From 01339b6f6e7fef00d8c57125391723cb757d0eec Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Tue, 7 Mar 2017 16:00:10 -0800
Subject: [PATCH] Add pre-commit-hooks/convert-markdown-into-html.py and use it

---
 .gitignore                                    |   2 +
 .pre-commit-config.yaml                       |   8 +
 fit_a_line/README.en.md                       |   2 +-
 fit_a_line/index.en.html                      | 208 +++----
 fit_a_line/index.html                         | 147 ++---
 gan/index.html                                |   6 +-
 image_caption/index.html                      |   6 +-
 image_classification/index.en.html            |   6 +-
 image_classification/index.html               |   6 +-
 image_detection/index.html                    |   6 +-
 image_qa/index.html                           |   6 +-
 index.html                                    |  74 ++-
 label_semantic_roles/index.en.html            |  46 +-
 label_semantic_roles/index.html               |   6 +-
 machine_translation/index.en.html             |   6 +-
 machine_translation/index.html                | 512 ++++++------------
 pre-commit-hooks/build.sh                     |   9 -
 ...-html.sh => convert_markdown_into_html.py} |  44 +-
 query_relationship/index.html                 |   6 +-
 recognize_digits/index.en.html                |   6 +-
 recognize_digits/index.html                   |   6 +-
 recommender_system/index.en.html              |   6 +-
 recommender_system/index.html                 | 462 +++++++++-------
 skip_thought/index.html                       |   6 +-
 speech_recognition/index.html                 |   6 +-
 understand_sentiment/index.en.html            |   6 +-
 understand_sentiment/index.html               |   6 +-
 word2vec/index.en.html                        |   6 +-
 word2vec/index.html                           | 209 ++++---
 29 files changed, 899 insertions(+), 926 deletions(-)
 delete mode 100755 pre-commit-hooks/build.sh
 rename pre-commit-hooks/{convert-markdown-into-html.sh => convert_markdown_into_html.py} (70%)
 mode change 100755 => 100644

diff --git a/.gitignore b/.gitignore
index 0a0dd02..b7dfec3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,4 @@
+deprecated
+*~
 pandoc.template
 .DS_Store
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0d8234b..cba2de8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,3 +14,11 @@
     -   id: check-symlinks
     -   id: detect-private-key
     -   id: end-of-file-fixer
+- repo: local
+  hooks:
+    -  id: convert-markdown-into-html
+       name: convert-markdown-into-html
+       description: "Convert README.md into index.html and README.en.md into index.en.html"
+       entry: python pre-commit-hooks/convert_markdown_into_html.py
+       language: system
+       files: \.md$
diff --git a/fit_a_line/README.en.md b/fit_a_line/README.en.md
index 21f457c..29aabda 100644
--- a/fit_a_line/README.en.md
+++ b/fit_a_line/README.en.md
@@ -202,4 +202,4 @@ This chapter introduces *Linear Regression* and how to train and test this model
 4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Common Creative License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a> This tutorial was created and published with [Creative Common License 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).
diff --git a/fit_a_line/index.en.html b/fit_a_line/index.en.html
index b2492b2..8e3a35f 100644
--- a/fit_a_line/index.en.html
+++ b/fit_a_line/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -40,61 +41,75 @@
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
 # Linear Regression
-Let us begin the tutorial with a classical problem called Linear Regression \[[1](#References)\]. In this chapter, we will train a model from a realistic dataset to predict house prices. Some important concepts in Machine Learning will be covered through this example.
+Let us begin the tutorial with a classical problem called Linear Regression \[[1](#References)\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example.
+
+The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).
 
-The source code for this tutorial is at [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). If this is your first time using PaddlePaddle, please refer to the [Install Guide](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).
+## Problem Setup
+Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.
 
-## Problem
-Suppose we have a dataset of $n$ houses. Each house $i$ has $d$ properties and the price $y_i$. A property $x_{i,d}$ describes one aspect of the house, for example, the number of rooms in the house, the number of schools or hospitals in the neighborhood, the nearby traffic condition, etc. Our task is to predict $y_i$ given a set of properties $\{x_{i,1}, ..., x_{i,d}\}$. We assume that the price is a linear combination of all the properties, i.e.,
+Each home is associated with $d$ attributes. The attributes describe characteristics such the number of rooms in the home, the number of schools or hospitals in the neighborhood, and the traffic condition nearby.
+
+In our problem setup, the attribute $x_{i,j}$ denotes the $j$th characteristic of the $i$th home. In addition, $y_i$ denotes the price of the $i$th home. Our task is to predict $y_i$ given a set of attributes $\{x_{i,1}, ..., x_{i,d}\}$. We assume that the price of a home is a linear combination of all of its attributes, namely,
 
 $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b,  i=1,\ldots,n$$
 
-where $\omega_{d}$ and $b$ are the model parameters we want to estimate. Once they are learned, given a set of properties of a house, we will be able to predict a price for that house. The model we have here is called Linear Regression, namely, we want to regress a value as a linear combination of several values. In practice this linear model for our problem is hardly true, because the real relationship between the house properties and the price is much more complicated. However, due to its simple formulation which makes the model training and analysis easy, Linear Regression has been applied to lots of real problems. It is always an important topic in many classical Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
+where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
 
 ## Results Demonstration
-We first show the training result of our model. We use the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) to train a linear model and predict the house prices in Boston. The figure below shows the predictions the model makes for some house prices. The $X$ coordinate of each point represents the median value of the prices of a certain type of houses, while the $Y$ coordinate represents the predicted value by our linear model. When $X=Y$, the point lies exactly on the dotted line. In other words, the more precise the model predicts, the closer the point is to the dotted line.
+We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of simlilar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the more precise the model predicts, the closer the point is to the dotted line.
 <p align="center">
-	<img src = "image/predictions_en.png" width=400><br/>
-	Figure 1. Predicted Value V.S. Actual Value
+    <img src = "image/predictions_en.png" width=400><br/>
+    Figure 1. Predicted Value V.S. Actual Value
 </p>
 
 ## Model Overview
 
 ### Model Definition
 
-In the UCI Housing Data Set, there are 13 house properties $x_{i,d}$ that are related to the median house price $y_i$. Thus our model is:
+In the UCI Housing Data Set, there are 13 home attributes $\{x_{i,j}\}$ that are related to the median home price $y_i$, which we aim to predict. Thus, our model can be written as:
 
 $$\hat{Y} = \omega_1X_{1} + \omega_2X_{2} + \ldots + \omega_{13}X_{13} + b$$
 
-where $\hat{Y}$ is the predicted value used to differentiate from the actual value $Y$. The model parameters to be learned are: $\omega_1, \ldots, \omega_{13}, b$, where $\omega$ are called the weights and $b$ is called the bias.
+where $\hat{Y}$ is the predicted value used to differentiate from actual value $Y$. The model learns parameters $\omega_1, \ldots, \omega_{13}, b$, where the entries of $\vec{\omega}$ are **weights** and $b$ is **bias**.
 
-Now we need an optimization goal, so that with the learned parameters, $\hat{Y}$ is close to $Y$ as much as possible. Here we introduce the concept of [Loss Function (Cost Function)](https://en.wikipedia.org/wiki/Loss_function). The Loss Function has such property: given any pair of the actual value $y_i$ and the predicted value $\hat{y_i}$, its output is always non-negative. This non-negative value reflects the model error.
+Now we need an objective to optimize, so that the learned parameters can make $\hat{Y}$ as close to $Y$ as possible. Let's refer to the concept of [Loss Function (Cost Function)](https://en.wikipedia.org/wiki/Loss_function). A loss function must output a non-negative value, given any pair of the actual value $y_i$ and the predicted value $\hat{y_i}$. This value reflects the magnitutude of the model error.
 
-For Linear Regression, the most common Loss Function is [Mean Square Error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error) which has the following form:
+For Linear Regression, the most common loss function is [Mean Square Error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error) which has the following form:
 
 $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 
-For a dataset of size $n$, MSE is the average value of the $n$ predicted errors.
+That is, for a dataset of size $n$, MSE is the average value of the the prediction sqaure errors.
 
 ### Training
 
-After defining our model, we have several major steps for the training:
-1. Initialize the parameters including the weights $\omega$ and the bias $b$. For example, we can set their mean values as 0s, and their standard deviations as 1s.
-2. Feedforward to compute the network output and the Loss Function.
-3. Backward to [backpropagate](https://en.wikipedia.org/wiki/Backpropagation) the errors. The errors will be propagated from the output layer back to the input layer, during which the model parameters will be updated with the corresponding errors.
+After setting up our model, there are several major steps to go through to train it:
+1. Initialize the parameters including the weights $\vec{\omega}$ and the bias $b$. For example, we can set their mean values as $0$s, and their standard deviations as $1$s.
+2. Feedforward. Evaluate the network output and compute the corresponding loss.
+3. [Backpropagate](https://en.wikipedia.org/wiki/Backpropagation) the errors. The errors will be propagated from the output layer back to the input layer, during which the model parameters will be updated with the corresponding errors.
 4. Repeat steps 2~3, until the loss is below a predefined threshold or the maximum number of repeats is reached.
 
-## Data Preparation
-Follow the command below to prepare data:
-```bash
-cd data && python prepare_data.py
+## Dataset
+
+### Python Dataset Modules
+
+Our program starts with importing necessary packages:
+
+```python
+import paddle.v2 as paddle
+import paddle.v2.dataset.uci_housing as uci_housing
 ```
-This line of code will download the dataset from the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) and perform some [preprocessing](#Preprocessing). The dataset is split into a training set and a test set.
 
-The dataset contains 506 lines in total, each line describing the properties and the median price of a certain type of houses in Boston. The meaning of each line is below:
+We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
 
+1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if not yet, and
+2.  [preprocesses](#preprocessing) the dataset.
 
-| Property Name | Explanation | Data Type |
+### An Introduction of the Dataset
+
+The UCI housing dataset has 506 instances. Each instance describes the attributes of a house in surburban Boston.  The attributes are explained below:
+
+| Attribute Name | Characteristic | Data Type |
 | ------| ------ | ------ |
 | CRIM | per capita crime rate by town | Continuous|
 | ZN | proportion of residential land zoned for lots over 25,000 sq.ft. | Continuous |
@@ -111,113 +126,115 @@ The dataset contains 506 lines in total, each line describing the properties and
 | LSTAT | % lower status of the population | Continuous |
 | MEDV | Median value of owner-occupied homes in $1000's | Continuous |
 
-The last entry is the median house price.
+The last entry is the median home price.
 
 ### Preprocessing
 #### Continuous and Discrete Data
-We define a feature vector of length 13 for each house, where each entry of the feature vector corresponds to a property of that house. Our first observation is that among the 13 dimensions, there are 12 continuous dimensions and 1 discrete dimension. Note that although a discrete value is also written as digits such as 0, 1, or 2, it has a quite different meaning from a continuous value. The reason is that the difference between two discrete values has no practical meaning. For example, if we use 0, 1, and 2 to represent `red`, `green`, and `blue` respectively, although the numerical difference between `red` and `green` is smaller than that between `red` and `blue`, we cannot say that the extent to which `blue` is different from `red` is greater than the extent to which `green` is different from `red`. Therefore, when handling a discrete feature that has $d$ possible values, we will usually convert it to $d$ new features where each feature can only take 0 or 1, indicating whether the original $d$th value is present or not. Or we can map the discrete feature to a continuous multi-dimensional vector through an embedding table. For our problem here, because CHAS itself is a binary discrete value, we do not need to do any preprocessing.
+We define a feature vector of length 13 for each home, where each entry corresponds to an attribute. Our first observation is that, among the 13 dimensions, there are 12 continuous dimensions and 1 discrete dimension.
+
+Note that although a discrete value is also written as numeric values such as 0, 1, or 2, its meaning differs from a continuous value drastically.  The linear difference between two discrete values has no meaning. For example, suppose $0$, $1$, and $2$ are used to represent colors *Red*, *Green*, and *Blue* respectively. Judging from the numeric representation of these colors, *Red* differs more from *Blue* than it does from *Green*. Yet in actuality, it is not true that extent to which the color *Blue* is different from *Red* is greater than the extent to which *Green* is different from *Red*. Therefore, when handling a discrete feature that has $d$ possible values, we usually convert it to $d$ new features where each feature takes a binary value, $0$ or $1$, indicating whether the original value is absent or present. Alternatively, the discrete features can be mapped onto a continuous multi-dimensional vector through an embedding table. For our problem here, because CHAS itself is a binary discrete value, we do not need to do any preprocessing.
 
 #### Feature Normalization
-Another observation we have is that there is a huge difference among the value ranges of the 13 features (Figure 2). For example, feature B has a value range of [0.32, 396.90] while feature NOX has a range of [0.3850, 0.8170]. For an effective optimization, here we need data normalization. The goal of data normalization is to scale each feature into roughly the same value range, for example [-0.5, 0.5]. In this example, we adopt a standard way of normalization: substracting the mean value from the feature and divide the result by the original value range.
+We also observe a huge difference among the value ranges of the 13 features (Figure 2). For instance, the values of feature *B* fall in $[0.32, 396.90]$, whereas those of feature *NOX* has a range of $[0.3850, 0.8170]$. An effective optimization would require data normalization. The goal of data normalization is to scale te values of each feature into roughly the same range, perhaps $[-0.5, 0.5]$. Here, we adopt a popular normalization technique where we substract the mean value from the feature value and divide the result by the width of the original range.
 
 There are at least three reasons for [Feature Normalization](https://en.wikipedia.org/wiki/Feature_scaling) (Feature Scaling):
 - A value range that is too large or too small might cause floating number overflow or underflow during computation.
-- Different value ranges might result in different importances of different features to the model (at least in the beginning of the training process), which however is an unreasonable assumption. Such assumption makes the optimization more difficult and increases the training time a lot.
-- Many Machine Learning techniques or models (e.g., L1/L2 regularization and Vector Space Model) are based on the assumption that all the features have roughly zero means and their value ranges are similar.
+- Different value ranges might result in varying *importances* of different features to the model (at least in the beginning of the training process). This assumption about the data is often unreasonable, making the optimization difficult, which in turn results in increased training time.
+- Many machine learning techniques or models (e.g., *L1/L2 regularization* and *Vector Space Model*) assumes that all the features have roughly zero means and their value ranges are similar.
 
 <p align="center">
-	<img src = "image/ranges_en.png" width=550><br/>
-	Figure 2. The value ranges of the features
+    <img src = "image/ranges_en.png" width=550><br/>
+    Figure 2. The value ranges of the features
 </p>
 
 #### Prepare Training and Test Sets
-We split the dataset into two subsets, one for estimating the model parameters, namely, model training, and the other for model testing. The model error on the former is called the **training error**, and the error on the latter is called the **test error**. Our goal of training a model is to find the statistical dependency between the outputs and the inputs, so that we can predict new outputs given new inputs. As a result, the test error reflects the performance of the model better than the training error does. We consider two things when deciding the ratio of the training set to the test set: 1) More training data will decrease the variance of the parameter estimation, yielding more reliable models; 2) More test data will decrease the variance of the test error, yielding more reliable test errors. One standard split ratio is $8:2$. You can try different split ratios to observe how the two variances change.
+We split the dataset in two, one for adjusting the model parameters, namely, for model training, and the other for model testing. The model error on the former is called the **training error**, and the error on the latter is called the **test error**. Our goal in training a model is to find the statistical dependency between the outputs and the inputs, so that we can predict new outputs given new inputs. As a result, the test error reflects the performance of the model better than the training error does. We consider two things when deciding the ratio of the training set to the test set: 1) More training data will decrease the variance of the parameter estimation, yielding more reliable models; 2) More test data will decrease the variance of the test error, yielding more reliable test errors. One standard split ratio is $8:2$.
+
+
+When training complex models, we usually have one more split: the validation set. Complex models usually have [Hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_optimization) that need to be set before the training process, such as the number of layers in the network. Because hyperparameters are not part of the model parameters, they cannot be trained using the same loss function. Thus we will try several sets of hyperparameters to train several models and cross-validate them on the validation set to pick the best one; finally, the selected trained model is tested on the test set. Because our model is relatively simple, we will omit this validation process.
+
+
+## Training
+
+`fit_a_line/trainer.py` demonstrates the training using [PaddlePaddle](http://paddlepaddle.org).
+
+### Initialize PaddlePaddle
 
-Executing the following command to split the dataset and write the training and test set into the `train.list` and `test.list` files, so that later PaddlePaddle can read from them.
 ```python
-python prepare_data.py -r 0.8 #8:2 is the default split ratio
+paddle.init(use_gpu=False, trainer_count=1)
 ```
 
-When training complex models, we usually have one more split: the validation set. Complex models usually have [Hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_optimization) that need to be set before the training process begins. These hyperparameters are not part of the model parameters and cannot be trained using the same Loss Function (e.g., the number of layers in the network). Thus we will try several sets of hyperparameters to get several models, and compare these trained models on the validation set to pick the best one, and finally it on the test set. Because our model is relatively simple in this problem, we ignore this validation process for now.
+### Model Configuration
 
-### Provide Data to PaddlePaddle
-After the data is prepared, we use a Python Data Provider to provide data for PaddlePaddle. A Data Provider is a Python function which will be called by PaddlePaddle during training. In this example, the Data Provider only needs to read the data and return it to the training process of PaddlePaddle line by line.
+Logistic regression is essentially a fully-connected layer with linear activation:
 
