From 266e439d366f8d3022f52c700a77229811d4cd77 Mon Sep 17 00:00:00 2001 From: Yao Wang Date: Sun, 6 Nov 2016 19:31:52 -0800 Subject: [PATCH] Fix linkand head issue (#3744) * Fix heading hierarchy and class title font size * Fix broken links --- docs/_static/mxnet.css | 41 ++++++++++++++----- docs/get_started/setup.md | 4 +- docs/how_to/faq.md | 2 +- docs/tutorials/computer_vision/detection.md | 2 +- .../computer_vision/image_classification.md | 2 +- .../computer_vision/imagenet_full.md | 4 +- docs/tutorials/r/CallbackFunctionTutorial.md | 2 +- docs/tutorials/r/charRnnModel.md | 2 +- .../r/classifyRealImageWithPretrainedModel.md | 2 +- docs/tutorials/r/fiveMinutesNeuralNetwork.md | 2 +- docs/tutorials/r/mnistCompetition.md | 2 +- docs/zh/api/r/CallbackFunctionTutorial.md | 2 +- docs/zh/api/r/fiveMinutesNeuralNetwork.md | 2 +- 13 files changed, 44 insertions(+), 25 deletions(-) diff --git a/docs/_static/mxnet.css b/docs/_static/mxnet.css index 103a13ea6b09..1c84e26ac3b4 100644 --- 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; } \ No newline at end of file diff --git a/docs/get_started/setup.md b/docs/get_started/setup.md index 620559f1d078..f09c0d591887 100644 --- 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 index 29509f2f7d8a..20ad44682c54 100644 --- 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 index c5db425f935e..8d3439ef789f 100644 --- 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 index 5acdd0009f53..6daa3b2cb0b8 100644 --- 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 index 2052e4546da8..24867021800c 100644 --- 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 index 89cbc0495b5b..5f50625b8104 100644 --- 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 index 5fa3bf8fdc28..2804115ba8ed 100644 --- 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 index e96ed4c5c3ab..208ba754dee0 100644 --- 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 index eb8bfa65085a..c1dc6cbf2b95 100644 --- 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 index 5cf230a2d48b..3ffb8a64fa93 100644 --- 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 index 3fa328f4d334..cd5ae010abad 100644 --- 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 index b372552975bf..ed2c204fb119 100644 --- 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