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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/10/16 17:54:25 UTC
[GitHub] [incubator-mxnet] TEChopra1000 commented on a change in pull
request #16500: Fixing broken links
TEChopra1000 commented on a change in pull request #16500: Fixing broken links
URL: https://github.com/apache/incubator-mxnet/pull/16500#discussion_r335616730
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File path: docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
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@@ -19,8 +19,8 @@
Loss functions are used to train neural networks and to compute the difference between output and target variable. A critical component of training neural networks is the loss function. A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels. Given this score, a network can improve by iteratively updating its weights to minimise this loss. Some tasks use a combination of multiple loss functions, but often you'll just use one. MXNet Gluon provides a number of the most commonly used loss functions, and you'll choose certain loss functions depending on your network and task. Some common task and loss function pairs include:
-- regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L2Loss.html)
-- classification: [SigmoidBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.html), [SoftmaxBinaryCrossEntropyLoss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.SoftmaxBinaryCrossEntropyLoss.html)
+- regression: [L1Loss](/api/python/docs/api/gluon/_autogen/mxnet.gluon.loss.L1Loss.html), [L2Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L2Loss)
Review comment:
```suggestion
- regression: [L1Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L1Loss), [L2Loss](/api/python/docs/api/gluon/loss/index.html#mxnet.gluon.loss.L2Loss)
```
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