From 5209fccea16dc01ea107f80d37938478e69c5447 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 6 Sep 2016 13:28:59 +0800 Subject: [PATCH] Update installation document and quick start. * Add wheel into installation package. * Improve quick start doc. Change-Id: I0254e48fd90b874ed3163654b0fab607c0a4195d --- doc/build/build_from_source.md | 2 +- doc/demo/quick_start/index_en.md | 2 +- doc_cn/demo/quick_start/index.md | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/build/build_from_source.md b/doc/build/build_from_source.md index a8de2c42d9..a191d31318 100644 --- a/doc/build/build_from_source.md +++ b/doc/build/build_from_source.md @@ -42,7 +42,7 @@ sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python sudo apt-get install libgoogle-glog-dev sudo apt-get install libgflags-dev sudo apt-get install libgtest-dev -pip install wheel +sudo pip install wheel pushd /usr/src/gtest cmake . make diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md index 179da870e8..ee3fa2a216 100644 --- a/doc/demo/quick_start/index_en.md +++ b/doc/demo/quick_start/index_en.md @@ -59,7 +59,7 @@ To build your text classification system, your code will need to perform five st ## Preprocess data into standardized format In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product. -`demo/quick_start` in the source code provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine). +`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine). ```bash cd demo/quick_start diff --git a/doc_cn/demo/quick_start/index.md b/doc_cn/demo/quick_start/index.md index 05089267b2..34cd4a840e 100644 --- a/doc_cn/demo/quick_start/index.md +++ b/doc_cn/demo/quick_start/index.md @@ -32,7 +32,7 @@ ## 数据格式准备(Data Preparation) 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)， -将评论分为好评(正样本)和差评(负样本)两类。源码的`demo/quick_start`里提供了数据下载脚本 +将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了数据下载脚本 和预处理脚本。 ```bash @@ -144,7 +144,7 @@ PyDataProviderWrapper。 我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置 连接请参考Layer文档。 -所有配置在源码`demo/quick_start`目录，首先列举逻辑回归网络。 +所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。 ### 逻辑回归模型(Logistic Regression) -- GitLab