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...@@ -33,11 +33,3 @@ ...@@ -33,11 +33,3 @@
entry: bash .clang_format.hook -i entry: bash .clang_format.hook -i
language: system language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$ files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
- repo: local
hooks:
- id: convert-markdown-into-html
name: convert-markdown-into-html
description: Convert README.md into index.html
entry: python .pre-commit-hooks/convert_markdown_into_html.py
language: system
files: .+README\.md$
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import argparse
import re
import sys
HEAD = """
<html>
<head>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
jax: ["input/TeX", "output/HTML-CSS"],
tex2jax: {
inlineMath: [ ['$','$'] ],
displayMath: [ ['$$','$$'] ],
processEscapes: true
},
"HTML-CSS": { availableFonts: ["TeX"] }
});
</script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
<script type="text/javascript" src="../.tools/theme/marked.js">
</script>
<link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
<script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
<link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
<link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
<link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
</head>
<style type="text/css" >
.markdown-body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
}
</style>
<body>
<div id="context" class="container-fluid markdown-body">
</div>
<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
<div id="markdown" style='display:none'>
"""
TAIL = """
</div>
<!-- You can change the lines below now. -->
<script type="text/javascript">
marked.setOptions({
renderer: new marked.Renderer(),
gfm: true,
breaks: false,
smartypants: true,
highlight: function(code, lang) {
code = code.replace(/&amp;/g, "&")
code = code.replace(/&gt;/g, ">")
code = code.replace(/&lt;/g, "<")
code = code.replace(/&nbsp;/g, " ")
return hljs.highlightAuto(code, [lang]).value;
}
});
document.getElementById("context").innerHTML = marked(
document.getElementById("markdown").innerHTML)
</script>
</body>
"""
def convert_markdown_into_html(argv=None):
parser = argparse.ArgumentParser()
parser.add_argument('filenames', nargs='*', help='Filenames to fix')
args = parser.parse_args(argv)
retv = 0
for filename in args.filenames:
with open(
re.sub(r"README", "index", re.sub(r"\.md$", ".html", filename)),
"w") as output:
output.write(HEAD)
with open(filename) as input:
for line in input:
output.write(line)
output.write(TAIL)
return retv
if __name__ == '__main__':
sys.exit(convert_markdown_into_html())
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<html>
<head>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
jax: ["input/TeX", "output/HTML-CSS"],
tex2jax: {
inlineMath: [ ['$','$'] ],
displayMath: [ ['$$','$$'] ],
processEscapes: true
},
"HTML-CSS": { availableFonts: ["TeX"] }
});
</script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
<script type="text/javascript" src="../.tools/theme/marked.js">
</script>
<link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
<script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
<link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
<link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
<link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
</head>
<style type="text/css" >
.markdown-body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
}
</style>
<body>
<div id="context" class="container-fluid markdown-body">
</div>
<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
<div id="markdown" style='display:none'>
# Click-Through Rate Prediction
## Introduction
CTR(Click-Through Rate)\[[1](https://en.wikipedia.org/wiki/Click-through_rate)\]
is a prediction of the probability that a user clicks on an advertisement. This model is widely used in the advertisement industry. Accurate click rate estimates are important for maximizing online advertising revenue.
When there are multiple ad slots, CTR estimates are generally used as a baseline for ranking. For example, in a search engine's ad system, when the user enters a query, the system typically performs the following steps to show relevant ads.
1. Get the ad collection associated with the user's search term.
2. Business rules and relevance filtering.
3. Rank by auction mechanism and CTR.
4. Show ads.
Here，CTR plays a crucial role.
### Brief history
Historically, the CTR prediction model has been evolving as follows.
- Logistic Regression(LR) / Gradient Boosting Decision Trees (GBDT) + feature engineering
- LR + Deep Neural Network (DNN)
- DNN + feature engineering
In the early stages of development LR dominated, but the recent years DNN based models are mainly used.
### LR vs DNN
The following figure shows the structure of LR and DNN model:
<p align="center">
<img src="images/lr_vs_dnn.jpg" width="620" hspace='10'/> <br/>
Figure 1. LR and DNN model structure comparison
</p>
We can see, LR and CNN have some common structures. However, DNN can have non-linear relation between input and output values by adding activation unit and further layers. This enables DNN to achieve better learning results in CTR estimates.
In the following, we demonstrate how to use PaddlePaddle to learn to predict CTR.
## Data and Model formation
Here `click` is the learning objective. There are several ways to learn the objectives.
1. Direct learning click, 0,1 for binary classification
2. Learning to rank, pairwise rank or listwise rank
3. Measure the ad click rate of each ad, then rank by the click rate.
In this example, we use the first method.
We use the Kaggle `Click-through rate prediction` task \[[2](https://www.kaggle.com/c/avazu-ctr-prediction/data)\].
Please see the [data process](./dataset.md) for pre-processing data.
The input data format for the demo model in this tutorial is as follows:
```
# <dnn input ids> \t <lr input sparse values> \t click
1 23 190 \t 230:0.12 3421:0.9 23451:0.12 \t 0
23 231 \t 1230:0.12 13421:0.9 \t 1
```
Description：
- `dnn input ids` one-hot coding.
- `lr input sparse values` Use `ID:VALUE` , values are preferaly scaled to the range `[-1, 1]`。
此外，模型训练时需要传入一个文件描述 dnn 和 lr两个子模型的输入维度，文件的格式如下：
```
dnn_input_dim: <int>
lr_input_dim: <int>
```
<int> represents an integer value.
`avazu_data_processor.py` can be used to download the data set \[[2](#参考文档)\]and pre-process the data.
```
usage: avazu_data_processer.py [-h] --data_path DATA_PATH --output_dir
OUTPUT_DIR
[--num_lines_to_detect NUM_LINES_TO_DETECT]
[--test_set_size TEST_SET_SIZE]
[--train_size TRAIN_SIZE]
PaddlePaddle CTR example
optional arguments:
-h, --help show this help message and exit
--data_path DATA_PATH
path of the Avazu dataset
--output_dir OUTPUT_DIR
directory to output
--num_lines_to_detect NUM_LINES_TO_DETECT
number of records to detect dataset's meta info
--test_set_size TEST_SET_SIZE
size of the validation dataset(default: 10000)
--train_size TRAIN_SIZE
size of the trainset (default: 100000)
```
- `data_path` The data path to be processed
- `output_dir` The output path of the data
- `num_lines_to_detect` The number of generated IDs
- `test_set_size` The number of rows for the test set
- `train_size` The number of rows of training set
## Wide & Deep Learning Model
Google proposed a model framework for Wide & Deep Learning to integrate the advantages of both DNNs suitable for learning abstract features and LR models for large sparse features.
### Introduction to the model
Wide & Deep Learning Model\[[3](#References)\] is a relatively mature model, but this model is still being used in the CTR predicting task. Here we demonstrate the use of this model to complete the CTR predicting task.
The model structure is as follows:
<p align="center">
<img src="images/wide_deep.png" width="820" hspace='10'/> <br/>
Figure 2. Wide & Deep Model
</p>
The wide part of the left side of the model can accommodate large-scale coefficient features and has some memory for some specific information (such as ID); and the Deep part of the right side of the model can learn the implicit relationship between features.
### Model Input
The model has three inputs as follows.
- `dnn_input` ，the Deep part of the input
- `lr_input` ，the wide part of the input
- `click` ， click on or not
```python
dnn_merged_input = layer.data(
name='dnn_input',
type=paddle.data_type.sparse_binary_vector(self.dnn_input_dim))
lr_merged_input = layer.data(
name='lr_input',
type=paddle.data_type.sparse_vector(self.lr_input_dim))
click = paddle.layer.data(name='click', type=dtype.dense_vector(1))
```
### Wide part
Wide part uses of the LR model, but the activation function changed to `RELU` for speed.
```python
def build_lr_submodel():
fc = layer.fc(
input=lr_merged_input, size=1, name='lr', act=paddle.activation.Relu())
return fc
```
### Deep part
The Deep part uses a standard multi-layer DNN.
```python
def build_dnn_submodel(dnn_layer_dims):
dnn_embedding = layer.fc(input=dnn_merged_input, size=dnn_layer_dims[0])
_input_layer = dnn_embedding
for i, dim in enumerate(dnn_layer_dims[1:]):
fc = layer.fc(
input=_input_layer,
size=dim,
act=paddle.activation.Relu(),
name='dnn-fc-%d' % i)
_input_layer = fc
return _input_layer
```
### Combine
The output section uses `sigmoid` function to output (0,1) as the prediction value.
```python
# conbine DNN and LR submodels
def combine_submodels(dnn, lr):
merge_layer = layer.concat(input=[dnn, lr])
fc = layer.fc(
input=merge_layer,
size=1,
name='output',
# use sigmoid function to approximate ctr, wihch is a float value between 0 and 1.
act=paddle.activation.Sigmoid())
return fc
```
### Training
```python
dnn = build_dnn_submodel(dnn_layer_dims)
lr = build_lr_submodel()
output = combine_submodels(dnn, lr)
# ==============================================================================
# cost and train period
# ==============================================================================
classification_cost = paddle.layer.multi_binary_label_cross_entropy_cost(
input=output, label=click)
paddle.init(use_gpu=False, trainer_count=11)
params = paddle.parameters.create(classification_cost)
optimizer = paddle.optimizer.Momentum(momentum=0)
trainer = paddle.trainer.SGD(
cost=classification_cost, parameters=params, update_equation=optimizer)
dataset = AvazuDataset(train_data_path, n_records_as_test=test_set_size)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
logging.warning("Pass %d, Samples %d, Cost %f" % (
event.pass_id, event.batch_id * batch_size, event.cost))
if event.batch_id % 1000 == 0:
result = trainer.test(
reader=paddle.batch(dataset.test, batch_size=1000),
feeding=field_index)
logging.warning("Test %d-%d, Cost %f" % (event.pass_id, event.batch_id,
result.cost))
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(dataset.train, buf_size=500),
batch_size=batch_size),
feeding=field_index,
event_handler=event_handler,
num_passes=100)
```
## Run training and testing
The model go through the following steps:
1. Prepare training data
1. Download train.gz from [Kaggle CTR](https://www.kaggle.com/c/avazu-ctr-prediction/data) .
2. Unzip train.gz to get train.txt
3. `mkdir -p output; python avazu_data_processer.py --data_path train.txt --output_dir output --num_lines_to_detect 1000 --test_set_size 100` 生成演示数据
2. Execute `python train.py --train_data_path ./output/train.txt --test_data_path ./output/test.txt --data_meta_file ./output/data.meta.txt --model_type=0`. Start training.
The argument options for `train.py` are as follows.
```
usage: train.py [-h] --train_data_path TRAIN_DATA_PATH
[--test_data_path TEST_DATA_PATH] [--batch_size BATCH_SIZE]
[--num_passes NUM_PASSES]
[--model_output_prefix MODEL_OUTPUT_PREFIX] --data_meta_file
DATA_META_FILE --model_type MODEL_TYPE
PaddlePaddle CTR example
optional arguments:
-h, --help show this help message and exit
--train_data_path TRAIN_DATA_PATH
path of training dataset
--test_data_path TEST_DATA_PATH
path of testing dataset
--batch_size BATCH_SIZE
size of mini-batch (default:10000)
--num_passes NUM_PASSES
number of passes to train
--model_output_prefix MODEL_OUTPUT_PREFIX
prefix of path for model to store (default:
./ctr_models)
--data_meta_file DATA_META_FILE
path of data meta info file
--model_type MODEL_TYPE
model type, classification: 0, regression 1 (default
classification)
```
- `train_data_path` ： The path of the training set
- `test_data_path` : The path of the testing set
- `num_passes`: number of rounds of model training
- `data_meta_file`: Please refer to [数据和任务抽象](### 数据和任务抽象)的描述。
- `model_type`: Model classification or regressio
## Use the training model for prediction
The training model can be used to predict new data, and the format of the forecast data is as follows.
```
# <dnn input ids> \t <lr input sparse values>
1 23 190 \t 230:0.12 3421:0.9 23451:0.12
23 231 \t 1230:0.12 13421:0.9
```
Here the only difference to the training data is that there is no label (i.e. `click` values).
We now can use `infer.py` to perform inference.
```
usage: infer.py [-h] --model_gz_path MODEL_GZ_PATH --data_path DATA_PATH
--prediction_output_path PREDICTION_OUTPUT_PATH
[--data_meta_path DATA_META_PATH] --model_type MODEL_TYPE
PaddlePaddle CTR example
optional arguments:
-h, --help show this help message and exit
--model_gz_path MODEL_GZ_PATH
path of model parameters gz file
--data_path DATA_PATH
path of the dataset to infer
--prediction_output_path PREDICTION_OUTPUT_PATH
path to output the prediction
--data_meta_path DATA_META_PATH
path of trainset's meta info, default is ./data.meta
--model_type MODEL_TYPE
model type, classification: 0, regression 1 (default
classification)
```
- `model_gz_path_model`：path for `gz` compressed data.