 ```python
-from paddle.trainer.PyDataProvider2 import *
-import numpy as np
-#define data type and dimensionality
-@provider(input_types=[dense_vector(13), dense_vector(1)])
-def process(settings, input_file):
-    data = np.load(input_file.strip())
-    for row in data:
-	    yield row[:-1].tolist(), row[-1:].tolist()
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+y_predict = paddle.layer.fc(input=x,
+                                size=1,
+                                act=paddle.activation.Linear())
+y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
+cost = paddle.layer.regression_cost(input=y_predict, label=y)
+```
+### Create Parameters
 
+```python
+parameters = paddle.parameters.create(cost)
 ```
 
-## Model Configuration
+### Create Trainer
 
-### Data Definition
-We first call the function `define_py_data_sources2` to let PaddlePaddle read training and test data from the `dataprovider.py` in the above. PaddlePaddle can accept configuration info from the command line, for example, here we pass a variable named `is_predict` to control the model to have different structures during training and test.
 ```python
-from paddle.trainer_config_helpers import *
+optimizer = paddle.optimizer.Momentum(momentum=0)
 
-is_predict = get_config_arg('is_predict', bool, False)
+trainer = paddle.trainer.SGD(cost=cost,
+                             parameters=parameters,
+                             update_equation=optimizer)
+```
 
-define_py_data_sources2(
-    train_list='data/train.list',
-    test_list='data/test.list',
-    module='dataprovider',
-    obj='process')
+### Feeding Data
 
-```
+PaddlePaddle provides the
+[reader mechanism](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/reader)
+for loadinng training data. A reader may return multiple columns, and we need a Python dictionary to specify the mapping from column index to data layers.
 
-### Algorithm Settings
-Next we need to set the details of the optimization algorithm. Due to the simplicity of the Linear Regression model, we only need to set the `batch_size` which defines how many samples are used every time for updating the parameters.
 ```python
-settings(batch_size=2)
+feeding={'x': 0, 'y': 1}
 ```
 
-### Network
-Finally, we use `fc_layer` and `LinearActivation` to represent the Linear Regression model.
+Moreover, an event handler is provided to print the training progress:
+
 ```python
-#input data of 13 dimensional house information
-x = data_layer(name='x', size=13)
-
-y_predict = fc_layer(
-    input=x,
-    param_attr=ParamAttr(name='w'),
-    size=1,
-    act=LinearActivation(),
-    bias_attr=ParamAttr(name='b'))
-
-if not is_predict: #when training, we use MSE (i.e., regression_cost) as the Loss Function
-    y = data_layer(name='y', size=1)
-    cost = regression_cost(input=y_predict, label=y)
-    outputs(cost) #output MSE to view the loss change
-else: #during test, output the prediction value
-    outputs(y_predict)
+# event_handler to print training and testing info
+def event_handler(event):
+    if isinstance(event, paddle.event.EndIteration):
+        if event.batch_id % 100 == 0:
+            print "Pass %d, Batch %d, Cost %f" % (
+                event.pass_id, event.batch_id, event.cost)
+
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(
+            reader=paddle.batch(
+                uci_housing.test(), batch_size=2),
+            feeding=feeding)
+        print "Test %d, Cost %f" % (event.pass_id, result.cost)
 ```
 
-## Training Model
-We can run the PaddlePaddle command line trainer in the root directory of the code. Here we name the configuration file as `trainer_config.py`. We train 30 passes and save the result in the directory `output`:
-```bash
-./train.sh
-```
+### Start Training
 
-## Use Model
-Now we can use the trained model to do prediction.
-```bash
-python predict.py
-```
-Here by default we use the model in `output/pass-00029` for prediction, and compare the actual house price with the predicted one. The result is shown in `predictions.png`.
-If you want to use another model or test on other data, you can pass in a new model path or data path:
-```bash
-python predict.py -m output/pass-00020 -t data/housing.test.npy
+```python
+trainer.train(
+    reader=paddle.batch(
+        paddle.reader.shuffle(
+            uci_housing.train(), buf_size=500),
+        batch_size=2),
+    feeding=feeding,
+    event_handler=event_handler,
+    num_passes=30)
 ```
 
 ## Summary
-In this chapter, we have introduced the Linear Regression model using the UCI Housing Data Set as an example. We have shown how to train and test this model with PaddlePaddle. Many more complex models and techniques are derived from this simple linear model, thus it is important for us to understand how it works.
+This chapter introduces *Linear Regression* and how to train and test this model with PaddlePaddle, using the UCI Housing Data Set. Because a large number of more complex models and techniques are derived from linear regression, it is important to understand its underlying theory and limitation.
 
 
 ## References
@@ -227,7 +244,8 @@ In this chapter, we have introduced the Linear Regression model using the UCI Ho
 4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.
 
 <br/>
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Common Creative License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a> This tutorial was created and published with [Creative Common License 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/fit_a_line/index.html b/fit_a_line/index.html
index 7bb9e8a..7266e7c 100644
--- a/fit_a_line/index.html
+++ b/fit_a_line/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -86,14 +87,25 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
  3. 根据损失函数进行反向误差传播 （[backpropagation](https://en.wikipedia.org/wiki/Backpropagation)），将网络误差从输出层依次向前传递, 并更新网络中的参数。
  4. 重复2~3步骤，直至网络训练误差达到规定的程度或训练轮次达到设定值。
 
+## 数据集
+
+### 数据集接口的封装
+首先加载需要的包
 
-## 数据准备
-执行以下命令来准备数据:
-```bash
-cd data && python prepare_data.py
+```python
+import paddle.v2 as paddle
+import paddle.v2.dataset.uci_housing as uci_housing
 ```
-这段代码将从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)下载数据并进行[预处理](#数据预处理)，最后数据将被分为训练集和测试集。
 
+我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
+
+其中，在uci_housing模块中封装了：
+
+1. 数据下载的过程。下载数据保存在~/.cache/paddle/dataset/uci_housing/housing.data。
+2. [数据预处理](#数据预处理)的过程。
+
+
+### 数据集介绍
 这份数据集共506行，每行包含了波士顿郊区的一类房屋的相关信息及该类房屋价格的中位数。其各维属性的意义如下：
 
 | 属性名 | 解释 | 类型 |
@@ -131,89 +143,89 @@ cd data && python prepare_data.py
 </p>
 
 #### 整理训练集与测试集
-我们将数据集分割为两份：一份用于调整模型的参数，即进行模型的训练，模型在这份数据集上的误差被称为**训练误差**；另外一份被用来测试，模型在这份数据集上的误差被称为**测试误差**。我们训练模型的目的是为了通过从训练数据中找到规律来预测未知的新数据，所以测试误差是更能反映模型表现的指标。分割数据的比例要考虑到两个因素：更多的训练数据会降低参数估计的方差，从而得到更可信的模型；而更多的测试数据会降低测试误差的方差，从而得到更可信的测试误差。一种常见的分割比例为$8:2$，感兴趣的读者朋友们也可以尝试不同的设置来观察这两种误差的变化。
+我们将数据集分割为两份：一份用于调整模型的参数，即进行模型的训练，模型在这份数据集上的误差被称为**训练误差**；另外一份被用来测试，模型在这份数据集上的误差被称为**测试误差**。我们训练模型的目的是为了通过从训练数据中找到规律来预测未知的新数据，所以测试误差是更能反映模型表现的指标。分割数据的比例要考虑到两个因素：更多的训练数据会降低参数估计的方差，从而得到更可信的模型；而更多的测试数据会降低测试误差的方差，从而得到更可信的测试误差。我们这个例子中设置的分割比例为$8:2$
+
+
+在更复杂的模型训练过程中，我们往往还会多使用一种数据集：验证集。因为复杂的模型中常常还有一些超参数（[Hyperparameter](https://en.wikipedia.org/wiki/Hyperparameter_optimization)）需要调节，所以我们会尝试多种超参数的组合来分别训练多个模型，然后对比它们在验证集上的表现选择相对最好的一组超参数，最后才使用这组参数下训练的模型在测试集上评估测试误差。由于本章训练的模型比较简单，我们暂且忽略掉这个过程。
+
+## 训练
+
+`fit_a_line/trainer.py`演示了训练的整体过程。
+
+### 初始化PaddlePaddle
 
-执行如下命令可以分割数据集，并将训练集和测试集的地址分别写入train.list 和 test.list两个文件中，供PaddlePaddle读取。
 ```python
-python prepare_data.py -r 0.8 #默认使用8:2的比例进行分割
+paddle.init(use_gpu=False, trainer_count=1)
 ```
 
-在更复杂的模型训练过程中，我们往往还会多使用一种数据集：验证集。因为复杂的模型中常常还有一些超参数（[Hyperparameter](https://en.wikipedia.org/wiki/Hyperparameter_optimization)）需要调节，所以我们会尝试多种超参数的组合来分别训练多个模型，然后对比它们在验证集上的表现选择相对最好的一组超参数，最后才使用这组参数下训练的模型在测试集上评估测试误差。由于本章训练的模型比较简单，我们暂且忽略掉这个过程。
+### 模型配置
 
-### 提供数据给PaddlePaddle
-准备好数据之后，我们使用一个Python data provider来为PaddlePaddle的训练过程提供数据。一个 data provider 就是一个Python函数，它会被PaddlePaddle的训练过程调用。在这个例子里，只需要读取已经保存好的数据，然后一行一行地返回给PaddlePaddle的训练进程即可。
+线性回归的模型其实就是一个采用线性激活函数（linear activation，`LinearActivation`）的全连接层（fully-connected layer，`fc_layer`）：
 
 ```python
-from paddle.trainer.PyDataProvider2 import *
-import numpy as np
-#定义数据的类型和维度
-@provider(input_types=[dense_vector(13), dense_vector(1)])
-def process(settings, input_file):
-    data = np.load(input_file.strip())
-    for row in data:
-	    yield row[:-1].tolist(), row[-1:].tolist()
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+y_predict = paddle.layer.fc(input=x,
+                                size=1,
+                                act=paddle.activation.Linear())
+y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
+cost = paddle.layer.regression_cost(input=y_predict, label=y)
+```
+### 创建参数
 
+```python
+parameters = paddle.parameters.create(cost)
 ```
 
