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图像分类教程
==========
在本教程中，我们将使用CIFAR-10数据集训练一个卷积神经网络，并使用这个神经网络来对图片进行分类。如下图所示，卷积神经网络可以辨识图片中的主体，并给出分类结果。
<center>![Image Classification](./image_classification.png)</center>
## 数据准备
首先下载CIFAR-10数据集。下面是CIFAR-10数据集的官方网址：
<https://www.cs.toronto.edu/~kriz/cifar.html>
我们准备了一个脚本，可以用于从官方网站上下载CIFAR-10数据集，转为jpeg文件并存入特定的目录。使用这个脚本前请确认已经安装了pillow及相关依赖模块。可以参照下面的命令进行安装：
1. 安装pillow
```bash
sudo apt-get install libjpeg-dev
pip install pillow
```
2. 下载数据集
```bash
cd demo/image_classification/data/
sh download_cifar.sh
```
CIFAR-10数据集包含60000张32x32的彩色图片。图片分为10类，每个类包含6000张。其中50000张图片作为训练集，10000张作为测试集。
下图展示了所有的图片类别，每个类别中随机抽取了10张图片。
<center>![Image Classification](./cifar.png)</center>
脚本运行完成后，我们应当会得到一个名为cifar-out的文件夹，其下子文件夹的结构如下
```
train
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
test
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
```
cifar-out下包含`train`和`test`两个文件夹，其中分别包含了CIFAR-10中的训练集和测试集。这两个文件夹下各自有10个子文件夹，每个子文件夹下存储相应分类的图片。将图片按照上述结构存储好之后，我们就可以着手对分类模型进行训练了。
## 预处理
数据下载之后，还需要进行预处理，将数据转换为Paddle的格式。我们可以通过如下命令进行预处理工作：
```
cd demo/image_classification/
sh preprocess.sh
```
其中`preprocess.sh` 调用 `./demo/image_classification/preprocess.py` 对图片进行预处理
```sh
export PYTHONPATH=$PYTHONPATH:../../
data_dir=./data/cifar-out
python preprocess.py -i $data_dir -s 32 -c 1
```
`./demo/image_classification/preprocess.py` 使用如下参数：
- `-i` 或 `--input` 给出输入数据所在路径；
- `-s` 或 `--size` 给出图片尺寸；
- `-c` 或 `--color` 标示图片是彩色图或灰度图
## 模型训练
在开始训练之前，我们需要先创建一个模型配置文件。下面我们给出了一个配置示例。**注意**，这里的列出的和`vgg_16_cifar.py`文件稍有差别，因为该文件可适用于预测。
```python
from paddle.trainer_config_helpers import *
data_dir='data/cifar-out/batches/'
meta_path=data_dir+'batches.meta'
args = {'meta':meta_path, 'mean_img_size': 32,
'img_size': 32, 'num_classes': 10,
'use_jpeg': 1, 'color': "color"}
define_py_data_sources2(train_list=data_dir+"train.list",
test_list=data_dir+'test.list',
module='image_provider',
obj='processData',
args=args)
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128))
img = data_layer(name='image', size=3*32*32)
lbl = data_layer(name="label", size=10)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
outputs(classification_cost(input=predict, label=lbl))
```
在第一行中我们载入用于定义网络的函数。
```python
from paddle.trainer_config_helpers import *
```
之后定义的`define_py_data_sources2`使用Python数据提供器，其中 `args`将在`image_provider.py`进行使用，该文件负责产生图片数据并传递给Paddle系统
- `meta`: 训练集平均值。
- `mean_img_size`: 平均特征图的高度及宽度。
- `img_size`：输入图片的高度及宽度。
- `num_classes`：类别个数。
- `use_jpeg`：处理过程中数据存储格式。
- `color`：标示是否为彩色图片。
`settings`用于设置训练算法。在下面的例子中，learning rate被设置为0.1除以batch size，而weight decay则为0.0005乘以batch size。
```python
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
```
`small_vgg`定义了网络结构。这里我们使用的是一个小的VGG网络。关于VGG卷积神经网络的描述可以参考：[http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/)。
```python
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
```
配置创建完毕后，可以运行脚本train.sh来训练模型。
```bash
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--save_dir=$output \
2>&1 | tee $log
python -m paddle.utils.plotcurve -i $log > plot.png
```
- 这里我们使用的是GPU模式进行训练。如果你没有GPU环境，可以设置`use_gpu=0`。
- `./demo/image_classification/vgg_16_cifar.py`是网络和数据配置文件。各项参数的详细说明可以在命令行参数相关文档中找到。
- 脚本`plotcurve.py`依赖于python的`matplotlib`模块。因此如果这个脚本运行失败，也许是因为需要安装`matplotlib`。
在训练完成后，训练及测试误差曲线图会被`plotcurve.py`脚本保存在 `plot.png`中。下面是一个误差曲线图的示例：
<center>![Training and testing curves.](./plot.png)</center>
## 预测
在训练完成后，模型及参数会被保存在路径`./cifar_vgg_model/pass-%05d`下。例如第300个pass的模型会被保存在`./cifar_vgg_model/pass-00299`。
要对一个图片的进行分类预测，我们可以使用`predict.sh`，该脚本将输出预测分类的标签：
```
sh predict.sh
```
predict.sh:
```
model=cifar_vgg_model/pass-00299/
image=data/cifar-out/test/airplane/seaplane_s_000978.png
use_gpu=1
python prediction.py $model $image $use_gpu
```
## 练习
在CUB-200数据集上使用VGG模型训练一个鸟类图片分类模型。相关的鸟类数据集可以从如下地址下载，其中包含了200种鸟类的照片（主要来自北美洲）。
<http://www.vision.caltech.edu/visipedia/CUB-200.html>
## 细节探究
### 卷积神经网络
卷积神经网络是一种使用卷积层的前向神经网络，很适合构建用于理解图片内容的模型。一个典型的神经网络如下图所示：
![Convolutional Neural Network](./lenet.png)
一个卷积神经网络包含如下层：
- 卷积层：通过卷积操作从图片或特征图中提取特征
- 池化层：使用max-pooling对特征图下采样
- 全连接层：使输入层到隐藏层的神经元是全部连接的。
卷积神经网络在图片分类上有着惊人的性能，这是因为它发掘出了图片的两类重要信息：局部关联性质和空间不变性质。通过交替使用卷积和池化处理， 卷积神经网络能够很好的表示这两类信息。
关于如何定义网络中的层，以及如何在层之间进行连接，请参考Layer文档。
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Image Classification Tutorial
==============================
This tutorial will guide you through training a convolutional neural network to classify objects using the CIFAR-10 image classification dataset.
As shown in the following figure, the convolutional neural network can recognize the main object in images, and output the classification result.
<center>![Image Classification](./image_classification.png)</center>
## Data Preparation
First, download CIFAR-10 dataset. CIFAR-10 dataset can be downloaded from its official website.
<https://www.cs.toronto.edu/~kriz/cifar.html>
We have prepared a script to download and process CIFAR-10 dataset. The script will download CIFAR-10 dataset from the official dataset.
It will convert it to jpeg images and organize them into a directory with the required structure for the tutorial. Make sure that you have installed pillow and its dependents.
Consider the following commands:
1. install pillow dependents
```bash
sudo apt-get install libjpeg-dev
pip install pillow
```
2. download data and preparation
```bash
cd demo/image_classification/data/
sh download_cifar.sh
```
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
Here are the classes in the dataset, as well as 10 random images from each:
<center>![Image Classification](./cifar.png)</center>
After downloading and converting, we should find a directory (cifar-out) containing the dataset in the following format:
```
train
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
test
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
```
It has two directories:`train` and `test`. These two directories contain training data and testing data of CIFAR-10, respectively. Each of these two folders contains 10 sub-folders, ranging from `airplane` to `truck`. Each sub-folder contains images with the corresponding label. After the images are organized into this structure, we are ready to train an image classification model.
## Preprocess
After the data has been downloaded, it needs to be pre-processed into the Paddle format. We can run the following command for preprocessing.
```
cd demo/image_classification/
sh preprocess.sh
```
`preprocess.sh` calls `./demo/image_classification/preprocess.py` to preprocess image data.
```sh
export PYTHONPATH=$PYTHONPATH:../../
data_dir=./data/cifar-out
python preprocess.py -i $data_dir -s 32 -c 1
```
`./demo/image_classification/preprocess.py` has the following arguments
- `-i` or `--input` specifes the input data directory.
- `-s` or `--size` specifies the processed size of images.
- `-c` or `--color` specifes whether images are color images or gray images.
## Model Training
We need to create a model config file before training the model. An example of the config file (vgg_16_cifar.py) is listed below. **Note**, it is slightly different from the `vgg_16_cifar.py` which also applies to the prediction.
```python
from paddle.trainer_config_helpers import *
data_dir='data/cifar-out/batches/'
meta_path=data_dir+'batches.meta'
args = {'meta':meta_path, 'mean_img_size': 32,
'img_size': 32, 'num_classes': 10,
'use_jpeg': 1, 'color': "color"}
define_py_data_sources2(train_list=data_dir+"train.list",
test_list=data_dir+'test.list',
module='image_provider',
obj='processData',
args=args)
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128))
img = data_layer(name='image', size=3*32*32)
lbl = data_layer(name="label", size=10)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
outputs(classification_cost(input=predict, label=lbl))
```
The first line imports python functions for defining networks.
```python
from paddle.trainer_config_helpers import *
```
Then define an `define_py_data_sources2` which use python data provider
interface. The arguments in `args` are used in `image_provider.py` which
yeilds image data and transform them to Paddle.
- `meta`: the mean value of training set.
- `mean_img_size`: the size of mean feature map.
- `img_size`: the height and width of input image.
- `num_classes`: the number of classes.
- `use_jpeg`: the data storage type when preprocessing.
- `color`: specify color image.
`settings` specifies the training algorithm. In the following example,
it specifies learning rate as 0.1, but divided by batch size, and the weight decay
is 0.0005 and multiplied by batch size.
```python
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
```
The `small_vgg` specifies the network. We use a small version of VGG convolutional network as our network
for classification. A description of VGG network can be found here [http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/).
```python
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
```
After writing the config, we can train the model by running the script train.sh.
```bash
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--save_dir=$output \
2>&1 | tee $log
python -m paddle.utils.plotcurve -i $log > plot.png
```
- Here we use GPU mode to train. If you have no gpu environment, just set `use_gpu=0`.
- `./demo/image_classification/vgg_16_cifar.py` is the network and data configuration file. The meaning of the other flags can be found in the documentation of the command line flags.
- The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`.
After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below:
<center>![Training and testing curves.](./plot.png)</center>
## Prediction
After we train the model, the model file as well as the model parameters are stored in path `./cifar_vgg_model/pass-%05d`. For example, the model of the 300-th pass is stored at `./cifar_vgg_model/pass-00299`.
To make a prediction for an image, one can run `predict.sh` as follows. The script will output the label of the classfiication.
```
sh predict.sh
```
predict.sh:
```
model=cifar_vgg_model/pass-00299/
image=data/cifar-out/test/airplane/seaplane_s_000978.png
use_gpu=1
python prediction.py $model $image $use_gpu
```
## Exercise
Train a image classification of birds using VGG model and CUB-200 dataset. The birds dataset can be downloaded here. It contains an image dataset with photos of 200 bird species (mostly North American).
<http://www.vision.caltech.edu/visipedia/CUB-200.html>
## Delve into Details
### Convolutional Neural Network
A Convolutional Neural Network is a feedforward neural network that uses convolution layers. It is very suitable for building neural networks that process and understand images. A standard convolutional neural network is shown below:
![Convolutional Neural Network](./lenet.png)
Convolutional Neural Network contains the following layers:
- Convolutional layer: It uses convolution operation to extract features from an image or a feature map.
- Pooling layer: It uses max-pooling to downsample feature maps.
- Fully Connected layer: It uses fully connected connections to transform features.
Convolutional Neural Network achieves amazing performance for image classification because it exploits two important characteristics of images: *local correlation* and *spatial invariance*. By iteratively applying convolution and max-pooing operations, convolutional neural network can well represent these two characteristics of images.
For more details of how to define layers and their connections, please refer to the documentation of layers.
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# 完整教程
* [快速入门](quick_start/index_cn.rst)
* [个性化推荐](rec/ml_regression_cn.rst)
* [图像分类](image_classification/index_cn.md)
* [情感分析](sentiment_analysis/index_cn.md)
* [语义角色标注](semantic_role_labeling/index_cn.md)
* [机器翻译](text_generation/index_cn.md)
## 常用模型
* [ResNet模型](imagenet_model/resnet_model_cn.md)
* [词向量模型](embedding_model/index_cn.md)
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# TUTORIALS
There are several examples and demos here.