- `data_path` ：
- `prediction_output_patj`：path for the predicted values s
- `data_meta_file` ：Please refer to [数据和任务抽象](### 数据和任务抽象)。
- `model_type` ：Classification or regression
The sample data can be predicted with the following command
```
python infer.py --model_gz_path <model_path> --data_path output/infer.txt --prediction_output_path predictions.txt --data_meta_path data.meta.txt
```
The final prediction is written in `predictions.txt`。
## References
1. <https://en.wikipedia.org/wiki/Click-through_rate>
2. <https://www.kaggle.com/c/avazu-ctr-prediction/data>
3. Cheng H T, Koc L, Harmsen J, et al. [Wide & deep learning for recommender systems](https://arxiv.org/pdf/1606.07792.pdf)[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
</div>
<!-- You can change the lines below now. -->
<script type="text/javascript">
marked.setOptions({
renderer: new marked.Renderer(),
gfm: true,
breaks: false,
smartypants: true,
highlight: function(code, lang) {
code = code.replace(/&amp;/g, "&")
code = code.replace(/&gt;/g, ">")
code = code.replace(/&lt;/g, "<")
code = code.replace(/&nbsp;/g, " ")
return hljs.highlightAuto(code, [lang]).value;
}
});
document.getElementById("context").innerHTML = marked(
document.getElementById("markdown").innerHTML)
</script>
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<html>
<head>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js", "TeX/AMSsymbols.js", "TeX/AMSmath.js"],
jax: ["input/TeX", "output/HTML-CSS"],
tex2jax: {
inlineMath: [ ['$','$'] ],
displayMath: [ ['$$','$$'] ],
processEscapes: true
},
"HTML-CSS": { availableFonts: ["TeX"] }
});
</script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
<script type="text/javascript" src="../.tools/theme/marked.js">
</script>
<link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
<script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
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<link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
<link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
</head>
<style type="text/css" >
.markdown-body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
}
</style>
<body>
<div id="context" class="container-fluid markdown-body">
</div>
<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
<div id="markdown" style='display:none'>
# Deep Structured Semantic Models (DSSM)
Deep Structured Semantic Models (DSSM) is simple but powerful DNN based model for matching web search queries and the URL based documents. This example demonstrates how to use PaddlePaddle to implement a generic DSSM model for modeling the semantic similarity between two strings.
## Background Introduction
DSSM \[[1](##References)]is a classic semantic model proposed by the Institute of Physics. It is used to study the semantic distance between two texts. The general implementation of DSSM is as follows.
1. The CTR predictor measures the degree of association between a user search query and a candidate web page.
2. Text relevance, which measures the degree of semantic correlation between two strings.
3. Automatically recommend, measure the degree of association between User and the recommended Item.
## Model Architecture
In the original paper \[[1](#References)] the DSSM model uses the implicit semantic relation between the user search query and the document as metric. The model structure is as follows
<p align="center">
<img src="./images/dssm.png"/><br/><br/>
Figure 1. DSSM In the original paper
</p>
With the subsequent optimization of the DSSM model to simplify the structure \[[3](#References)]，the model becomes：
<p align="center">
<img src="./images/dssm2.png" width="600"/><br/><br/>
Figure 2. DSSM generic structure
</p>
The blank box in the figure can be replaced by any model, such as fully connected FC, convoluted CNN, RNN, etc. The structure is designed to measure the semantic distance between two elements (such as strings).
In practice，DSSM model serves as a basic building block, with different loss functions to achieve specific functions, such as
- In ranking system, the pairwise rank loss function.
- In the CTR estimate, instead of the binary classification on the click, use cross-entropy loss for a classification model
- In regression model, the cosine similarity is used to calculate the similarity
## Model Implementation
At a high level, DSSM model is composed of three components: the left and right DNN, and loss function on top of them. In complex tasks, the structure of the left DNN and the light DNN can be different. In this example, we keep these two DNN structures the same. And we choose any of FC, CNN, and RNN for the DNN architecture.
In PaddlePaddle, the loss functions are supported for any of classification, regression, and ranking. Among them, the distance between the left and right DNN is calculated by the cosine similarity. In the classification task, the predicted distribution is calculated by softmax.
Here we demonstrate:
- How CNN, FC do text information extraction can refer to [text classification](https://github.com/PaddlePaddle/models/blob/develop/text_classification/README.md#模型详解)
- The contents of the RNN / GRU can be found in [Machine Translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md#gated-recurrent-unit-gru)
- For Pairwise Rank learning, please refer to [learn to rank](https://github.com/PaddlePaddle/models/blob/develop/ltr/README.md)
Figure 3 shows the general architecture for both regression and classification models.
<p align="center">
<img src="./images/dssm3.jpg"/><br/><br/>
Figure 3. DSSM for REGRESSION or CLASSIFICATION
</p>
The structure of the Pairwise Rank is more complex, as shown in Figure 4.
<p align="center">
<img src="./images/dssm2.jpg"/><br/><br/>
图 4. DSSM for Pairwise Rank
</p>
In below, we describe how to train DSSM model in PaddlePaddle. All the codes are included in `./network_conf.py`.
### Create a word vector table for the text
```python
def create_embedding(self, input, prefix=''):
"""
Create word embedding. The `prefix` is added in front of the name of
embedding"s learnable parameter.
"""
logger.info("Create embedding table [%s] whose dimention is %d" %
(prefix, self.dnn_dims[0]))
emb = paddle.layer.embedding(
input=input,
size=self.dnn_dims[0],
param_attr=ParamAttr(name='%s_emb.w' % prefix))
return emb
```
Since the input (embedding table) is a list of the IDs of the words corresponding to a sentence, the word vector table outputs the sequence of word vectors.
### CNN implementation
```python
def create_cnn(self, emb, prefix=''):
"""
A multi-layer CNN.
:param emb: The word embedding.
:type emb: paddle.layer
:param prefix: The prefix will be added to of layers' names.
:type prefix: str
"""
def create_conv(context_len, hidden_size, prefix):
key = "%s_%d_%d" % (prefix, context_len, hidden_size)
conv = paddle.networks.sequence_conv_pool(
input=emb,
context_len=context_len,
hidden_size=hidden_size,
# set parameter attr for parameter sharing
context_proj_param_attr=ParamAttr(name=key + "contex_proj.w"),
fc_param_attr=ParamAttr(name=key + "_fc.w"),
fc_bias_attr=ParamAttr(name=key + "_fc.b"),
pool_bias_attr=ParamAttr(name=key + "_pool.b"))
return conv
conv_3 = create_conv(3, self.dnn_dims[1], "cnn")
conv_4 = create_conv(4, self.dnn_dims[1], "cnn")
return conv_3, conv_4
```
CNN accepts the word sequence of the embedding table, then process the data by convolution and pooling, and finally outputs a semantic vector.
### RNN implementation
RNN is suitable for learning variable length of the information
```python
def create_rnn(self, emb, prefix=''):
"""
A GRU sentence vector learner.
"""
gru = paddle.networks.simple_gru(
input=emb,
size=self.dnn_dims[1],
mixed_param_attr=ParamAttr(name='%s_gru_mixed.w' % prefix),
mixed_bias_param_attr=ParamAttr(name="%s_gru_mixed.b" % prefix),
gru_param_attr=ParamAttr(name='%s_gru.w' % prefix),
gru_bias_attr=ParamAttr(name="%s_gru.b" % prefix))
sent_vec = paddle.layer.last_seq(gru)
return sent_vec
```
### FC implementation
```python
def create_fc(self, emb, prefix=''):
"""
A multi-layer fully connected neural networks.
:param emb: The output of the embedding layer
:type emb: paddle.layer
:param prefix: A prefix will be added to the layers' names.
:type prefix: str
"""
_input_layer = paddle.layer.pooling(
input=emb, pooling_type=paddle.pooling.Max())
fc = paddle.layer.fc(
input=_input_layer,
size=self.dnn_dims[1],
param_attr=ParamAttr(name='%s_fc.w' % prefix),
bias_attr=ParamAttr(name="%s_fc.b" % prefix))
return fc
```
In the construction of FC, we use `paddle.layer.pooling` for the maximum pooling operation on the word vector sequence. Then we transform the sequence into a fixed dimensional vector.
### Multi-layer DNN implementation
```python
def create_dnn(self, sent_vec, prefix):
if len(self.dnn_dims) > 1:
_input_layer = sent_vec
for id, dim in enumerate(self.dnn_dims[1:]):
name = "%s_fc_%d_%d" % (prefix, id, dim)
fc = paddle.layer.fc(
input=_input_layer,
size=dim,
act=paddle.activation.Tanh(),
param_attr=ParamAttr(name='%s.w' % name),
bias_attr=ParamAttr(name='%s.b' % name),
)
_input_layer = fc
return _input_layer
```
### Classification / Regression
The structure of classification and regression is similar. Below function can be used for both tasks.
Please check the function `_build_classification_or_regression_model` in [network_conf.py]( https://github.com/PaddlePaddle/models/blob/develop/dssm/network_conf.py) for detail implementation.
### Pairwise Rank
Please check the function `_build_rank_model` in [network_conf.py]( https://github.com/PaddlePaddle/models/blob/develop/dssm/network_conf.py) for implementation.
## Data Format
Below is a simple example for the data in `./data`
### Regression data format
```
# 3 fields each line:
# - source's word ids
# - target's word ids
# - target
<ids> \t <ids> \t <float>
```
The example of this format is as follows.
```
3 6 10 \t 6 8 33 \t 0.7
6 0 \t 6 9 330 \t 0.03
```
### Classification data format
```
# 3 fields each line:
# - source's word ids
# - target's word ids
# - target
<ids> \t <ids> \t <label>
```
The example of this format is as follows.
```
3 6 10 \t 6 8 33 \t 0
6 10 \t 8 3 1 \t 1
```
### Ranking data format
```
# 4 fields each line:
# - source's word ids
# - target1's word ids
# - target2's word ids
# - label
<ids> \t <ids> \t <ids> \t <label>
```
The example of this format is as follows.
```
7 2 4 \t 2 10 12 \t 9 2 7 10 23 \t 0
7 2 4 \t 10 12 \t 9 2 21 23 \t 1
```
## Training
We use `python train.py -y 0 --model_arch 0` with the data in `./data/classification` to train a DSSM model for classification. The paremeters to execute the script `train.py` can be found by execution `python infer.py --help`. Some important parameters are：
- `train_data_path` Training data path
- `test_data_path` Test data path, optional
- `source_dic_path` Source dictionary path
- `target_dic_path` 目Target dictionary path
- `model_type` The type of loss function of the model: classification 0, sort 1, regression 2
- `model_arch` Model structure: FC 0，CNN 1, RNN 2
- `dnn_dims` The dimension of each layer of the model is set, the default is `256,128,64,32`，with 4 layers.
## To predict using the trained model
The paremeters to execute the script `infer.py` can be found by execution `python infer.py --help`. Some important parameters are：
- `data_path` Path for the data to predict
- `prediction_output_path` Prediction output path
## References
1. Huang P S, He X, Gao J, et al. Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013: 2333-2338.
2. [Microsoft Learning to Rank Datasets](https://www.microsoft.com/en-us/research/project/mslr/)
3. [Gao J, He X, Deng L. Deep Learning for Web Search and Natural Language Processing[J]. Microsoft Research Technical Report, 2015.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/wsdm2015.v3.pdf)
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# 使用循环神经网语言模型生成文本
语言模型(Language Model)是一个概率分布模型，简单来说，就是用来计算一个句子的概率的模型。利用它可以确定哪个词序列的可能性更大，或者给定若干个词，可以预测下一个最可能出现的词。语言模型是自然语言处理领域里一个重要的基础模型。
## 应用场景
**语言模型被应用在很多领域**，如：
* **自动写作**：语言模型可以根据上文生成下一个词，递归下去可以生成整个句子、段落、篇章。
* **QA**：语言模型可以根据Question生成Answer。
* **机器翻译**：当前主流的机器翻译模型大多基于Encoder-Decoder模式，其中Decoder就是一个待条件的语言模型，用来生成目标语言。
* **拼写检查**：语言模型可以计算出词序列的概率，一般在拼写错误处序列的概率会骤减，可以用来识别拼写错误并提供改正候选集。
* **词性标注、句法分析、语音识别......**
## 关于本例
本例实现基于RNN的语言模型，以及利用语言模型生成文本，本例的目录结构如下：
```text
.