-## 模型配置说明
+### 创建Trainer
 
-### 数据定义
-首先，通过 `define_py_data_sources2` 来配置PaddlePaddle从上面的`dataprovider.py`里读入训练数据和测试数据。 PaddlePaddle接受从命令行读入的配置信息，例如这里我们传入一个名为`is_predict`的变量来控制模型在训练和测试时的不同结构。
 ```python
-from paddle.trainer_config_helpers import *
+optimizer = paddle.optimizer.Momentum(momentum=0)
 
-is_predict = get_config_arg('is_predict', bool, False)
+trainer = paddle.trainer.SGD(cost=cost,
+                             parameters=parameters,
+                             update_equation=optimizer)
+```
 
-define_py_data_sources2(
-    train_list='data/train.list',
-    test_list='data/test.list',
-    module='dataprovider',
-    obj='process')
+### 读取数据且打印训练的中间信息
 
-```
+PaddlePaddle提供一个
+[reader机制](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/reader)
+来读取数据。 Reader返回的数据可以包括多列，我们需要一个Python dict把列
+序号映射到网络里的数据层。
 
-### 算法配置
-接着，指定模型优化算法的细节。由于线性回归模型比较简单，我们只要设置基本的`batch_size`即可，它指定每次更新参数的时候使用多少条数据计算梯度信息。
 ```python
-settings(batch_size=2)
+feeding={'x': 0, 'y': 1}
 ```
 
-### 网络结构
-最后，使用`fc_layer`和`LinearActivation`来表示线性回归的模型本身。
+此外，我们还可以提供一个 event handler，来打印训练的进度：
+
 ```python
-#输入数据，13维的房屋信息
-x = data_layer(name='x', size=13)
-
-y_predict = fc_layer(
-    input=x,
-    param_attr=ParamAttr(name='w'),
-    size=1,
-    act=LinearActivation(),
-    bias_attr=ParamAttr(name='b'))
-
-if not is_predict: #训练时，我们使用MSE，即regression_cost作为损失函数
-    y = data_layer(name='y', size=1)
-    cost = regression_cost(input=y_predict, label=y)
-    outputs(cost) #训练时输出MSE来监控损失的变化
-else: #测试时，输出预测值
-    outputs(y_predict)
+# event_handler to print training and testing info
+def event_handler(event):
+    if isinstance(event, paddle.event.EndIteration):
+        if event.batch_id % 100 == 0:
+            print "Pass %d, Batch %d, Cost %f" % (
+                event.pass_id, event.batch_id, event.cost)
+
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(
+            reader=paddle.batch(
+                uci_housing.test(), batch_size=2),
+            feeding=feeding)
+        print "Test %d, Cost %f" % (event.pass_id, result.cost)
 ```
 
-## 训练模型
-在对应代码的根目录下执行PaddlePaddle的命令行训练程序。这里指定模型配置文件为`trainer_config.py`，训练30轮，结果保存在`output`路径下。
-```bash
-./train.sh
-```
+### 开始训练
 
-## 应用模型
-现在来看下如何使用已经训练好的模型进行预测。
-```bash
-python predict.py
-```
-这里默认使用`output/pass-00029`中保存的模型进行预测，并将数据中的房价与预测结果进行对比，结果保存在 `predictions.png`中。
-如果你想使用别的模型或者其它的数据进行预测，只要传入新的路径即可：
-```bash
-python predict.py -m output/pass-00020 -t data/housing.test.npy
+```python
+trainer.train(
+    reader=paddle.batch(
+        paddle.reader.shuffle(
+            uci_housing.train(), buf_size=500),
+        batch_size=2),
+    feeding=feeding,
+    event_handler=event_handler,
+    num_passes=30)
 ```
 
 ## 总结
@@ -228,6 +240,7 @@ python predict.py -m output/pass-00020 -t data/housing.test.npy
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/gan/index.html b/gan/index.html
index 1f88c35..1063860 100644
--- a/gan/index.html
+++ b/gan/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -40,6 +41,7 @@
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
 TODO: Write about https://github.com/PaddlePaddle/Paddle/tree/develop/demo/gan
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/image_caption/index.html b/image_caption/index.html
index bd5f85a..de7844e 100644
--- a/image_caption/index.html
+++ b/image_caption/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
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diff --git a/image_classification/index.en.html b/image_classification/index.en.html
index b6a8078..43fb382 100644
--- a/image_classification/index.en.html
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@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -589,6 +590,7 @@ Traditional image classification methods involve multiple stages of processing a
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/image_classification/index.html b/image_classification/index.html
index ca6d789..2c8e011 100644
--- a/image_classification/index.html
+++ b/image_classification/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -535,6 +536,7 @@ Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/image_detection/index.html b/image_detection/index.html
index bd5f85a..de7844e 100644
--- a/image_detection/index.html
+++ b/image_detection/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
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diff --git a/image_qa/index.html b/image_qa/index.html
index bd5f85a..de7844e 100644
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+++ b/image_qa/index.html
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+
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 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
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+
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diff --git a/index.html b/index.html
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+++ b/index.html
@@ -1,7 +1,77 @@
+
 <html>
 <head>
-	<meta http-equiv="refresh" content="0; url=https://github.com/paddlepaddle/book" />
+  <script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
+    jax: ["input/TeX", "output/HTML-CSS"],
+    tex2jax: {
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
+      processEscapes: true
+    },
+    "HTML-CSS": { availableFonts: ["TeX"] }
+  });
+  </script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
+  <script type="text/javascript" src="../.tmpl/marked.js">
+  </script>
+  <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
+  <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
+  <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
+  <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
+  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
 </head>
+<style type="text/css" >
+.markdown-body {
+    box-sizing: border-box;
+    min-width: 200px;
+    max-width: 980px;
+    margin: 0 auto;
+    padding: 45px;
+}
+</style>
+
+
 <body>
-	<a href="https://github.com/paddlepaddle/book">Please access github home page</a>
+
+<div id="context" class="container markdown-body">
+</div>
+
+<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
+<div id="markdown" style='display:none'>
+# 深度学习入门
+
+1. 新手入门 [[fit_a_line](fit_a_line/)] [[html](http://book.paddlepaddle.org/fit_a_line)]
+1. 识别数字 [[recognize_digits](recognize_digits/)] [[html](http://book.paddlepaddle.org/recognize_digits)]
+1. 图像分类 [[image_classification](image_classification/)] [[html](http://book.paddlepaddle.org/image_classification)]
+1. 词向量 [[word2vec](word2vec/)] [[html](http://book.paddlepaddle.org/word2vec)]
+1. 情感分析 [[understand_sentiment](understand_sentiment/)] [[html](http://book.paddlepaddle.org/understand_sentiment)]
+1. 语义角色标注 [[label_semantic_roles](label_semantic_roles/)] [[html](http://book.paddlepaddle.org/label_semantic_roles)]
+1. 机器翻译 [[machine_translation](machine_translation/)] [[html](http://book.paddlepaddle.org/machine_translation)]
+1. 个性化推荐 [[recommender_system](recommender_system/)] [[html](http://book.paddlepaddle.org/recommender_system)]
+
+<br/>
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
+</div>
+<!-- You can change the lines below now. -->
+
+<script type="text/javascript">
+marked.setOptions({
+  renderer: new marked.Renderer(),
+  gfm: true,
+  breaks: false,
+  smartypants: true,
+  highlight: function(code, lang) {
+    code = code.replace(/&amp;/g, "&")
+    code = code.replace(/&gt;/g, ">")
+    code = code.replace(/&lt;/g, "<")
+    code = code.replace(/&nbsp;/g, " ")
+    return hljs.highlightAuto(code, [lang]).value;
+  }
+});
+document.getElementById("context").innerHTML = marked(
+		document.getElementById("markdown").innerHTML)
+</script>
 </body>
diff --git a/label_semantic_roles/index.en.html b/label_semantic_roles/index.en.html
index a3fc663..84c2273 100644
--- a/label_semantic_roles/index.en.html
+++ b/label_semantic_roles/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -63,34 +64,20 @@ Standard SRL system mostly builds on top of Syntactic Analysis and contains five
 
 
 <div  align="center">
-<img src="image/dependency_parsing.png" width = "80%" align=center /><br>
+<img src="image/dependency_parsing_en.png" width = "80%" align=center /><br>
 Fig 1. Syntactic parse tree
 </div>
 
-核心关系-> HED
-定中关系-> ATT
-主谓关系-> SBV
-状中结构-> ADV
-介宾关系-> POB
-右附加关系-> RAD
-动宾关系-> VOB
-标点-> WP
-
 
 However, complete syntactic analysis requires identifying the relation among all constitutes and the performance of SRL is sensitive to the precision of syntactic analysis, which makes SRL a very challenging task. To reduce the complexity and obtain some syntactic structure information, we often use shallow syntactic analysis. Shallow Syntactic Analysis is also called partial parsing or chunking. Unlike complete syntactic analysis which requires the construction of the complete parsing tree, Shallow Syntactic Analysis only need to identify some independent components with relatively simple structure, such as verb phrases (chunk). To avoid difficulties in constructing a syntactic tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking based SRL methods, which convert SRL as a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using BIO representation. For syntactic chunks forming a chunk of type A, the first chunk receives the B-A tag (Begin), the remaining ones receive the tag I-A (Inside), and all chunks outside receive the tag O-A.
 
 The BIO representation of above example is shown in Fig.1.
 
 <div  align="center">
-<img src="image/bio_example.png" width = "90%"  align=center /><br>
+<img src="image/bio_example_en.png" width = "90%"  align=center /><br>
 Fig 2. BIO represention
 </div>
 
-输入序列-> input sequence
-语块-> chunk
-标注序列-> label sequence
-角色-> role
-
 This example illustrates the simplicity of sequence tagging because (1) shallow syntactic analysis reduces the precision requirement of syntactic analysis; (2) pruning candidate arguments is removed; 3) argument identification and tagging are finished at the same time. Such unified methods simplify the procedure, reduce the risk of accumulating errors and boost the performance further.
 
 In this tutorial, our SRL system is built as an end-to-end system via a neural network. We take only text sequences, without using any syntactic parsing results or complex hand-designed features. We give public dataset [CoNLL-2004 and CoNLL-2005 Shared Tasks](http://www.cs.upc.edu/~srlconll/) as an example to illustrate: given a sentence with predicates marked, identify the corresponding arguments and their semantic roles by sequence tagging method.
@@ -112,13 +99,10 @@ The operation of a single LSTM cell contain 3 parts: (1) input-to-hidden: map in
 Fig.3 illustrate the final stacked recurrent neural networks.
 
 <p align="center">    
-<img src="./image/stacked_lstm.png" width = "40%"  align=center><br>
+<img src="./image/stacked_lstm_en.png" width = "40%"  align=center><br>
 Fig 3. Stacked Recurrent Neural Networks
 </p>
 
-线性变换-> linear transformation
-输入层到隐层-> input-to-hidden
-
 ### Bidirectional Recurrent Neural Network
 
 LSTMs can summarize the history of previous inputs seen up to now, but can not see the future. In most of NLP (natural language processing) tasks, the entire sentences are ready to use. Therefore, sequential learning might be much efficient if the future can be encoded as well like histories.
@@ -127,15 +111,10 @@ To address the above drawbacks, we can design bidirectional recurrent neural net
 
 
 <p align="center">    
-<img src="./image/bidirectional_stacked_lstm.png" width = "60%" align=center><br>
+<img src="./image/bidirectional_stacked_lstm_en.png" width = "60%" align=center><br>
 Fig 4. Bidirectional LSTMs
 </p>
 
-线性变换-> linear transformation
-输入层到隐层-> input-to-hidden
-正向处理输出序列->process sequence in the forward direction
-反向处理上一层序列-> process sequence from the previous layer in backward direction
-
 Note that, this bidirectional RNNs is different with the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following tasks[machine translation](https://github.com/PaddlePaddle/book/blob/develop/machine_translation/README.md)
 
 ### Conditional Random Field
@@ -197,18 +176,10 @@ After modification, the model is as follows:
 
 
 <div  align="center">    
-<img src="image/db_lstm_network.png" width = "60%"  align=center /><br>
+<img src="image/db_lstm_en.png" width = "60%"  align=center /><br>
 Fig 6. DB-LSTM for SRL tasks
 </div>
 
-论元-> argu
-谓词-> pred
-谓词上下文-> ctx-p
-谓词上下文区域标记-> $m_r$
-输入-> input
-原句-> sentence
-反向LSTM-> LSTM Reverse
-
 ## Data Preparation
 
 In the tutorial, we use [CoNLL 2005](http://www.cs.upc.edu/~srlconll/) SRL task open dataset as an example. It is important to note that the training set and development set of the CoNLL 2005 SRL task are not free to download after the competition. Currently, only the test set can be obtained, including 23 sections of the Wall Street Journal and three sections of the Brown corpus. In this tutorial, we use the WSJ corpus as the training dataset to explain the model. However, since the training set is small, if you want to train a usable neural network SRL system, consider paying for the full corpus.
@@ -542,6 +513,7 @@ Semantic Role Labeling is an important intermediate step in a wide range of natu
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/label_semantic_roles/index.html b/label_semantic_roles/index.html
index 044b3bd..b8e7a9f 100644
--- a/label_semantic_roles/index.html
+++ b/label_semantic_roles/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -509,6 +510,7 @@ trainer.train(
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/machine_translation/index.en.html b/machine_translation/index.en.html
index 558e368..cb48df1 100644
--- a/machine_translation/index.en.html
+++ b/machine_translation/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -764,6 +765,7 @@ End-to-end neural machine translation is a recently developed way to perform mac
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/machine_translation/index.html b/machine_translation/index.html
index 09d4a4e..17a9b8f 100644
--- a/machine_translation/index.html
+++ b/machine_translation/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -191,17 +192,19 @@ e_{ij}&=align(z_i,h_j)\\\\
 
