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Posted to commits@mxnet.apache.org by jx...@apache.org on 2018/03/20 23:09:48 UTC
[incubator-mxnet] branch master updated: Fix broken link (#10177)
This is an automated email from the ASF dual-hosted git repository.
jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 067590b Fix broken link (#10177)
067590b is described below
commit 067590bd22721fcb08ba09e8d898997e5c1905b1
Author: Haibin Lin <li...@gmail.com>
AuthorDate: Tue Mar 20 16:09:43 2018 -0700
Fix broken link (#10177)
* Update row_sparse.md
* Update row_sparse.md
* Update row_sparse.md
---
docs/tutorials/sparse/row_sparse.md | 12 ++++--------
1 file changed, 4 insertions(+), 8 deletions(-)
diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md
index d4f6884..65b7d05 100644
--- a/docs/tutorials/sparse/row_sparse.md
+++ b/docs/tutorials/sparse/row_sparse.md
@@ -17,9 +17,6 @@ Y = mx.nd.dot(X, W)
{'X': X, 'W': W, 'Y': Y}
```
-
-
-
{'W':
[[ 3. 4. 5.]
[ 6. 7. 8.]]
@@ -30,7 +27,6 @@ Y = mx.nd.dot(X, W)
<NDArray 1x3 @cpu(0)>}
-
As you can see,
```
@@ -80,7 +76,7 @@ In this tutorial, we will describe what the row sparse format is and how to use
To complete this tutorial, we need:
-- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/install/index.html)
+- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.incubator.apache.org/install/index.html)
- [Jupyter](http://jupyter.org/)
```
pip install jupyter
@@ -391,7 +387,7 @@ rsp_retained = mx.nd.sparse.retain(rsp, mx.nd.array([0, 1]))
## Sparse Operators and Storage Type Inference
-Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](http://mxnet.io/versions/master/api/python/ndarray/sparse.html) to find what sparse operators are available.
+Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](http://mxnet.incubator.apache.org/api/python/ndarray/sparse.html) to find what sparse operators are available.
```python
@@ -537,8 +533,8 @@ sgd.update(0, weight, grad, momentum)
-Note that both [mxnet.optimizer.SGD](https://mxnet.incubator.apache.org/api/python/optimization.html#mxnet.optimizer.SGD)
-and [mxnet.optimizer.Adam](https://mxnet.incubator.apache.org/api/python/optimization.html#mxnet.optimizer.Adam) support sparse updates in MXNet.
+Note that both [mxnet.optimizer.SGD](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.SGD)
+and [mxnet.optimizer.Adam](https://mxnet.incubator.apache.org/api/python/optimization/optimization.html#mxnet.optimizer.Adam) support sparse updates in MXNet.
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