* [Quick Start](quick_start/index_en.md)
* [MovieLens Regression](rec/ml_regression_en.rst)
* [Image Classification](image_classification/index_en.md)
* [Sentiment Analysis](sentiment_analysis/index_en.md)
* [Semantic Role Labeling](semantic_role_labeling/index_en.md)
* [Text Generation](text_generation/index_en.md)
* [Image Auto-Generation](gan/index_en.md)
## Model Zoo
* [ImageNet: ResNet](imagenet_model/resnet_model_en.md)
* [Embedding: Chinese Word](embedding_model/index_en.md)
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```eval_rst
.. _demo_ml_dataset:
```
# MovieLens数据集
[MovieLens 数据集](http://grouplens.org/datasets/movielens/)由GroupLens Research实验室搜集整理。
该数据集包含一些用户信息、电影信息以及电影评分\[1-5\]。根据数据量规模，该数据及有很多不同的版本。
我们用[MovieLens 百万数据集](http://files.grouplens.org/datasets/movielens/ml-1m.zip)作为示例数据
集，其中包含6,000位用户对4,000部电影的1,000,000条评价。该数据集于2003年2月发布。
## 数据集特征
在[ml-1m 数据集](http://files.grouplens.org/datasets/movielens/ml-1m.zip)中有许多的特征。在[ml-1m 数据集]
(http://files.grouplens.org/datasets/movielens/ml-1m.zip)中的这些数据文件(含有".dat"的后缀)实际上是CSV文件，
分隔符为"::"。以下我们翻译数据集网站中README文件的描述:
### 评分文件描述(ratings.dat)
所有的评分数据都包含在"ratings.dat"文件中，遵循如下的格式:
用户ID::电影ID::评分::时间戳
- 用户ID范围从1到6040
- 电影ID范围从1到3952
- 评分被调整为5星的规模(只允许整数的星级)
- 时间戳表示为从1970-01-01(UTC)来的秒数，与time(2)的返回值一致
- 每位用户至少有20条评分
### 用户文件描述(users.dat)
所有的用户信息都包含在"users.dat"文件中，遵循如下的格式:
用户ID::性别::年龄::职业::邮编
所有的人口统计学信息由用户自愿提供，没有进行正确性的检查。只有含有人
口统计学信息的用户才被包含在数据集中。
- 性别，用"M"表示男性，"F"表示女性
- 年龄从下列列表范围中选取:
* 1: "18岁以下"
* 18: "18-24岁"
* 25: "25-34岁"
* 35: "35-44岁"
* 45: "45-49岁"
* 50: "50-55岁"
* 56: "56+"
- 职业从下面所列中选择:
* 0: "其他"或不确定
* 1: "学术/教育工作者"
* 2: "艺术家"
* 3: "文书工作/管理员"
* 4: "大学生/研究生"
* 5: "客户服务"
* 6: "医生/医疗保健"
* 7: "行政工作/管理人员"
* 8: "农民"
* 9: "操持家务者"
* 10: "高中毕业生"
* 11: "律师"
* 12: "程序员"
* 13: "退休人员"
* 14: "销售/市场"
* 15: "科学家"
* 16: "自由职业者"
* 17: "技术员/工程师"
* 18: "推销员/手工艺者"
* 19: "无业人士"
* 20: "作家"
### 电影文件描述(movies.dat)
所有的电影信息都包含在"movies.dat"文件中，遵循如下的格式:
电影ID::电影名称::电影类型
- 电影名称（包括发行时间）与IMDB网站提供的一致
- 电影类型如符合多种用管道符号|分割，选自下列类型:
* 动作片
* 冒险片
* 动画片
* 儿童片
* 喜剧片
* 犯罪片
* 纪录片
* 戏剧
* 奇幻片
* 黑色电影
* 恐怖片
* 音乐剧
* 悬疑片
* 浪漫片
* 科幻片
* 惊险电影
* 战争片
* 西部片
- 由于意外的副本记录和测试记录，有些电影ID可能与实际电影不相符合
- 电影大部分是手工输入数据，因此可能会有一些错误和不一致发生
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```eval_rst
.. _demo_ml_dataset:
```
# MovieLens Dataset
The [MovieLens Dataset](http://grouplens.org/datasets/movielens/) was collected by GroupLens Research.
The data set contains some user information, movie information, and many movie ratings from \[1-5\].
The data sets have many version depending on the size of set.
We use [MovieLens 1M Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip) as a demo dataset, which contains
1 million ratings from 6000 users on 4000 movies. Released 2/2003.
## Dataset Features
In [ml-1m Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip), there are many features in these dataset.
The data files (which have ".dat" extension) in [ml-1m Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip)
is basically CSV file that delimiter is "::". The description in README we quote here.
### RATINGS FILE DESCRIPTION(ratings.dat)
All ratings are contained in the file "ratings.dat" and are in the
following format:
UserID::MovieID::Rating::Timestamp
- UserIDs range between 1 and 6040
- MovieIDs range between 1 and 3952
- Ratings are made on a 5-star scale (whole-star ratings only)
- Timestamp is represented in seconds since the epoch as returned by time(2)
- Each user has at least 20 ratings
### USERS FILE DESCRIPTION(users.dat)
User information is in the file "users.dat" and is in the following
format:
UserID::Gender::Age::Occupation::Zip-code
All demographic information is provided voluntarily by the users and is
not checked for accuracy. Only users who have provided some demographic
information are included in this data set.
- Gender is denoted by a "M" for male and "F" for female
- Age is chosen from the following ranges:
* 1: "Under 18"
* 18: "18-24"
* 25: "25-34"
* 35: "35-44"
* 45: "45-49"
* 50: "50-55"
* 56: "56+"
- Occupation is chosen from the following choices:
* 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"
### MOVIES FILE DESCRIPTION(movies.dat)
Movie information is in the file "movies.dat" and is in the following
format:
MovieID::Title::Genres
- Titles are identical to titles provided by the IMDB (including
year of release)
- Genres are pipe-separated and are selected from the following genres:
* Action
* Adventure
* Animation
* Children's
* Comedy
* Crime
* Documentary
* Drama
* Fantasy
* Film-Noir
* Horror
* Musical
* Mystery
* Romance
* Sci-Fi
* Thriller
* War
* Western
- Some MovieIDs do not correspond to a movie due to accidental duplicate
entries and/or test entries
- Movies are mostly entered by hand, so errors and inconsistencies may exist
doc/tutorials/rec/ml_regression_cn.rst 已删除 100644 → 0
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MovieLens数据集评分回归模型
===========================
这里我们在MovieLens数据集描述一种 **余弦相似度回归** 任务。
该示例将展示paddle如何进行词向量嵌入，处理相似度回归，针对文本
的单词级别的卷积神经网络，以及paddle如何处理多种类型的输入。
需要注意的是，该模型网络只是用于进行demo展示paddle如何工作，而
没有进行结构的微调。
**我们非常欢迎您用PADDLEPADDLE构建更好的示例，如果您有好的建议来
让这个示例变得更好，希望能让我们知晓。**
数据准备
`````````
下载并解压数据集
'''''''''''''''''
这里我们使用 :ref:`demo_ml_dataset` 。
要下载和解压数据集，只需要简单的运行下面的命令即可。
.. code-block:: bash
cd demo/recommendation/data
./ml_data.sh
:code:`demo/recommendation/data/ml-1m` 的目录结构为:
.. code-block:: text
+--ml-1m
+--- movies.dat # 电影特征
+--- ratings.dat # 评分
+--- users.dat # 用户特征
+--- README # 数据集描述
字段配置文件
'''''''''''''
**字段配置文件** 用来具体说明数据集的字段和文件格式，
例如，说明每个特征文件具体字段是 **什么** 类型。
ml-1m的字段配置文件在目录 :code:`demo/recommendation/data/config.json` 中。
其具体说明了字段类型和文件名称:
1) 用户文件中有四种类型的字段\: 编号，性别，年龄和职业；
2) 文件名称为"users.dat"，文件的分隔符为"::"。
.. include:: ../../../demo/recommendation/data/config.json
:code: json
:literal:
准备数据
`````````
你需要安装python的第三方库。
**强烈推荐使用VIRTUALENV来创造一个干净的python环境。**
.. code-block:: bash
pip install -r requirements.txt
预处理数据一般的命令为:
.. code-block:: bash
cd demo/recommendation
./preprocess.sh
下面介绍预处理过程具体的步骤。
提取电影或用户的特征并生成python对象
'''''''''''''''''''''''''''''''''''''
在movielens 1m数据集中，电影和用户有许多的特征。
评分文件的每一行仅仅提供电影或用户的编号来代表相应的电影或用户。
我们首先处理电影或用户的特征文件，然后用pickle命令将特征( **Meta** )对象存储为文件。
Meta配置文件
.............
**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。
该文件可以从字段配置文件生成，或是手动编辑生成。文件的格式可以
为json或yaml格式。解析器能通过文件的扩展名自动识别文件的格式。
要将字段配置文件转化为meta配置文件，只需要运行：
.. code-block:: bash
cd demo/recommendation/data
python config_generator.py config.json > meta_config.json
生成的meta配置文件如下所示：
.. include:: ../../../demo/recommendation/data/meta_config.json
:code: json
:literal:
在meta文件中有两种特征\: 电影和用户。
* 在电影文件movies.dat中
* 我们仅用"::"来分隔每一行
* pos 0 代表编号
* pos 1 特征：
* name是电影名
* 利用正则表达式来解析该特征
* 基于字母的词嵌入特征
* 是序列
* pos 2 特征：
* name是体裁
* type是one hot稠密向量
* dictionary由解析自动生成，每一个key由'|'分隔
* 在用户文件users.dat中
* 我们仅用"::"来分隔每一行
* pos 0 代表编号
* pos 1 特征：
* name是性别
* 简单的基于字母的词嵌入
* pos 2 特征：
* name是年龄
* 是整个的词嵌入
* 嵌入编号会根据单词排序
* pos 3 特征：
* name是职业
* 简单的整个词嵌入
Meta文件
''''''''
有了meta配置文件之后，我们可以生成 **Meta文件** ，该文件是python的pickle对象，
存储着电影或用户信息。可以运行下面的命令来生成。
.. code-block:: bash
python meta_generator.py ml-1m meta.bin --config=meta_config.json
meta文件 :code:`meta.bin` 的结构如下：
.. code-block:: text
+--+ movie
| +--+ __meta__
| | +--+ raw_meta # 每个特征的meta配置。列表
| | | +
| | | | # 编号字段，我们用编号作为key
| | | +--+ {'count': 3883, 'max': 3952, 'is_key': True, 'type': 'id', 'min': 1}
| | | |
| | | | # 电影名字段，嵌入特征字典
| | | +--+ {'dict': [ ... ], 'type': 'embedding', 'name': 'title', 'seq': 'sequence'}
| | | |
| | | | # 体裁字段，体裁字典
| | | +--+ {'dict': [ ... ], 'type': 'one_hot_dense', 'name': 'genres'}
| | |
| | +--+ feature_map [1, 2] # a list for raw_meta index for feature field.
| | # it means there are 2 features for each key.
| | # * 0 offset of feature is raw_meta[1], Title.
| | # * 1 offset of feature is raw_meta[2], Genres.
| |
| +--+ 1 # 电影1的特征
| | +
| | +---+ [[...], [...]] # title ids, genres dense vector
| |
| +--+ 2
| |
| +--+ ...
|
+--- user
+--+ __meta__
| +
| +--+ raw_meta
| | +
| | +--+ id field as user
| | |
| | +--+ {'dict': ['F', 'M'], 'type': 'embedding', 'name': 'gender', 'seq': 'no_sequence'}
| | |
| | +--+ {'dict': ['1', '18', '25', '35', '45', '50', '56'], 'type': 'embedding', 'name': 'age', 'seq': 'no_sequence'}
| | |
| | +--+ {'dict': [...], 'type': 'embedding', 'name': 'occupation', 'seq': 'no_sequence'}
| |
| +--+ feature_map [1, 2, 3]
|
+--+ 1 # 用户1的特征
|
+--+ 2
+--+ ...
分割训练/测试文件
''''''''''''''''''
我们将 :code:`ml-1m/ratings.dat` 文件分割为训练和测试文件。分割文件的方法是：对于每位用户，我们将评分分成两部分。
这样的话每位用户在测试文件中将与训练文件含有同样的信息。
用 :code:`separate.py` 来分离训练和测试文件。
.. code-block:: bash
python split.py ml-1m/ratings.dat --delimiter="::" --test_ratio=0.1
这样就会生成两个文件：:code:`ml-1m/ratings.dat.train` 和 :code:`ml-1m/ratings.data.test` 。
将他们移动到目录 :code:`data` ，然后进行随机打乱，再为paddle的训练过程提供文件列表。
.. code-block:: bash
shuf ml-1m/ratings.dat.train > ratings.dat.train
cp ml-1m/ratings.dat.test .