 注意：$z_{i+1}$和$p_{i+1}$的计算公式同[解码器](#解码器)中的一样。且由于生成时的每一步都是通过贪心法实现的，因此并不能保证得到全局最优解。
 
-## 数据准备
+## 数据介绍
 
 ### 下载与解压缩
 
 本教程使用[WMT-14](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/)数据集中的[bitexts(after selection)](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz)作为训练集，[dev+test data](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz)作为测试集和生成集。
 
 在Linux下，只需简单地运行以下命令：
+
 ```bash
 cd data
 ./wmt14_data.sh
 ```
+
 得到的数据集`data/wmt14`包含如下三个文件夹：
 <p align = "center">
 <table>
@@ -239,29 +242,6 @@ cd data
 - `XXX.src`是源法语文件，`XXX.trg`是目标英语文件，文件中的每行存放一个句子
 - `XXX.src`和`XXX.trg`的行数一致，且两者任意第$i$行的句子之间都有着一一对应的关系。
 
-### 用户自定义数据集（可选）
-
-如果您想使用自己的数据集，只需按照如下方式组织，并将它们放在`data`目录下：
-```text
-user_dataset
-├── train
-│   ├── train_file1.src
-│   ├── train_file1.trg
-│   └── ...
-├── test
-│   ├── test_file1.src
-│   ├── test_file1.trg
-│   └── ...
-├── gen
-│   ├── gen_file1.src
-│   ├── gen_file1.trg
-│   └── ...
-```
-  
-- 一级目录`user_dataset`：用户自定义的数据集名字。
-- 二级目录`train`、`test`和`gen`：必须使用这三个文件夹名字。
-- 三级目录：存放源语言到目标语言的平行语料库文件，后缀名必须使用`.src`和`.trg`。
-
 ### 数据预处理
 
 我们的预处理流程包括两步：
@@ -270,225 +250,80 @@ user_dataset
   - `XXX`中的第$i$行内容为`XXX.src`中的第$i$行和`XXX.trg`中的第$i$行连接，用'\t'分隔。
 - 创建训练数据的“源字典”和“目标字典”。每个字典都有**DICTSIZE**个单词，包括：语料中词频最高的（DICTSIZE - 3）个单词，和3个特殊符号`<s>`（序列的开始）、`<e>`（序列的结束）和`<unk>`（未登录词）。
 
-预处理可以使用`preprocess.py`：
-```python
-python preprocess.py -i INPUT [-d DICTSIZE] [-m]
-```
-- `-i INPUT`：输入的原始数据集路径。
-- `-d DICTSIZE`：指定的字典单词数，如果没有设置，字典会包含输入数据集中的所有单词。
-- `-m --mergeDict`：合并“源字典”和“目标字典”，即这两个字典的内容完全一样。
-
-本教程的具体命令如下：
-```python
-python preprocess.py -i data/wmt14 -d 30000
-```
-请耐心等待几分钟的时间，您会在屏幕上看到：
-```text
-concat parallel corpora for dataset
-build source dictionary for train data
-build target dictionary for train data
-dictionary size is 30000
-```
-预处理好的数据集存放在`data/pre-wmt14`目录下：
-```text
-pre-wmt14
-├── train
-│   └── train
-├── test
-│   └── test
-├── gen
-│   └── gen
-├── train.list
-├── test.list
-├── gen.list
-├── src.dict
-└── trg.dict
-```
-- `train`、`test`和`gen`：分别包含了法英平行语料库的训练、测试和生成数据。其每个文件的每一行以“\t”分为两列，第一列是法语序列，第二列是对应的英语序列。
-- `train.list`、`test.list`和`gen.list`：分别记录了`train`、`test`和`gen`文件夹中的文件路径。
-- `src.dict`和`trg.dict`：源（法语）和目标（英语）字典。每个字典都含有30000个单词，包括29997个最高频单词和3个特殊符号。
-
-### 提供数据给PaddlePaddle
-
-我们通过`dataprovider.py`将数据提供给PaddlePaddle。具体步骤如下：
+### 示例数据
 
-1. 首先，引入PaddlePaddle的PyDataProvider2包，并定义三个特殊符号。
+因为完整的数据集数据量较大，为了验证训练流程，PaddlePaddle接口paddle.dataset.wmt14中默认提供了一个经过预处理的[较小规模的数据集](http://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz)。
 
-   ```python
-   from paddle.trainer.PyDataProvider2 import *
-   UNK_IDX = 2    #未登录词
-   START = "<s>"  #序列的开始
-   END = "<e>"    #序列的结束
-   ```
-2. 其次，使用初始化函数`hook`，分别定义了训练模式和生成模式下的数据输入格式（`input_types`）。
-   - 训练模式：有三个输入序列，其中“源语言序列”和“目标语言序列”作为输入数据，“目标语言的下一个词序列”作为标签数据。
-   - 生成模式：有两个输入序列，其中“源语言序列”作为输入数据，“源语言序列编号”作为输入数据的编号（该输入非必须，可以省略）。
-  
-  `hook`函数中的`src_dict_path`是源语言字典路径，`trg_dict_path`是目标语言字典路径，`is_generating`（训练或生成模式）是从模型配置中传入的对象。`hook`函数的具体调用方式请见[模型配置说明](#模型配置说明)。
+该数据集有193319条训练数据，6003条测试数据，词典长度为30000。因为数据规模限制，使用该数据集训练出来的模型效果无法保证。
 
-   ```python
-   def hook(settings, src_dict_path, trg_dict_path, is_generating, file_list, 
-            **kwargs):
-       # job_mode = 1: 训练模式；0: 生成模式
-       settings.job_mode = not is_generating
-
-       def fun(dict_path): # 根据字典路径加载字典
-           out_dict = dict()
-           with open(dict_path, "r") as fin:
-               out_dict = {
-                   line.strip(): line_count
-                   for line_count, line in enumerate(fin)
-               }
-           return out_dict
-
-       settings.src_dict = fun(src_dict_path)
-       settings.trg_dict = fun(trg_dict_path)
-
-       if settings.job_mode:                                  #训练模式
-           settings.input_types = {
-               'source_language_word':                        #源语言序列
-               integer_value_sequence(len(settings.src_dict)),
-               'target_language_word':                        #目标语言序列
-               integer_value_sequence(len(settings.trg_dict)),
-               'target_language_next_word':                   #目标语言的下一个词序列
-               integer_value_sequence(len(settings.trg_dict))
-           }
-       else:                                                  #生成模式
-           settings.input_types = {
-               'source_language_word':                        #源语言序列
-               integer_value_sequence(len(settings.src_dict)),
-               'sent_id':                                     #源语言序列编号
-               integer_value_sequence(len(open(file_list[0], "r").readlines()))
-           }
-   ```
-3. 最后，使用`process`函数打开文本文件`file_name`，读取每一行，将行中的数据转换成与`input_types`一致的格式，再用`yield`关键字返回给PaddlePaddle进程。具体来说，
+## 训练流程说明
 
-   - 在源语言序列的每句话前面补上开始符号`<s>`、末尾补上结束符号`<e>`，得到“source_language_word”；
-   - 在目标语言序列的每句话前面补上`<s>`，得到“target_language_word”；
-   - 在目标语言序列的每句话末尾补上`<e>`，作为目标语言的下一个词序列（“target_language_next_word”）。
+### paddle初始化
 
-   ```python
-   def _get_ids(s, dictionary): # 获得源语言序列中的每个单词在字典中的位置
-       words = s.strip().split()
-       return [dictionary[START]] + \
-              [dictionary.get(w, UNK_IDX) for w in words] + \
-              [dictionary[END]]
-
-   @provider(init_hook=hook, pool_size=50000)
-   def process(settings, file_name):
-       with open(file_name, 'r') as f:
-           for line_count, line in enumerate(f):
-               line_split = line.strip().split('\t')
-               if settings.job_mode and len(line_split) != 2:
-                   continue
-               src_seq = line_split[0]
-               src_ids = _get_ids(src_seq, settings.src_dict)
-
-               if settings.job_mode:
-                   trg_seq = line_split[1]
-                   trg_words = trg_seq.split()
-                   trg_ids = [settings.trg_dict.get(w, UNK_IDX) for w in trg_words]
-
-                   # 如果任意一个序列长度超过80个单词，在训练模式下会移除这条样本，以防止RNN过深。
-                   if len(src_ids) > 80 or len(trg_ids) > 80:
-                       continue
-                   trg_ids_next = trg_ids + [settings.trg_dict[END]]
-                   trg_ids = [settings.trg_dict[START]] + trg_ids
-                   yield {
-                       'source_language_word': src_ids,
-                       'target_language_word': trg_ids,
-                       'target_language_next_word': trg_ids_next
-                   }
-               else:
-                   yield {'source_language_word': src_ids, 'sent_id': [line_count]}
-   ```
-注意：由于本示例中的训练数据有3.55G，对于内存较小的机器，不能一次性加载进内存，所以推荐使用`pool_size`变量来设置内存中暂存的数据条数。
+```python
+# 加载 paddle的python包
+import paddle.v2 as paddle
 
-## 模型配置说明
+# 配置只使用cpu，并且使用一个cpu进行训练
+paddle.init(use_gpu=False, trainer_count=1)
+```
 
 ### 数据定义
 
-1. 首先，定义数据集路径和源/目标语言字典路径，并用`is_generating`变量定义当前配置是训练模式（默认）还是生成模式。该变量接受从命令行传入的参数，使用方法见[应用命令与结果](#应用命令与结果)。
-
-   ```python
-   import os
-   from paddle.trainer_config_helpers import *
-
-   data_dir = "./data/pre-wmt14" # 数据集路径
-   src_lang_dict = os.path.join(data_dir, 'src.dict') # 源语言字典路径
-   trg_lang_dict = os.path.join(data_dir, 'trg.dict') # 目标语言字典路径
-   is_generating = get_config_arg("is_generating", bool, False) # 配置模式
-   ```
-2. 其次，通过`define_py_data_sources2`函数从`dataprovider.py`中读取数据，并用`args`变量传入源/目标语言的字典路径以及配置模式。
-
-   ```python
-   if not is_generating:
-       train_list = os.path.join(data_dir, 'train.list')
-       test_list = os.path.join(data_dir, 'test.list')
-   else:
-       train_list = None
-       test_list = os.path.join(data_dir, 'gen.list')
-
-   define_py_data_sources2(
-       train_list,
-       test_list,
-       module="dataprovider",
-       obj="process",
-       args={
-           "src_dict_path": src_lang_dict, # 源语言字典路径
-           "trg_dict_path": trg_lang_dict, # 目标语言字典路径
-           "is_generating": is_generating  # 配置模式
-       })
-   ```
-
-### 算法配置
+首先要定义词典大小，数据生成和网络配置都需要用到。然后获取wmt14的dataset reader。
 
 ```python
-settings(
-    learning_method = AdamOptimizer(),
-    batch_size = 50,
-    learning_rate = 5e-4)
+# source and target dict dim.
+dict_size = 30000
+
+feeding = {
+    'source_language_word': 0,
+    'target_language_word': 1,
+    'target_language_next_word': 2
+}
+wmt14_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.wmt14.train(dict_size=dict_size), buf_size=8192),
+    batch_size=5)
 ```
-本教程使用默认的SGD随机梯度下降算法和Adam学习方法，并指定学习率为5e-4。注意：生成模式下的`batch_size = 50`，表示同时生成50条序列。
 
