Fix linkand head issue (#3744)

* Fix heading hierarchy and class title font size * Fix broken links
apache · Nov 7, 2016 · 266e439 · 266e439
1 parent 9107fc3
commit 266e439
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Showing 13 changed files with 44 additions and 25 deletions.
diff --git a/docs/_static/mxnet.css b/docs/_static/mxnet.css
@@ -582,33 +582,42 @@ div.sphinxsidebar ul ul { margin-left: 15px }
 
 
 .section h1 {
-    padding-top: 90px;
+    padding-top: 100px;
     margin-top: -60px;
     padding-bottom: 10px;
-    font-size: 28px;
+    font-size: 32px;
 }
 
 .section h2 {
     padding-top: 100px;
     margin-top: -60px;
     padding-bottom: 10px;
-    font-size: 25px;
+    font-size: 29px;
 }
 
 .section h3 {
-    padding-top: 80px;
-    margin-top: -64px;
+    padding-top: 100px;
+    margin-top: -60px;
     padding-bottom: 8px;
+    font-size: 26px;
 }
 
 .section h4 {
-    padding-top: 80px;
-    margin-top: -64px;
+    padding-top: 100px;
+    margin-top: -60px;
     padding-bottom: 8px;
+    font-size: 23px;
 }
 
-.section ul ul {
-    display: none
+.section h5 {
+    padding-top: 100px;
+    margin-top: -60px;
+    padding-bottom: 8px;
+    font-size: 20px;
+}
+
+div.content a[href="#module-mxnet.symbol"] + ul {
+    display: none;
 }
 
 dt {
@@ -687,8 +696,13 @@ div.highlight-python, div.highlight-none {
     padding-left: 20px 
 }
 
-.content ul {
-    padding-left: 20px 
+.content ol {
+    padding-left: 20px !important
+}
+
+.content ul ul {
+    padding-left: 40px;
+    padding-bottom: 40px
 }
 
 /*API function formation*/
@@ -707,4 +721,9 @@ td {
 
 td p.first {
     margin-bottom: 0
+}
+
+/*Class title font size*/
+dl.class > dt {
+    font-size: 1.2em;
 }
diff --git a/docs/get_started/setup.md b/docs/get_started/setup.md
@@ -65,7 +65,7 @@ If you are running Python on Amazon Linux or Ubuntu, you can use Git Bash script
 
 For users of Python on Amazon Linux and Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.
 
-**Note:** To contribute easy installation scripts for other operating systems and programming languages, see [community page](http://mxnet.io/how_to/contribute.html).
+**Note:** To contribute easy installation scripts for other operating systems and programming languages, see [community page](http://mxnet.io/community/contribute.html).
 