echo "./data/ratings.dat.train" > train.list
echo "./data/ratings.dat.test" > test.list
神经网络结构配置
`````````````````
训练器配置文件
'''''''''''''''
网络结构如下图所示：
.. image:: rec_regression_network.png
:align: center
:alt: rec_regression_network
该示例的神经网络配置文件 :code:`trainer_config.py` 如下所示：
.. literalinclude:: ../../../demo/recommendation/trainer_config.py
:language: python
:lines: 15-
在文件 :code:`trainer_config.py` 中，我们仅仅是将每个特征种类映射到一个特征向量中，以下
展示了如何将每个特征映射到一个向量。
* :code:`id` \: 仅仅是简单的嵌入，然后添加一个全连接层。
* :code:`embedding` \:
- 如果是序列，则先做嵌入，然后再做一次文本卷积网络操作，
然后得到平均采样的结果。
- 如果不是序列，则先做嵌入，然后添加一个全连接层。
* :code:`one_host_dense` \:
- 仅仅是两个全连接层。
然后我们利用多输入的:code:`fc_layer` 全连接层将电影的每个特征结合成一个电影特征，
并且对用户的特征做同样的操作，也得到一个用户特征。然后我们求这两个特征的余弦相似度。
在这些网络中，我们用以下的一些:ref:`api_trainer_config` 中的接口。
* 数据层， :ref:`api_trainer_config_helpers_layers_data_layer`
* 全连接层， :ref:`api_trainer_config_helpers_layers_fc_layer`
* 嵌入层， :ref:`api_trainer_config_helpers_layers_embedding_layer`
* 文本投影层， :ref:`api_trainer_config_helpers_layers_context_projection`
* 采样层， :ref:`api_trainer_config_helpers_layers_pooling_layer`
* 余弦相似度层， :ref:`api_trainer_config_helpers_layers_cos_sim`
* 文本卷积采样层， :ref:`api_trainer_config_helpers_network_text_conv_pool`
* 声明Python数据源， :ref:`api_trainer_config_helpers_data_sources`
数据提供脚本
'''''''''''''
.. literalinclude:: ../../../demo/recommendation/dataprovider.py
:language: python
:lines: 15-
数据提供脚本仅仅是读取meta.bin和评分文件，生成训练需要的样本。
在脚本 :code:`dataprovider.py` 中，我们需要设置：
* obj.slots\: 特征的类型和维度。
* use_seq\: :code:`dataprovider.py` 中的数据是否为序列模式。
* process\: 返回数据的每一条样本给 :code:`paddle` 。
数据提供脚本的细节文档可以参考 :ref:`api_pydataprovider2` 。
训练
````
准备好数据，配置了网络，编写好数据提供脚本后，现在我们可以开始paddle训练了。
代码 :code:`run.sh` 如下：
.. literalinclude:: ../../../demo/recommendation/run.sh
:language: bash
:lines: 16-
该脚本仅仅是开始一个paddle训练过程，将日志写入文件 :code:`log.txt` ，然后
打印在屏幕上。
脚本 :code:`run.sh` 中的每一行命令，请参考页面 :ref:`cmd_line_index` 。
这些参数的简短介绍如下：
* config\: 告诉paddle哪个文件是神经网络的配置文件。
* save_dir\: 告诉paddle将模型保存在: code:`./output` 中。
* use_gpu\: 是否使用GPU，默认为不使用。
* trainer_count\: 一台机器上面的线程数量。
* test_all_data_in_one_period\: 每一个测试周期测试一次所有数据。否则，
每个测试周期测试: code:`batch_size` 批次的数据。
* log_period\: 在训练了: code:`log_period` 批次后打印日志。
* dot_period\: 在每训练: code:`dot_period` 个批次后打印一个 :code:`.` 。
* num_passes\: 训练至多: code:`num_passes` 轮。
如果训练过程启动成功的话，输出应该类似如下：
.. code-block:: text
I0601 08:07:22.832059 10549 TrainerInternal.cpp:157] Batch=100 samples=160000 AvgCost=4.13494 CurrentCost=4.13494 Eval: CurrentEval:
I0601 08:07:50.672627 10549 TrainerInternal.cpp:157] Batch=200 samples=320000 AvgCost=3.80957 CurrentCost=3.48421 Eval: CurrentEval:
I0601 08:08:18.877369 10549 TrainerInternal.cpp:157] Batch=300 samples=480000 AvgCost=3.68145 CurrentCost=3.42519 Eval: CurrentEval:
I0601 08:08:46.863963 10549 TrainerInternal.cpp:157] Batch=400 samples=640000 AvgCost=3.6007 CurrentCost=3.35847 Eval: CurrentEval:
I0601 08:09:15.413025 10549 TrainerInternal.cpp:157] Batch=500 samples=800000 AvgCost=3.54811 CurrentCost=3.33773 Eval: CurrentEval:
I0601 08:09:36.058670 10549 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=3.52368 Eval:
I0601 08:09:46.215489 10549 Tester.cpp:101] Test samples=97383 cost=3.32155 Eval:
I0601 08:09:46.215966 10549 GradientMachine.cpp:132] Saving parameters to ./output/model/pass-00000
I0601 08:09:46.233397 10549 ParamUtil.cpp:99] save dir ./output/model/pass-00000
I0601 08:09:46.233438 10549 Util.cpp:209] copy trainer_config.py to ./output/model/pass-00000
I0601 08:09:46.233541 10549 ParamUtil.cpp:147] fileName trainer_config.py
模型被保存在 :code:`output/` 目录中。你可以在任何时候用 :code:`Ctrl-C` 来停止训练。
模型评估和预测
```````````````
在训练了几个轮次以后，你可以对模型进行评估，得到最好轮次下的模型。运行下面命令即可：
.. code-block:: bash
./evaluate.sh
你将看到如下的信息：
.. code-block:: text
Best pass is 00009, error is 3.06949, which means predict get error as 0.875998002281
evaluating from pass output/pass-00009
然后，你可以预测任何用户对于任何一部电影的评价，运行下面命令即可：
.. code-block:: bash
python prediction.py 'output/pass-00009/'
预测程序将读取用户的输入，然后输出预测分数。用户预测的命令行界面如下：
.. code-block:: text
Input movie_id: 9
Input user_id: 4
Prediction Score is 2.56
Input movie_id: 8
Input user_id: 2
Prediction Score is 3.13
doc/tutorials/rec/ml_regression_en.rst 已删除 100644 → 0
浏览文件 @ 5c5250e3
Regression MovieLens Ratting
============================
Here we demonstrate a **Cosine Similarity Regression** job in movie lens dataset.
This demo will show how paddle does (word) embedding job,
handles the similarity regression,
the character-level convolutional networks for text, and how does paddle handle
multiple types of inputs.
Note that the model structure is not fine-tuned and just a demo to show how paddle works.
YOU ARE WELCOME TO BUILD A BETTER DEMO
BY USING PADDLEPADDLE, AND LET US KNOW TO MAKE THIS DEMO BETTER.
Data Preparation
````````````````
Download and extract dataset
''''''''''''''''''''''''''''
We use :ref:`demo_ml_dataset` here.
To download and unzip the dataset, simply run the following commands.
.. code-block:: bash
cd demo/recommendation/data
./ml_data.sh
And the directory structure of :code:`demo/recommendation/data/ml-1m` is:
.. code-block:: text
+--ml-1m
+--- movies.dat # movie features
+--- ratings.dat # ratings
+--- users.dat # user features
+--- README # dataset description
Field config file
'''''''''''''''''
**Field config file** is used to specify the fields of the dataset and the file format,
i.e, specific **WHAT** type it is in each feature file.
The field config file of ml-1m shows in :code:`demo/recommendation/data/config.json`.
It specifics the field types and file names: 1) there are four types of field for user file\: id, gender, age and occupation;
2) the filename is "users.dat", and the delimiter of file is "::".
.. include:: ../../../demo/recommendation/data/config.json
:code: json
:literal:
Preprocess Data
```````````````
You need to install python 3rd party libraries.
IT IS HIGHLY RECOMMEND TO USE VIRTUALENV MAKE A CLEAN PYTHON ENVIRONMENT.
.. code-block:: bash
pip install -r requirements.txt
The general command for preprocessing the dataset is:
.. code-block:: bash
cd demo/recommendation
./preprocess.sh
And the detail steps are introduced as follows.
Extract Movie/User features to python object
'''''''''''''''''''''''''''''''''''''''''''''
There are many features in movie or user in movielens 1m dataset.
Each line of rating file just provides a Movie/User id to refer each movie or user.
We process the movie/user feature file first, and pickle the feature (**Meta**) object as a file.
Meta config file
................
**Meta config file** is used to specific **HOW** to parse each field in dataset.
It could be translated from field config file, or written by hand.
Its file format could be either json or yaml syntax file. Parser will automatically choose the file format by extension name.
To convert Field config file to meta config file, just run:
.. code-block:: bash
cd demo/recommendation/data
python config_generator.py config.json > meta_config.json
The meta config file shows below:
.. include:: ../../../demo/recommendation/data/meta_config.json
:code: json
:literal:
There are two kinds of features in meta\: movie and user.
* in movie file, whose name is movies.dat
* we just split each line by "::"
* pos 0 is id.
* pos 1 feature:
* name is title.
* it uses regex to parse this feature.
* it is a char based word embedding feature.
* it is a sequence.
* pos 2 feature:
* name is genres.
* type is one hot dense vector.
* dictionary is auto generated by parsing, each key is split by '|'
* in user file, whose name is users.dat
* we just split each line by "::"
* pos 0 is id.
* pos 1 feature:
* name is gender
* just simple char based embedding.
* pos 2 feature:
* name is age
* just whole word embedding.
* embedding id will be sort by word.
* pos 3 feature:
* name is occupation.
* just simple whole word embedding.
Meta file
'''''''''
After having meta config file, we can generate **Meta file**, a python pickle object which stores movie/user information.
The following commands could be run to generate it.
.. code-block:: bash
python meta_generator.py ml-1m meta.bin --config=meta_config.json
And the structure of the meta file :code:`meta.bin` is:
.. code-block:: text
+--+ movie
| +--+ __meta__
| | +--+ raw_meta # each feature meta config. list
| | | +
| | | | # ID Field, we use id as key
| | | +--+ {'count': 3883, 'max': 3952, 'is_key': True, 'type': 'id', 'min': 1}
| | | |
| | | | # Titile field, the dictionary list of embedding.
| | | +--+ {'dict': [ ... ], 'type': 'embedding', 'name': 'title', 'seq': 'sequence'}
| | | |
| | | | # Genres field, the genres dictionary
| | | +--+ {'dict': [ ... ], 'type': 'one_hot_dense', 'name': 'genres'}
| | |
| | +--+ feature_map [1, 2] # a list for raw_meta index for feature field.
| | # it means there are 2 features for each key.
| | # * 0 offset of feature is raw_meta[1], Title.
| | # * 1 offset of feature is raw_meta[2], Genres.
| |
| +--+ 1 # movie 1 features
| | +
| | +---+ [[...], [...]] # title ids, genres dense vector
| |
| +--+ 2
| |
| +--+ ...
|
+--- user
+--+ __meta__
| +
| +--+ raw_meta
| | +
| | +--+ id field as user
| | |
| | +--+ {'dict': ['F', 'M'], 'type': 'embedding', 'name': 'gender', 'seq': 'no_sequence'}
| | |
| | +--+ {'dict': ['1', '18', '25', '35', '45', '50', '56'], 'type': 'embedding', 'name': 'age', 'seq': 'no_sequence'}
| | |
| | +--+ {'dict': [...], 'type': 'embedding', 'name': 'occupation', 'seq': 'no_sequence'}
| |
| +--+ feature_map [1, 2, 3]
|
+--+ 1 # user 1 features
|
+--+ 2
+--+ ...
Split Training/Testing files
''''''''''''''''''''''''''''
We split :code:`ml-1m/ratings.dat` into a training and testing file. The way to split file is for each user, we split the
rating by two parts. So each user in testing file will have some rating information in training file.
Use :code:`separate.py` to separate the training and testing file.
.. code-block:: bash
python split.py ml-1m/ratings.dat --delimiter="::" --test_ratio=0.1
Then two files will be generated\: :code:`ml-1m/ratings.dat.train` and :code:`ml-1m/rating.data.test`.
Move them to workspace :code:`data`, shuffle the train file, and prepare the file list for paddle train.
.. code-block:: bash
shuf ml-1m/ratings.dat.train > ratings.dat.train
cp ml-1m/ratings.dat.test .
echo "./data/ratings.dat.train" > train.list
echo "./data/ratings.dat.test" > test.list
Neural Network Configuration
````````````````````````````
Trainer Config File
'''''''''''''''''''
The network structure shows below.
.. image:: rec_regression_network.png
:align: center
:alt: rec_regression_network
The demo's neural network config file :code:`trainer_config.py` show as below.
.. literalinclude:: ../../../demo/recommendation/trainer_config.py
:language: python
:lines: 15-
In this :code:`trainer_config.py`, we just map each feature type to
a feature vector, following shows how to map each feature to a vector shows below.
* :code:`id`\: Just simple embedding, and then add to fully connected layer.
* :code:`embedding`\:
- if is_sequence, get the embedding and do a text convolutional operation,
get the average pooling result.
- if not sequence, get the embedding and add to fully connected layer.
* :code:`one_host_dense`\:
- just two fully connected layer.
Then we combine each features of movie into one movie feature by a
:code:`fc_layer` with multiple inputs, and do the same thing to user features,
get one user feature. Then we calculate the cosine similarity of these two
features.
In these networks, we use several APIs in :ref:`api_trainer_config` . There are
* Data Layer, :ref:`api_trainer_config_helpers_layers_data_layer`
* Fully Connected Layer, :ref:`api_trainer_config_helpers_layers_fc_layer`
* Embedding Layer, :ref:`api_trainer_config_helpers_layers_embedding_layer`
* Context Projection Layer, :ref:`api_trainer_config_helpers_layers_context_projection`
* Pooling Layer, :ref:`api_trainer_config_helpers_layers_pooling_layer`
* Cosine Similarity Layer, :ref:`api_trainer_config_helpers_layers_cos_sim`
* Text Convolution Pooling Layer, :ref:`api_trainer_config_helpers_network_text_conv_pool`
* Declare Python Data Sources :ref:`api_trainer_config_helpers_data_sources`.
Data Provider
'''''''''''''
.. literalinclude:: ../../../demo/recommendation/dataprovider.py
:language: python
:lines: 15-
The data provider just read the meta.bin and rating file, yield each sample for training.
In this :code:`dataprovider.py`, we should set\:
* obj.slots\: The feature types and dimension.
* use_seq\: Whether this :code:`dataprovider.py` in sequence mode or not.
* process\: Return each sample of data to :code:`paddle`.
The data provider details document see :ref:`api_pydataprovider2`.
Train
`````
After prepare data, config network, writting data provider, now we can run paddle training.
