Update installation document and quick start document.

* Add wheel into installation package.
* Improve quick start doc.

Change-Id: I6151f0569fef8b73248ad9aa3452ef5f0c1bb900

doc/build/build_from_source.md

@@ -42,6 +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
 pushd /usr/src/gtest
 cmake .
 make

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` 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 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
@@ -423,7 +423,7 @@ paddle train \
 
 mv rank-00000 result.txt
 ```
-There are several differences between training and inference network configurations.
+User can choose the best model base on the training log instead of model `output/pass-00003`. There are several differences between training and inference network configurations.
 - You do not need labels during inference.
 - Outputs need to be specified to the classification probability layer (the output of softmax layer), or the id of maximum probability (`max_id` layer). An example to output the id and probability is given in the code snippet.
 - batch_size = 1.

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。`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</a>。
 
 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<a href = "../../../doc/layer.html">Layer文档</a>。
-所有配置在`demo/quick_start`目录，首先列举逻辑回归网络。
+所有配置在源码`demo/quick_start`目录，首先列举逻辑回归网络。
 
 ### 逻辑回归模型(Logistic Regression)
 
@@ -407,7 +407,7 @@ paddle train \
 
 mv rank-00000 result.txt
 ```
-与训练网络配置不同的是：无需label相关的层，指定outputs输出概率层(softmax输出)，
+这里以`output/pass-00003`为例进行预测，用户可以根据训练log选择test结果最好的模型来预测。与训练网络配置不同的是：无需label相关的层，指定outputs输出概率层(softmax输出)，
 指定batch_size=1，数据传输无需label数据，预测数据指定test_list的位置。
 
 预测结果以文本的形式保存在`result.txt`中，一行为一个样本，格式如下：