├── data
│ └── train_data_examples.txt # 示例数据，可参考示例数据的格式，提供自己的数据
├── config.py # 配置文件，包括data、train、infer相关配置
├── generate.py # 预测任务脚本，即生成文本
├── beam_search.py # beam search 算法实现
├── network_conf.py # 本例中涉及的各种网络结构均定义在此文件中，希望进一步修改模型结构，请修改此文件
├── reader.py # 读取数据接口
├── README.md
├── train.py # 训练任务脚本
└── utils.py # 定义通用的函数，例如：构建字典、加载字典等
```
## RNN 语言模型
### 简介
RNN是一个序列模型，基本思路是：在时刻$t$，将前一时刻$t-1$的隐藏层输出和$t$时刻的词向量一起输入到隐藏层从而得到时刻$t$的特征表示，然后用这个特征表示得到$t$时刻的预测输出，如此在时间维上递归下去。可以看出RNN善于使用上文信息、历史知识，具有“记忆”功能。理论上RNN能实现“长依赖”（即利用很久之前的知识），但在实际应用中发现效果并不理想，研究提出了LSTM和GRU等变种，通过引入门机制对传统RNN的记忆单元进行了改进，弥补了传统RNN在学习长序列时遇到的难题。本例模型使用了LSTM或GRU，可通过配置进行修改。下图是RNN（广义上包含了LSTM、GRU等）语言模型“循环”思想的示意图：
<p align=center><img src='images/rnn.png' width='500px'/></p>
### 模型实现
本例中RNN语言模型的实现简介如下：
- **定义模型参数**：`config.py`中定义了模型的参数变量。
- **定义模型结构**：`network_conf.py`中的`rnn_lm`**函数**中定义了模型的**结构**，如下：
- 输入层：将输入的词（或字）序列映射成向量，即词向量层： `embedding`。
- 中间层：根据配置实现RNN层，将上一步得到的`embedding`向量序列作为输入。
- 输出层：使用`softmax`归一化计算单词的概率。
- loss：定义多类交叉熵作为模型的损失函数。
- **训练模型**：`train.py`中的`main`方法实现了模型的训练，实现流程如下：
- 准备输入数据：建立并保存词典、构建train和test数据的reader。
- 初始化模型：包括模型的结构、参数。
- 构建训练器：demo中使用的是Adam优化算法。
- 定义回调函数：构建`event_handler`来跟踪训练过程中loss的变化，并在每轮训练结束时保存模型的参数。
- 训练：使用trainer训练模型。
- **生成文本**：`generate.py` 实现了文本的生成，实现流程如下：
- 加载训练好的模型和词典文件。
- 读取`gen_file`文件，每行是一个句子的前缀，用[柱搜索算法(Beam Search)](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.cn.md#柱搜索算法)根据前缀生成文本。
- 将生成的文本及其前缀保存到文件`gen_result`。
## 使用说明
运行本例的方法如下：
* 1，运行`python train.py`命令，开始train模型（默认使用RNN），待训练结束。
* 2，运行`python generate.py`运行文本生成。（输入的文本默认为`data/train_data_examples.txt`，生成的文本默认保存到`data/gen_result.txt`中。）
**如果需要使用自己的语料、定制模型，需要修改`config.py`中的配置，细节和适配工作详情如下：**
### 语料适配
* 清洗语料：去除原文中空格、tab、乱码，按需去除数字、标点符号、特殊符号等。
* 内容格式：每个句子占一行；每行中的各词之间使用一个空格符分开。
* 按需要配置`config.py`中的如下参数：
```python
train_file = "data/train_data_examples.txt"
test_file = ""
vocab_file = "data/word_vocab.txt"
model_save_dir = "models"
```
1. `train_file`：指定训练数据的路径，**需要预先分词**。
2. `test_file`：指定测试数据的路径，如果训练数据不为空，将在每个 `pass` 训练结束对指定的测试数据进行测试。
3. `vocab_file`：指定字典的路径，如果字典文件不存在，将会对训练语料进行词频统计，构建字典。
4. `model_save_dir`：指定模型保存的路径，如果指定的文件夹不存在，将会自动创建。
### 构建字典的策略
- 当指定的字典文件不存在时，将对训练数据进行词频统计，自动构建字典`config.py` 中有如下两个参数与构建字典有关：
```python
max_word_num = 51200 - 2
cutoff_word_fre = 0
```
1. `max_word_num`：指定字典中含有多少个词。
2. `cutoff_word_fre`：字典中词语在训练语料中出现的最低频率。
- 加入指定了 `max_word_num = 5000`，并且 `cutoff_word_fre = 10`，词频统计发现训练语料中出现频率高于10次的词语仅有3000个，那么最终会取3000个词构成词典。
- 构建词典时，会自动加入两个特殊符号：
1. `<unk>`：不出现在字典中的词
2. `<e>`：句子的结束符
*注：需要注意的是，词典越大生成的内容越丰富，但训练耗时越久。一般中文分词之后，语料中不同的词能有几万乃至几十万，如果`max_word_num`取值过小则导致`<unk>`占比过高，如果`max_word_num`取值较大，则严重影响训练速度（对精度也有影响）。所以，也有“按字”训练模型的方式，即：把每个汉字当做一个词，常用汉字也就几千个，使得字典的大小不会太大、不会丢失太多信息，但汉语中同一个字在不同词中语义相差很大，有时导致模型效果不理想。建议多试试、根据实际情况选择是“按词训练”还是“按字训练”。*
### 模型适配、训练
* 按需调整`config.py`中如下配置，来修改 rnn 语言模型的网络结果：
```python
rnn_type = "lstm" # "gru" or "lstm"
emb_dim = 256
hidden_size = 256
stacked_rnn_num = 2
```
1. `rnn_type`：支持 ”gru“ 或者 ”lstm“ 两种参数，选择使用何种 RNN 单元。
2. `emb_dim`：设置词向量的维度。
3. `hidden_size`：设置 RNN 单元隐层大小。
4. `stacked_rnn_num`：设置堆叠 RNN 单元的个数，构成一个更深的模型。
* 运行`python train.py`命令训练模型，模型将被保存到`model_save_dir`指定的目录。
### 按需生成文本
* 按需调整`config.py`中以下变量，详解如下：
```python
gen_file = "data/train_data_examples.txt"
gen_result = "data/gen_result.txt"
max_gen_len = 25 # the max number of words to generate
beam_size = 5
model_path = "models/rnn_lm_pass_00000.tar.gz"
```
1. `gen_file`：指定输入数据文件，每行是一个句子的前缀，**需要预先分词**。
2. `gen_result`：指定输出文件路径，生成结果将写入此文件。
3. `max_gen_len`：指定每一句生成的话最长长度，如果模型无法生成出`<e>`，当生成 `max_gen_len` 个词语后，生成过程会自动终止。
4. `beam_size`：Beam Search 算法每一步的展开宽度。
5. `model_path`：指定训练好的模型的路径。
其中，`gen_file` 中保存的是待生成的文本前缀，每个前缀占一行，形如：
```text
若隐若现 地像 幽灵 , 像 死神
```
将需要生成的文本前缀按此格式存入文件即可；
* 运行`python generate.py`命令运行beam search 算法为输入前缀生成文本，下面是模型生成的结果：
```text
81 若隐若现 地像 幽灵 , 像 死神
-12.2542 一样 。 他 是 个 怪物 <e>
-12.6889 一样 。 他 是 个 英雄 <e>
-13.9877 一样 。 他 是 我 的 敌人 <e>
-14.2741 一样 。 他 是 我 的 <e>
-14.6250 一样 。 他 是 我 的 朋友 <e>
```
其中：
1. 第一行 `81 若隐若现 地像 幽灵 , 像 死神`以`\t`为分隔，共有两列：
- 第一列是输入前缀在训练样本集中的序号。
- 第二列是输入的前缀。
2. 第二 ~ `beam_size + 1` 行是生成结果，同样以 `\t` 分隔为两列：
- 第一列是该生成序列的对数概率（log probability）。
- 第二列是生成的文本序列，正常的生成结果会以符号`<e>`结尾，如果没有以`<e>`结尾，意味着超过了最大序列长度，生成强制终止。
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# Globally Normalized Reader
This model implements the work in the following paper:
Jonathan Raiman and John Miller. Globally Normalized Reader. Empirical Methods in Natural Language Processing (EMNLP), 2017.
If you use the dataset/code in your research, please cite the above paper:
```text
@inproceedings{raiman2015gnr,
author={Raiman, Jonathan and Miller, John},
booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
title={Globally Normalized Reader},
year={2017},
}
```
You can also visit https://github.com/baidu-research/GloballyNormalizedReader to get more information.
# Installation
1. Please use [docker image](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html) to install the latest PaddlePaddle, by running:
```bash
docker pull paddledev/paddle
```
2. Download all necessary data by running:
```bash
cd data && ./download.sh && cd ..
```
3. Preprocess and featurizer data:
```bash
python featurize.py --datadir data --outdir data/featurized --glove-path data/glove.840B.300d.txt
```
# Training a Model
- Configurate the model by modifying `config.py` if needed, and then run:
```bash
python train.py 2>&1 | tee train.log
```
# Inferring by a Trained Model
- Infer by a trained model by running:
```bash
python infer.py \
--model_path models/pass_00000.tar.gz \
--data_dir data/featurized/ \
--batch_size 2 \
--use_gpu 0 \
--trainer_count 1 \
2>&1 | tee infer.log
```
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# Hsigmoid加速词向量训练
## 背景介绍
在自然语言处理领域中，传统做法通常使用one-hot向量来表示词，比如词典为['我', '你', '喜欢']，可以用[1,0,0]、[0,1,0]和[0,0,1]这三个向量分别表示'我'、'你'和'喜欢'。这种表示方式比较简洁，但是当词表很大时，容易产生维度爆炸问题；而且任意两个词的向量是正交的，向量包含的信息有限。为了避免或减轻one-hot表示的缺点，目前通常使用词向量来取代one-hot表示，词向量也就是word embedding，即使用一个低维稠密的实向量取代高维稀疏的one-hot向量。训练词向量的方法有很多种，神经网络模型是其中之一，包括CBOW、Skip-gram等，这些模型本质上都是一个分类模型，当词表较大即类别较多时，传统的softmax将非常消耗时间。PaddlePaddle提供了Hsigmoid Layer、NCE Layer，来加速模型的训练过程。本文主要介绍如何使用Hsigmoid Layer来加速训练，词向量相关内容请查阅PaddlePaddle Book中的[词向量章节](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)。
## Hsigmoid Layer
Hsigmoid Layer引用自论文\[[1](#参考文献)\]，Hsigmoid指Hierarchical-sigmoid，原理是通过构建一个分类二叉树来降低计算复杂度，二叉树中每个叶子节点代表一个类别，每个非叶子节点代表一个二类别分类器。例如我们一共有4个类别分别是0、1、2、3，softmax会分别计算4个类别的得分，然后归一化得到概率。当类别数很多时，计算每个类别的概率非常耗时，Hsigmoid Layer会根据类别数构建一个平衡二叉树，如下：
<p align="center">
<img src="images/binary_tree.png" width="220" hspace='10'/> <img src="images/path_to_1.png" width="220" hspace='10'/> <br/>
图1. （a）为平衡二叉树，（b）为根节点到类别1的路径
</p>
二叉树中每个非叶子节点是一个二类别分类器（sigmoid），如果类别是0，则取左子节点继续分类判断，反之取右子节点，直至达到叶节点。按照这种方式，每个类别均对应一条路径，例如从root到类别1的路径编码为0、1。训练阶段我们按照真实类别对应的路径，依次计算对应分类器的损失，然后综合所有损失得到最终损失。预测阶段，模型会输出各个非叶节点分类器的概率，我们可以根据概率获取路径编码，然后遍历路径编码就可以得到最终预测类别。传统softmax的计算复杂度为N（N为词典大小），Hsigmoid可以将复杂度降至log(N)，详细理论细节可参照论文\[[1](#参考文献)\]。
## 数据准备
### PTB数据
本文采用Penn Treebank (PTB)数据集（[Tomas Mikolov预处理版本](http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz)），共包含train、valid和test三个文件。其中使用train作为训练数据，valid作为测试数据。本文训练的是5-gram模型，即用每条数据的前4个词来预测第5个词。PaddlePaddle提供了对应PTB数据集的python包[paddle.dataset.imikolov](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/dataset/imikolov.py) ，自动做数据的下载与预处理。预处理会把数据集中的每一句话前后加上开始符号\<s>以及结束符号\<e>，然后依据窗口大小（本文为5），从头到尾每次向右滑动窗口并生成一条数据。如"I have a dream that one day"可以生成\<s> I have a dream、I have a dream that、have a dream that one、a dream that one day、dream that one day \<e>，PaddlePaddle会把词转换成id数据作为预处理的输出。
### 自定义数据
用户可以使用自己的数据集训练模型，自定义数据集最关键的地方是实现reader接口做数据处理，reader需要产生一个迭代器，迭代器负责解析文件中的每一行数据，返回一个python list，例如[1, 2, 3, 4, 5]，分别是第一个到第四个词在字典中的id，PaddlePaddle会进一步将该list转化成`paddle.data_type.inter_value`类型作为data layer的输入，一个封装样例如下：
```python
def reader_creator(filename, word_dict, n):
def reader():
with open(filename) as f:
UNK = word_dict['<unk>']
for l in f:
l = ['<s>'] + l.strip().split() + ['<e>']
if len(l) >= n:
l = [word_dict.get(w, UNK) for w in l]
for i in range(n, len(l) + 1):
yield tuple(l[i - n:i])
return reader
def train_data(filename, word_dict, n):
"""
Reader interface for training data.
It returns a reader creator, each sample in the reader is a word ID tuple.