 ### 模型结构
 1. 首先，定义了一些全局变量。
 
    ```python
-   source_dict_dim = len(open(src_lang_dict, "r").readlines()) # 源语言字典维度
-   target_dict_dim = len(open(trg_lang_dict, "r").readlines()) # 目标语言字典维度
+   source_dict_dim = dict_size # 源语言字典维度
+   target_dict_dim = dict_size # 目标语言字典维度
    word_vector_dim = 512 # 词向量维度
    encoder_size = 512 # 编码器中的GRU隐层大小
    decoder_size = 512 # 解码器中的GRU隐层大小
-
-   if is_generating:
-       beam_size=3  # 柱搜索算法中的宽度
-       max_length=250 # 生成句子的最大长度
-       gen_trans_file = get_config_arg("gen_trans_file", str, None) # 生成后的文件
   ```
 
 2. 其次，实现编码器框架。分为三步：
 
-   2.1 传入已经在`dataprovider.py`转换成one-hot vector表示的源语言序列$\mathbf{w}$。
+   2.1 将在dataset reader中生成的用每个单词在字典中的索引表示的源语言序列
+   转换成one-hot vector表示的源语言序列$\mathbf{w}$，其类型为integer_value_sequence。
 
    ```python
-   src_word_id = data_layer(name='source_language_word', size=source_dict_dim)
+    src_word_id = paddle.layer.data(
+        name='source_language_word',
+        type=paddle.data_type.integer_value_sequence(source_dict_dim))
    ```
    2.2 将上述编码映射到低维语言空间的词向量$\mathbf{s}$。
 
    ```python
-   src_embedding = embedding_layer(
-       input=src_word_id,
-       size=word_vector_dim,
-       param_attr=ParamAttr(name='_source_language_embedding'))
+    src_embedding = paddle.layer.embedding(
+        input=src_word_id,
+        size=word_vector_dim,
+        param_attr=paddle.attr.ParamAttr(name='_source_language_embedding'))
    ```
    2.3 用双向GRU编码源语言序列，拼接两个GRU的编码结果得到$\mathbf{h}$。
   
    ```python
-   src_forward = simple_gru(input=src_embedding, size=encoder_size)
-   src_backward = simple_gru(
-       input=src_embedding, size=encoder_size, reverse=True)
-   encoded_vector = concat_layer(input=[src_forward, src_backward])
+    src_forward = paddle.networks.simple_gru(
+        input=src_embedding, size=encoder_size)
+    src_backward = paddle.networks.simple_gru(
+        input=src_embedding, size=encoder_size, reverse=True)
+    encoded_vector = paddle.layer.concat(input=[src_forward, src_backward])
    ```
 
 3. 接着，定义基于注意力机制的解码器框架。分为三步：
@@ -496,17 +331,18 @@ settings(
    3.1 对源语言序列编码后的结果（见2.3），过一个前馈神经网络（Feed Forward Neural Network），得到其映射。
    
    ```python
-   with mixed_layer(size=decoder_size) as encoded_proj:
-       encoded_proj += full_matrix_projection(input=encoded_vector)
+    with paddle.layer.mixed(size=decoder_size) as encoded_proj:
+        encoded_proj += paddle.layer.full_matrix_projection(
+            input=encoded_vector)
    ```
    3.2 构造解码器RNN的初始状态。由于解码器需要预测时序目标序列，但在0时刻并没有初始值，所以我们希望对其进行初始化。这里采用的是将源语言序列逆序编码后的最后一个状态进行非线性映射，作为该初始值，即$c_0=h_T$。
 
    ```python
-   backward_first = first_seq(input=src_backward)
-   with mixed_layer(
-           size=decoder_size,
-           act=TanhActivation(), ) as decoder_boot:
-       decoder_boot += full_matrix_projection(input=backward_first)
+    backward_first = paddle.layer.first_seq(input=src_backward)
+    with paddle.layer.mixed(
+            size=decoder_size, act=paddle.activation.Tanh()) as decoder_boot:
+        decoder_boot += paddle.layer.full_matrix_projection(
+            input=backward_first)
    ```
    3.3 定义解码阶段每一个时间步的RNN行为，即根据当前时刻的源语言上下文向量$c_i$、解码器隐层状态$z_i$和目标语言中第$i$个词$u_i$，来预测第$i+1$个词的概率$p_{i+1}$。
 
@@ -515,42 +351,46 @@ settings(
       - decoder_inputs融合了$c_i$和当前目标词current_word（即$u_i$）的表示。
       - gru_step通过调用`gru_step_layer`函数，在decoder_inputs和decoder_mem上做了激活操作，即实现公式$z_{i+1}=\phi _{\theta '}\left ( c_i,u_i,z_i \right )$。
       - 最后，使用softmax归一化计算单词的概率，将out结果返回，即实现公式$p\left ( u_i|u_{&lt;i},\mathbf{x} \right )=softmax(W_sz_i+b_z)$。 
-        
+
+
    ```python
-   def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
-       decoder_mem = memory(
-           name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
-
-       context = simple_attention(
-           encoded_sequence=enc_vec,
-           encoded_proj=enc_proj,
-           decoder_state=decoder_mem, )
-
-       with mixed_layer(size=decoder_size * 3) as decoder_inputs:
-           decoder_inputs += full_matrix_projection(input=context)
-           decoder_inputs += full_matrix_projection(input=current_word)
-
-       gru_step = gru_step_layer(
-           name='gru_decoder',
-           input=decoder_inputs,
-           output_mem=decoder_mem,
-           size=decoder_size)
-
-       with mixed_layer(
-               size=target_dict_dim, bias_attr=True,
-               act=SoftmaxActivation()) as out:
-           out += full_matrix_projection(input=gru_step)
-       return out
+    def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
+
+        decoder_mem = paddle.layer.memory(
+            name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
+
+        context = paddle.networks.simple_attention(
+            encoded_sequence=enc_vec,
+            encoded_proj=enc_proj,
+            decoder_state=decoder_mem)
+
+        with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
+            decoder_inputs += paddle.layer.full_matrix_projection(input=context)
+            decoder_inputs += paddle.layer.full_matrix_projection(
+                input=current_word)
+
+        gru_step = paddle.layer.gru_step(
+            name='gru_decoder',
+            input=decoder_inputs,
+            output_mem=decoder_mem,
+            size=decoder_size)
+
+        with paddle.layer.mixed(
+                size=target_dict_dim,
+                bias_attr=True,
+                act=paddle.activation.Softmax()) as out:
+            out += paddle.layer.full_matrix_projection(input=gru_step)
+        return out
     ```
 4. 训练模式与生成模式下的解码器调用区别。
 
    4.1 定义解码器框架名字，和`gru_decoder_with_attention`函数的前两个输入。注意：这两个输入使用`StaticInput`，具体说明可见[StaticInput文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/deep_model/rnn/recurrent_group_cn.md#输入)。
 
    ```python
-   decoder_group_name = "decoder_group"
-   group_input1 = StaticInput(input=encoded_vector, is_seq=True)
-   group_input2 =  StaticInput(input=encoded_proj, is_seq=True)
-   group_inputs = [group_input1, group_input2]
+    decoder_group_name = "decoder_group"
+    group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+    group_input2 = paddle.layer.StaticInputV2(input=encoded_proj, is_seq=True)
+    group_inputs = [group_input1, group_input2]
    ```
    4.2 训练模式下的解码器调用：
 
@@ -560,99 +400,85 @@ settings(
       - 最后，用多类交叉熵损失函数`classification_cost`来计算损失值。
 
    ```python
-   if not is_generating:
-       trg_embedding = embedding_layer(
-           input=data_layer(
-               name='target_language_word', size=target_dict_dim),
-           size=word_vector_dim,
-           param_attr=ParamAttr(name='_target_language_embedding'))
-       group_inputs.append(trg_embedding)
-
-       decoder = recurrent_group(
-           name=decoder_group_name,
-           step=gru_decoder_with_attention,
-           input=group_inputs)
-
-       lbl = data_layer(name='target_language_next_word', size=target_dict_dim)
-       cost = classification_cost(input=decoder, label=lbl)
-       outputs(cost)
-   ```
-   4.3 生成模式下的解码器调用：
-
-      - 首先，在序列生成任务中，由于解码阶段的RNN总是引用上一时刻生成出的词的词向量，作为当前时刻的输入，因此，使用`GeneratedInput`来自动完成这一过程。具体说明可见[GeneratedInput文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/deep_model/rnn/recurrent_group_cn.md#输入)。
-      - 其次，使用`beam_search`函数循环调用`gru_decoder_with_attention`函数，生成出序列id。
-      - 最后，使用`seqtext_printer_evaluator`函数，根据目标字典`trg_lang_dict`，打印出完整的句子保存在`gen_trans_file`中。
-     
-   ```python
-   else:
-       trg_embedding = GeneratedInput(
-           size=target_dict_dim,
-           embedding_name='_target_language_embedding',
-           embedding_size=word_vector_dim)
-       group_inputs.append(trg_embedding)
-
-       beam_gen = beam_search(
-           name=decoder_group_name,
-           step=gru_decoder_with_attention,
-           input=group_inputs,
-           bos_id=0,
-           eos_id=1,
-           beam_size=beam_size,
-           max_length=max_length)
-
-       seqtext_printer_evaluator(
-           input=beam_gen,
-           id_input=data_layer(
-               name="sent_id", size=1),
-           dict_file=trg_lang_dict,
-           result_file=gen_trans_file)
-       outputs(beam_gen)
+    trg_embedding = paddle.layer.embedding(
+        input=paddle.layer.data(
+            name='target_language_word',
+            type=paddle.data_type.integer_value_sequence(target_dict_dim)),
+        size=word_vector_dim,
+        param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
+    group_inputs.append(trg_embedding)
+
+    # For decoder equipped with attention mechanism, in training,
+    # target embeding (the groudtruth) is the data input,
+    # while encoded source sequence is accessed to as an unbounded memory.
+    # Here, the StaticInput defines a read-only memory
+    # for the recurrent_group.
+    decoder = paddle.layer.recurrent_group(
+        name=decoder_group_name,
+        step=gru_decoder_with_attention,
+        input=group_inputs)
+
+    lbl = paddle.layer.data(
+        name='target_language_next_word',
+        type=paddle.data_type.integer_value_sequence(target_dict_dim))
+    cost = paddle.layer.classification_cost(input=decoder, label=lbl)
    ```
 注意：我们提供的配置在Bahdanau的论文\[[4](#参考文献)\]上做了一些简化，可参考[issue #1133](https://github.com/PaddlePaddle/Paddle/issues/1133)。
 
+### 参数定义
 
-## 训练模型
+首先依据模型配置的`cost`定义模型参数。
+    
+```python
+# create parameters
+parameters = paddle.parameters.create(cost)
+```
+    
+可以打印参数名字，如果在网络配置中没有指定名字，则默认生成。
+    
+```python
+for param in parameters.keys():
+    print param
+```
 
-可以通过以下命令来训练模型：
+### 训练模型
+1. 构造trainer
 
-```bash
-./train.sh
+    根据优化目标cost,网络拓扑结构和模型参数来构造出trainer用来训练，在构造时还需指定优化方法，这里使用最基本的SGD方法。
+    
+    ```python
+    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer)
 ```
-其中`train.sh` 的内容为：
 
-```bash
-paddle train \
---config='seqToseq_net.py' \
---save_dir='model' \
---use_gpu=false \
---num_passes=16 \
---show_parameter_stats_period=100 \
---trainer_count=4 \
---log_period=10 \
---dot_period=5 \
-2>&1 | tee 'train.log'
-```
-- config: 设置神经网络的配置文件。
-- save_dir: 设置保存模型的输出路径。
-- use_gpu: 是否使用GPU训练，这里使用CPU。
-- num_passes: 设置passes的数量。PaddlePaddle中的一个pass表示对数据集中所有样本的一次完整训练。
-- show_parameter_stats_period: 这里每隔100个batch显示一次参数统计信息。
-- trainer_count: 设置CPU线程数或者GPU设备数。
-- log_period: 这里每隔10个batch打印一次日志。
-- dot_period: 这里每个5个batch打印一个点"."。
-
-训练的损失函数每隔10个batch打印一次，您将会看到如下消息：
-```text
-I0719 19:16:45.952062 15563 TrainerInternal.cpp:160]  Batch=10 samples=500 AvgCost=198.475 CurrentCost=198.475 Eval: classification_error_evaluator=0.737155  CurrentEval: classification_error_evaluator=0.737155
-I0719 19:17:56.707319 15563 TrainerInternal.cpp:160]  Batch=20 samples=1000 AvgCost=157.479 CurrentCost=116.483 Eval: classification_error_evaluator=0.698392  CurrentEval: classification_error_evaluator=0.659065
-.....
+2. 构造event_handler
+
+    可以通过自定义回调函数来评估训练过程中的各种状态，比如错误率等。下面的代码通过event.batch_id % 10 == 0 指定没10个batch打印一次日志，包含cost等信息。
+    ```python
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 10 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+    ```
+3. 启动训练：
+
+    ```python
+    trainer.train(
+        reader=wmt14_reader,
+        event_handler=event_handler,
+        num_passes=10000,
+        feeding=feeding)
+    ```
+    训练开始后，可以观察到event_handler输出的日志如下：
+    ```text
+    Pass 0, Batch 0, Cost 247.408008, {'classification_error_evaluator': 1.0}
+    Pass 0, Batch 10, Cost 212.058789, {'classification_error_evaluator': 0.8737863898277283}
+    ...
 ```
-- AvgCost：从第0个batch到当前batch的平均损失值。
-- CurrentCost：当前batch的损失值。
-- classification\_error\_evaluator(Eval)：从第0个评估到当前评估中，每个单词的预测错误率。
-- classification\_error\_evaluator(CurrentEval)：当前评估中，每个单词的预测错误率。
 
-当classification\_error\_evaluator的值低于0.35时，模型就训练成功了。
 
 ## 应用模型
 
@@ -666,30 +492,7 @@ cd pretrained
 
 ### 应用命令与结果
 
-可以通过以下命令来进行法英翻译：
-
-```bash
-./gen.sh
-```
-其中`gen.sh` 的内容为：
-
-```bash
-paddle train \
---job=test \
---config='seqToseq_net.py' \
---save_dir='pretrained/wmt14_model' \
---use_gpu=true \
---num_passes=13 \
---test_pass=12 \
---trainer_count=1 \
---config_args=is_generating=1,gen_trans_file="gen_result" \
-2>&1 | tee 'translation/gen.log'
-```
-与训练命令不同的参数如下：
-- job：设置任务的模式为测试。
-- save_dir：设置存放预训练模型的路径。
-- num_passes和test_pass：加载第$i\epsilon \left [ test\_pass,num\_passes-1 \right ]$轮的模型参数，这里只加载 `data/wmt14_model/pass-00012`。
-- config_args：将命令行中的自定义参数传递给模型配置。`is_generating=1`表示当前为生成模式，`gen_trans_file="gen_result"`表示生成结果的存储文件。
+新版api尚未支持机器翻译的翻译过程，尽请期待。
 
 翻译结果请见[效果展示](#效果展示)。
 
@@ -728,6 +531,7 @@ BLEU = 26.92
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/pre-commit-hooks/build.sh b/pre-commit-hooks/build.sh
deleted file mode 100755
index 8497a3d..0000000
--- a/pre-commit-hooks/build.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-for i in $(du -a | grep '\.\/.\+\/README.md' | cut -f 2); do
-    .tmpl/convert-markdown-into-html.sh $i > $(dirname $i)/index.html
-done
-
-for i in $(du -a | grep '\.\/.\+\/README.en.md' | cut -f 2); do
-    .tmpl/convert-markdown-into-html.sh $i > $(dirname $i)/index.en.html
-done
diff --git a/pre-commit-hooks/convert-markdown-into-html.sh b/pre-commit-hooks/convert_markdown_into_html.py
old mode 100755
new mode 100644
similarity index 70%
rename from pre-commit-hooks/convert-markdown-into-html.sh
rename to pre-commit-hooks/convert_markdown_into_html.py
index 149c686..088d9fa
--- a/pre-commit-hooks/convert-markdown-into-html.sh
+++ b/pre-commit-hooks/convert_markdown_into_html.py
@@ -1,8 +1,8 @@
-markdown_file=$1
+import argparse
+import re
+import sys
 
-# Notice: the single-quotes around EOF below make outputs
-# verbatium. c.f. http://stackoverflow.com/a/9870274/724872
-cat <<'EOF'
+HEAD = """
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -10,8 +10,8 @@ cat <<'EOF'
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -44,11 +44,9 @@ cat <<'EOF'
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
-EOF
+"""
 
-cat $markdown_file
-
-cat <<'EOF'
+TAIL = """
 </div>
 <!-- You can change the lines below now. -->
 
@@ -70,4 +68,28 @@ document.getElementById("context").innerHTML = marked(
 		document.getElementById("markdown").innerHTML)
 </script>
 </body>
-EOF
+"""
+
+
+def convert_markdown_into_html(argv=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('filenames', nargs='*', help='Filenames to fix')
+    args = parser.parse_args(argv)
+
+    retv = 0
+
+    for filename in args.filenames:
+        with open(
+                re.sub(r"README", "index", re.sub(r"\.md$", ".html", filename)),
+                "w") as output:
+            output.write(HEAD)
+            with open(filename) as input:
+                for line in input:
+                    output.write(line)
+            output.write(TAIL)
+
+    return retv
+
+
+if __name__ == '__main__':
+    sys.exit(convert_markdown_into_html())
diff --git a/query_relationship/index.html b/query_relationship/index.html
index bd5f85a..de7844e 100644
--- a/query_relationship/index.html
+++ b/query_relationship/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/recognize_digits/index.en.html b/recognize_digits/index.en.html
index bec542c..15f30e4 100644
--- a/recognize_digits/index.en.html
+++ b/recognize_digits/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -338,6 +339,7 @@ This tutorial describes a few basic Deep Learning models viz. Softmax regression
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This book</span> is created by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and uses <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Shared knowledge signature - non commercial use-Sharing 4.0 International Licensing Protocal</a>.
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/recognize_digits/index.html b/recognize_digits/index.html
index a3fa4d9..a3a3cea 100644
--- a/recognize_digits/index.html
+++ b/recognize_digits/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -340,6 +341,7 @@ trainer.train(
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/recommender_system/index.en.html b/recommender_system/index.en.html
index b5a4cfd..a3c12dd 100644
--- a/recommender_system/index.en.html
+++ b/recommender_system/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -150,6 +151,7 @@ This tutorial goes over traditional approaches in recommender system and a deep
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial</span> was created by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">the PaddlePaddle community</a> and published under <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Common Creative 4.0 License</a>。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/recommender_system/index.html b/recommender_system/index.html
index 2bcc0d9..816ae63 100644
--- a/recommender_system/index.html
+++ b/recommender_system/index.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -132,278 +133,330 @@ $$P(\omega=i|u)=\frac{e^{v_{i}u}}{\sum_{j \in V}e^{v_{j}u}}$$
 
 我们以 [MovieLens 百万数据集（ml-1m）](http://files.grouplens.org/datasets/movielens/ml-1m.zip)为例进行介绍。ml-1m 数据集包含了 6,000 位用户对 4,000 部电影的 1,000,000 条评价（评分范围 1~5 分，均为整数），由 GroupLens Research 实验室搜集整理。
 
-您可以运行 `data/getdata.sh` 下载数据，如果数椐获取成功，您将在目录`data/ml-1m`中看到下面的文件：
+Paddle在API中提供了自动加载数据的模块。数据模块为 `paddle.dataset.movielens`
+
 
+```python
+import paddle.v2 as paddle
+paddle.init(use_gpu=False)
 ```
-movies.dat  ratings.dat  users.dat  README 
+
+
+```python
+# Run this block to show dataset's documentation
+# help(paddle.dataset.movielens)
 ```
 
-- movies.dat：电影特征数据，格式为`电影ID::电影名称::电影类型`
-- ratings.dat：评分数据，格式为`用户ID::电影ID::评分::时间戳`
-- users.dat：用户特征数据，格式为`用户ID::性别::年龄::职业::邮编`
-- README：数据集的详细描述
+在原始数据中包含电影的特征数据，用户的特征数据，和用户对电影的评分。
 
-### 数据预处理
+例如，其中某一个电影特征为:
 
-首先安装 Python 第三方库（推荐使用 Virtualenv）：
 
-```shell
-pip install -r data/requirements.txt
+```python
+movie_info = paddle.dataset.movielens.movie_info()
+print movie_info.values()[0]
 ```
 