 ### Quick Installation on Amazon Linux
 
@@ -406,7 +406,7 @@ You might want to add this command to your ```~/.bashrc``` file. If you do, you
 Pkg.add("MXNet")
 ```
 
-For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://mxnetjl.readthedocs.org/en/latest/user-guide/install.html)
+For more details about installing and using MXNet with Julia, see the [MXNet Julia documentation](http://dmlc.ml/MXNet.jl/latest/user-guide/install/)
 
 #### Install the MXNet Package for Scala
 There are four ways to install the MXNet package for Scala:

diff --git a/docs/how_to/faq.md b/docs/how_to/faq.md
@@ -63,5 +63,5 @@ memory efficient than cxxnet, purine and more flexible than minerva.
 
 
 #### What is the Relation to Tensorflow
-Both MXNet and [Tensorflow] (https://www.tensorflow.org/) use a computation graph abstraction, which is initially used by Theano, then also adopted by other packages such as CGT, caffe2, purine. Currently TensorFlow adopts an optimized symbolic API. While MXNet supports a more [mixed flavour](https://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming together. 
+Both MXNet and [Tensorflow] (https://www.tensorflow.org/) use a computation graph abstraction, which is initially used by Theano, then also adopted by other packages such as CGT, caffe2, purine. Currently TensorFlow adopts an optimized symbolic API. While MXNet supports a more [mixed flavour](http://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming together. 
 In short, MXNet is lightweight and “mixed”, with flexibility from imperative programming, while getting similar advantages by using a computation graph to make it very fast and memory efficient. That being said, most systems will involve and we expect both systems can learn and benefit from each other.
diff --git a/docs/tutorials/computer_vision/detection.md b/docs/tutorials/computer_vision/detection.md
@@ -22,7 +22,7 @@ and Fast R-CNN. Fast R-CNN weights are used to initiate RPN for training.
 ## Getting Started
 * Install python package `easydict`, `cv2`, `matplotlib`. MXNet require `numpy`.
 * Install MXNet with version no later than Commit 8a3424e, preferably the latest master.
-  Follow the instructions at http://mxnet.readthedocs.io/en/latest/how_to/build.html. Install the python interface.
+  Follow the instructions at http://mxnet.io/get_started/setup.html#quick-installation. Install the python interface.
 * Try out detection result by running `python demo.py --prefix final --epoch 0 --image myimage.jpg --gpu 0`.
   Suppose you have downloaded pretrained network and place the extracted file `final-0000.params` in this folder and there is an image named `myimage.jpg`.
 

diff --git a/docs/tutorials/computer_vision/image_classification.md b/docs/tutorials/computer_vision/image_classification.md
@@ -10,7 +10,7 @@ width=400/>
 
 ## How to use
 
-First build mxnet by following the [guide](http://mxnet.readthedocs.io/en/latest/how_to/build.html)
+First build mxnet by following the [guide](http://mxnet.io/get_started/setup.html#quick-installation)
 
 ### Train
 

diff --git a/docs/tutorials/computer_vision/imagenet_full.md b/docs/tutorials/computer_vision/imagenet_full.md
@@ -40,9 +40,9 @@ After packing, together with threaded buffer iterator, we can simply achieve an
 
 
 Now we have data. We need to consider which network structure to use. We use Inception-BN [3] style model, compared to other models such as VGG, it has fewer parameters, less parameters simplified sync problem. Considering our problem is much more challenging than 1k classes problem, we add suitable capacity into original Inception-BN structure, by increasing the size of filter by factor of 1.5 in bottom layers of original Inception-BN network.
-This however, creates a challenge for GPU memory. As GTX980 only have 4G of GPU RAM. We really need to minimize the memory consumption to fit larger batch-size into the training. To solve this problem we use the techniques such as node memory reuse, and inplace optimization, which reduces the memory consumption by half, more details can be found in  [memory optimization note](http://mxnet.readthedocs.org/en/latest/developer-guide/note_memory.html)
+This however, creates a challenge for GPU memory. As GTX980 only have 4G of GPU RAM. We really need to minimize the memory consumption to fit larger batch-size into the training. To solve this problem we use the techniques such as node memory reuse, and inplace optimization, which reduces the memory consumption by half, more details can be found in  [memory optimization note](http://mxnet.io/architecture/note_memory.html)
 