The :code:`run.sh` is shown as follow:
.. literalinclude:: ../../../demo/recommendation/run.sh
:language: bash
:lines: 16-
It just start a paddle training process, write the log to :code:`log.txt`,
then print it on screen.
Each command line argument in :code:`run.sh`, please refer to the :ref:`cmd_line_index` page. The short description of these arguments is shown as follow.
* config\: Tell paddle which file is neural network configuration.
* save_dir\: Tell paddle save model into :code:`./output`.
* use_gpu\: Use gpu or not. Default is false.
* trainer_count\: The compute thread in one machine.
* test_all_data_in_one_period\: Test All Data during one test period. Otherwise,
will test a :code:`batch_size` data in one test period.
* log_period\: Print log after train :code:`log_period` batches.
* dot_period\: Print a :code:`.` after train :code:`dot_period` batches.
* num_passes\: Train at most :code:`num_passes`.
If training process starts successfully, the output likes follow:
.. code-block:: text
I0601 08:07:22.832059 10549 TrainerInternal.cpp:157] Batch=100 samples=160000 AvgCost=4.13494 CurrentCost=4.13494 Eval: CurrentEval:
I0601 08:07:50.672627 10549 TrainerInternal.cpp:157] Batch=200 samples=320000 AvgCost=3.80957 CurrentCost=3.48421 Eval: CurrentEval:
I0601 08:08:18.877369 10549 TrainerInternal.cpp:157] Batch=300 samples=480000 AvgCost=3.68145 CurrentCost=3.42519 Eval: CurrentEval:
I0601 08:08:46.863963 10549 TrainerInternal.cpp:157] Batch=400 samples=640000 AvgCost=3.6007 CurrentCost=3.35847 Eval: CurrentEval:
I0601 08:09:15.413025 10549 TrainerInternal.cpp:157] Batch=500 samples=800000 AvgCost=3.54811 CurrentCost=3.33773 Eval: CurrentEval:
I0601 08:09:36.058670 10549 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=3.52368 Eval:
I0601 08:09:46.215489 10549 Tester.cpp:101] Test samples=97383 cost=3.32155 Eval:
I0601 08:09:46.215966 10549 GradientMachine.cpp:132] Saving parameters to ./output/model/pass-00000
I0601 08:09:46.233397 10549 ParamUtil.cpp:99] save dir ./output/model/pass-00000
I0601 08:09:46.233438 10549 Util.cpp:209] copy trainer_config.py to ./output/model/pass-00000
I0601 08:09:46.233541 10549 ParamUtil.cpp:147] fileName trainer_config.py
The model is saved in :code:`output/` directory. You can use :code:`Ctrl-C` to stop training whenever you want.
Evaluate and Predict
````````````````````
After training several passes, you can evaluate them and get the best pass. Just run
.. code-block:: bash
./evaluate.sh
You will see messages like this:
.. code-block:: text
Best pass is 00009, error is 3.06949, which means predict get error as 0.875998002281
evaluating from pass output/pass-00009
Then, you can predict what any user will rate a movie. Just run
.. code-block:: bash
python prediction.py 'output/pass-00009/'
Predictor will read user input, and predict scores. It has a command-line user interface as follows:
.. code-block:: text
Input movie_id: 9
Input user_id: 4
Prediction Score is 2.56
Input movie_id: 8
Input user_id: 2
Prediction Score is 3.13
doc/tutorials/semantic_role_labeling/index_cn.md 已删除 100644 → 0
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# 语义角色标注教程 #
语义角色标注（Semantic role labeling, SRL）是浅层语义解析的一种形式，其目的是在给定的输入句子中发现每个谓词的谓词论元结构。 SRL作为很多自然语言处理任务中的中间步骤是很有用的，如信息提取、文档自动分类和问答。 实例如下 [1]:
[ <sub>A0</sub> He ] [ <sub>AM-MOD</sub> would ][ <sub>AM-NEG</sub> n’t ] [ <sub>V</sub> accept] [ <sub>A1</sub> anything of value ] from [<sub>A2</sub> those he was writing about ].
- V: 动词
- A0: 接受者
- A1: 接受的东西
- A2: 从……接受
- A3: 属性
- AM-MOD: 情态动词
- AM-NEG: 否定
给定动词“accept”，句子中的组块将会扮演某些语义角色。这里，标签方案来自 Penn Proposition Bank。
到目前为止，大多数成功的SRL系统是建立在某种形式的句法分析结果之上的，使用了基于句法结构的预定义特征模板。 本教程将介绍使用深度双向长短期记忆（DB-LSTM）模型[2]的端到端系统来解决SRL任务，这在很大程度上优于先前的最先进的系统。 这个系统将SRL任务视为序列标注问题。
## 数据描述
相关论文[2]采用 CoNLL-2005＆2012 共享任务中设置的数据进行训练和测试。由于数据许可的原因，演示采用 CoNLL-2005 的测试数据集，可以在网站上找到。
用户只需执行以下命令就可以下载并处理原始数据：
```bash
cd data
./get_data.sh
```
`data `目录会出现如下几个新的文件：
```bash
conll05st-release：the test data set of CoNll-2005 shared task
test.wsj.words：the Wall Street Journal data sentences
test.wsj.props: the propositional arguments
feature: the extracted features from data set
```
## 训练
### DB-LSTM
请参阅情感分析的演示以了解有关长期短期记忆单元的更多信息。
与在 Sentiment Analysis 演示中使用的 Bidirectional-LSTM 不同，DB-LSTM 采用另一种方法来堆叠LSTM层。首先，标准LSTM以正向处理该序列。该 LSTM 层的输入和输出作为下一个 LSTM 层的输入，并被反向处理。这两个标准 LSTM 层组成一对 LSTM。然后我们堆叠一对对的 LSTM 层后得到深度 LSTM 模型。
下图展示了时间扩展的2层 DB-LSTM 网络。
<center>
![pic](./network_arch.png)
</center>
### 特征
两个输入特征在这个流程中起着至关重要的作用：predicate（pred）和argument（arguments）。 还采用了两个其他特征：谓词上下文（ctx-p）和区域标记（mr）。 因为单个谓词不能精确地描述谓词信息，特别是当相同的词在句子中出现多于一次时。 使用谓词上下文，可以在很大程度上消除歧义。类似地，如果它位于谓词上下文区域中，则使用区域标记 m<sub>r</sub> = 1 来表示参数位置，反之则 m<sub>r</sub> = 0。这四个简单的特征是我们的SRL系统所需要的。上下文大小设置为1的一个样本的特征如下[2]所示：
<center>
![pic](./feature.jpg)
</center>
在这个示例中，相应的标记句子是：
[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] .
在演示中, 我们采用上面的特征模板, 包括： `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` 并使用 `B/I/O` 方案来标记每个参数。这些特征和标签存储在 `feature` 文件中, 用`\t`分割。
### 数据提供
`dataprovider.py` 是一个包装数据的 Python 文件。 函数 `hook()` 定义了网络的数据槽。六个特征和标签都是索引槽。
```
def hook(settings, word_dict, label_dict, **kwargs):
settings.word_dict = word_dict
settings.label_dict = label_dict
#all inputs are integral and sequential type
settings.slots = [
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(2),
integer_value_sequence(len(label_dict))]
```
相应的数据迭代器如下：
```
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \
line.strip().split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
label_list = label.split()
label_slot = [settings.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
```
函数 `process` 返回8个特征list和1个标签list。
### 神经网络配置
`db_lstm.py` 是在训练过程中加载字典并定义数据提供程序模块和网络架构的神经网络配置文件。
九个 `data_layer` 从数据提供程序加载实例。八个特征分别转换为向量，并由`mixed_layer`混合。 深度双向LSTM层提取softmax层的特征。目标函数是标签的交叉熵。
### 训练
训练的脚本是 `train.sh`，用户只需执行:
```bash
./train.sh
```
`train.sh` 中的内容：
```
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--save_dir=./output \
--num_passes=10000 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
```
- \--config=./db_lstm.py : 网络配置文件
- \--use_gpu=false: 使用 CPU 训练（如果已安装 PaddlePaddle GPU版本并想使用 GPU 训练可以设置为true，目前 crf_layer 不支持 GPU）
- \--log_period=500: 每20个batch输出日志
- \--trainer_count=1: 设置线程数（或 GPU 数）
- \--show_parameter_stats_period=5000: 每100个batch显示参数统计
- \--save_dir=./output: 模型输出路径
- \--num_passes=10000: 设置数据遍历次数，一个pass意味着PaddlePaddle训练数据集中的所有样本被遍历一次
- \--average_test_period=10000000: 每个 average_test_period 批次对平均参数进行测试
- \--init_model_path=./data: 参数初始化路径
- \--load_missing_parameter_strategy=rand: 随机初始不存在的参数
- \--test_all_data_in_one_period=1: 在一个周期内测试所有数据
训练后，模型将保存在目录`output`中。 我们的训练曲线如下：
<center>
![pic](./src/curve.jpg)
</center>
### 测试
测试脚本是 `test.sh`, 执行:
```bash
./test.sh
```
`tesh.sh` 的主要部分：
```
paddle train \
--config=./db_lstm.py \
--model_list=$model_list \
--job=test \
--config_args=is_test=1 \
```
- \--config=./db_lstm.py: 网络配置文件
- \--model_list=$model_list.list: 模型列表文件
- \--job=test: 指示测试任务
- \--config_args=is_test=1: 指示测试任务的标记
- \--test_all_data_in_one_period=1: 在一个周期内测试所有数据
### 预测
预测脚本是 `predict.sh`，用户只需执行：
```bash
./predict.sh
```
在`predict.sh`中，用户应该提供网络配置文件，模型路径，标签文件，字典文件，特征文件。
```
python predict.py
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file \
-o $output_file
```
`predict.py` 是主要的可执行python脚本，其中包括函数：加载模型，加载数据，数据预测。网络模型将输出标签的概率分布。 在演示中，我们使用最大概率的标签作为结果。用户还可以根据概率分布矩阵实现柱搜索或维特比解码。
预测后，结果保存在 `predict.res` 中。
## 引用
[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005.
[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
doc/tutorials/semantic_role_labeling/index_en.md 已删除 100644 → 0
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```eval_rst
.. _semantic_role_labeling:
```
# Semantic Role labeling Tutorial #
Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]:
[ <sub>A0</sub> He ] [ <sub>AM-MOD</sub> would ][ <sub>AM-NEG</sub> n’t ] [ <sub>V</sub> accept] [ <sub>A1</sub> anything of value ] from [<sub>A2</sub> those he was writing about ].
- V: verb
- A0: acceptor
- A1: thing accepted
- A2: accepted-from
- A3: Attribute
- AM-MOD: modal
- AM-NEG: negation
Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank.
To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem.
## Data Description
The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license, the demo adopts the test data set of CoNLL-2005, which can be reached on website.
To download and process the original data, user just need to execute the following command:
```bash
cd data
./get_data.sh
```
Several new files appear in the `data `directory as follows.
```bash
conll05st-release：the test data set of CoNll-2005 shared task
test.wsj.words：the Wall Street Journal data sentences
test.wsj.props: the propositional arguments
feature: the extracted features from data set
```
## Training
### DB-LSTM
Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit.
Unlike Bidirectional-LSTM that used in Sentiment Analysis demo, the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model.
The following figure shows a temporal expanded 2-layer DB-LSTM network.
<center>
![pic](./src/network_arch.png)
</center>
### Features
Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark m<sub>r</sub> = 1 to denote the argument position if it locates in the predicate context region, or m<sub>r</sub> = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]:
<center>
![pic](./src/feature.jpg)
</center>
In this sample, the coresponding labelled sentence is:
[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] .
In the demo, we adopt the feature template as above, consists of : `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`.
### Data Provider
`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The Six features and label are all IndexSlots.
```
def hook(settings, word_dict, label_dict, **kwargs):
settings.word_dict = word_dict
settings.label_dict = label_dict
#all inputs are integral and sequential type
settings.slots = [
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(2),
integer_value_sequence(len(label_dict))]
```
The corresponding data iterator is as following:
```
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \
line.strip().split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
label_list = label.split()
label_slot = [settings.label_dict.get(w) for w in label_list]
yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
```
The `process`function yield 9 lists which are 8 features and label.
### Neural Network Config
`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure.
Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
### Run Training
The script for training is `train.sh`, user just need to execute:
```bash
./train.sh
```
The content in `train.sh`:
```
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--save_dir=./output \
--num_passes=10000 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
```
- \--config=./db_lstm.py : network config file.
- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
- \--log_period=500: print log every 20 batches.
- \--trainer_count=1: set thread number (or GPU count).
- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
- \--save_dir=./output: output path to save models.
- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
- \--average_test_period=10000000: do test on average parameter every average_test_period batches
- \--init_model_path=./data: parameter initialization path
- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
- \--test_all_data_in_one_period=1: test all data in one period
After training, the models will be saved in directory `output`. Our training curve is as following:
<center>
![pic](./src/curve.jpg)
</center>
### Run testing
The script for testing is `test.sh`, user just need to execute:
```bash
./test.sh
```
The main part in `tesh.sh`
```
paddle train \
--config=./db_lstm.py \
--model_list=$model_list \
--job=test \
--config_args=is_test=1 \
```
- \--config=./db_lstm.py: network config file
- \--model_list=$model_list.list: model list file
- \--job=test: indicate the test job
- \--config_args=is_test=1: flag to indicate test
- \--test_all_data_in_one_period=1: test all data in 1 period
### Run prediction
The script for prediction is `predict.sh`, user just need to execute:
```bash
./predict.sh
```
In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
```
python predict.py
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file \
-o $output_file
```
`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
After prediction, the result is saved in `predict.res`.