:param filename: path of data file
:type filename: str
:param word_dict: word dictionary
:type word_dict: dict
:param n: sliding window size
:type n: int
"""
return reader_creator(filename, word_dict, n)
```
## 网络结构
本文通过训练N-gram语言模型来获得词向量，具体地使用前4个词来预测当前词。网络输入为词在字典中的id，然后查询词向量词表获取词向量，接着拼接4个词的词向量，然后接入一个全连接隐层，最后是`Hsigmoid`层。详细网络结构见图2：
<p align="center">
<img src="images/network_conf.png" width = "70%" align="center"/><br/>
图2. 网络配置结构
</p>
代码如下：
```python
def ngram_lm(hidden_size, embed_size, dict_size, gram_num=4, is_train=True):
emb_layers = []
embed_param_attr = paddle.attr.Param(
name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0)
for i in range(gram_num):
word = paddle.layer.data(
name="__word%02d__" % (i),
type=paddle.data_type.integer_value(dict_size))
emb_layers.append(
paddle.layer.embedding(
input=word, size=embed_size, param_attr=embed_param_attr))
target_word = paddle.layer.data(
name="__target_word__", type=paddle.data_type.integer_value(dict_size))
embed_context = paddle.layer.concat(input=emb_layers)
hidden_layer = paddle.layer.fc(
input=embed_context,
size=hidden_size,
act=paddle.activation.Sigmoid(),
layer_attr=paddle.attr.Extra(drop_rate=0.5),
bias_attr=paddle.attr.Param(learning_rate=2),
param_attr=paddle.attr.Param(
initial_std=1. / math.sqrt(embed_size * 8), learning_rate=1))
return paddle.layer.hsigmoid(
input=hidden_layer,
label=target_word,
param_attr=paddle.attr.Param(name="sigmoid_w"),
bias_attr=paddle.attr.Param(name="sigmoid_b"))
```
需要注意在 PaddlePaddle 中，hsigmoid 层将可学习参数存储为一个 `[类别数目 - 1 × 隐层向量宽度]` 大小的矩阵。预测时，需要将 hsigmoid 层替换为全连接运算**并固定以 `sigmoid` 为激活**。预测时输出一个宽度为`[batch_size x 类别数目 - 1]` 维度的矩阵（`batch_size = 1`时退化为一个向量）。矩阵行向量的每一维计算了一个输入向量属于一个内部结点的右孩子的概率。**全连接运算在加载 hsigmoid 层学习到的参数矩阵时，需要对参数矩阵进行一次转置**。代码片段如下：
```python
return paddle.layer.mixed(
size=dict_size - 1,
input=paddle.layer.trans_full_matrix_projection(
hidden_layer, param_attr=paddle.attr.Param(name="sigmoid_w")),
act=paddle.activation.Sigmoid(),
bias_attr=paddle.attr.Param(name="sigmoid_b"))
```
上述代码片段中的 `paddle.layer.mixed` 必须以 PaddlePaddle 中 `paddle.layer.×_projection` 为输入。`paddle.layer.mixed` 将多个 `projection` （输入可以是多个）计算结果求和作为输出。`paddle.layer.trans_full_matrix_projection` 在计算矩阵乘法时会对参数$W$进行转置。
## 训练阶段
训练比较简单，直接运行``` python train.py ```。程序第一次运行会检测用户缓存文件夹中是否包含imikolov数据集，如果未包含，则自动下载。运行过程中，每100个iteration会打印模型训练信息，主要包含训练损失和测试损失，每个pass会保存一次模型。
## 预测阶段
在命令行运行 :
```bash
python infer.py \
--model_path "models/XX" \
--batch_size 1 \
--use_gpu false \
--trainer_count 1
```
参数含义如下：
- `model_path`：指定训练好的模型所在的路径。必选。
- `batch_size`：一次预测并行的样本数目。可选，默认值为 `1`。
- `use_gpu`：是否使用 GPU 进行预测。可选，默认值为 `False`。
- `trainer_count` : 预测使用的线程数目。可选，默认为 `1`。**注意：预测使用的线程数目必选大于一次预测并行的样本数目**。
预测阶段根据多个二分类概率得到编码路径，遍历路径获取最终的预测类别，逻辑如下：
```python
def decode_res(infer_res, dict_size):
"""
Inferring probabilities are orginized as a complete binary tree.
The actual labels are leaves (indices are counted from class number).
This function travels paths decoded from inferring results.
If the probability >0.5 then go to right child, otherwise go to left child.
param infer_res: inferring result
param dict_size: class number
return predict_lbls: actual class
"""
predict_lbls = []
infer_res = infer_res > 0.5
for i, probs in enumerate(infer_res):
idx = 0
result = 1
while idx < len(probs):
result <<= 1
if probs[idx]:
result |= 1
if probs[idx]:
idx = idx * 2 + 2 # right child
else:
idx = idx * 2 + 1 # left child
predict_lbl = result - dict_size
predict_lbls.append(predict_lbl)
return predict_lbls
```
预测程序的输入数据格式与训练阶段相同，如`have a dream that one`，程序会根据`have a dream that`生成一组概率，通过对概率解码生成预测词，`one`作为真实词，方便评估。解码函数的输入是一个batch样本的预测概率以及词表的大小，里面的循环是对每条样本的输出概率进行解码，解码方式就是按照左0右1的准则，不断遍历路径，直至到达叶子节点。
## 参考文献
1. Morin, F., & Bengio, Y. (2005, January). [Hierarchical Probabilistic Neural Network Language Model](http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf). In Aistats (Vol. 5, pp. 246-252).
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图像分类
=======================
这里将介绍如何在PaddlePaddle下使用AlexNet、VGG、GoogLeNet和ResNet模型进行图像分类。图像分类问题的描述和这四种模型的介绍可以参考[PaddlePaddle book](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)。
## 训练模型
### 初始化
在初始化阶段需要导入所用的包，并对PaddlePaddle进行初始化。
```python
import gzip
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import reader
import vgg
import resnet
import alexnet
import googlenet
# PaddlePaddle init
paddle.init(use_gpu=False, trainer_count=1)
```
### 定义参数和输入
设置算法参数（如数据维度、类别数目和batch size等参数），定义数据输入层`image`和类别标签`lbl`。
```python
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 102
BATCH_SIZE = 128
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(CLASS_DIM))
```
### 获得所用模型
这里可以选择使用AlexNet、VGG、GoogLeNet和ResNet模型中的一个模型进行图像分类。通过调用相应的方法可以获得网络最后的Softmax层。
1. 使用AlexNet模型
指定输入层`image`和类别数目`CLASS_DIM`后，可以通过下面的代码得到AlexNet的Softmax层。
```python
out = alexnet.alexnet(image, class_dim=CLASS_DIM)
```
2. 使用VGG模型
根据层数的不同，VGG分为VGG13、VGG16和VGG19。使用VGG16模型的代码如下：
```python
out = vgg.vgg16(image, class_dim=CLASS_DIM)
```
类似地，VGG13和VGG19可以分别通过`vgg.vgg13`和`vgg.vgg19`方法获得。
3. 使用GoogLeNet模型
GoogLeNet在训练阶段使用两个辅助的分类器强化梯度信息并进行额外的正则化。因此`googlenet.googlenet`共返回三个Softmax层，如下面的代码所示：
```python
out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM)
loss1 = paddle.layer.cross_entropy_cost(
input=out1, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out1, label=lbl)
loss2 = paddle.layer.cross_entropy_cost(
input=out2, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out2, label=lbl)
extra_layers = [loss1, loss2]
```
对于两个辅助的输出，这里分别对其计算损失函数并评价错误率，然后将损失作为后文SGD的extra_layers。
4. 使用ResNet模型
ResNet模型可以通过下面的代码获取：
```python
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
```
### 定义损失函数
```python
cost = paddle.layer.classification_cost(input=out, label=lbl)
```
### 创建参数和优化方法
```python
# Create parameters
parameters = paddle.parameters.create(cost)
# Create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
BATCH_SIZE),
learning_rate=0.001 / BATCH_SIZE,
learning_rate_decay_a=0.1,
learning_rate_decay_b=128000 * 35,
learning_rate_schedule="discexp", )
```
通过 `learning_rate_decay_a` (简写$a$） 、`learning_rate_decay_b` (简写$b$) 和 `learning_rate_schedule` 指定学习率调整策略，这里采用离散指数的方式调节学习率，计算公式如下， $n$ 代表已经处理过的累计总样本数，$lr_{0}$ 即为参数里设置的 `learning_rate`。
$$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
### 定义数据读取
首先以[花卉数据](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html)为例说明如何定义输入。下面的代码定义了花卉数据训练集和验证集的输入：
```python
train_reader = paddle.batch(
paddle.reader.shuffle(
flowers.train(),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
flowers.valid(),
batch_size=BATCH_SIZE)
```
若需要使用其他数据，则需要先建立图像列表文件。`reader.py`定义了这种文件的读取方式，它从图像列表文件中解析出图像路径和类别标签。
图像列表文件是一个文本文件，其中每一行由一个图像路径和类别标签构成，二者以跳格符（Tab）隔开。类别标签用整数表示，其最小值为0。下面给出一个图像列表文件的片段示例：
```
dataset_100/train_images/n03982430_23191.jpeg 1
dataset_100/train_images/n04461696_23653.jpeg 7
dataset_100/train_images/n02441942_3170.jpeg 8
dataset_100/train_images/n03733281_31716.jpeg 2
dataset_100/train_images/n03424325_240.jpeg 0
dataset_100/train_images/n02643566_75.jpeg 8
```
训练时需要分别指定训练集和验证集的图像列表文件。这里假设这两个文件分别为`train.list`和`val.list`，数据读取方式如下：
```python
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.train_reader('train.list'),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
reader.test_reader('val.list'),
batch_size=BATCH_SIZE)
```
### 定义事件处理程序
```python
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if isinstance(event, paddle.event.EndPass):
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=test_reader)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
```
### 定义训练方法
对于AlexNet、VGG和ResNet，可以按下面的代码定义训练方法：
```python
# Create trainer
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=optimizer)
```
GoogLeNet有两个额外的输出层，因此需要指定`extra_layers`，如下所示：
```python
# Create trainer
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=extra_layers)
```
### 开始训练
```python
trainer.train(
reader=train_reader, num_passes=200, event_handler=event_handler)
```
## 应用模型
模型训练好后，可以使用下面的代码预测给定图片的类别。
```python
# load parameters
with gzip.open('params_pass_10.tar.gz', 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
file_list = [line.strip() for line in open(image_list_file)]
test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
.flatten().astype('float32'), )
for image_file in file_list]
probs = paddle.infer(
output_layer=out, parameters=parameters, input=test_data)
lab = np.argsort(-probs)
for file_name, result in zip(file_list, lab):
print "Label of %s is: %d" % (file_name, result[0])
```
首先从文件中加载训练好的模型（代码里以第10轮迭代的结果为例），然后读取`image_list_file`中的图像。`image_list_file`是一个文本文件，每一行为一个图像路径。代码使用`paddle.infer`判断`image_list_file`中每个图像的类别，并进行输出。
## 使用预训练模型
为方便进行测试和fine-tuning，我们提供了一些对应于示例中模型配置的预训练模型，目前包括在ImageNet 1000类上训练的ResNet50、ResNet101和Vgg16，请使用`models`目录下的脚本`model_download.sh`进行模型下载，如下载ResNet50可进入`models`目录并执行"`sh model_download.sh ResNet50`"，完成后同目录下的`Paddle_ResNet50.tar.gz`即是训练好的模型，可以在代码中使用如下两种方式进行加载模：
```python
parameters = paddle.parameters.Parameters.from_tar(gzip.open('Paddle_ResNet50.tar.gz', 'r'))
```
```python
parameters = paddle.parameters.create(cost)
parameters.init_from_tar(gzip.open('Paddle_ResNet50.tar.gz', 'r'))
```
### 注意事项
模型压缩包中所含各文件的文件名和模型配置中的参数名一一对应，是加载模型参数的依据。我们提供的预训练模型均使用了示例代码中的配置，如需修改网络配置，请多加注意，需要保证网络配置中的参数名和压缩包中的文件名能够正确对应。
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# 排序学习(Learning To Rank)
排序学习技术\[[1](#参考文献1)\]是构建排序模型的机器学习方法，在信息检索、自然语言处理，数据挖掘等机器学场景中具有重要作用。排序学习的主要目的是对给定一组文档，对任意查询请求给出反映相关性的文档排序。在本例子中，利用标注过的语料库训练两种经典排序模型RankNet[[4](#参考文献4)\]和LamdaRank[[6](#参考文献6)\]，分别可以生成对应的排序模型，能够对任意查询请求，给出相关性文档排序。
RankNet模型在命令行输入：
```bash
bash ./run_ranknet.sh
```
LambdaRank模型在命令行输入：
```bash
bash ./run_lambdarank.sh
```
用户只需要使用以上命令就完成排序模型的训练和预测，程序会自动下载内置数据集，无需手动下载。
## 背景介绍
排序学习技术随着互联网的快速增长而受到越来越多关注，是机器学习中的常见任务之一。一方面人工排序规则不能处理海量规模的候选数据，另一方面无法为不同渠道的候选数据给于合适的权重，因此排序学习在日常生活中应用非常广泛。排序学习起源于信息检索领域，目前仍然是许多信息检索场景中的核心模块，例如搜索引擎搜索结果排序，推荐系统候选集排序，在线广告排序等等。本例以文档检索任务阐述排序学习模型。
<p align="center">
<img src="images/search_engine_example.png" width="30%" ><br/>
图1. 排序模型在文档检索的典型应用搜索引擎中的作用
</p>
假定有一组文档$S$，文档检索任务是依据和请求的相关性，给出文档排列顺序。查询引擎根据查询请求，排序模型会给每个文档打出分数，依据打分情况倒序排列文档，得到查询结果。在训练模型时，给定一条查询，并给出对应的文档最佳排序和得分。在预测时候，给出查询请求，排序模型生成文档排序。常见的排序学习方法划分为以下三类：
- Pointwise 方法
Pointwise方法是通过近似为回归问题解决排序问题，输入的单条样本为**得分-文档**，将每个查询-文档对的相关性得分作为实数分数或者序数分数，使得单个查询-文档对作为样本点(Pointwise的由来)，训练排序模型。预测时候对于指定输入，给出查询-文档对的相关性得分。
- Pairwise方法
Pairwise方法是通过近似为分类问题解决排序问题，输入的单条样本为**标签-文档对**。对于一次查询的多个结果文档，组合任意两个文档形成文档对作为输入样本。即学习一个二分类器，对输入的一对文档对AB（Pairwise的由来），根据A相关性是否比B好，二分类器给出分类标签1或0。对所有文档对进行分类，就可以得到一组偏序关系，从而构造文档全集的排序关系。该类方法的原理是对给定的文档全集$S$，降低排序中的逆序文档对的个数来降低排序错误，从而达到优化排序结果的目的。
- Listwise方法
Listwise方法是直接优化排序列表，输入为单条样本为一个**文档排列**。通过构造合适的度量函数衡量当前文档排序和最优排序差值，优化度量函数得到排序模型。由于度量函数很多具有非连续性的性质，优化困难。
<p align="center">
<img src="images/learning_to_rank.jpg" width="50%" ><br/>
图2. 排序模型三类方法
</p>
## 实验数据
本例中的实验数据采用了排序学习中的基准数据[LETOR]([http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar](http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar))语料库，部分来自于Gov2网站的查询请求结果，包含了约1700条查询请求结果文档列表，并对文档相关性做出了人工标注。其中，一条查询含有唯一的查询id，对应于多个具有相关性的文档，构成了一次查询请求结果文档列表。每个文档由一个一维数组的特征向量表示，并对应一个人工标注与查询的相关性分数。
本例在第一次运行的时会自动下载LETOR MQ2007数据集并缓存，无需手动下载。
`mq2007`数据集分别提供了三种类型排序模型的生成格式，需要指定生成格式`format`
例如调用接口
```python
pairwise_train_dataset = functools.partial(paddle.dataset.mq2007.train, format="pairwise")
for label, left_doc, right_doc in pairwise_train_dataset():
...