-其次在预处理`./preprocess.sh`过程中，我们将字段配置文件`data/config.json`转化为meta配置文件`meta_config.json`，并生成对应的meta文件`meta.bin`，以完成数据文件的序列化。然后再将`ratings.dat`分为训练集、测试集两部分，把它们的地址写入`train.list`和`test.list`。
+    <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
+
 
-运行成功后目录`./data` 新增以下文件：
+这表示，电影的id是1，标题是《Toy Story》，该电影被分为到三个类别中。这三个类别是动画，儿童，喜剧。
 
+
+```python
+user_info = paddle.dataset.movielens.user_info()
+print user_info.values()[0]
 ```
-meta_config.json  meta.bin  ratings.dat.train  ratings.dat.test  train.list  test.list
+
+    <UserInfo id(1), gender(F), age(1), job(10)>
+
+
+这表示，该用户ID是1，女性，年龄比18岁还年轻。职业ID是10。
+
+
+其中，年龄使用下列分布
+*  1:  "Under 18"
+* 18:  "18-24"
+* 25:  "25-34"
+* 35:  "35-44"
+* 45:  "45-49"
+* 50:  "50-55"
+* 56:  "56+"
+
+职业是从下面几种选项里面选则得出:
+*  0:  "other" or not specified
+*  1:  "academic/educator"
+*  2:  "artist"
+*  3:  "clerical/admin"
+*  4:  "college/grad student"
+*  5:  "customer service"
+*  6:  "doctor/health care"
+*  7:  "executive/managerial"
+*  8:  "farmer"
+*  9:  "homemaker"
+* 10:  "K-12 student"
+* 11:  "lawyer"
+* 12:  "programmer"
+* 13:  "retired"
+* 14:  "sales/marketing"
+* 15:  "scientist"
+* 16:  "self-employed"
+* 17:  "technician/engineer"
+* 18:  "tradesman/craftsman"
+* 19:  "unemployed"
+* 20:  "writer"
+
+而对于每一条训练/测试数据，均为 <用户特征> + <电影特征> + 评分。
+
+例如，我们获得第一条训练数据:
+
+
+```python
+train_set_creator = paddle.dataset.movielens.train()
+train_sample = next(train_set_creator())
+uid = train_sample[0]
+mov_id = train_sample[len(user_info[uid].value())]
+print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
 ```
 
-- meta.bin: meta文件是Python的pickle对象， 存储着电影和用户信息。
-- meta_config.json: meta配置文件，用来具体描述如何解析数据集中的每一个字段，由字段配置文件生成。
-- ratings.dat.train和ratings.dat.test: 训练集和测试集，训练集已经随机打乱。
-- train.list和test.list: 训练集和测试集的文件地址列表。
+    User <UserInfo id(1), gender(F), age(1), job(10)> rates Movie <MovieInfo id(1193), title(One Flew Over the Cuckoo's Nest ), categories(['Drama'])> with Score [5.0]
+
 
-### 提供数据给 PaddlePaddle
+即用户1对电影1193的评价为5分。
+
+## 模型配置说明
+
+下面我们开始根据输入数据的形式配置模型。
 
-我们使用 Python 接口传递数据给系统，下面 `dataprovider.py` 给出了完整示例。
 
 ```python
-from paddle.trainer.PyDataProvider2 import *
-from common_utils import meta_to_header
-
-def __list_to_map__(lst):  # 将list转为map
-    ret_val = dict()
-    for each in lst:
-        k, v = each
-        ret_val[k] = v
-    return ret_val
-
-def hook(settings, meta, **kwargs): # 读取meta.bin
-    # 定义电影特征
-    movie_headers = list(meta_to_header(meta, 'movie'))
-    settings.movie_names = [h[0] for h in movie_headers]
-    headers = movie_headers
-    
-    # 定义用户特征
-    user_headers = list(meta_to_header(meta, 'user'))
-    settings.user_names = [h[0] for h in user_headers]
-    headers.extend(user_headers)
-    
-    # 加载评分信息
-    headers.append(("rating", dense_vector(1)))
-    
-    settings.input_types = __list_to_map__(headers)
-    settings.meta = meta
-    
-@provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, filename):
-    with open(filename, 'r') as f:
-        for line in f:
-            # 从评分文件中读取评分
-            user_id, movie_id, score = map(int, line.split('::')[:-1])
-            # 将评分平移到[-2, +2]范围内的整数
-            score = float(score - 3)
-            
-            movie_meta = settings.meta['movie'][movie_id]
-            user_meta = settings.meta['user'][user_id]
+uid = paddle.layer.data(
+        name='user_id',
+        type=paddle.data_type.integer_value(
+            paddle.dataset.movielens.max_user_id() + 1))
+usr_emb = paddle.layer.embedding(input=uid, size=32)
+
+usr_gender_id = paddle.layer.data(
+        name='gender_id', type=paddle.data_type.integer_value(2))
+usr_gender_emb = paddle.layer.embedding(input=usr_gender_id, size=16)
+
+usr_age_id = paddle.layer.data(
+        name='age_id',
+        type=paddle.data_type.integer_value(
+            len(paddle.dataset.movielens.age_table)))
+usr_age_emb = paddle.layer.embedding(input=usr_age_id, size=16)
+
+usr_job_id = paddle.layer.data(
+        name='job_id',
+        type=paddle.data_type.integer_value(paddle.dataset.movielens.max_job_id(
+        ) + 1))
+usr_job_emb = paddle.layer.embedding(input=usr_job_id, size=16)
+```
 
-            # 添加电影ID与电影特征
-            outputs = [('movie_id', movie_id - 1)]
-            for i, each_meta in enumerate(movie_meta):
-                outputs.append((settings.movie_names[i + 1], each_meta))
-            
-            # 添加用户ID与用户特征
-            outputs.append(('user_id', user_id - 1))
-            for i, each_meta in enumerate(user_meta):
-                outputs.append((settings.user_names[i + 1], each_meta))
-            
-            # 添加评分
-            outputs.append(('rating', [score]))
-            # 将数据返回给 paddle
-            yield __list_to_map__(outputs)
+如上述代码所示，对于每个用户，我们输入4维特征。其中包括`user_id`,`gender_id`,`age_id`,`job_id`。这几维特征均是简单的整数值。为了后续神经网络处理这些特征方便，我们借鉴NLP中的语言模型，将这几维离散的整数值，变换成embedding取出。分别形成`usr_emb`, `usr_gender_emb`, `usr_age_emb`, `usr_job_emb`。
+
+
+```python
+usr_combined_features = paddle.layer.fc(
+        input=[usr_emb, usr_gender_emb, usr_age_emb, usr_job_emb],
+        size=200,
+        act=paddle.activation.Tanh())
 ```
 
-## 模型配置说明
+然后，我们对于所有的用户特征，均输入到一个全连接层(fc)中。将所有特征融合为一个200维度的特征。
 
-### 数据定义
+进而，我们对每一个电影特征做类似的变换，网络配置为:
 
-加载`meta.bin`文件并定义通过`define_py_data_sources2`从dataprovider中读入数据：
 
 ```python
-from paddle.trainer_config_helpers import *
+mov_id = paddle.layer.data(
+    name='movie_id',
+    type=paddle.data_type.integer_value(
+        paddle.dataset.movielens.max_movie_id() + 1))
+mov_emb = paddle.layer.embedding(input=mov_id, size=32)
+
+mov_categories = paddle.layer.data(
+    name='category_id',
+    type=paddle.data_type.sparse_binary_vector(
+        len(paddle.dataset.movielens.movie_categories())))
+
+mov_categories_hidden = paddle.layer.fc(input=mov_categories, size=32)
+
+
+movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
+mov_title_id = paddle.layer.data(
+    name='movie_title',
+    type=paddle.data_type.integer_value_sequence(len(movie_title_dict)))
+mov_title_emb = paddle.layer.embedding(input=mov_title_id, size=32)
+mov_title_conv = paddle.networks.sequence_conv_pool(
+    input=mov_title_emb, hidden_size=32, context_len=3)
+
+mov_combined_features = paddle.layer.fc(
+    input=[mov_emb, mov_categories_hidden, mov_title_conv],
+    size=200,
+    act=paddle.activation.Tanh())
+```
 
-try:
-    import cPickle as pickle
-except ImportError:
-    import pickle
+电影ID和电影类型分别映射到其对应的特征隐层。对于电影标题名称(title)，一个ID序列表示的词语序列，在输入卷积层后，将得到每个时间窗口的特征（序列特征），然后通过在时间维度降采样得到固定维度的特征，整个过程在text_conv_pool实现。
 
-is_predict = get_config_arg('is_predict', bool, False)
+最后再将电影的特征融合进`mov_combined_features`中。
 
-META_FILE = 'data/meta.bin'
 
-# 加载 meta 文件
-with open(META_FILE, 'rb') as f:
-    meta = pickle.load(f)
+```python
+inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_features, size=1, scale=5)
+```
+
+进而，我们使用余弦相似度计算用户特征与电影特征的相似性。并将这个相似性拟合(回归)到用户评分上。
 
-if not is_predict:
-    define_py_data_sources2(
-        'data/train.list',
-        'data/test.list',
-        module='dataprovider',
-        obj='process',
-        args={'meta': meta})
+
+```python
+cost = paddle.layer.regression_cost(
+        input=inference,
+        label=paddle.layer.data(
+            name='score', type=paddle.data_type.dense_vector(1)))
 ```
 