## Reference
[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005.
[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.
doc/tutorials/sentiment_analysis/index_cn.md 已删除 100644 → 0
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# 情感分析教程
情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性，给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如：把用户在购物网站、旅游网站、团购网站（亚马逊、天猫、淘宝等）上发表的评论分成正面评论和负面评论两类。
情感分析也常用于基于大量评论和个人博客来监控社会媒体。 例如，研究人员分析了几个关于消费者信心和政治观点的调查，结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]。 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]。
另一方面，抓取产品的用户评论并分析他们的情感，有助于理解用户对不同公司，不同产品，甚至不同竞争对手产品的偏好。
本教程将指导您完成长期短期记忆（LSTM）网络的训练过程，以分类来自[大型电影评论数据集](http://ai.stanford.edu/~amaas/data/sentiment/)（有时称为[互联网电影数据库 (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf)）的句子的情感 。 此数据集包含电影评论及其相关联的类别标签，即正面和负面。
## 数椐准备
### IMDB 数椐介绍
训练模型之前, 我们需要预处理数椐并构建一个字典。 首先, 你可以使用下面的脚本下载 IMDB 数椐集和[Moses](http://www.statmt.org/moses/)工具, 这是一个基于统计的机器翻译系统. 我们提供了一个数据预处理脚本，它不仅能够处理IMDB数据，还能处理其他用户自定义的数据。 为了使用提前编写的脚本，需要将标记的训练和测试样本移动到另一个路径，这已经在`get_imdb.sh`中完成。
```
cd demo/sentiment/data
./get_imdb.sh
```
如果数椐获取成功，你将在目录```./demo/sentiment/data```中看到下面的文件：
```
aclImdb get_imdb.sh imdb mosesdecoder-master
```
* aclImdb: 从外部网站上下载的原始数椐集。
* imdb: 仅包含训练和测试数椐集。
* mosesdecoder-master: Moses 工具。
IMDB数据集包含25,000个已标注过的高极性电影评论用于训练，25,000个用于测试。负面的评论的得分小于等于4,正面的评论的得大于等于7，总评分10分。 运行完脚本 `./get_imdb.sh`后, 我们可以看到在目录 `aclImdb`中的数椐集的结构如下：
```
imdbEr.txt imdb.vocab README test train
```
* train: 训练数椐集。
* test : 测试数椐集。
* imdb.vocab: 字典文件。
* imdbEr.txt: 字典imdb.vocab中每个切分单词的预期评级。
* README: 数椐说明文档。
测试集和训练集目录包含下面的文件:
```
labeledBow.feat neg pos unsup unsupBow.feat urls_neg.txt urls_pos.txt urls_unsup.txt
```
* pos: 正面评价样本，包含12,500个txt文件，每个文件是一个电影评论。
* neg: 负面评价样本，包含12,500个txt文件，每个文件是一个电影评论。
* unsup: 未标记的评价样本，包含50,000个txt文件。
* urls_xx.txt: 每个评论的网址。
* xxBow.feat: 用于统计词频的Bow模型特征。
### IMDB 数椐准备
在这个例子中，我们只使用已经标注过的训练集和测试集，且默认在训练集上构建字典，而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。
```
cd demo/sentiment/
./preprocess.sh
```
preprocess.sh:
```
data_dir="./data/imdb"
python preprocess.py -i data_dir
```
* data_dir: 输入数椐所在目录。
* preprocess.py: 预处理脚本。
运行成功后目录`demo/sentiment/data/pre-imdb` 结构如下:
```
dict.txt labels.list test.list test_part_000 train.list train_part_000
```
* test\_part\_000 and train\_part\_000: 所有标记的测试集和训练集， 训练集已经随机打乱。
* train.list and test.list: 训练集和测试集文件列表。
* dict.txt: 利用训练集生成的字典。
* labels.txt: neg 0, pos 1, 含义：标签0表示负面的评论，标签1表示正面的评论。
### 用户自定义数椐预处理
如果你执行其它的用情感分析来分类文本的任务，可以按如下的结构来准备数椐. 我们提供了脚本来构建字典和预处理数椐。所以你只用按下面的结构来组织数椐就行了。
```
dataset
|----train
| |----class1
| | |----text_files
| |----class2
| | |----text_files
| | ...
|----test
| |----class1
| | |----text_files
| |----class2
| | |----text_files
| | ...
```
* dataset: 一级目录。
* train, test: 二级目录。
* class1,class2,...: 三级目录。
* text_files: 文本格式的实例文件。
所有同目录下的文本实例文件都是同级别的。 每个文本文件包含一个或者多个实例，每一行表示一个实例。 为了充分的随机打乱训练集, 在预处理含有多行数椐的文本文件时参数设置稍有不同, 执行`preprocess.sh`脚本时需要加上`-m True`参数。 tokenizer.perl 默认用来切分单记和标点符号，如果你不需要这个操作，在运行`preprocess.sh`时加上`-t False`参数即可。
## 训练模型
在这步任务中,我们使用了循环神经网络（RNN）的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标准循环神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元，忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息，而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内，存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。
<center>![LSTM](src/lstm.png)</center>
<center>图表 1. LSTM [3]</center>
情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ，仅仅是一些关键词，如形容词和副词，在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长，例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先，它能够从词级到具有可变上下文长度的上下文级别来总结表示。 第二，它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三，它直接学习段落表示，而不是组合上下文级别信息。
在本演示中，我们提供两个网络，即双向LSTM和三层堆叠LSTM。
#### 双向LSTM
图2是双向LSTM网络，后面连全连接层和softmax层。
<center>![BiLSTM](src/bi_lstm.jpg)</center>
<center>图 2. Bidirectional-LSTM </center>
#### Stacked-LSTM
图3是三层LSTM结构。图的底部是word embedding(对文档处理后形成的单词向量)。 接下来，连接三个LSTM隐藏层，并且第二个是反向LSTM。然后提取隐藏LSTM层的所有时间步长的最大词向量作为整个序列的表示。 最后，使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。
<center>![StackedLSTM](src/stacked_lstm.jpg)</center>
<center>图 3. Stacked-LSTM for sentiment analysis </center>
**配置**
进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置。第一行从`sentiment_net.py`中导出预定义的网络。
trainer_config.py:
```python
from sentiment_net import *
data_dir = "./data/pre-imdb"
# whether this config is used for test
is_test = get_config_arg('is_test', bool, False)
# whether this config is used for prediction
is_predict = get_config_arg('is_predict', bool, False)
dict_dim, class_dim = sentiment_data(data_dir, is_test, is_predict)
################## Algorithm Config #####################
settings(
batch_size=128,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25
)
#################### Network Config ######################
stacked_lstm_net(dict_dim, class_dim=class_dim,
stacked_num=3, is_predict=is_predict)
#bidirectional_lstm_net(dict_dim, class_dim=class_dim, is_predict=is_predict)
```
* **数椐定义**:
* get\_config\_arg(): 获取通过 `--config_args=xx` 设置的命令行参数。
* 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档
* **算法配置**:
* 使用随机梯度下降（sgd）算法。
* 使用 adam 优化。
* 设置batch size大小为128。
* 设置平均sgd窗口。
* 设置全局学习率。
* **网络配置**:
* dict_dim: 获取字典维度。
* class_dim: 设置类别数，IMDB有两个标签，即正面评价标签和负面评价标签。
* `stacked_lstm_net`: 预定义网络如图3所示，默认情况下使用此网络
* `bidirectional_lstm_net`: 预定义网络，如图2所示。
**训练**
首先安装PaddlePaddle。 然后使用下面的脚本 `train.sh` 来开启本地的训练。
```
cd demo/sentiment/
./train.sh
```
train.sh:
```
config=trainer_config.py
output=./model_output
paddle train --config=$config \
--save_dir=$output \
--job=train \
--use_gpu=false \
--trainer_count=4 \
--num_passes=10 \
--log_period=20 \
--dot_period=20 \
--show_parameter_stats_period=100 \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
```
* \--config=$config: 设置网络配置。
* \--save\_dir=$output: 设置输出路径以保存训练完成的模型。
* \--job=train: 设置工作模式为训练。
* \--use\_gpu=false: 使用CPU训练，如果你安装GPU版本的PaddlePaddle，并想使用GPU来训练设置为true。
* \--trainer\_count=4:设置线程数（或GPU个数）。
* \--num\_passes=15: 设置pass，PaddlePaddle中的一个pass意味着对数据集中的所有样本进行一次训练。
* \--log\_period=20: 每20个batch打印一次日志。
* \--show\_parameter\_stats\_period=100: 每100个batch打印一次统计信息。
* \--test\_all_data\_in\_one\_period=1: 每次测试都测试所有数据。
如果运行成功，输出日志保存在路径 `demo/sentiment/train.log`中，模型保存在目录`demo/sentiment/model_output/`中。 输出日志说明如下：
```
Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification_error_evaluator=0.36875 CurrentEval: classification_error_evaluator=0.36875
...
Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922
Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406
```
- Batch=xx: 表示训练了xx个Batch。
- samples=xx: 表示训练了xx个样本。。
- AvgCost=xx: 从第0个batch到当前batch的平均损失。
- CurrentCost=xx: 最新log_period个batch处理的当前损失。
- Eval: classification\_error\_evaluator=xx: 表示第0个batch到当前batch的分类错误。
- CurrentEval: classification\_error\_evaluator: 最新log_period个batch的分类错误。
- Pass=0: 通过所有训练集一次称为一遍。 0表示第一次经过训练集。
默认情况下，我们使用`stacked_lstm_net`网络，当传递相同的样本数时，它的收敛速度比`bidirectional_lstm_net`快。如果要使用双向LSTM，只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。
## 测试模型
测试模型是指使用训练出的模型评估已标记的验证集。
```
cd demo/sentiment
./test.sh
```
test.sh:
```bash
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \
sed -r 'N;s/Test.* error=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \
sort | head -n 1
}
log=train.log
LOG=`get_best_pass $log`
LOG=(${LOG})
evaluate_pass="model_output/pass-${LOG[1]}"
echo 'evaluating from pass '$evaluate_pass
model_list=./model.list
touch $model_list | echo $evaluate_pass > $model_list
net_conf=trainer_config.py
paddle train --config=$net_conf \
--model_list=$model_list \
--job=test \
--use_gpu=false \
--trainer_count=4 \
--config_args=is_test=1 \
2>&1 | tee 'test.log'
```
函数`get_best_pass`依据分类错误率获得最佳模型进行测试。 在本示例中，我们默认使用IMDB的测试数据集作为验证。 与训练不同，它需要在这里指定`--job = test`和模型路径，即`--model_list = $model_list`。如果运行成功，日志将保存在“demo / sentiment / test.log”的路径中。例如，在我们的测试中，最好的模型是`model_output / pass-00002`，分类误差是0.115645，如下：
```
Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645
```
## 预测
`predict.py`脚本提供了一个预测接口。在使用它之前请安装PaddlePaddle的python api。 预测IMDB的未标记评论的一个实例如下：
```
cd demo/sentiment
./predict.sh
```
predict.sh:
```
#Note the default model is pass-00002, you shold make sure the model path
#exists or change the mode path.
model=model_output/pass-00002/
config=trainer_config.py
label=data/pre-imdb/labels.list
cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
--tconf=$config\
--model=$model \
--label=$label \
--dict=./data/pre-imdb/dict.txt \
--batch_size=1
```
* `cat ./data/aclImdb/test/pos/10007_10.txt` : 输入预测样本。
* `predict.py` : 预测接口脚本。
* `--tconf=$config` : 设置网络配置。
* `--model=$model` : 设置模型路径。
* `--label=$label` : 设置标签类别字典，这个字典是整数标签和字符串标签的一个对应。
* `--dict=data/pre-imdb/dict.txt` : 设置字典文件。
* `--batch_size=1` : 设置batch size。
注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。
本示例的预测结果：
```
Loading parameters from model_output/pass-00002/
./data/aclImdb/test/pos/10014_7.txt: predicting label is pos
```
我们真诚地感谢您的关注，并欢迎您来参与贡献。
## 参考文档
[1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010. <br>
[2] Johan Bollen, Huina Mao, Xiaojun Zeng. 2011. [Twitter mood predicts the stock market](http://arxiv.org/abs/1010.3003), Journal of Computational Science.<br>
[3] Alex Graves, Marcus Liwicki, Santiago Fernan- dez, Roman Bertolami, Horst Bunke, and Ju ̈rgen Schmidhuber. 2009. [A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine In- telligence](http://www.cs.toronto.edu/~graves/tpami_2009.pdf), 31(5):855–868.<br>
[4] Zachary C. Lipton, [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/abs/1506.00019v1), arXiv:1506.00019. <br>
[5] Jie Zhou and Wei Xu; [End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf); ACL-IJCNLP 2015. <br>
doc/tutorials/sentiment_analysis/index_en.md 已删除 100644 → 0
浏览文件 @ 5c5250e3
# Sentiment Analysis Tutorial
Sentiment analysis has many applications. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence or feature/aspect level. One simple example is to classify the customer reviews in a shopping website, a tourism website, and group buying websites like Amazon, TaoBao, Tmall etc.
Sentiment analysis is also used to monitor social media based on large amount of reviews or blogs. For example, the researchers analyzed several surveys on consumer confidence and political opinion, found they correlate to sentiment word frequencies in contemporaneous Twitter messages [1]. Another example is to forecast stock movements through analyzing the text content of a daily Twitter blog [2].