-Finally, we cannot train the model using a single GPU because this is a really large net, and a lot of data. We use data parallelism on four GPUs to train this model, which involves smart synchronization of parameters between different GPUs, and overlap the communication and computation. A [runtime denpdency engine](https://mxnet.readthedocs.org/en/latest/developer-guide/note_engine.html) is used to simplify this task, allowing us to run the training at around 170 images/sec.
+Finally, we cannot train the model using a single GPU because this is a really large net, and a lot of data. We use data parallelism on four GPUs to train this model, which involves smart synchronization of parameters between different GPUs, and overlap the communication and computation. A [runtime denpdency engine](http://mxnet.io/architecture/note_engine.html) is used to simplify this task, allowing us to run the training at around 170 images/sec.
 
 Here is a learning curve of the training process:
 ![alt text](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/imagenet_full/curve.png "Learning Curve")

diff --git a/docs/tutorials/r/CallbackFunctionTutorial.md b/docs/tutorials/r/CallbackFunctionTutorial.md
@@ -6,7 +6,7 @@ which can very useful in model training.
 
 This tutorial is written in Rmarkdown.
 
-- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/api/r/CallbackFunctionTutorial.html)
+- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html)
 
 - You can find the Rmarkdown source from [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/CallbackFunctionTutorial.Rmd)
 

diff --git a/docs/tutorials/r/charRnnModel.md b/docs/tutorials/r/charRnnModel.md
@@ -8,7 +8,7 @@ Data can be found at [here](https://github.com/dmlc/web-data/tree/master/mxnet/t
 Preface
 -------
 This tutorial is written in Rmarkdown.
-- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/api/r/CharRnnModel.html)
+- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/api/tutorials/charRnnModel.html)
 - You can find the download the Rmarkdown source from [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/CharRnnModel.Rmd)
 
 Load Data 

diff --git a/docs/tutorials/r/classifyRealImageWithPretrainedModel.md b/docs/tutorials/r/classifyRealImageWithPretrainedModel.md
@@ -12,7 +12,7 @@ This model gives the recent state-of-art prediction accuracy on image net datase
 Preface
 -------
 This tutorial is written in Rmarkdown.
-- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/api/r/classifyRealImageWithPretrainedModel.html)
+- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
 - You can find the download the Rmarkdown source from [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/classifyRealImageWithPretrainedModel.Rmd)
 
 Package Loading

diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
@@ -8,7 +8,7 @@ We will show you how to do classification and regression tasks respectively. The
 Preface
 -------
 This tutorial is written in Rmarkdown.
-- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/api/r/fiveMinutesNeuralNetwork.html)
+- You can directly view the hosted version of the tutorial from [MXNet R Document](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
 - You can find the download the Rmarkdown source from [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/fiveMinutesNeuralNetwork.Rmd)
 
 ## Classification

diff --git a/docs/tutorials/r/mnistCompetition.md b/docs/tutorials/r/mnistCompetition.md
@@ -5,7 +5,7 @@ Handwritten Digits Classification Competition
 We will present the basic usage of [MXNet](https://github.com/dmlc/mxnet/tree/master/R-package) to compete in this challenge.
 
 This tutorial is written in Rmarkdown. You can download the source [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/mnistCompetition.Rmd) and view a
-hosted version of tutorial [here](http://mxnet.io/api/r/mnistCompetition.html).
+hosted version of tutorial [here](http://mxnet.io/tutorials/r/mnistCompetition.html).
 
 ## Data Loading
 

diff --git a/docs/zh/api/r/CallbackFunctionTutorial.md b/docs/zh/api/r/CallbackFunctionTutorial.md
@@ -2,7 +2,7 @@
 
 本文将概述我们如何在模型训练的时候使用或者自定义一些回调函数。本教程使用 Rmarkdown 编写。
 
-- 你可以直接看我们在主站上的教程： [MXNet R Document](http://mxnet.io/api/r/CallbackFunctionTutorial.html)
+- 你可以直接看我们在主站上的教程： [MXNet R Document](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html)
 
 - 你可以在这里找到Rmarkdown的源码： [here](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/CallbackFunctionTutorial.Rmd)
 

diff --git a/docs/zh/api/r/fiveMinutesNeuralNetwork.md b/docs/zh/api/r/fiveMinutesNeuralNetwork.md
@@ -8,7 +8,7 @@
 
 本教程由Rmd编辑完成。
 
-- 你可以直接访问主站版本的教程：[MXNet R Document](http://mxnet.io/api/r/fiveMinutesNeuralNetwork.html)
+- 你可以直接访问主站版本的教程：[MXNet R Document](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
 - 你也可以从[这里](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/fiveMinutesNeuralNetwork.Rmd) 下载到Rmarkdown源文件
 
 ## 分类