```
## 模型概览
对于排序模型，本例中提供了Pairwise方法的模型RankNet和Listwise方法的模型LambdaRank，分别代表了两类学习方法。Pointwise方法的排序模型退化为回归问题，请参考PaddleBook中[推荐系统](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)一课。
## RankNet排序模型
[RankNet](http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)是一种经典的Pairwise的排序学习方法，是典型的前向神经网络排序模型。在文档集合$S$中的第$i$个文档记做$U_i$，它的文档特征向量记做$x_i$，对于给定的一个文档对$U_i$, $U_j$，RankNet将输入的单个文档特征向量$x$映射到$f(x)$，得到$s_i=f(x_i)$, $s_j=f(x_j)$。将$U_i$相关性比$U_j$好的概率记做$P_{i,j}$，则
$$P_{i,j}=P(U_{i}>U_{j})=\frac{1}{1+e^{-\sigma (s_{i}-s_{j}))}}$$
由于排序度量函数大多数非连续，非光滑，因此RankNet需要一个可以优化的度量函数$C$。首先使用交叉熵作为度量函数衡量预测代价，将损失函数$C$记做
$$C_{i,j}=-\bar{P_{i,j}}logP_{i,j}-(1-\bar{P_{i,j}})log(1-P_{i,j})$$
其中$\bar{P_{i,j}}$代表真实概率，记做
$$\bar{P_{i,j}}=\frac{1}{2}(1+S_{i,j})$$
而$S_{i,j}$ = {+1,0}，表示$U_i$和$U_j$组成的Pair的标签，即Ui相关性是否好于$U_j$。
最终得到了可求导的度量损失函数
$$C=\frac{1}{2}(1-S_{i,j})\sigma (s_{i}-s{j})+log(1+e^{-\sigma (s_{i}-s_{j})})$$
可以使用常规的梯度下降方法进行优化。细节见[RankNet](http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
同时，得到文档$U_i$在排序优化过程的梯度信息为
$$\lambda _{i,j}=\frac{\partial C}{\partial s_{i}} = \frac{1}{2}(1-S_{i,j})-\frac{1}{1+e^{\sigma (s_{i}-s_{j})}}$$
表示的含义是本轮排序优化过程中文档$U_i$的上升或者下降量。
根据以上推论构造RankNet网络结构，由若干层隐藏层和全连接层构成，如图所示，将文档特征使用隐藏层，全连接层逐层变换，完成了底层特征空间到高层特征空间的变换。其中docA和docB结构对称，分别输入到最终的RankCost层中。
<p align="center">
<img src="images/ranknet.jpg" width="50%" ><br/>
图3. RankNet网络结构示意图
</p>
- 全连接层(fully connected layer) : 指上一层中的每个节点都连接到下层网络。本例子中同样使用`paddle.layer.fc`实现，注意输入到RankCost层的全连接层维度为1。
- RankCost层： RankCost层是排序网络RankNet的核心，度量docA相关性是否比docB好，给出预测值并和label比较。使用了交叉熵(cross enctropy)作为度量损失函数，使用梯度下降方法进行优化。细节可见[RankNet](http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)[4]。
由于Pairwise中的网络结构是左右对称，可定义一半网络结构，另一半共享网络参数。在PaddlePaddle中允许网络结构中共享连接，具有相同名字的参数将会共享参数。使用PaddlePaddle实现RankNet排序模型，定义网络结构的示例代码如下：
```python
import paddle.v2 as paddle
def half_ranknet(name_prefix, input_dim):
"""
parameter with a same name will be shared in PaddlePaddle framework,
these parameters in ranknet can be used in shared state, e.g. left network and right network in detail
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/api.md
"""
# data layer
data = paddle.layer.data(name_prefix+"/data", paddle.data_type.dense_vector(input_dim))
# fully connect layer
hd1 = paddle.layer.fc(
input=data,
size=10,
act=paddle.activation.Tanh(),
param_attr=paddle.attr.Param(initial_std=0.01, name="hidden_w1"))
# fully connected layer/ output layer
output = paddle.layer.fc(
input=hd1,
size=1,
act=paddle.activation.Linear(),
param_attr=paddle.attr.Param(initial_std=0.01, name="output"))
return output
def ranknet(input_dim):
# label layer
label = paddle.layer.data("label", paddle.data_type.integer_value(1))
# reuse the parameter in half_ranknet
output_left = half_ranknet("left", input_dim)
output_right = half_ranknet("right", input_dim)
# rankcost layer
cost = paddle.layer.rank_cost(name="cost", left=output_left, right=output_right, label=label)
return cost
```
上述结构中使用了和图3相同的模型结构：两层隐藏层，分别是`hidden_size=10`的全连接层和`hidden_size=1`的全连接层。本例中的input_dim指输入**单个文档**的特征的维度，label取值为1，0。每条输入样本为`<label>，<docA, docB>`的结构，以docA为例，输入`input_dim`的文档特征，依次变换成10维，1维特征，最终输入到RankCost层中，比较docA和docB在RankCost输出得到预测值。
### RankNet模型训练
RankNet的训练只需要运行命令：
```bash
bash ./run_ranknet.sh
```
将会自动下载数据，训练RankNet模型，并将每个轮次的模型参数存储下来。
### RankNet模型预测
本例提供了rankNet模型的训练和预测两个部分。完成训练后的模型分为拓扑结构(需要注意`rank_cost`不是模型拓扑结构的一部分)和模型参数文件两部分。在本例子中复用了`ranknet`训练时的模型拓扑结构`half_ranknet`，模型参数从外存中加载。模型预测的输入为单个文档的特征向量，模型会给出相关性得分。将预测得分排序即可得到最终的文档相关性排序结果。
## 用户自定义RankNet数据
上述的代码使用了PaddlePaddle内置的排序数据，如果希望使用自定义格式数据，可以参考PaddlePaddle内置的`mq2007`数据集，编写一个新的生成器函数。例如输入数据为如下格式，只包含doc0-doc2三个文档。
\<query_id\> \<relevance_score\> \<feature_vector\>的格式(featureid: feature_value)
```
query_id : 1, relevance_score:1, feature_vector 0:0.1, 1:0.2, 2:0.4 #doc0
query_id : 1, relevance_score:2, feature_vector 0:0.3, 1:0.1, 2:0.4 #doc1
query_id : 1, relevance_score:0, feature_vector 0:0.2, 1:0.4, 2:0.1 #doc2
query_id : 2, relevance_score:0, feature_vector 0:0.1, 1:0.4, 2:0.1 #doc0
.....
```
需要将输入样本转换为Pairwise的输入格式，例如组合生成格式与mq2007 Pairwise格式相同的结构
\<label\> \<docA_feature_vector\>\<docB_feature_vector\>
```
1 doc1 doc0
1 doc1 doc2
1 doc0 doc2
....
```
注意，一般在Pairwise格式的数据中，label=1表示docA和查询的相关性好于docB，事实上label信息隐含在docA和docB组合pair中。如果存在`0 docA docB`，交换顺序构造`1 docB docA`即可。
另外组合所有的pair会有训练数据冗余，因为可以从部分偏序关系恢复文档集上的全序关系。相关研究见[PairWise approach](http://www.machinelearning.org/proceedings/icml2007/papers/139.pdf)[[5](#参考文献5)\]，本例不予赘述。
```python
# a customized data generator
def gen_pairwise_data(text_line_of_data):
"""
return :
------
label : np.array, shape=(1)
docA_feature_vector : np.array, shape=(1, feature_dimension)
docA_feature_vector : np.array, shape=(1, feature_dimension)
"""
return label, docA_feature_vector, docB_feature_vector
```
对应于paddle的输入中，`integer_value`为单个整数，`dense_vector`为实数一维向量，与生成器对应，需要在训练模型之前指明输入数据对应关系。
```python
# Define the input data order
feeding = { "label":0,
"left/data" :1,
"right/data":2}
```
## LambdaRank排序模型
[LambdaRank](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf)\[[6](#参考文献))\]是Listwise的排序方法，是Bugers[6]等人从RankNet发展而来，使用构造lambda函数(LambdaRank名字的由来)的方法优化度量标准NDCG(Normalized Discounted Cumulative Gain)，每个查询后得到的结果文档列表都单独作为一个训练样本。NDCG是信息论中很衡量文档列表排序质量的标准之一，前$K$个文档的NDCG得分记做
$$NDCG@K=Z_{k}\sum (2^{rel_{i}})1/log(k+1)$$
前文中RankNet中推导出，文档排序需要的是排序错误的梯度信息。NDCG度量函数是非光滑，非连续的，不能直接求得梯度信息，因此将|delta(NDCG)|=|NDCG(new) - NDCG(old)|引入，构造lambda函数为
$$\lambda _{i,j}=\frac{\partial C}{\partial s_{i}}=-\frac{\sigma }{1+e^{\sigma (s_{i}-s{j})}}|\Delta NDCG|$$
替换RankNet中的梯度表示，得到的排序模型称为[LambdaRank](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf)
由以上推导可知，LambdaRank网络结构和RankNet结构非常相似。如图所示
<p align="center">
<img src="images/lambdarank.jpg" width="50%" ><br/>
图4. LambdaRank的网络结构示意图
</p>
一个查询得到的结果文档列表作为一条样本输入到网络中，替换RankCost为LambdaCost层，其他结构与RankNet相同。
- LambdaCost层 : LambdaCost层使用NDCG差值作为Lambda函数，score是一个一维的序列，对于单调训练样本全连接层输出的是1x1的序列，二者的序列长度都等于该条查询得到的文档数量。Lambda函数的构造详细见[LambdaRank](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf)
使用PaddlePaddle定义LambdaRank网络结构的示例代码如下：
```python
import paddle.v2 as paddle
def lambda_rank(input_dim):
"""
lambda_rank is a ListWise Rank Model, input data and label must be sequence
https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf
parameters :
input_dim, one document's dense feature vector dimension
dense_vector_sequence format
[[f, ...], [f, ...], ...], f is represent for an float or int number
"""
label = paddle.layer.data("label",
paddle.data_type.dense_vector_sequence(1))
data = paddle.layer.data("data",
paddle.data_type.dense_vector_sequence(input_dim))
# hidden layer
hd1 = paddle.layer.fc(
input=data,
size=10,
act=paddle.activation.Tanh(),
param_attr=paddle.attr.Param(initial_std=0.01))
output = paddle.layer.fc(
input=hd1,
size=1,
act=paddle.activation.Linear(),
param_attr=paddle.attr.Param(initial_std=0.01))
# cost layer
cost = paddle.layer.lambda_cost(
input=output, score=label, NDCG_num=6, max_sort_size=-1)
return cost, output
```
上述结构中使用了和图3相同的模型结构。和RankNet相似，分别使用了`hidden_size=10`和`hidden_size=1`的两个全连接层。本例中的input_dim指输入**单个文档**的特征的维度。每条输入样本为label，\<docA, docB\>的结构，以docA为例，输入input_dim的文档特征，依次变换成10维，1维特征，最终输入到LambdaCost层中。需要注意这里的label和data格式为**dense_vector_sequence**，表示一列文档得分或者文档特征组成的**序列**。
### LambdaRank模型训练
训练LambdaRank模型只需要运行命令：
```bash
bash ./run_lambdarank.sh
```
脚本会自动下载数据，训练LambdaRank模型，并将每个轮次的模型存储下来。
### LambdaRank模型预测
LambdaRank模型预测过程和RankNet相同。预测时的模型拓扑结构复用代码中的模型定义，从外存加载对应的参数文件。预测时的输入是文档列表，输出是该文档列表的各个文档相关性打分，根据打分对文档进行重新排序，即可得到最终的文档排序结果。
## 自定义 LambdaRank数据
上面的代码使用了PaddlePaddle内置的mq2007数据，如果希望使用自定义格式数据，可以参考PaddlePaddle内置的`mq2007`数据集，编写一个生成器函数。例如输入数据为如下格式，只包含doc0-doc2三个文档。
\<query_id\> \<relevance_score\> \<feature_vector\>的格式
```
query_id : 1, relevance_score:1, feature_vector 0:0.1, 1:0.2, 2:0.4 #doc0
query_id : 1, relevance_score:2, feature_vector 0:0.3, 1:0.1, 2:0.4 #doc1
query_id : 1, relevance_score:0, feature_vector 0:0.2, 1:0.4, 2:0.1 #doc2
query_id : 2, relevance_score:0, feature_vector 0:0.1, 1:0.4, 2:0.1 #doc0
query_id : 2, relevance_score:2, feature_vector 0:0.1, 1:0.4, 2:0.1 #doc1
.....