-### 算法配置
+至此，我们的优化目标就是这个网络配置中的`cost`了。
+
+## 训练模型
+
+### 定义参数
+神经网络的模型，我们可以简单的理解为网络拓朴结构+参数。之前一节，我们定义出了优化目标`cost`。这个`cost`即为网络模型的拓扑结构。我们开始训练模型，需要先定义出参数。定义方法为:
 
-这里我们设置了batch size、网络初始学习率和RMSProp自适应优化方法。
 
 ```python
-settings(
-    batch_size=1600, learning_rate=1e-3, learning_method=RMSPropOptimizer())
+parameters = paddle.parameters.create(cost)
 ```
 
-### 模型结构
+    [INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
+    [INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__regression_cost_0__]
 
-1. 定义数据输入和参数维度。
 
-   ```python
-   movie_meta = meta['movie']['__meta__']['raw_meta']
-   user_meta = meta['user']['__meta__']['raw_meta']
+`parameters`是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候，我们没有指定参数名称，这里参数名称是自动生成的。当然，我们也可以指定每一个参数名称，方便日后维护。
 
-   movie_id = data_layer('movie_id', size=movie_meta[0]['max'])    # 电影ID
-   title = data_layer('title', size=len(movie_meta[1]['dict']))    # 电影名称
-   genres = data_layer('genres', size=len(movie_meta[2]['dict']))  # 电影类型
-   user_id = data_layer('user_id', size=user_meta[0]['max'])	    # 用户ID
-   gender = data_layer('gender', size=len(user_meta[1]['dict']))   # 用户性别
-   age = data_layer('age', size=len(user_meta[2]['dict']))			# 用户年龄
-   occupation = data_layer('occupation', size=len(user_meta[3]['dict'])) # 用户职业
 
-   embsize = 256  # 向量维度
-   ```
+```python
+print parameters.keys()
+```
 
-2. 构造“电影”特征。
+    [u'___fc_layer_2__.wbias', u'___fc_layer_2__.w2', u'___embedding_layer_3__.w0', u'___embedding_layer_5__.w0', u'___embedding_layer_2__.w0', u'___embedding_layer_1__.w0', u'___fc_layer_1__.wbias', u'___fc_layer_0__.wbias', u'___fc_layer_1__.w0', u'___fc_layer_0__.w2', u'___fc_layer_0__.w3', u'___fc_layer_0__.w0', u'___fc_layer_0__.w1', u'___fc_layer_2__.w1', u'___fc_layer_2__.w0', u'___embedding_layer_4__.w0', u'___sequence_conv_pool_0___conv_fc.w0', u'___embedding_layer_0__.w0', u'___sequence_conv_pool_0___conv_fc.wbias']
 
-   ```python
-   # 电影ID和电影类型分别映射到其对应的特征隐层（256维）。
-   movie_id_emb = embedding_layer(input=movie_id, size=embsize)
-   movie_id_hidden = fc_layer(input=movie_id_emb, size=embsize)
 
-   genres_emb = fc_layer(input=genres, size=embsize)
+### 构造训练(trainer)
 
-   # 对于电影名称，一个ID序列表示的词语序列，在输入卷积层后，
-   # 将得到每个时间窗口的特征（序列特征），然后通过在时间维度
-   # 降采样得到固定维度的特征，整个过程在text_conv_pool实现
-   title_emb = embedding_layer(input=title, size=embsize)
-   title_hidden = text_conv_pool(
-       input=title_emb, context_len=5, hidden_size=embsize)
+下面，我们根据网络拓扑结构和模型参数来构造出一个本地训练(trainer)。在构造本地训练的时候，我们还需要指定这个训练的优化方法。这里我们使用Adam来作为优化算法。
 
-   # 将三个属性的特征表示分别全连接并相加，结果即是电影特征的最终表示
-   movie_feature = fc_layer(
-       input=[movie_id_hidden, title_hidden, genres_emb], size=embsize)
-   ```
 
-3. 构造“用户”特征。
+```python
+trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, 
+                            update_equation=paddle.optimizer.Adam(learning_rate=1e-4))
+```
 
-   ```python
-   # 将用户ID，性别，职业，年龄四个属性分别映射到其特征隐层。
-   user_id_emb = embedding_layer(input=user_id, size=embsize)
-   user_id_hidden = fc_layer(input=user_id_emb, size=embsize)
+    [INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
+    [INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__regression_cost_0__]
 
-   gender_emb = embedding_layer(input=gender, size=embsize)
-   gender_hidden = fc_layer(input=gender_emb, size=embsize)
 
-   age_emb = embedding_layer(input=age, size=embsize)
-   age_hidden = fc_layer(input=age_emb, size=embsize)
+### 训练
 
-   occup_emb = embedding_layer(input=occupation, size=embsize)
-   occup_hidden = fc_layer(input=occup_emb, size=embsize)
+下面我们开始训练过程。
 
-   # 同样将这四个属性分别全连接并相加形成用户特征的最终表示。
-   user_feature = fc_layer(
-       input=[user_id_hidden, gender_hidden, age_hidden, occup_hidden],
-       size=embsize)
-   ```
+我们直接使用Paddle提供的数据集读取程序。`paddle.dataset.movielens.train()`和`paddle.dataset.movielens.test()`分别做训练和预测数据集。并且通过`reader_dict`来指定每一个数据和data_layer的对应关系。
 
-4. 计算余弦相似度，定义损失函数和网络输出。
+例如，这里的reader_dict表示的是，对于数据层 `user_id`，使用了reader中每一条数据的第0个元素。`gender_id`数据层使用了第1个元素。以此类推。
 
-   ```python
-   similarity = cos_sim(a=movie_feature, b=user_feature, scale=2)
+训练过程是完全自动的。我们可以使用event_handler来观察训练过程，或进行测试等。这里我们在event_handler里面绘制了训练误差曲线和测试误差曲线。并且保存了模型。
 
-   # 训练时，采用regression_cost作为损失函数计算回归误差代价，并作为网络的输出。
-   # 预测时，网络的输出即为余弦相似度。
-   if not is_predict:
-       lbl=data_layer('rating', size=1)
-   	cost=regression_cost(input=similarity, label=lbl)
-   	outputs(cost)
-   else:
-       outputs(similarity)
-   ```
 
-## 训练模型
+```python
+%matplotlib inline
+
+import matplotlib.pyplot as plt
+from IPython import display
+import cPickle
+
+feeding = {
+    'user_id': 0,
+    'gender_id': 1,
+    'age_id': 2,
+    'job_id': 3,
+    'movie_id': 4,
+    'category_id': 5,
+    'movie_title': 6,
+    'score': 7
+}
 
-执行`sh train.sh` 开始训练模型，将日志写入文件 `log.txt` 并打印在屏幕上。其中指定了总共需要执行 50 个pass。
-
-```shell
-set -e
-paddle train \
-    --config=trainer_config.py \		 # 神经网络配置文件
-    --save_dir=./output \				 # 模型保存路径
-    --use_gpu=false \					 # 是否使用GPU(默认不使用)
-    --trainer_count=4\					 # 一台机器上面的线程数量
-    --test_all_data_in_one_period=true \ # 每个训练周期训练一次所有数据，否则每个训练周期测试batch_size个batch数据
-    --log_period=100 \					 # 训练log_period个batch后打印日志
-    --dot_period=1 \					 # 每训练dot_period个batch后打印一个"."
-    --num_passes=50  2>&1 | tee 'log.txt'
-```
+step=0
+
+train_costs=[],[]
+test_costs=[],[]
 
-成功的输出类似如下：
-
-```bash
-I0117 01:01:48.585651  9998 TrainerInternal.cpp:165]  Batch=100 samples=160000 AvgCost=0.600042 CurrentCost=0.600042 Eval:  CurrentEval:
-...................................................................................................
-I0117 01:02:53.821918  9998 TrainerInternal.cpp:165]  Batch=200 samples=320000 AvgCost=0.602855 CurrentCost=0.605668 Eval:  CurrentEval:
-...................................................................................................
-I0117 01:03:58.937922  9998 TrainerInternal.cpp:165]  Batch=300 samples=480000 AvgCost=0.605199 CurrentCost=0.609887 Eval:  CurrentEval:
-...................................................................................................
-I0117 01:05:04.083251  9998 TrainerInternal.cpp:165]  Batch=400 samples=640000 AvgCost=0.608693 CurrentCost=0.619175 Eval:  CurrentEval:
-...................................................................................................
-I0117 01:06:09.155859  9998 TrainerInternal.cpp:165]  Batch=500 samples=800000 AvgCost=0.613273 CurrentCost=0.631591 Eval:  CurrentEval:
-.................................................................I0117 01:06:51.109654  9998 TrainerInternal.cpp:181]
- Pass=49 Batch=565 samples=902826 AvgCost=0.614772 Eval:
-I0117 01:07:04.205142  9998 Tester.cpp:115]  Test samples=97383 cost=0.721995 Eval:
-I0117 01:07:04.205281  9998 GradientMachine.cpp:113] Saving parameters to ./output/pass-00049
+def event_handler(event):
+    global step
+    global train_costs
+    global test_costs
+    if isinstance(event, paddle.event.EndIteration):
+        need_plot = False
+        if step % 10 == 0:  # every 10 batches, record a train cost
+            train_costs[0].append(step)
+            train_costs[1].append(event.cost)
+            
+        if step % 1000 == 0: # every 1000 batches, record a test cost
+            result = trainer.test(reader=paddle.batch(
+                  paddle.dataset.movielens.test(), batch_size=256))
+            test_costs[0].append(step)
+            test_costs[1].append(result.cost)
+        
+        if step % 100 == 0: # every 100 batches, update cost plot
+            plt.plot(*train_costs)
+            plt.plot(*test_costs)
+            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
+            display.clear_output(wait=True)
+            display.display(plt.gcf())
+            plt.gcf().clear()
+        step += 1
+
+trainer.train(
+    reader=paddle.batch(
+            paddle.reader.shuffle(
+            paddle.dataset.movielens.train(), buf_size=8192),
+                            batch_size=256),
+    event_handler=event_handler,
+    feeding=feeding,
+    num_passes=2)
 ```
 
+
+![png](./image/output_32_0.png)
+
 ## 应用模型
 
-在训练了几轮以后，您可以对模型进行评估。运行以下命令，可以通过选择最小训练误差的一轮参数得到最好轮次的模型。
+在训练了几轮以后，您可以对模型进行推断。我们可以使用任意一个用户ID和电影ID，来预测该用户对该电影的评分。示例程序为:
 
-```shell
-./evaluate.py log.txt
-```
 
-您将看到：
+```python
+import copy
+user_id = 234
+movie_id = 345
 
-```shell
-Best pass is 00036, error is 0.719281, which means predict get error as 0.424052
-evaluating from pass output/pass-00036
-```
+user = user_info[user_id]
+movie = movie_info[movie_id]
+
+feature = user.value() + movie.value()
 
-预测任何用户对于任何一部电影评价的命令如下：
+infer_dict = copy.copy(feeding)
+del infer_dict['score']
 
-```shell
-python prediction.py 'output/pass-00036/'
+prediction = paddle.infer(output=inference, parameters=parameters, input=[feature], feeding=infer_dict)
+score = (prediction[0][0] + 5.0) / 2
+print "[Predict] User %d Rating Movie %d With Score %.2f"%(user_id, movie_id, score)
 ```
 
-预测程序将读取用户的输入，然后输出预测分数。您会看到如下命令行界面：
+    [INFO 2017-03-06 17:17:08,132 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title]
+    [INFO 2017-03-06 17:17:08,134 networks.py:1478] The output order is [__cos_sim_0__]
+
+
+    [Predict] User 234 Rating Movie 345 With Score 4.16
 
-```
-Input movie_id: 1962
-Input user_id: 1
-Prediction Score is 4.25
-```
 
 ## 总结
 
@@ -421,6 +474,7 @@ Prediction Score is 4.25
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
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diff --git a/skip_thought/index.html b/skip_thought/index.html
index bd5f85a..de7844e 100644
--- a/skip_thought/index.html
+++ b/skip_thought/index.html
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+
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 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
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diff --git a/speech_recognition/index.html b/speech_recognition/index.html
index bd5f85a..de7844e 100644
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+++ b/speech_recognition/index.html
@@ -1,3 +1,4 @@
+
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 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
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diff --git a/understand_sentiment/index.en.html b/understand_sentiment/index.en.html
index 43f14c1..4a06747 100644
--- a/understand_sentiment/index.en.html
+++ b/understand_sentiment/index.en.html
@@ -1,3 +1,4 @@
+
 <html>
 <head>
   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -532,6 +533,7 @@ In this chapter, we use sentiment analysis as an example to introduce applying d
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
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diff --git a/understand_sentiment/index.html b/understand_sentiment/index.html
index aa2c6bc..97550ff 100644
--- a/understand_sentiment/index.html
+++ b/understand_sentiment/index.html
@@ -1,3 +1,4 @@
+
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   <script type="text/x-mathjax-config">
@@ -5,8 +6,8 @@
     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -353,6 +354,7 @@ Test with Pass 0, {'classification_error_evaluator': 0.11432000249624252}
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
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diff --git a/word2vec/index.en.html b/word2vec/index.en.html
index f20c2b3..9494c09 100644
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+++ b/word2vec/index.en.html
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+
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     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -219,6 +220,7 @@ In information retrieval, the relevance between the query and document keyword c
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
 <!-- You can change the lines below now. -->
 
diff --git a/word2vec/index.html b/word2vec/index.html
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+
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     extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
     jax: ["input/TeX", "output/HTML-CSS"],
     tex2jax: {
-      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-      displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+      inlineMath: [ ['$','$'] ],
+      displayMath: [ ['$$','$$'] ],
       processEscapes: true
     },
     "HTML-CSS": { availableFonts: ["TeX"] }
@@ -39,6 +40,7 @@
 
