans23](https://discuss.pytorch.org/u/bhushans23)\
**Post date:** [March 4, 2019, 3:47am UTC](https://discuss.pytorch.org/t/understanding-transform-normalize/21730/8 "2019-03-04T03:47:19Z")

</div>

this is very well explained by @InnovArul above [Understanding transform.Normalize( )](https://discuss.pytorch.org/t/understanding-transform-normalize/21730/2?u=bhushans23)  
It depends which normalization method are you using.  
Using normalization transform mentioned above will transform dataset into normalized range [-1, 1]  
If dataset is already in range [0, 1] and normalized, you can choose to skip the normalization in transformation.  
You can choose to normalize and get data in range [0, 1] by tweaking mean and std in transform

---

_[View the full topic](https://discuss.pytorch.org/t/understanding-transform-normalize/21730)._