On the other hand, grabbing the user comments of products and analyzing their sentiment are useful to understand user preferences for companies, products, even competing products.
This tutorial will guide you through the process of training a Long Short Term Memory (LSTM) Network to classify the sentiment of sentences from [Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/), sometimes known as the Internet Movie Database (IMDB). This dataset contains movie reviews along with their associated binary sentiment polarity labels, namely positive and negative. So randomly guessing yields 50% accuracy.
## Data Preparation
### IMDB Data Introduction
Before training models, we need to preprocess the data and build a dictionary. First, you can use following script to download IMDB dataset and [Moses](http://www.statmt.org/moses/) tool, which is a statistical machine translation system. We provide a data preprocessing script, which is capable of handling not only IMDB data, but also other user-defined data. In order to use the pre-written script, it needs to move labeled train and test samples to another path, which has been done in `get_imdb.sh`.
```
cd demo/sentiment/data
./get_imdb.sh
```
If the data is obtained successfuly, you will see the following files at ```./demo/sentiment/data```:
```
aclImdb get_imdb.sh imdb mosesdecoder-master
```
* aclImdb: raw dataset downloaded from website.
* imdb: only contains train and test data.
* mosesdecoder-master: Moses tool.
IMDB dataset contains 25,000 highly polar movie reviews for training, and 25,000 for testing. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. After running `./get_imdb.sh`, we can find the dataset has the following structure in `aclImdb`.
```
imdbEr.txt imdb.vocab README test train
```
* train: train sets.
* test : test sets.
* imdb.vocab: dictionary.
* imdbEr.txt: expected rating for each token in imdb.vocab.
* README: data documentation.
The file in train set directory is as follows. The test set also contains them except `unsup` and `urls_unsup.txt`.
```
labeledBow.feat neg pos unsup unsupBow.feat urls_neg.txt urls_pos.txt urls_unsup.txt
```
* pos: positive samples, contains 12,500 txt files, each file is one movie review.
* neg: negative samples, contains 12,500 txt files, each file is one movie review.
* unsup: unlabeled samples, contains 50,000 txt files.
* urls_xx.txt: urls of each reviews.
* xxBow.feat: already-tokenized bag of words (BoW) features.
### IMDB Data Preparation
In this demo, we only use labled train and test set and not use imdb.vocab as dictionary. By default, dictionary is builded on train set. Train set is shuffled and test set is not. `tokenizer.perl` in Moses tool is used to tokenize the words and punctuation. Simply execute the following command to preprcess data.
```
cd demo/sentiment/
./preprocess.sh
```
preprocess.sh:
```
data_dir="./data/imdb"
python preprocess.py -i data_dir
```
* data_dir: input data directory.
* preprocess.py: preprocess script.
If running successfully, you will see `demo/sentiment/data/pre-imdb` directory as follows:
```
dict.txt labels.list test.list test_part_000 train.list train_part_000
```
* test\_part\_000 and train\_part\_000: all labeled test and train sets. Train sets have be shuffled.
* train.list and test.list: train and test file lists.
* dict.txt: dictionary generated on train sets by default.
* labels.txt: neg 0, pos 1, means label 0 is negative review, label 1 is positive review.
### User-defined Data Preparation
If you perform other sentiment classifcation task, you can prepare data as follows. We have provided the scripts to build dictionary and preprocess data. So just organize data as follows.
```
dataset
|----train
| |----class1
| | |----text_files
| |----class2
| | |----text_files
| | ...
|----test
| |----class1
| | |----text_files
| |----class2
| | |----text_files
| | ...
```
* dataset: 1st directory.
* train, test: 2nd directory.
* class1,class2,...: 3rd directory.
* text_files: samples with text file format.
All samples with text files format under the same folder are same category. Each text file contains one or more samples and each line is one sample. In order to shuffle fully, the preprocessing is a little different for data with multiple lines in one text file, which needs to set `-m True` in `preprocess.sh`. And tokenizer.perl is used by default. If you don't need it, only set `-t False` in `preprocess.sh'.
## Training
In this task, we use Recurrent Neural Network (RNN) of LSTM architecure to train sentiment analysis model. LSTM model was introduced primarily in order to overcome the problem of vanishing gradients. LSTM network resembles a standard recurrent neural network with a hidden layer, but each ordinary node in the hidden layer is replaced by a memory cell. Each memory cell contains four main elements: an input gate, a neuron with a self-recurrent connection, a forget gate and an output gate. More details can be found in the literature [4]. The biggest advantage of the LSTM architecture is that it learns to memorize information over long time intervals without the loss of short time memory. At each time step with a new coming word, historical information stored in the memory block is updated to iteratively learn the sequence representation.
<center>![LSTM](./lstm.png)</center>
<center>Figure 1. LSTM [3]</center>
Sentiment analysis is among the most typical problems in natural language understanding. It aims at predicting the attitude expressed in a sequence. Usually, only some key words, like adjectives and adverbs words, play a major role in predicting the sentiment of sequences or paragraphs. However, some review or comment contexts are very long, such as IMDB dataset. We use LSTM to perform this task for its improved design with the gate mechanism. First, it is able to summarize the representation from word level to context level with variable context length which is adapted by the gate values. Second, it can utilize the expanded context at the sentence level, while most methods are good at utilizing n-gram level knowledge. Third, it learns the paragraph representation directly rather than combining the context level information. This results in this end-to-end framework.
In this demo we provide two network, namely bidirectional-LSTM and three layers of stacked-LSTM.
#### Bidirectional-LSTM
One is a bidirectional LSTM network, connected by fully connected layer and softmax, as shown in Figure 2.
<center>![BiLSTM](./bi_lstm.jpg)</center>
<center>Figure 2. Bidirectional-LSTM </center>
#### Stacked-LSTM
Another is three-layer LSTM structure in Figure 3. The bottom of the figure is word embedding. Next, three LSTM-Hidden layers are connected and the second LSTM is reversed. Then extract the maximum hidden vectors of all time step of hidden and LSTM layer as the representation for the entire sequence. Finally, a fully connected feed forward layer with softmax activation is used to perform the classification task. This network is refered to paper [5].
<center>![StackedLSTM](./stacked_lstm.jpg)</center>
<center>Figure 3. Stacked-LSTM for sentiment analysis </center>
**Config**
Switch into `demo/sentiment` directory, `trainer_config.py` file is an example of the config, containing algorithm and newtork configure. The first line imports predefined networks from `sentiment_net.py`.
trainer_config.py:
```python
from sentiment_net import *
data_dir = "./data/pre-imdb"
# whether this config is used for test
is_test = get_config_arg('is_test', bool, False)
# whether this config is used for prediction
is_predict = get_config_arg('is_predict', bool, False)
dict_dim, class_dim = sentiment_data(data_dir, is_test, is_predict)
################## Algorithm Config #####################
settings(
batch_size=128,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
average_window=0.5,
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25
)
#################### Network Config ######################
stacked_lstm_net(dict_dim, class_dim=class_dim,
stacked_num=3, is_predict=is_predict)
#bidirectional_lstm_net(dict_dim, class_dim=class_dim, is_predict=is_predict)
```
* **Data Definition**:
* get\_config\_arg(): get arguments setted by `--config_args=xx` in commandline argument.
* Define data provider, here using Python interface to load data. For details, you can refer to the document of PyDataProvider2.
* **Algorithm Configuration**:
* set batch size of 128.
* set global learning rate.
* use adam optimization.
* set average sgd window.
* set L2 regularization.
* set gradient clipping threshold.
* **Network Configuration**:
* dict_dim: dictionary dimension.
* class_dim: category number, IMDB has two label, namely positive and negative label.
* `stacked_lstm_net`: predefined network as shown in Figure 3, use this network by default.
* `bidirectional_lstm_net`: predefined network as shown in Figure 2.
**Training**
Install PaddlePaddle first if necessary. Then you can use script `train.sh` as follows to launch local training.
```
cd demo/sentiment/
./train.sh
```
train.sh:
```
config=trainer_config.py
output=./model_output
paddle train --config=$config \
--save_dir=$output \
--job=train \
--use_gpu=false \
--trainer_count=4 \
--num_passes=10 \
--log_period=20 \
--dot_period=20 \
--show_parameter_stats_period=100 \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
```
* \--config=$config: set network config.
* \--save\_dir=$output: set output path to save models.
* \--job=train: set job mode to train.
* \--use\_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
* \--trainer\_count=4: set thread number (or GPU count).
* \--num\_passes=15: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
* \--log\_period=20: print log every 20 batches.
* \--show\_parameter\_stats\_period=100: show parameter statistic every 100 batches.
* \--test\_all_data\_in\_one\_period=1: test all data every testing.
If the run succeeds, the output log is saved in path of `demo/sentiment/train.log` and model is saved in path of `demo/sentiment/model_output/`. The output log is explained as follows.
```
Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification_error_evaluator=0.36875 CurrentEval: classification_error_evaluator=0.36875
...
Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922
Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406
```
- Batch=xx: means passing xx batches.
- samples=xx: means passing xx samples.
- AvgCost=xx: averaged cost from 0-th batch to current batch.
- CurrentCost=xx: current cost of latest log_period batches.
- Eval: classification\_error\_evaluator=xx: means classfication error from 0-th batch ro current batch.
- CurrentEval: classification\_error\_evaluator: current classfication error of the lates log_period batches.
- Pass=0: Going through all training set one time is called one pass. 0 means going through training set first time.
By default, we use the `stacked_lstm_net` network, which converges at a faster rate than `bidirectional_lstm_net` when passing same sample number. If you want to use bidirectional LSTM, just remove comment in the last line and comment `stacked_lstm_net`.
## Testing
Testing means evaluating the labeled validation set using trained model.
```
cd demo/sentiment
./test.sh
```
test.sh:
```bash
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \
sed -r 'N;s/Test.* error=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \
sort | head -n 1
}
log=train.log
LOG=`get_best_pass $log`
LOG=(${LOG})
evaluate_pass="model_output/pass-${LOG[1]}"
echo 'evaluating from pass '$evaluate_pass
model_list=./model.list
touch $model_list | echo $evaluate_pass > $model_list
net_conf=trainer_config.py
paddle train --config=$net_conf \
--model_list=$model_list \
--job=test \
--use_gpu=false \
--trainer_count=4 \
--config_args=is_test=1 \
2>&1 | tee 'test.log'
```
The function `get_best_pass` gets the best model by classification error rate for testing. In this example, We use test dataset of IMDB as validation by default. Unlike training, it needs to specify `--job=test` and model path, namely `--model_list=$model_list` here. If running successfully, the log is saved in path of `demo/sentiment/test.log`. For example, in our test, the best model is `model_output/pass-00002`, the classification error is 0.115645 as follows.
```
Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645
```
## Prediction
`predict.py` provides a predicting interface. You should install python api of PaddlePaddle before using it. One example to predict unlabeled review of IMDB is as follows. Simply running:
```
cd demo/sentiment
./predict.sh
```
predict.sh:
```
#Note the default model is pass-00002, you shold make sure the model path
#exists or change the mode path.
model=model_output/pass-00002/
config=trainer_config.py
label=data/pre-imdb/labels.list
cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
--tconf=$config\
--model=$model \
--label=$label \
--dict=./data/pre-imdb/dict.txt \
--batch_size=1
```
* `cat ./data/aclImdb/test/pos/10007_10.txt` : the input sample.
* `predict.py` : predicting interface.
* `--tconf=$config` : set network configure.
* ` --model=$model` : set model path.
* `--label=$label` : set dictionary about corresponding relation between integer label and string label.
* `--dict=data/pre-imdb/dict.txt` : set dictionary.
* `--batch_size=1` : set batch size.
Note you should make sure the default model path `model_output/pass-00002`
exists or change the model path.
Predicting result of this example:
```
Loading parameters from model_output/pass-00002/
./data/aclImdb/test/pos/10014_7.txt: predicting label is pos
```
We sincerely appreciate your interest and welcome your contributions.