```
需要转换为Listwise格式，例如
`<query_id><relevance_score> <feature_vector>`
```tex
1 1 0.1,0.2,0.4
1 2 0.3,0.1,0.4
1 0 0.2,0.4,0.1
2 0 0.1,0.4,0.1
2 2 0.1,0.4,0.1
......
```
**数据格式注意**
- 数据中每条样本对应的文档数量都必须大于`lambda_cost`层的NDCG_num
- 若单条样本对应的文档都为0，文档相关性都为0，NDCG计算无效，那么可以判定该query无效，我们在训练中过滤掉了这样的query。
```python
# self define data generator
def gen_listwise_data(text_all_lines_of_data):
"""
return :
------
label : np.array, shape=(samples_num, )
querylist : np.array, shape=(samples_num, feature_dimension)
"""
return label_list, query_docs_feature_vector_matrix
```
对应于PaddlePaddle输入，`label`的类型为`dense_vector_sequence`，是得分的序列，`data`的类型为`dense_vector_sequence`，是特征向量的序列输入，`input_dim`为单个文档的一维特征向量维度，与生成器对应，需要在训练模型之前指明输入数据对应关系。
```python
# Define the input data order
feeding = {"label":0,
"data" : 1}
```
## 总结
LTR在实际生活中有着广泛的应用。排序模型构造方法一般可划分为PointWise方法，Pairwise方法，Listwise方法，本例以LETOR的mq2007数据为例子，阐述了Pairwise的经典方法RankNet和Listwise方法中的LambdaRank，展示如何使用PaddlePaddle框架构造对应的排序模型结构，并提供了自定义数据类型样例。PaddlePaddle提供了灵活的编程接口，并可以使用一套代码运行在单机单GPU和多机分布式多GPU下实现LTR类型任务。
## 注意事项
本例作为LTR的演示示例，所采用的网络规模较小，在应用中须结合实际情况进行设置。本例实验数据中的特征向量为**查询-文档对**的联合特征，当使用查询和文档的独立特征时，可参考[DSSM](https://github.com/PaddlePaddle/models/tree/develop/dssm)。
## 参考文献
1. https://en.wikipedia.org/wiki/Learning_to_rank
2. Liu T Y. [Learning to rank for information retrieval](http://ftp.nowpublishers.com/article/DownloadSummary/INR-016)[J]. Foundations and Trends® in Information Retrieval, 2009, 3(3): 225-331.
3. Li H. [Learning to rank for information retrieval and natural language processing](http://www.morganclaypool.com/doi/abs/10.2200/S00607ED2V01Y201410HLT026)[J]. Synthesis Lectures on Human Language Technologies, 2014, 7(3): 1-121.
4. Burges C, Shaked T, Renshaw E, et al. [Learning to rank using gradient descent](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2005_BurgesSRLDHH05.pdf)[C]//Proceedings of the 22nd international conference on Machine learning. ACM, 2005: 89-96.
5. Cao Z, Qin T, Liu T Y, et al. [Learning to rank: from pairwise approach to listwise approach](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_CaoQLTL07.pdf)[C]//Proceedings of the 24th international conference on Machine learning. ACM, 2007: 129-136.
6. Burges C J C, Ragno R, Le Q V. [Learning to rank with nonsmooth cost functions](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf)[C]//NIPS. 2006, 6: 193-200.
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# 使用噪声对比估计加速语言模型训练
## 为什么需要噪声对比估计
语言模型是许多自然语言处理任务的基础，也是获得词向量表示的一种有效方法。神经概率语言模型（Neural Probabilistic Language Model, NPLM）刻画了词语序列 $\omega_1,...,\omega_T$ 属于某个固定语言的概率 $P(\omega_1^T)$ ：
$$P(\omega_1^T)= \prod_{t=1}^{T}P(\omega_t|\omega_1^{t-1})$$
为了降低建模和求解的难度，通常会引入一定条件独立假设：词语$w_t$的概率只受之前$n-1$个词语的影响，于是有：
$$ P(\omega_1^T) \approx \prod P(\omega_t|\omega_{t-n-1}^{t-1}) \tag{1}$$
从式($1$)中看到，可以通过建模条件概率 $P(\omega_t|w_{t-n-1},...,\omega_{t-1})$ 进而计算整个序列 $\omega_1,...,\omega_T$ 的概率。于是，我们可以将语言模型求解的任务简单地概括为：
**给定词语序列的向量表示 $h$ ，称之为上下文（context），模型预测下一个目标词语 $\omega$ 的概率。**
在[$n$-gram 语言模型](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)中，上下文取固定的 $n-1$ 个词，[RNN 语言模型](https://github.com/PaddlePaddle/models/tree/develop/generate_sequence_by_rnn_lm)可以处理任意长度的上下文。
给定上下文 $h$，NPLM 学习一个分值函数（scoring function）$s_\theta(\omega, h)$，$s$ 刻画了上下文 $h$ 向量和所有可能的下一个词的向量表示 $\omega'$ 之间的相似度，再通过在全词表空间对打分函数 $s$ 的取值进行归一化（除以归一化因子 $Z$），得到目标词 $\omega$ 的概率分布，其中：$\theta$ 是可学习参数，这一过程用式($2$)表示，也就是 `Softmax` 函数的计算过程。
$$P_\theta^h(\omega) = \frac{\text{exp}{s_\theta(\omega, h)}}{Z}，Z=\sum_{\omega'} \exp{s_\theta(\omega', h)}\tag{2}$$
极大似然估计（MLE，Maximum Likelihood Estimation）是求解概率($2$)最常用的学习准则。然而，不论是估计概率 $P_\theta^h(\omega)$ 还是计算似然（likelihood）的梯度时，都要计算归一化因子$Z$。$Z$ 的计算随着词典大小线性增长，当训练大规模语言模型时，例如，当词典增长到百万级别甚至更大，训练时间将变得十分漫长，因此，我们**需要其它可能的学习准则，他的求解过程从计算上应该更加轻便可解。**
models 的另一篇介绍了使用[Hsigmoid加速词向量训练](https://github.com/PaddlePaddle/models/tree/develop/hsigmoid) ，这里我们介绍另一种基于采样的提高语言模型训练速度的方法：使用噪声对比估计（Noise-contrastive estimation, NCE）\[[1](#参考文献)\]。
## 什么是噪声对比估计
噪声对比估计是一种基于采样思想的概率密度估计准则，用于估计/拟合：概率函数由非归一化的分值函数和归一化因子两部分构成，这样一类特殊的概率函数\[[1](#参考文献)\] 。噪声对比估计通过构造下面这样一个辅助问题避免在全词典空间计算归一化因子 $Z$ ，从而降低计算代价：
给定上下文 $h$ 和任意已知的噪声分布 $P_n$ ，学习一个二类分类器来拟合：目标 $\omega$ 来自真实分布 $P_\theta$ ($D = 1$) 还是噪声分布 $P_n$（$D = 0$）的概率。假设来自噪声分布的负类样本的数量 $k$ 倍于目标样本，于是有：
$$P(D=1|h,\omega) = \frac{P_\theta(h, \omega)}{P_\theta (h, \omega) + kP_n} \tag{3}$$
我们直接用`Sigmoid`函数来刻画式($3$)这样一个二分类概率：
$$P(D=1|h,\omega) = \sigma (\Delta s_\theta(w,h)) \tag{4}$$
有了上面的问题设置便可以基于二分类来进行极大似然估计：增大正样本的概率同时降低负样本的概率[[2,3](#参考文献)]，也就是最小化下面这样一个损失函数：
$$
J^h(\theta )=E_{ P_d^h }\left[ \log { P^h(D=1|w,\theta ) } \right] +kE_{ P_n }\left[ \log P^h (D=0|w,\theta ) \right]$$
$$
\\\\\qquad =E_{ P_d^h }\left[ \log { \sigma (\Delta s_\theta(w,h)) } \right] +kE_{ P_n }\left[ \log (1-\sigma (\Delta s_\theta(w,h))) \right] \tag{5}$$
式($5$)便是基于噪声对比估计而定义的NCE损失函数，至此，我们还剩下两个问题：
1. 式($5$)中的 $s_\theta(w,h)$ 是什么？
- 在神经网络的实现中，$s_\theta(h,\omega)$ 是未归一化的分值。
- NCE cost 层的可学习参数 $W$ 是一个 $|V| \times d$ 维度的矩阵，$|V|$ 是词典大小，$d$ 是上下文向量$h$的维度；
- 训练时下一个词的真实类别 $t$ 是正类，从指定的噪声分布中采样 $k$ 个负类样本它们的类别分别记作： $\{n_1, ..., n_k\}$；
- 抽取 $W$ 中第 $\{t, n_1, ..., n_k\}$ 行（共计 $k + 1$ 行）分别与 $h$ 计算分值 $s_\theta(w,h)$ ，再通过($5$)式计算最终的损失；
2. 噪声分布如何选择？
- 实践中，可以任意选择合适的噪声分布（噪声分布暗含着一定的先验）。
- 最常用选择有：使用基于全词典之上的`unigram`分布（词频统计），无偏的均匀分布。
- 在PaddlePaddle中用户如果用户未指定噪声分布，默认采用均匀分布。
使用NCE准确训练时，最后一层的计算代价只与负采样数目线性相关，当负采样数目逐渐增大时，NCE 估计准则会收敛到极大似然估计。因此，在使用NCE准则训练时，可以通过控制负采样数目来控制对归一化的概率分布近似的质量。
## 实验数据
本例采用 Penn Treebank (PTB) 数据集（[Tomas Mikolov预处理版本](http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz)）来训练一个 5-gram 语言模型。PaddlePaddle 提供了 [paddle.dataset.imikolov](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/dataset/imikolov.py) 接口来方便地使用PTB数据。当没有找到下载好的数据时，脚本会自动下载并验证文件的完整性。语料语种为英文，共有42068句训练数据，3761句测试数据。
## 网络结构
在 5-gram 神经概率语言模型详细网络结构见图1：
<p align="center">
<img src="images/network_conf.png" width = "70%" align="center"/><br/>
图1. 5-gram 网络配置结构
</p>
模型主要分为如下几个部分构成：
1. **输入层**：输入样本由原始英文单词组成，每个英文单词首先被转换为字典中的 id 表示。
2. **词向量层**：id 表示通过词向量层作用得到连续表示的词向量表示，能够更好地体现词与词之间的语义关系。训练完成之后，词语之间的语义相似度可以使用词向量之间的距离来表示，语义越相似，距离越近。
3. **词向量拼接层**：将词向量进行串联，并将词向量首尾相接形成一个长向量。这样可以方便后面全连接层的处理。
4. **全连接隐层**：将上一层获得的长向量输入到一层隐层的神经网络，输出特征向量。全连接的隐层可以增强网络的学习能力。
5. **NCE层**：训练时可以直接实用 PaddlePaddle 提供的 `paddle.layer.nce` 作为损失函数。
## 训练
在命令行窗口运行命令``` python train.py ```可以直接开启训练任务。
- 程序第一次运行会检测用户缓存文件夹中是否包含 ptb 数据集，如果未包含，则自动下载。
- 运行过程中，每10个 batch 会打印模型训练在训练集上的代价值
- 每个 pass 结束后，会计算测试数据集上的损失，并同时会保存最新的模型快照。
在模型文件`network_conf.py`中 NCE 调用代码如下：
```python
return paddle.layer.nce(
input=hidden_layer,
label=next_word,
num_classes=dict_size,
param_attr=paddle.attr.Param(name="nce_w"),
bias_attr=paddle.attr.Param(name="nce_b"),
num_neg_samples=25,
neg_distribution=None)
```
NCE 层的一些重要参数解释如下：
| 参数名 | 参数作用 | 介绍 |
|:------ |:-------| :--------|
| param\_attr / bias\_attr | 用来设置参数名字 |方便预测阶段加载参数，具体在预测一节中介绍。|
| num\_neg\_samples | 负样本采样个数|可以控制正负样本比例，这个值取值区间为 [1, 字典大小-1]，负样本个数越多则整个模型的训练速度越慢，模型精度也会越高 |
| neg\_distribution | 生成负样例标签的分布，默认是一个均匀分布| 可以自行控制负样本采样时各个类别的采样权重。例如：希望正样例为“晴天”时，负样例“洪水”在训练时更被着重区分，则可以将“洪水”这个类别的采样权重增加|
| act | 使用何种激活函数| 根据 NCE 的原理，这里应该使用 sigmoid 函数 |
## 预测
1. 在命令行运行 :
```bash
python infer.py \
--model_path "models/XX" \
--batch_size 1 \
--use_gpu false \
--trainer_count 1
```
参数含义如下：
- `model_path`：指定训练好的模型所在的路径。必选。
- `batch_size`：一次预测并行的样本数目。可选，默认值为 `1`。
- `use_gpu`：是否使用 GPU 进行预测。可选，默认值为 `False`。
- `trainer_count` : 预测使用的线程数目。可选，默认为 `1`。**注意：预测使用的线程数目必选大于一次预测并行的样本数目**。
2. 需要注意的是：**预测和训练的计算逻辑不同**。预测使用全连接矩阵乘法后接`softmax`激活，输出基于各类别的概率分布，需要替换训练中使用的`paddle.train.nce`层。在PaddlePaddle中，NCE层将可学习参数存储为一个 `[类别数目 × 上一层输出向量宽度]` 大小的矩阵，预测时，**全连接运算在加载NCE层学习到参数时，需要进行转置**，代码如下：
```python
return paddle.layer.mixed(
size=dict_size,
input=paddle.layer.trans_full_matrix_projection(
hidden_layer, param_attr=paddle.attr.Param(name="nce_w")),
act=paddle.activation.Sigmoid(),
bias_attr=paddle.attr.Param(name="nce_b"))
```
上述代码片段中的 `paddle.layer.mixed` 必须以 PaddlePaddle 中 `paddle.layer.×_projection` 为输入。`paddle.layer.mixed` 将多个 `projection` （输入可以是多个）计算结果求和作为输出。`paddle.layer.trans_full_matrix_projection` 在计算矩阵乘法时会对参数$W$进行转置。
3. 预测的输出格式如下：
```text
0.6734 their may want to move
```
每一行是一条预测结果，内部以“\t”分隔，共计3列：
- 第一列：下一个词的概率。
- 第二列：模型预测的下一个词。
- 第三列：输入的 $n$ 个词语，内部以空格分隔。
## 参考文献
1. Gutmann M, Hyvärinen A. [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf)[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 2010: 297-304.