 <!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
 <div id="markdown" style='display:none'>
+
 # 词向量
 
 本教程源代码目录在[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/word2vec)， 初次使用请参考PaddlePaddle[安装教程](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。
@@ -250,7 +252,7 @@ N = 5 # 训练5-Gram
 
 - 将$w_t$之前的$n-1$个词 $w_{t-n+1},...w_{t-1}$，通过$|V|\times D$的矩阵映射到D维词向量（本例中取D=32）。
 	
-```python	
+```python
 def wordemb(inlayer):
     wordemb = paddle.layer.table_projection(
         input=inlayer,
@@ -266,54 +268,54 @@ def wordemb(inlayer):
 - 定义输入层接受的数据类型以及名字。
 
 ```python
-def main():
-    paddle.init(use_gpu=False, trainer_count=1) # 初始化PaddlePaddle
-    word_dict = paddle.dataset.imikolov.build_dict()
-    dict_size = len(word_dict)
-	# 每个输入层都接受整形数据，这些数据的范围是[0, dict_size)
-    firstword = paddle.layer.data(
-        name="firstw", type=paddle.data_type.integer_value(dict_size))
-    secondword = paddle.layer.data(
-        name="secondw", type=paddle.data_type.integer_value(dict_size))
-    thirdword = paddle.layer.data(
-        name="thirdw", type=paddle.data_type.integer_value(dict_size))
-    fourthword = paddle.layer.data(
-        name="fourthw", type=paddle.data_type.integer_value(dict_size))
-    nextword = paddle.layer.data(
-        name="fifthw", type=paddle.data_type.integer_value(dict_size))
-
-    Efirst = wordemb(firstword)
-    Esecond = wordemb(secondword)
-    Ethird = wordemb(thirdword)
-    Efourth = wordemb(fourthword)
+paddle.init(use_gpu=False, trainer_count=3) # 初始化PaddlePaddle
+word_dict = paddle.dataset.imikolov.build_dict()
+dict_size = len(word_dict)
+# 每个输入层都接受整形数据，这些数据的范围是[0, dict_size)
+firstword = paddle.layer.data(
+    name="firstw", type=paddle.data_type.integer_value(dict_size))
+secondword = paddle.layer.data(
+    name="secondw", type=paddle.data_type.integer_value(dict_size))
+thirdword = paddle.layer.data(
+    name="thirdw", type=paddle.data_type.integer_value(dict_size))
+fourthword = paddle.layer.data(
+    name="fourthw", type=paddle.data_type.integer_value(dict_size))
+nextword = paddle.layer.data(
+    name="fifthw", type=paddle.data_type.integer_value(dict_size))
+
+Efirst = wordemb(firstword)
+Esecond = wordemb(secondword)
+Ethird = wordemb(thirdword)
+Efourth = wordemb(fourthword)
+
 ```
 
 - 将这n-1个词向量经过concat_layer连接成一个大向量作为历史文本特征。
 
 ```python
-    contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
+contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
 ```
 
 - 将历史文本特征经过一个全连接得到文本隐层特征。
 
 ```python
-    hidden1 = paddle.layer.fc(input=contextemb,
-                              size=hiddensize,
-                              act=paddle.activation.Sigmoid(),
-                              layer_attr=paddle.attr.Extra(drop_rate=0.5),
-                              bias_attr=paddle.attr.Param(learning_rate=2),
-                              param_attr=paddle.attr.Param(
-                                  initial_std=1. / math.sqrt(embsize * 8),
-                                  learning_rate=1))
+hidden1 = paddle.layer.fc(input=contextemb,
+                          size=hiddensize,
+                          act=paddle.activation.Sigmoid(),
+                          layer_attr=paddle.attr.Extra(drop_rate=0.5),
+                          bias_attr=paddle.attr.Param(learning_rate=2),
+                          param_attr=paddle.attr.Param(
+                                initial_std=1. / math.sqrt(embsize * 8),
+                                learning_rate=1))
 ```
 	
 - 将文本隐层特征，再经过一个全连接，映射成一个$|V|$维向量，同时通过softmax归一化得到这`|V|`个词的生成概率。
 
 ```python
-    predictword = paddle.layer.fc(input=hidden1,
-                                  size=dict_size,
-                                  bias_attr=paddle.attr.Param(learning_rate=2),
-                                  act=paddle.activation.Softmax())
+predictword = paddle.layer.fc(input=hidden1,
+                              size=dict_size,
+                              bias_attr=paddle.attr.Param(learning_rate=2),
+                              act=paddle.activation.Softmax())
 ```
 
 - 网络的损失函数为多分类交叉熵，可直接调用`classification_cost`函数。
@@ -329,11 +331,11 @@ cost = paddle.layer.classification_cost(input=predictword, label=nextword)
 - 正则化（regularization）： 是防止网络过拟合的一种手段，此处采用L2正则化。
 
 ```python
-    parameters = paddle.parameters.create(cost)
-    adam_optimizer = paddle.optimizer.Adam(
-        learning_rate=3e-3,
-        regularization=paddle.optimizer.L2Regularization(8e-4))
-    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+parameters = paddle.parameters.create(cost)
+adam_optimizer = paddle.optimizer.Adam(
+    learning_rate=3e-3,
+    regularization=paddle.optimizer.L2Regularization(8e-4))
+trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
 ```
 
 下一步，我们开始训练过程。`paddle.dataset.imikolov.train()`和`paddle.dataset.imikolov.test()`分别做训练和测试数据集。这两个函数各自返回一个reader——PaddlePaddle中的reader是一个Python函数，每次调用的时候返回一个Python generator。
@@ -341,111 +343,93 @@ cost = paddle.layer.classification_cost(input=predictword, label=nextword)
 `paddle.batch`的输入是一个reader，输出是一个batched reader —— 在PaddlePaddle里，一个reader每次yield一条训练数据，而一个batched reader每次yield一个minbatch。
 
 ```python
-    def event_handler(event):
-        if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                result = trainer.test(
+import gzip
+    
+def event_handler(event):
+    if isinstance(event, paddle.event.EndIteration):
+        if event.batch_id % 100 == 0:
+            print "Pass %d, Batch %d, Cost %f, %s" % (
+                event.pass_id, event.batch_id, event.cost, event.metrics)
+            
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(
                     paddle.batch(
                         paddle.dataset.imikolov.test(word_dict, N), 32))
-                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics,
-                    result.metrics)
-
-    trainer.train(
-        paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
-        num_passes=30,
-        event_handler=event_handler)
+        print "Pass %d, Testing metrics %s" % (event.pass_id, result.metrics)
+        with gzip.open("model_%d.tar.gz"%event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
+trainer.train(
+    paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
+    num_passes=100,
+    event_handler=event_handler)
 ```
 
-训练过程是完全自动的，event_handler里打印的日志类似如下所示：
+    ...
+    Pass 0, Batch 25000, Cost 4.251861, {'classification_error_evaluator': 0.84375}
+    Pass 0, Batch 25100, Cost 4.847692, {'classification_error_evaluator': 0.8125}
+    Pass 0, Testing metrics {'classification_error_evaluator': 0.7417652606964111}
+
+
+训练过程是完全自动的，event_handler里打印的日志类似如上所示：
 
-```text
-.............................
-I1222 09:27:16.477841 12590 TrainerInternal.cpp:162]  Batch=3000 samples=300000 AvgCost=5.36135 CurrentCost=5.36135 Eval: classification_error_evaluator=0.818653  CurrentEval: class
-ification_error_evaluator=0.818653 
-.............................
-I1222 09:27:22.416700 12590 TrainerInternal.cpp:162]  Batch=6000 samples=600000 AvgCost=5.29301 CurrentCost=5.22467 Eval: classification_error_evaluator=0.814542  CurrentEval: class
-ification_error_evaluator=0.81043 
-.............................
-I1222 09:27:28.343756 12590 TrainerInternal.cpp:162]  Batch=9000 samples=900000 AvgCost=5.22494 CurrentCost=5.08876 Eval: classification_error_evaluator=0.810088  CurrentEval: class
-ification_error_evaluator=0.80118 
-..I1222 09:27:29.128582 12590 TrainerInternal.cpp:179]  Pass=0 Batch=9296 samples=929600 AvgCost=5.21786 Eval: classification_error_evaluator=0.809647 
-I1222 09:27:29.627616 12590 Tester.cpp:111]  Test samples=73760 cost=4.9594 Eval: classification_error_evaluator=0.79676 
-I1222 09:27:29.627713 12590 GradientMachine.cpp:112] Saving parameters to model/pass-00000
-```
 经过30个pass，我们将得到平均错误率为classification_error_evaluator=0.735611。
 
 
 ## 应用模型
-训练模型后，我们可以加载模型参数，用训练出来的词向量初始化其他模型，也可以将模型参数从二进制格式转换成文本格式进行后续应用。
-
-### 初始化其他模型
+训练模型后，我们可以加载模型参数，用训练出来的词向量初始化其他模型，也可以将模型查看参数用来做后续应用。
 
-训练好的模型参数可以用来初始化其他模型。具体方法如下：
-在PaddlePaddle 训练命令行中，用`--init_model_path` 来定义初始化模型的位置，用`--load_missing_parameter_strategy`指定除了词向量以外的新模型其他参数的初始化策略。注意，新模型需要和原模型共享被初始化参数的参数名。
 	
 ### 查看词向量
-PaddlePaddle训练出来的参数为二进制格式，存储在对应训练pass的文件夹下。这里我们提供了文件`format_convert.py`用来互转PaddlePaddle训练结果的二进制文件和文本格式特征文件。
 
-```bash
-python format_convert.py --b2t -i INPUT -o OUTPUT -d DIM
-```
-其中，INPUT是输入的（二进制）词向量模型名称，OUTPUT是输出的文本模型名称，DIM是词向量参数维度。
+PaddlePaddle训练出来的参数可以直接使用`parameters.get()`获取出来。例如查看单词的word的词向量，即为
 
-用法如：
 
-```bash
-python format_convert.py --b2t -i model/pass-00029/_proj -o model/pass-00029/_proj.txt -d 32
-```
-转换后得到的文本文件如下：
+```python
+embeddings = parameters.get("_proj").reshape(len(word_dict), embsize)
 
-```text
-0,4,62496
--0.7444070,-0.1846171,-1.5771370,0.7070392,2.1963732,-0.0091410, ......
--0.0721337,-0.2429973,-0.0606297,0.1882059,-0.2072131,-0.7661019, ......
-......
+print embeddings[word_dict['word']]
 ```
 
-其中，第一行是PaddlePaddle 输出文件的格式说明，包含3个属性：<br/>
-1) PaddlePaddle的版本号，本例中为0;<br/>
-2) 浮点数占用的字节数，本例中为4;<br/>
-3) 总计的参数个数, 本例中为62496（即1953*32）;<br/>
-第二行及之后的每一行都按顺序表示字典里一个词的特征，用逗号分隔。
+    [-0.38961065 -0.02392169 -0.00093231  0.36301503  0.13538605  0.16076435
+     -0.0678709   0.1090285   0.42014077 -0.24119169 -0.31847557  0.20410083
+      0.04910378  0.19021918 -0.0122014  -0.04099389 -0.16924137  0.1911236
+     -0.10917275  0.13068172 -0.23079982  0.42699069 -0.27679482 -0.01472992
+      0.2069038   0.09005053 -0.3282454   0.12717034 -0.24218646  0.25304323
+      0.19072419 -0.24286366]
+
+
 	
 ### 修改词向量
 
-我们可以对词向量进行修改，并转换成PaddlePaddle参数二进制格式，方法：	
+获得到的embedding为一个标准的numpy矩阵。我们可以对这个numpy矩阵进行修改，然后赋值回去。
 
-```bash
-python format_convert.py --t2b -i INPUT -o OUTPUT
-```
 
-其中，INPUT是输入的输入的文本词向量模型名称，OUTPUT是输出的二进制词向量模型名称
-
-输入的文本格式如下（注意，不包含上面二进制转文本后第一行的格式说明）：
+```python
+def modify_embedding(emb):
+    # Add your modification here.
+    pass
 
-```text
--0.7444070,-0.1846171,-1.5771370,0.7070392,2.1963732,-0.0091410, ......
--0.0721337,-0.2429973,-0.0606297,0.1882059,-0.2072131,-0.7661019, ......
-......
+modify_embedding(embeddings)
+parameters.set("_proj", embeddings)
 ```
-	
-	
 
 ### 计算词语之间的余弦距离
 
 两个向量之间的距离可以用余弦值来表示，余弦值在$[-1,1]$的区间内，向量间余弦值越大，其距离越近。这里我们在`calculate_dis.py`中实现不同词语的距离度量。
 用法如下：
 
-```bash
-python calculate_dis.py VOCABULARY EMBEDDINGLAYER` 
-```
 
-其中，`VOCABULARY`是字典，`EMBEDDINGLAYER`是词向量模型，示例如下：
+```python
+from scipy import spatial
 
-```bash
-python calculate_dis.py data/vocabulary.txt model/pass-00029/_proj.txt
+emb_1 = embeddings[word_dict['world']]
+emb_2 = embeddings[word_dict['would']]
+
+print spatial.distance.cosine(emb_1, emb_2)
 ```
+
+    0.99375076448
  
  
 ## 总结
@@ -461,6 +445,7 @@ python calculate_dis.py data/vocabulary.txt model/pass-00029/_proj.txt
 
 <br/>
 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
 </div>
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