## Reference
[1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010. <br>
[2] Johan Bollen, Huina Mao, Xiaojun Zeng. 2011. [Twitter mood predicts the stock market](http://arxiv.org/abs/1010.3003), Journal of Computational Science.<br>
[3] Alex Graves, Marcus Liwicki, Santiago Fernan- dez, Roman Bertolami, Horst Bunke, and Ju ̈rgen Schmidhuber. 2009. [A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine In- telligence](http://www.cs.toronto.edu/~graves/tpami_2009.pdf), 31(5):855–868.<br>
[4] Zachary C. Lipton, [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/abs/1506.00019v1), arXiv:1506.00019. <br>
[5] Jie Zhou and Wei Xu; [End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf); ACL-IJCNLP 2015. <br>
doc/tutorials/text_generation/index_cn.md 已删除 100644 → 0
浏览文件 @ 5c5250e3
# 文本生成教程 #
在语言生成领域中，“序列到序列”（sequence to sequence）的方法已被证明是一种强大的模型。它可以被应用于进行机器翻译（machine translation）、query改写（query rewriting）、图像描述（image captioning）等等。
本篇教程将会指导你通过训练一个“序列到序列”的神经网络机器翻译（NMT）模型来将法语翻译成英语。
我们遵循 [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473) 这篇文章，其中详细说明了模型架构，以及在WMT-14数据集上得到良好表现的训练过程。本篇教程在PaddlePaddle中重现了这一良好的训练结果。
我们感谢@caoying的pull request，其中定义了模型架构和solver配置。
## 数据准备 ##
### 下载与解压缩 ###
从该链接 [http://www-lium.univ-lemans.fr/~schwenk/cslm\_joint\_paper/](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/) 下载WMT-14数据集，然后解压，并将Develop和Test数据分别放入不同的文件夹。
- **Train data**: [bitexts (选择过后的)](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz)
- **Develop and Test data**: [dev 与 test 数据](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz)
在Linux下，只需要简单地运行以下命令。否则你需要自己下载、解压、拆分到不同文件夹、并且分别重命名文件后缀。
```bash
cd demo/seqToseq/data
./wmt14_data.sh
```
我们会发现数据集 `wmt14` 中包含如下表所示的3个文件夹。
<table border="2" cellspacing="0" cellpadding="6" rules="all" frame="border">
<colgroup>
<col class="left" />
<col class="left" />
<col class="left" />
<col class="left" />
</colgroup>
<thead>
<tr>
<th scope="col" class="left">folder name</th>
<th scope="col" class="left">French-English parallel corpora file</th>
<th scope="col" class="left">number of total file</th>
<th scope="col" class="left">size</th>
</tr>
</thead>
<tbody>
<tr>
<td class="left">train_data</td>
<td class="left">ccb2_pc30.src, ccb2_pc30.trg, etc</td>
<td class="left">12</td>
<td class="left">3.55G</td>
</tr>
<tr>
<td class="left">test_data</td>
<td class="left">ntst1213.src, ntst1213.trg</td>
<td class="left">2</td>
<td class="left">1636k</td>
</tr>
<tr>
<td class="left">gen_data</td>
<td class="left">ntst14.src, ntst14.trg</td>
<td class="left">2</td>
<td class="left">864k</td>
</tr>
</tbody>
</table>
<br/>
- 每个文件夹都包含法语到英语的平行语料库
- **XXX.src** 是原始法语文件；**XXX.trg** 是目标英语文件
- **XXX.src** 和 **XXX.trg** 的行数应该一致
- 每行都是一个法语或者英语的句子
- **XXX.src** 和 **XXX.trg** 中任意第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 ###
如果你想进行诸如语义转述（Paraphrasing）等其他“序列到序列”的任务，你只需要按照如下方式组织数据，并将它们放在`demo/seqToseq/data`目录下：
dataset
train
file1.src file1.trg
file2.src file2.trg
......
test
file1.src file1.trg
file2.src file2.trg
......
gen
file1.src file1.trg
file2.src file2.trg
......
- 一级目录：数据集文件夹名称
- 二级目录：train、test和gen这三个文件夹是固定的
- 三级目录：源语言到目标语言的平行语料库文件
- **XXX.src** 是源语言的文件，**XXX.trg** 时目标语言的文件
- 文件中的每行都必须是一个句子
- **XXX.src** 和 **XXX.trg** 中任意第i行的句子之间都必须有着一一对应的关系
## 数据预处理 ##
### 预处理工作流程 ###
- 将每个源语言到目标语言的平行语料库文件合并为一个文件：
- 合并每个 **XXX.src** 和 **XXX.trg** 文件为 **XXX**
- **XXX** 中的第i行 = **XXX.src** 中的第i行 + '\t' + **XXX.trg**中的第i行
- 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：
- 词频最高的（DICTSIZE - 3）个单词
- 3个特殊符号
- `<s>`：序列的开始
- `<e>`：序列的结束
- `<unk>`：未包含在字典中的单词
### 预处理命令和结果
对数据集进行预处理的基本命令是：
```python
cd demo/seqToseq/
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”，使得两个字典有相同的上下文
你将会看到如下消息：
concat parallel corpora for dataset
build source dictionary for train data
build target dictionary for train data
dictionary size is XXX
然后你只需要运行以下命令：
```python
python preprocess.py -i data/wmt14 -d 30000
```
这将花费数分钟的时间，并且将预处理好的数据集存放在`demo/seqToseq/data/pre-wmt14`目录下。目录结构如下：
train test gen train.list test.list gen.list src.dict trg.dict# Text generation Tutorial #
- **train, test, gen**：分别包含了法语到英语的平行语料库的训练数据、测试数据和生成数据。文件夹中的每个文件的每一行包含两部分，首先是法语序列，然后是对应的英语序列。
- **train.list, test.list, gen.list**：分别为train，test，gen文件夹中的文件列表
- **src.dict, trg.dict**：源（法语）/目标（英语）字典，每个字典包含总共30000个单词：29997个最高频单词和3个特殊符号
## 模型训练 ##
### 简介###
神经网络机器翻译（NMT）旨在建立一个可以被协同调至最优翻译效果的单神经元网络。近期提出的NMT模型通常都属于编解码模型（encoder–decoder models）的一种。编解码模型将一个源语句编码为一个定长的向量，然后解码器通过这个向量生成一个目标语句。
在这个任务中，我们使用了一个编解码模型的扩展，它同时学习排列(align)与翻译。每当模型在翻译过程中生成了一个单词，它就会在源语句中搜索出最相关信息的位置的集合。解码器根据上下文向量预测出一个目标单词，这个向量与源中搜索出的位置和所有之前生成的目标单词有关。如想了解更多详细的解释，可以参考 [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473)。
这个模型对于编解码模型来说，最不同的特色是它并没有将输入语句编码为一个单独的定长向量。相反，它将输入语句编码为向量的序列，其中每个向量对应输入语句中的一个元素。然后在解码被翻译的语句时，会自适应地从这些向量中选择一个子集出来。这使得NMT模型得以解放出来，不必再将任意长度源语句中的所有信息压缩至一个定长的向量中。该模型在长语句翻译的场景下效果提升更加明显，在任意长度语句翻译的场景下都可以观察到其效果的提升。
<center>![](./encoder-decoder-attention-model.png)</center>
<center>Figure 1. Encoder-Decoder-Attention-Model</center>
### 使用PaddlePaddle训练模型 ###
我们在训练之前需要常见一个模型配置文件，这里是一个例子`demo/seqToseq/translation/train.conf`。前三行import了定义network，job_mode和attention_mode的python函数。
```python
from seqToseq_net import *
is_generating = False
### Data Definiation
train_conf = seq_to_seq_data(data_dir = "./data/pre-wmt14",
is_generating = is_generating)
### Algorithm Configuration
settings(
learning_method = AdamOptimizer(),
batch_size = 50,
learning_rate = 5e-4)
### Network Architecture
gru_encoder_decoder(train_conf, is_generating)
```
1. **Data Definiation**：在示例中我们定义了一个序列到序列的训练和测试数据。它返回train_conf作为配置，其输入参数如下：
- data_dir：训练数据和测试数据的目录
- is_generating：这个配置是否用来生成，这里设置为False
2. **Algorithm Configuration**：在示例中我们使用SGD训练算法（默认），和ADAM学习方法，指定batch_size为50，learning_rate为5e-4
3. **Network Architecture**：在示例中我们使用attention版本的GRU编解码网络。它包括了一个双向的GRU作为编码器和解码器，它模拟了解码翻译过程中在源语句中的搜索。
### 训练模型的命令与结果###
写完模型配置之后，我们可以通过以下命令来训练模型：
```bash
cd demo/seqToseq/translation
./train.sh
```
`train.sh` 的内容如下所示：
```bash
paddle train \
--config='translation/train.conf' \
--save_dir='translation/model' \
--use_gpu=false \
--num_passes=16 \
--show_parameter_stats_period=100 \
--trainer_count=4 \
--log_period=10 \
--dot_period=5 \
2>&1 | tee 'translation/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，这里设置为使用CPU
- num_passes: 设置passes的数量。paddle中的一条pass表示训练数据集中所有的样本一次
- 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"."
训练的损失函数默认每隔10个batch打印一次，你将会看到如下消息：
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
.....
- AvgCost：从第0个batch到当前batch的平均cost
- CurrentCost:：当前batch的cost
- 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。
## 文本生成 ##
### 简介###
一般而言，NMT模型受制于源语句的编码，并且通过给出当前目标单词来预测下一个目标单词。在训练过程中，当前单词在相比之下总是被当作真值（ground truth）。在生成过程中，当前单词是解码器最后一步的输出，这来自于PaddlePaddle的内存中。
而且，我们使用集束搜索（Beam Search）来生成序列。集束搜索使用广度优先搜索来构建搜索树。对于树的每一层，生成当前层的所有后继状态，并将它们按照启发代价（heuristic cost）升序排列。但是这种方法在每层只保存预设数量的最优状态（这个数量称为beam size）。
### 预训练的模型 ###
我们在拥有50个节点的集群中训练模型，每个节点有两个6核CPU。我们在5天里训练了16个pass，其中每条pass花费了7个小时。model_dir中有16个子目录，每个里面都包含202MB的全部的模型参数。然后我们发现pass-00012的模型有着最高的BLEU值27.77（参考文献[BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf)）。要下载解压这个模型，只需在linux下运行如下命令：
```bash
cd demo/seqToseq/data
./wmt14_model.sh
```
### 使用PaddlePaddle生成模型 ###
在翻译法语句子之前，我们需要创建模型配置文件。这里是一个例子`demo/seqToseq/translation/gen.conf`。前三行import了定义network，job_mode和attention_mode的python函数。
```python
from seqToseq_net import *
is_generating = True
################## Data Definiation #####################
gen_conf = seq_to_seq_data(data_dir = "./data/pre-wmt14",
is_generating = is_generating,
gen_result = "./translation/gen_result")
############## Algorithm Configuration ##################
settings(
learning_method = AdamOptimizer(),
batch_size = 1,
learning_rate = 0)
################# Network configure #####################
gru_encoder_decoder(gen_conf, is_generating)
```
1. **Data Definiation**：在示例中我们定义了一个序列到序列的生成数据。它返回gen_conf作为配置，其输入参数如下：
- data_dir：生成数据的目录
 - is_generating：这个配置是否用来生成，这里设置为True
 - gen_result：保存生成结果的文件
2. **Algorithm Configuration**：在生成过程中我们使用SGD训练算法，并指定batch_size为1（每次生成1个序列），learning_rate为0
3. **Network Architecture**：本质上与训练模型一样
### 生成模型的命令与结果 ###
写完模型配置之后，我们可以通过以下命令来进行从法语到英语的文本翻译：
```bash
cd demo/seqToseq/translation
./gen.sh
```
`gen.sh` 的内容如下所示。与训练模型不同的是，这里有一些不同的参数需要指定：
```bash
paddle train \
--job=test \
--config='translation/gen.conf' \
--save_dir='data/wmt14_model' \
--use_gpu=true \
--num_passes=13 \
--test_pass=12 \
--trainer_count=1 \
2>&1 | tee 'translation/gen.log'
```
- job：设置任务的模式为测试
- save_dir：存储模型的路径
- num_passes and test_pass：从test_pass到（num_passes - 1）加载模型参数，这里只加载 `data/wmt14_model/pass-00012`
你将会看到这样的消息：
I0706 14:48:31.178915 31441 GradientMachine.cpp:143] Loading parameters from data/wmt14_model/pass-00012
I0706 14:48:40.012039 31441 Tester.cpp:125] Batch=100 samples=100 AvgCost=0
I0706 14:48:48.898632 31441 Tester.cpp:125] Batch=200 samples=200 AvgCost=0
...
然后在`demo/seqToseq/translation/gen_result`中的生成结果如下所示：
0
0 -11.1314 The <unk> <unk> about the width of the seats while large controls are at stake <e>
1 -11.1519 The <unk> <unk> on the width of the seats while large controls are at stake <e>
2 -11.5988 The <unk> <unk> about the width of the seats while large controls are at stake . <e>
1
0 -24.4149 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of the Dubai <unk> . <e>
1 -26.9524 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai &apos; s <unk> . <e>
2 -27.9574 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai &apos; s Dubai <unk> . <e>
...
- 这是集束搜索的结果，其中beam size是3
- 第一行的“0”和第6行的“1”表示生成数据的序列id
- 其他六行列出了集束搜索的结果
- 第二列是集束搜索的得分（从大到小）
- 第三列是生成的英语序列
- 有两个特殊标识：
- `<e>`：序列的结尾
- `<unk>`：不包含在字典中的单词
### BLEU评估 ###
对机器翻译的人工评估工作很广泛但也很昂贵。一篇论文 [BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf) 展示了一种方法，当需要快速或者频繁的评估时，使用自动的替补来替代经验丰富的人工评判。[Moses](http://www.statmt.org/moses/) 是一个统计学的机器翻译系统，我们使用其中的 [multi-bleu.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) 来做BLEU评估。运行以下命令来下载这个脚本：
```bash
cd demo/seqToseq/translation
./moses_bleu.sh
```
由于标准的翻译结果已经下载到这里`data/wmt14/gen/ntst14.trg`，我们可以运行以下命令来做BLEU评估。
```bash
cd demo/seqToseq/translation
./eval_bleu.sh FILE BEAMSIZE
```
- FILE：生成的结果文件
- BEAMSIZE：集束搜索中的扩展广度
doc/tutorials/text_generation/index_en.md 已删除 100644 → 0
浏览文件 @ 5c5250e3
# Text generation Tutorial #
Sequence to sequence has been proven to be a powerful model for language generation. It can be used for machine translation, query rewriting, image captioning, etc.
This tutorial guides you through training a sequence to sequence model for neural machine translation (NMT) network that translates French to English.
We follow the paper [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473) , which details the model architecture and training procedure for good performance on WMT-14 dataset. This tutorial reproduces this result in PaddlePaddle.
We thank @caoying for the pull request that defines the model architecture and solver configurations.
## Data Preparation ##
### Download and Extract ###
Download the WMT-14 dataset from [http://www-lium.univ-lemans.fr/~schwenk/cslm\_joint\_paper/](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/), extract it, and divide Develop and Test data into separate folder.
