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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/01/25 01:17:23 UTC
[GitHub] [incubator-mxnet] TEChopra1000 opened a new pull request #17435:
Chopt docfixes
TEChopra1000 opened a new pull request #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435
## Description ##
* A variety of python tutorial pages has incorrectly formatted hyperlinks, resulting in link-rendering issues in the public docs (e.g. https://mxnet.apache.org/api/python/docs/tutorials/packages/ndarray/sparse/train_gluon.html#Sparse-NDArrays-with-Gluon). I took out the back-slashes surrounding hyperlinked words to fix this issues.
* Fixed broken links on the Gluon tutorial index page.
* Small typo fixes
## Testing ##
I tested the doc-build locally and previewed the changes.
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[GitHub] [incubator-mxnet] vishaalkapoor commented on issue #17435: Chopt
docfixes
Posted by GitBox <gi...@apache.org>.
vishaalkapoor commented on issue #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#issuecomment-578363409
Modulo Ehsan's comments, approved.
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[GitHub] [incubator-mxnet] ehsanmok commented on a change in pull request
#17435: Chopt docfixes
Posted by GitBox <gi...@apache.org>.
ehsanmok commented on a change in pull request #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r370903348
##########
File path: docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
##########
@@ -160,7 +160,7 @@ In classification, we often apply the
softmax operator to the predicted outputs to obtain prediction probabilities,
and then apply the cross entropy loss against the true labels:
-$$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
+$$ \begin{align}\begin{aligned}p = softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
Review comment:
Think latex wise using `\text{softmax}` is better that plain "softmax".
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[GitHub] [incubator-mxnet] ehsanmok commented on a change in pull request
#17435: Chopt docfixes
Posted by GitBox <gi...@apache.org>.
ehsanmok commented on a change in pull request #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r370903113
##########
File path: docs/python_docs/python/tutorials/extend/custom_layer.md
##########
@@ -26,7 +26,6 @@ In this article, I will cover how to create a new layer from scratch, how to use
To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
-- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation]({{'/get_started'|relative_url}}).
The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you.
Review comment:
Could you point to a specific tag commit when linking to code part for `forward(self, x)`, `hybridize` etc. ?
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[GitHub] [incubator-mxnet] TEChopra1000 commented on a change in pull
request #17435: Docs: Python tutorials doc fixes
Posted by GitBox <gi...@apache.org>.
TEChopra1000 commented on a change in pull request #17435: Docs: Python tutorials doc fixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r371404167
##########
File path: docs/python_docs/python/tutorials/extend/custom_layer.md
##########
@@ -26,7 +26,6 @@ In this article, I will cover how to create a new layer from scratch, how to use
To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
Review comment:
```suggestion
To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/c9818480680f84daa6e281a974ab263691302ba8/python/mxnet/gluon/block.py#L128) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
```
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[GitHub] [incubator-mxnet] TEChopra1000 commented on a change in pull
request #17435: Docs: Python tutorials doc fixes
Posted by GitBox <gi...@apache.org>.
TEChopra1000 commented on a change in pull request #17435: Docs: Python tutorials doc fixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r371400896
##########
File path: docs/python_docs/python/tutorials/extend/custom_layer.md
##########
@@ -26,7 +26,6 @@ In this article, I will cover how to create a new layer from scratch, how to use
To create a new layer in Gluon API, one must create a class that inherits from [Block](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L123) class. This class provides the most basic functionality, and all pre-defined layers inherit from it directly or via other subclasses. Because each layer in Apache MxNet inherits from `Block`, words "layer" and "block" are used interchangeable inside of the Apache MxNet community.
-- MXNet [7b24137](https://github.com/apache/incubator-mxnet/commit/7b24137ed45df605defa4ce72ec91554f6e445f0). See Instructions in [Setup and Installation]({{'/get_started'|relative_url}}).
The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L415), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you.
Review comment:
```suggestion
The only instance method needed to be implemented is [forward(self, x)](https://github.com/apache/incubator-mxnet/blob/c9818480680f84daa6e281a974ab263691302ba8/python/mxnet/gluon/block.py#L909), which defines what exactly your layer is going to do during forward propagation. Notice, that it doesn't require to provide what the block should do during back propogation. Back propogation pass for blocks is done by Apache MxNet for you.
```
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[GitHub] [incubator-mxnet] TEChopra1000 commented on a change in pull
request #17435: Chopt docfixes
Posted by GitBox <gi...@apache.org>.
TEChopra1000 commented on a change in pull request #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r370907244
##########
File path: docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
##########
@@ -160,7 +160,7 @@ In classification, we often apply the
softmax operator to the predicted outputs to obtain prediction probabilities,
and then apply the cross entropy loss against the true labels:
-$$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
+$$ \begin{align}\begin{aligned}p = softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
Review comment:
Isn't softmax the name of the function, not a LaTex command? I made this change because a ticket pointed out that there was a LaTex rendering issue here: https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/loss/loss.html#Cross-Entropy-Loss-with-Softmax
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[GitHub] [incubator-mxnet] TEChopra1000 commented on a change in pull
request #17435: Docs: Python tutorials doc fixes
Posted by GitBox <gi...@apache.org>.
TEChopra1000 commented on a change in pull request #17435: Docs: Python tutorials doc fixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r371019613
##########
File path: docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
##########
@@ -160,7 +160,7 @@ In classification, we often apply the
softmax operator to the predicted outputs to obtain prediction probabilities,
and then apply the cross entropy loss against the true labels:
-$$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
+$$ \begin{align}\begin{aligned}p = softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
Review comment:
```suggestion
$$ \begin{align}\begin{aligned}p = \text{softmax}({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
```
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[GitHub] [incubator-mxnet] aaronmarkham commented on issue #17435: Chopt
docfixes
Posted by GitBox <gi...@apache.org>.
aaronmarkham commented on issue #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#issuecomment-578835938
@TEChopra1000 please update the title of the PR. These titles end up in the release notes and they should be properly descriptive... like 'documentation update to fix latex rendering" (or similar)
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[GitHub] [incubator-mxnet] ehsanmok commented on a change in pull request
#17435: Chopt docfixes
Posted by GitBox <gi...@apache.org>.
ehsanmok commented on a change in pull request #17435: Chopt docfixes
URL: https://github.com/apache/incubator-mxnet/pull/17435#discussion_r370907772
##########
File path: docs/python_docs/python/tutorials/packages/gluon/loss/loss.md
##########
@@ -160,7 +160,7 @@ In classification, we often apply the
softmax operator to the predicted outputs to obtain prediction probabilities,
and then apply the cross entropy loss against the true labels:
-$$ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
+$$ \begin{align}\begin{aligned}p = softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align}
Review comment:
What you did works. Previously it was `\softmax` with the rendering issue, but enclosing it in `\text{softmax}` tells latex to properly handle text written in latex.
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[GitHub] [incubator-mxnet] aaronmarkham merged pull request #17435: Docs:
Python tutorials doc fixes
Posted by GitBox <gi...@apache.org>.
aaronmarkham merged pull request #17435: Docs: Python tutorials doc fixes
URL: https://github.com/apache/incubator-mxnet/pull/17435
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