1. Mnih A, Kavukcuoglu K. [Learning word embeddings efficiently with noise-contrastive estimation](https://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf)[C]//Advances in neural information processing systems. 2013: 2265-2273.
1. Mnih A, Teh Y W. [A Fast and Simple Algorithm for Training Neural Probabilistic Language Models](http://xueshu.baidu.com/s?wd=paperuri%3A%280735b97df93976efb333ac8c266a1eb2%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fabs%2F1206.6426&ie=utf-8&sc_us=5770715420073315630)[J]. Computer Science, 2012:1751-1758.
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## 简介
序列是许多机器学习和数据挖掘任务面对的一种输入数据类型，以自然语言处理任务为例：句子由词语构成，而多个句子进一步构成了段落。因此，段落可以看作是一个嵌套的序列（或者叫作：双层序列），这个序列的每个元素又是一个序列。
双层序列是 PaddlePaddle 支持的一种非常灵活的数据组织方式， 能够帮助我们更好地描述段落、多轮对话等更为复杂的数据。以双层序列作为输入，我们可以设计一个层次化的网络，从而更好地完成一些复杂的任务。
本单元将介绍如何在 PaddlePaddle 中使用双层序列。
- [基于双层序列的文本分类](https://github.com/PaddlePaddle/models/tree/develop/nested_sequence/text_classification)
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# 基于双层序列的文本分类
## 简介
本例将演示如何在 PaddlePaddle 中将长文本输入（通常能达到段落或者篇章基本）组织为双层序列，完成对长文本的分类任务。
## 模型介绍
我们将一段文本看成句子的序列，而每个句子又是词语的序列。
我们首先用卷积神经网络编码段落中的每一句话；然后，将每句话的表示向量经过池化层得到段落的编码向量；最后将段落的编码向量作为分类器（以softmax层的全连接层）输入，得到最终的分类结果。
**模型结构如下图所示**
<p align="center">
<img src="images/model.jpg" width = "60%" align="center"/><br/>
图1. 基于双层序列的文本分类模型
</p>
PaddlePaddle 实现该网络结构的代码见 `network_conf.py`。
对双层时间序列的处理，需要先将双层时间序列数据变换成单层时间序列数据，再对每一个单层时间序列进行处理。 在 PaddlePaddle 中 ，`recurrent_group` 是帮助我们构建处理双层序列的层次化模型的主要工具。这里，我们使用两个嵌套的 `recurrent_group` 。外层的 `recurrent_group` 将段落拆解为句子，`step` 函数中拿到的输入是句子序列；内层的 `recurrent_group` 将句子拆解为词语，`step` 函数中拿到的输入是非序列的词语。
在词语级别，我们通过 CNN 网络以词向量为输入输出学习到的句子表示；在段落级别，将每个句子的表示通过池化作用得到段落表示。
``` python
nest_group = paddle.layer.recurrent_group(input=[paddle.layer.SubsequenceInput(emb),
hidden_size],
step=cnn_cov_group)
```
拆解后的单层序列数据经过一个CNN网络学习对应的向量表示，CNN的网络结构包含以下部分：
- **卷积层**： 文本分类中的卷积在时间序列上进行，卷积核的宽度和词向量层产出的矩阵一致，卷积后得到的结果为“特征图”， 使用多个不同高度的卷积核，可以得到多个特征图。本例代码默认使用了大小为 3（图1红色框）和 4（图1蓝色框）的卷积核。
- **最大池化层**： 对卷积得到的各个特征图分别进行最大池化操作。由于特征图本身已经是向量，因此最大池化实际上就是选出各个向量中的最大元素。将所有最大元素又被拼接在一起，组成新的向量。
- **线性投影层**： 将不同卷积得到的结果经过最大池化层之后拼接为一个长向量， 然后经过一个线性投影得到对应单层序列的表示向量。
CNN网络具体代码实现如下：
```python
def cnn_cov_group(group_input, hidden_size):
"""
Convolution group definition.
:param group_input: The input of this layer.
:type group_input: LayerOutput
:params hidden_size: The size of the fully connected layer.
:type hidden_size: int
"""
conv3 = paddle.networks.sequence_conv_pool(
input=group_input, context_len=3, hidden_size=hidden_size)
conv4 = paddle.networks.sequence_conv_pool(
input=group_input, context_len=4, hidden_size=hidden_size)
linear_proj = paddle.layer.fc(input=[conv3, conv4],
size=hidden_size,
param_attr=paddle.attr.ParamAttr(name='_cov_value_weight'),
bias_attr=paddle.attr.ParamAttr(name='_cov_value_bias'),
act=paddle.activation.Linear())
return linear_proj
```
PaddlePaddle 中已经封装好的带有池化的文本序列卷积模块：`paddle.networks.sequence_conv_pool`，可直接调用。
在得到每个句子的表示向量之后， 将所有句子表示向量经过一个平均池化层， 得到一个样本的向量表示， 向量经过一个全连接层输出最终的预测结果。 代码如下：
```python
avg_pool = paddle.layer.pooling(input=nest_group,
pooling_type=paddle.pooling.Avg(),
agg_level=paddle.layer.AggregateLevel.TO_NO_SEQUENCE)
prob = paddle.layer.mixed(size=class_num,
input=[paddle.layer.full_matrix_projection(input=avg_pool)],
act=paddle.activation.Softmax())
```
## 安装依赖包
```bash
pip install -r requirements.txt
```
## 指定训练配置参数
通过 `config.py` 脚本修改训练和模型配置参数，脚本中有对可配置参数的详细解释，示例如下：
```python
class TrainerConfig(object):
# whether to use GPU for training
use_gpu = False
# the number of threads used in one machine
trainer_count = 1
# train batch size
batch_size = 32
...
class ModelConfig(object):
# embedding vector dimension
emb_size = 28
...
```
修改 `config.py` 对参数进行调整。例如，通过修改 `use_gpu` 参数来指定是否使用 GPU 进行训练。
## 使用 PaddlePaddle 内置数据运行
### 训练
在终端执行：
```bash
python train.py
```
将以 PaddlePaddle 内置的情感分类数据集: `imdb` 运行本例。
### 预测
训练结束后模型将存储在指定目录当中（默认models目录），在终端执行：
```bash
python infer.py --model_path 'models/params_pass_00000.tar.gz'
```
默认情况下，预测脚本将加载训练一个pass的模型对 `imdb的测试集` 进行测试。
## 使用自定义数据训练和预测
### 训练
1.数据组织
输入数据格式如下：每一行为一条样本，以 `\t` 分隔，第一列是类别标签，第二列是输入文本的内容。以下是两条示例数据：
```
positive This movie is very good. The actor is so handsome.
negative What a terrible movie. I waste so much time.
```
2.编写数据读取接口
自定义数据读取接口只需编写一个 Python 生成器实现**从原始输入文本中解析一条训练样本**的逻辑。以下代码片段实现了读取原始数据返回类型为： `paddle.data_type.integer_value_sub_sequence` 和 `paddle.data_type.integer_value`
```python
def train_reader(data_dir, word_dict, label_dict):
"""
Reader interface for training data
:param data_dir: data directory
:type data_dir: str
:param word_dict: path of word dictionary,
the dictionary must has a "UNK" in it.
:type word_dict: Python dict
:param label_dict: path of label dictionary.
:type label_dict: Python dict
"""
def reader():
UNK_ID = word_dict['<unk>']
word_col = 1
lbl_col = 0
for file_name in os.listdir(data_dir):
file_path = os.path.join(data_dir, file_name)
if not os.path.isfile(file_path):
continue
with open(file_path, "r") as f:
for line in f:
line_split = line.strip().split("\t")
doc = line_split[word_col]
doc_ids = []
for sent in doc.strip().split("."):
sent_ids = [
word_dict.get(w, UNK_ID)
for w in sent.split()]
if sent_ids:
doc_ids.append(sent_ids)
yield doc_ids, label_dict[line_split[lbl_col]]
return reader
```
需要注意的是， 本例中以英文句号`'.'`作为分隔符， 将一段文本分隔为一定数量的句子， 且每个句子表示为对应词表的索引数组（`sent_ids`）。 由于当前样本的表示(`doc_ids`)中包含了该段文本的所有句子， 因此，它的类型为：`paddle.data_type.integer_value_sub_sequence`。
3.指定命令行参数进行训练
`train.py`训练脚本中包含以下参数：
```
Options:
--train_data_dir TEXT The path of training dataset (default: None). If
this parameter is not set, imdb dataset will be
used.
--test_data_dir TEXT The path of testing dataset (default: None). If this
parameter is not set, imdb dataset will be used.
--word_dict_path TEXT The path of word dictionary (default: None). If this
parameter is not set, imdb dataset will be used. If
this parameter is set, but the file does not exist,
word dictionay will be built from the training data
automatically.
--label_dict_path TEXT The path of label dictionary (default: None).If this
parameter is not set, imdb dataset will be used. If
this parameter is set, but the file does not exist,
label dictionay will be built from the training data
automatically.
--model_save_dir TEXT The path to save the trained models (default:
'models').
--help Show this message and exit.
```
修改`train.py`脚本中的启动参数，可以直接运行本例。 以`data`目录下的示例数据为例，在终端执行：
```bash
python train.py \
--train_data_dir 'data/train_data' \
--test_data_dir 'data/test_data' \
--word_dict_path 'word_dict.txt' \
--label_dict_path 'label_dict.txt'
```
即可对样例数据进行训练。
### 预测
1.指定命令行参数
`infer.py`训练脚本中包含以下参数：
```
Options:
--data_path TEXT The path of data for inference (default: None). If
this parameter is not set, imdb test dataset will be
used.
--model_path TEXT The path of saved model. [required]
--word_dict_path TEXT The path of word dictionary (default: None). If this
parameter is not set, imdb dataset will be used.