- **Train data**: [bitexts (after selection)](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz)
- **Develop and Test data**: [dev+test data](http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz)
To do this, simply run the following commands in linux, otherwise, you need to download, extract, divide, and rename the file suffix respectively.
```bash
cd demo/seqToseq/data
./wmt14_data.sh
```
We should find that the dataset `wmt14` has three folders as shown in the following table.
<table border="2" cellspacing="0" cellpadding="6" rules="all" frame="border">
<colgroup>
<col class="left" />
<col class="left" />
<col class="left" />
<col class="left" />
</colgroup>
<thead>
<tr>
<th scope="col" class="left">folder name</th>
<th scope="col" class="left">French-English parallel corpora file</th>
<th scope="col" class="left">number of total file</th>
<th scope="col" class="left">size</th>
</tr>
</thead>
<tbody>
<tr>
<td class="left">train_data</td>
<td class="left">ccb2_pc30.src, ccb2_pc30.trg, etc</td>
<td class="left">twelve</td>
<td class="left">3.55G</td>
</tr>
<tr>
<td class="left">test_data</td>
<td class="left">ntst1213.src, ntst1213.trg</td>
<td class="left">two</td>
<td class="left">1636k</td>
</tr>
<tr>
<td class="left">gen_data</td>
<td class="left">ntst14.src, ntst14.trg</td>
<td class="left">two</td>
<td class="left">864k</td>
</tr>
</tbody>
</table>
<br/>
- Each folder has French-English parallel corpora
- **XXX.src** are source French files; **XXX.trg** are target English files.
- The number of lines of **XXX.src** and **XXX.trg** should be the same.
- Each line is a French/English sentence.
- There is a one-to-one correspondence between the sentence at the i-th line of **XXX.src** and **XXX.trg**.
### User Defined Dataset ###
If you need to do other sequence-to-sequence tasks, such as Paraphrasing, you only need to organize the data as follows, and place them in `demo/seqToseq/data`:
dataset
train
file1.src file1.trg
file2.src file2.trg
......
test
file1.src file1.trg
file2.src file2.trg
......
gen
file1.src file1.trg
file2.src file2.trg
......
- 1st directory: dataset folder name
- 2nd directory: folder of train, test, and gen. The names of these three folders are fixed.
- 3rd file: Source-Target parallel corpora files.
- **XXX.src** are source files, **XXX.trg** are target files.
- Each line of the file must be a sequence.
- There should be a one-to-one correspondence between the i-th sequence of **XXX.src** and **XXX.trg**.
## Data Preprocess ##
### Preprocessing Workflow ###
- Concat each Source-Target parallel corpora to be one file:
- concat each **XXX.src** and **XXX.trg** to be **XXX**.
- the i-th line of **XXX** = the i-th line of **XXX.src** + '\t' + the i-th line of **XXX.trg**
- Build source and target dictionary of train data, each dictionary has DICTSIZE words:
- the most frequent (DICTSIZE-3) words
- 3 special token:
- `<s>`: the start of a sequence
- `<e>`: the end of a sequence
- `<unk>`: a word not included in dictionary
### Preprocessing Command and Result
The general command for preprocessing the dataset is:
```python
cd demo/seqToseq/
python preprocess.py -i INPUT [-d DICTSIZE] [-m]
```
- `-i INPUT`: the path of input original dataset
- `-d DICTSIZE`: the specified word count of dictionary, if not set, dictionary will contain all the words in input dataset
- `-m --mergeDict`: merge source and target dictionary, thus, two dictionaries have the same context
And you will see messages like this:
concat parallel corpora for dataset
build source dictionary for train data
build target dictionary for train data
dictionary size is XXX
Here, you can simply run the command:
```python
python preprocess.py -i data/wmt14 -d 30000
```
It will take several minutes, and store the preprocessed dataset in `demo/seqToseq/data/pre-wmt14`, the directory has following structure.
train test gen train.list test.list gen.list src.dict trg.dict
- **train, test, gen**: folder contains French-English parallel corpora of train data, test data and gen data respectively. Each line of file in folder contains two parts, the former is a French sequence, and the latter is a corresponding English sequence.
- **train.list, test.list, gen.list**: text contains a file list in train folder, test folder and gen folder respectively
- **src.dict, trg.dict**: source (French) / target (English) dictionary, each dictionary has 30000 words: the most frequent 29997 words and 3 special token
## Model Training ##
### Introduction ###
Neural machine translation (NMT) aims at building a single neural network that can be jointly tuned to maximize translation performance. Recently proposed NMT models often belong to a family of encoder–decoder models. Encoder-Decoder models encode a source sentence into a fixed-length vector from which a decoder generates a target sentence.
In this task, we use an extension to the encoder–decoder model which learns to align and translate jointly. Each time the model generates a word in a translation, it searches for a set of positions in the source sentence for the most relevant information. The decoder predicts a target word based on the context vectors associated with these source positions and all the previous generated target words. For more detailed explanation, readers can refer to paper [Neural Machine Translation by Jointly Learning to Align and Translate](http://arxiv.org/abs/1409.0473).
The most distinguishing feature of this model is that it doesn't encode an input sentence into a single fixed-length vector. Instead, it encodes the input sentence into a sequence of vectors, where one vector corresponds to an input element. A subset of these vectors is chosen adaptively while decoding the translated sentence. This frees a NMT model from having to squash all the information of a source sentence, regardless of its length, into a fixed-length vector. The improvement of this model is more apparent for longer sentences, but the improvement can be observed for sentences of any length.
<center>![](./encoder-decoder-attention-model.png)</center>
<center>Figure 1. Encoder-Decoder-Attention-Model</center>
### Training Model in PaddlePaddle ###
We need to create a model config file before training. Here is an example `demo/seqToseq/translation/train.conf`. The first three lines import python function for defining network, and define the job_mode and attention_mode.
```python
from seqToseq_net import *
is_generating = False
### Data Definiation
train_conf = seq_to_seq_data(data_dir = "./data/pre-wmt14",
is_generating = is_generating)
### Algorithm Configuration
settings(
learning_method = AdamOptimizer(),
batch_size = 50,
learning_rate = 5e-4)
### Network Architecture
gru_encoder_decoder(train_conf, is_generating)
```
1. **Data Definiation**: We define a SeqToSeq train and test data in our example. It returns train_conf as the configuration, following is its input arguments:
- data_dir: directory of train data and test data
- is\_generating: whether this config is used for generating, here is false
2. **Algorithm Configuration**: We use the SGD training algorithm (default), ADAM learning method in our example, specify batch_size as 50, and learning rate as 5e-4.
3. **Network Architecture**: We use an attention version of GRU Encoder-Decoder network in our example. It consists a bidirectional GRU as an encoder and a decoder that emulates searching through a source sentence during decoding a translation.
### Training Command and Result###
After writing the model config, we can train the model by running the command:
```bash
cd demo/seqToseq/translation
./train.sh
```
The `train.sh` is shown as follows:
```bash
paddle train \
--config='translation/train.conf' \
--save_dir='translation/model' \
--use_gpu=false \
--num_passes=16 \
--show_parameter_stats_period=100 \
--trainer_count=4 \
--log_period=10 \
--dot_period=5 \
2>&1 | tee 'translation/train.log'
```
- config: set config of neural network
- save_dir: set output path to save models
- use_gpu: whether to use GPU to train, here use CPU
- num_passes: set number of passes. One pass in paddle means training all samples in dataset one time
- show_parameter_stats_period: here show parameter statistic every 100 batches
- trainer_count: set number of CPU threads or GPU devices
- log_period: here print log every 10 batches
- dot_period: here print '.' every 5 batches
The training loss function is printed every 10 batch by default, and you will see messages like this:
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
.....
- AvgCost: Average Cost from 0th batch to current batch
- CurrentCost: Cost in current batch
- classification\_error\_evaluator(Eval): False prediction rate for each word from 0th evaluation to current evaluation
- classification\_error\_evaluator(CurrentEval): False prediction rate for each word in current evaluation
And when the classification\_error\_evaluator is less than 0.35, the model is trained sucessfully.
## Text Generation ##
### Introduction ###
Generally speaking, the NMT model is conditioned on the encodings of the source sentence, and then to predict the next target word by given the current target word. In the training process, the current word is always knowns as the ground truth, by contrast. In the generating process, the current word is the output of the decoder in last time step, which is accessed to from a memory in PaddlePaddle.
Besides, we use Beam Search to generate sequences. Beam search uses breadth-first search to build its search tree. At each level of the tree, it generates all successors of the states at the current level, sorting them in increasing order of heuristic cost. However, it only stores a predetermined number of best states at each level (called the beam size).
### Pretrained model ###
We trained the model on a cluster with 50 nodes, each node has two 6-core CPUs. We trained 16 passes in 5 days, where each pass takes 7 hours. The model_dir has 16 sub-folder, each of which contains the whole model parameters with 202MB size. And we find pass-00012 model has the highest BLEU 27.77 (see paper [BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf)). To download and extract this model, simply run the following commands in linux.
```bash
cd demo/seqToseq/data
./wmt14_model.sh
```
### Generating Model in PaddlePaddle ###
We need to create a model config file before translating French sequence. Here is an example `demo/seqToseq/translation/gen.conf`, the first three lines import python function for defining network, and define the job\_mode and attention\_mode.
```python
from seqToseq_net import *
is_generating = True
################## Data Definiation #####################
gen_conf = seq_to_seq_data(data_dir = "./data/pre-wmt14",
is_generating = is_generating,
gen_result = "./translation/gen_result")
############## Algorithm Configuration ##################
settings(
learning_method = AdamOptimizer(),
batch_size = 1,
learning_rate = 0)
################# Network configure #####################
gru_encoder_decoder(gen_conf, is_generating)
```
1. **Data Definiation**: We defines an SeqToSeq gen data in our example. It returns gen_conf as the configuration, following is its input arguments:
- data\_dir: directory of gen data
  - is\_generating: whether this config is used for generating, here is true
  - gen\_result: file to store the generation result
2. **Algorithm Configuration**: We use SGD traing algorithm in generation, and specify batch_size as 1 (each time generate one sequence), and learning rate as 0.
3. **Network Architecture**: Essentially the same as the training model.
### Generating Command and Result ###
After writing the model config, we can do text translation from French to English by running the command:
```bash
cd demo/seqToseq/translation
./gen.sh
```
The `gen.sh` is shown as follows, unlike training, there are some different arguments to specify:
```bash
paddle train \
--job=test \
--config='translation/gen.conf' \
--save_dir='data/wmt14_model' \
--use_gpu=true \
--num_passes=13 \
--test_pass=12 \
--trainer_count=1 \
2>&1 | tee 'translation/gen.log'
```
- job: set job mode to test
- save_dir: the path of saved models
- num_passes and test_pass: loading model parameters from test_pass to (num_passes - 1), here only loads `data/wmt14_model/pass-00012`
You will see messages like this:
I0706 14:48:31.178915 31441 GradientMachine.cpp:143] Loading parameters from data/wmt14_model/pass-00012
I0706 14:48:40.012039 31441 Tester.cpp:125] Batch=100 samples=100 AvgCost=0
I0706 14:48:48.898632 31441 Tester.cpp:125] Batch=200 samples=200 AvgCost=0
...
And the generating result in `demo/seqToseq/translation/gen_result` likes:
0
0 -11.1314 The <unk> <unk> about the width of the seats while large controls are at stake <e>
1 -11.1519 The <unk> <unk> on the width of the seats while large controls are at stake <e>
2 -11.5988 The <unk> <unk> about the width of the seats while large controls are at stake . <e>
1
0 -24.4149 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of the Dubai <unk> . <e>
1 -26.9524 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai &apos; s <unk> . <e>
2 -27.9574 The dispute is between the major aircraft manufacturers about the width of the tourist seats on the <unk> flights , paving the way for a <unk> confrontation during the month of Dubai &apos; s Dubai <unk> . <e>
...
- This is the beam search result, where beam size is 3
- '0' in 1st-line and '1' in 6th-line mean the sequence-id in gen data
- Other six lines list the beam search results
- The 2nd-column is the score of beam search (from large to small)
- The 3rd-colunm is the generating English sequence
- There is 2 special tokens:
- `<e>`: the end of a sequence
- `<unk>`: a word not included in dictionary
### Bleu Evalutaion ###
Human evaluations of machine translation are extensive but expensive. Paper [BLEU: a Method for Automatic Evaluation of Machine Translation](http://www.aclweb.org/anthology/P02-1040.pdf) presents a method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations. [Moses](http://www.statmt.org/moses/) is a statistical machine translation system, and we use [multi-bleu.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) of it to do Bleu Evalution. To download this script, simply run the following command:
```bash
cd demo/seqToseq/translation
./moses_bleu.sh
```
Since the standard translation is alrealy downloaded as `data/wmt14/gen/ntst14.trg`, we can do Bleu Evalution by running the command:
```bash
cd demo/seqToseq/translation
./eval_bleu.sh FILE BEAMSIZE
```
- FILE: the generation result file
- BEAMSIZE: expand width in beam search
doc/v1_api_tutorials/README.md 0 → 100644
浏览文件 @ 17b3de9d
The tutorials in v1_api_tutorials are using v1_api now, and will be upgraded into v2_api later.
Thus, v1_api_tutorials is a temporary directory. We decide not to maintain it and will delete it in future.
Please go to [PaddlePaddle/book](https://github.com/PaddlePaddle/book) and
[PaddlePaddle/models](https://github.com/PaddlePaddle/models) to learn PaddlePaddle.
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