--label_dict_path TEXT The path of label dictionary (default: None).If this
parameter is not set, imdb dataset will be used.
--batch_size INTEGER The number of examples in one batch (default: 32).
--help Show this message and exit.
```
2.以`data`目录下的示例数据为例，在终端执行：
```bash
python infer.py \
--data_path 'data/infer.txt' \
--word_dict_path 'word_dict.txt' \
--label_dict_path 'label_dict.txt' \
--model_path 'models/params_pass_00000.tar.gz'
```
即可对样例数据进行预测。
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# Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
This model implements the work in the following paper:
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. [arXiv:1607.06275](https://arxiv.org/abs/1607.06275).
If you use the dataset/code in your research, please cite the above paper:
```text
@article{li:2016:arxiv,
author = {Li, Peng and Li, Wei and He, Zhengyan and Wang, Xuguang and Cao, Ying and Zhou, Jie and Xu, Wei},
title = {Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering},
journal = {arXiv:1607.06275v2},
year = {2016},
url = {https://arxiv.org/abs/1607.06275v2},
}
```
## Installation
1. Install PaddlePaddle v0.10.5 by the following commond. Note that v0.10.0 is not supported.
```bash
# either one is OK
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
2. Download the [WebQA](http://idl.baidu.com/WebQA.html) dataset by running
```bash
cd data && ./download.sh && cd ..
```
## Hyperparameters
All the hyperparameters are defined in `config.py`. The default values are aligned with the paper.
## Training
Training can be launched using the following command:
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python train.py 2>&1 | tee train.log
```
## Validation and Test
WebQA provides two versions of validation and test sets. Automatic validation and test can be lauched by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python val_and_test.py models [ann|ir]
```
where
* `models`: the directory where model files are stored. You can use `models` if `config.py` is not changed.
* `ann`: using the validation and test sets with annotated evidence.
* `ir`: using the validation and test sets with retrieved evidence.
Note that validation and test can run simultaneously with training. `val_and_test.py` will handle the synchronization related problems.
Intermediate results are stored in the directory `tmp`. You can delete them safely after validation and test.
The results should be comparable with those shown in Table 3 in the paper.
## Inferring using a Trained Model
Infer using a trained model by running:
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
MODEL_FILE \
INPUT_DATA \
OUTPUT_FILE \
2>&1 | tee infer.log
```
where
* `MODEL_FILE`: a trained model produced by `train.py`.
* `INPUT_DATA`: input data in the same format as the validation/test sets of the WebQA dataset.
* `OUTPUT_FILE`: results in the format specified in the WebQA dataset for the evaluation scripts.
## Pre-trained Models
We have provided two pre-trained models, one for the validation and test sets with annotated evidence, and one for those with retrieved evidence. These two models are selected according to the performance on the corresponding version of validation set, which is consistent with the paper.
The models can be downloaded with
```bash
cd pre-trained-models && ./download-models.sh && cd ..
```
The evaluation result on the test set with annotated evidence can be achieved by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
pre-trained-models/params_pass_00010.tar.gz \
data/data/test.ann.json.gz \
test.ann.output.txt.gz
PYTHONPATH=data/evaluation:$PYTHONPATH \
python data/evaluation/evaluate-tagging-result.py \
test.ann.output.txt.gz \
data/data/test.ann.json.gz \
--fuzzy --schema BIO2
# The result should be
# chunk_f1=0.739091 chunk_precision=0.686119 chunk_recall=0.800926 true_chunks=3024 result_chunks=3530 correct_chunks=2422
```
And the evaluation result on the test set with retrieved evidence can be achieved by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
pre-trained-models/params_pass_00021.tar.gz \
data/data/test.ir.json.gz \
test.ir.output.txt.gz
PYTHONPATH=data/evaluation:$PYTHONPATH \
python data/evaluation/evaluate-voting-result.py \
test.ir.output.txt.gz \
data/data/test.ir.json.gz \
--fuzzy --schema BIO2
# The result should be
# chunk_f1=0.749358 chunk_precision=0.727868 chunk_recall=0.772156 true_chunks=3024 result_chunks=3208 correct_chunks=2335
```
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# Neural Machine Translation Model
## Background Introduction
Neural Machine Translation (NMT) is a simple new architecture for getting machines to learn to translate. Traditional machine translation methods are mainly based on phrase-based statistical translation approaches that use separately engineered subcomponents rules or statistical models. NMT models use deep learning and representation learning. This example describes how to construct an end-to-end neural machine translation (NMT) model using the recurrent neural network (RNN) in PaddlePaddle.
## Model Overview
RNN-based neural machine translation follows the encoder-decoder architecture. A common choice for the encoder and decoder is the recurrent neural network (RNN), used by most NMT models. Below is an example diagram of a general approach for NMT.
<p align="center"><img src="images/encoder-decoder.png" width = "90%" align="center"/><br/>Figure 1. Encoder - Decoder frame </ p>
The input and output of the neural machine translation model can be any of character, word or phrase. This example illustrates the word-based NMT.
- **Encoder**: Encodes the source language sentence into a vector as input to the decoder. The original input of the decoder is the `id` sequence $ w = {w_1, w_2, ..., w_T} $ of the word, expressed in the one-hot code. In order to reduce the input dimension, and to establish the semantic association between words, the model is a word that is expressed by hot independent code. Word embedding is a word vector. For more information about word vector, please refer to PaddleBook [word vector] (https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.cn.md) chapter. Finally, the RNN unit processes the input word by word to get the encoding vector of the complete sentence.
- **Decoder**: Accepts the input of the encoder, decoding the target language sequence $ u = {u_1, u_2, ..., u_ {T '}} $ one by one. For each time step, the RNN unit outputs a hidden vector. Then the conditional probability of the next target word is calculated by `Softmax` normalization, i.e. $ P (u_i | w, u_1, u_2, ..., u_ {t- 1}) $. Thus, given the input $ w $, the corresponding translation result is $ u $
$$ P(u_1,u_2,...,u_{T'} | w) = \prod_{t=1}^{t={T'}}p(u_t|w, u_1, u_2, u_{t-1})$$
In Chinese to English translation, for example, the source language is Chinese, and the target language is English. The following is a sentence after the source language word segmentation.
```
祝愿 祖国 繁荣 昌盛
```
Corresponding target language English translation results for:
```
Wish motherland rich and powerful
```
In the preprocessing step, we prepare the parallel corpus data of the source language and the target language. Then we construct the dictionaries of the source language and the target language respectively. In the training stage, we use the pairwise parallel corpus training model. In the model test stage, the model automatically generates the corresponding English translation, and then it evaluates the resulting results with standard translations. For the evaluation metric, BLEU is most commonly used.
### RNN unit
The original structure of the RNN uses a vector to store the hidden state. However, the RNN of this structure is prone to have gradient vanishing problem, which is difficult to model for a long time. This issue can be addressed by using LSTM \[[1](#References)] and GRU (Gated Recurrent Unit) \[[2](#References)]. This solves the problem of long-term dependency by carefully forgetting the previous information. In this example, we demonstrate the GRU based model.
<p align="center">
<img src="images/gru.png" width = "90%" align="center"/><br/>
图 2. GRU 单元
</p>
We can see that, in addition to the implicit state, the GRU also contains two gates: the Update Gate and the Reset Gate. At each time step, the update of the threshold and the hidden state is determined by the formula on the right side of Figure 2. These two thresholds determine how the state is updated.
### Bi-directional Encoder
In the above basic model, when the encoder sequentially processes the input sentence sequence, the state of the current time contains only the history input information without the sequence information of the future time. For sequence modeling, the context of the future also contains important information. With the bi-directional encoder (Figure 3), we can get both information at the same time:
<p align="center">
<img src="images/bidirectional-encoder.png" width = "90%" align="center"/><br/>
Figure 3. Bi-directional encoder structure diagram
</p>
The bi-directional encoder \[[3](#References)\] shown in Figure 3 consists of two independent RNNs that encode the input sequence from the forward and backward, respectively. Then it combines the outputs of the two RNNs together, as the final encoding output.
In PaddlePaddle, bi-directional encoders can easily call using APIs:
```python
src_word_id = paddle.layer.data(
name='source_language_word',
type=paddle.data_type.integer_value_sequence(source_dict_dim))
# source embedding
src_embedding = paddle.layer.embedding(
input=src_word_id, size=word_vector_dim)
# bidirectional GRU as encoder
encoded_vector = paddle.networks.bidirectional_gru(
input=src_embedding,
size=encoder_size,
fwd_act=paddle.activation.Tanh(),
fwd_gate_act=paddle.activation.Sigmoid(),
bwd_act=paddle.activation.Tanh(),
bwd_gate_act=paddle.activation.Sigmoid(),
return_seq=True)
```
### Beam Search Algorithm
After the training is completed, the model will input and decode the corresponding target language translation result according to the source language. Decoding, a direct way is to take each step conditional probability of the largest word, as the current moment of output. But the local optimal does not necessarily guarantee the global optimal. If the search for the full space is large, the cost is too large. In order to solve this problem, beam search algorithm is commonly used. Beam search is a heuristic graph search algorithm that controls the search width with a parameter $ k $] as follows:
**1**. During decoding, always maintain $ k $ decoded sub-sequences;
**2**. At the middle of time $ t $, for each sequence in the $ k $ sub-sequence, calculate the probability of the next word and take the maximum of $ k $ words with the largest probability, combining $ k ^ 2 $ New child sequence;
**3**. Take the maximum probability of $ k $ in these combination sequences to update the original subsequence;
**4**. iterate through it until you get $ k $ complete sentences as candidates for translation results.
For more information on beam search, refer to the [beam search] chapter in PaddleBook [machine translation] (https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.cn.md) (https://github.com/PaddlePaddle/book/blob/develop.org.machine_translation/README.cn.md# beam search algorithm) section.
### Decoder without Attention mechanism
- In the relevant section of PaddleBook (https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.cn.md), the Attention Mechanism has been introduced. This example demonstrates Encoder-Decoder structure without attention mechanism. With regard to the attention mechanism, please refer to PaddleBook and references \[[3](#References)].
In PaddlePaddle, commonly used RNN units can be conveniently called using APIs. For example, `recurrent_layer_group` can be used to implement custom actions at each point in the RNN. First, customize the single-step logic function, and then use the function `recurrent_group ()` to cycle through the single-step logic function to process the entire sequence. In this case, the unattended mechanism of the decoder uses `recurrent_layer_group` to implement the function` gru_decoder_without_attention () `. Corresponding code is as follows:
```python
# the initialization state for decoder GRU
encoder_last = paddle.layer.last_seq(input=encoded_vector)
encoder_last_projected = paddle.layer.fc(
size=decoder_size, act=paddle.activation.Tanh(), input=encoder_last)
# the step function for decoder GRU
def gru_decoder_without_attention(enc_vec, current_word):
'''
Step function for gru decoder
:param enc_vec: encoded vector of source language
:type enc_vec: layer object
:param current_word: current input of decoder
:type current_word: layer object
'''
decoder_mem = paddle.layer.memory(
name="gru_decoder",
size=decoder_size,
boot_layer=encoder_last_projected)
context = paddle.layer.last_seq(input=enc_vec)
decoder_inputs = paddle.layer.fc(
size=decoder_size * 3, input=[context, current_word])
gru_step = paddle.layer.gru_step(
name="gru_decoder",
act=paddle.activation.Tanh(),
gate_act=paddle.activation.Sigmoid(),
input=decoder_inputs,
output_mem=decoder_mem,
size=decoder_size)
out = paddle.layer.fc(
size=target_dict_dim,
bias_attr=True,
act=paddle.activation.Softmax(),
input=gru_step)
return out
```
In the model training and testing phase, the behavior of the decoder is different:
- **Training phase**: The word vector of the target translation `trg_embedding` is passed as a parameter to the single step logic` gru_decoder_without_attention () `. The function` recurrent_group () `loop calls the single step logic execution, and finally calculates the target translation with the actual decoding;
- **Testing phase**: The decoder predicts the next word based on the last generated word, `GeneratedInput ()`. The automatic fetch model predicts the highest probability of the $ k $ word vector passed to the single step logic. Then the beam_search () function calls the function `gru_decoder_without_attention ()` to complete the beam search and returns as a result.
The training and generated returns are implemented in the following `if-else` conditional branches:
```python
group_input1 = paddle.layer.StaticInput(input=encoded_vector)
group_inputs = [group_input1]
decoder_group_name = "decoder_group"
if is_generating:
trg_embedding = paddle.layer.GeneratedInput(
size=target_dict_dim,
embedding_name="_target_language_embedding",
embedding_size=word_vector_dim)
group_inputs.append(trg_embedding)
beam_gen = paddle.layer.beam_search(
name=decoder_group_name,
step=gru_decoder_without_attention,
input=group_inputs,
bos_id=0,
eos_id=1,
beam_size=beam_size,
max_length=max_length)
return beam_gen
else:
trg_embedding = paddle.layer.embedding(
input=paddle.layer.data(
name="target_language_word",
type=paddle.data_type.integer_value_sequence(target_dict_dim)),
size=word_vector_dim,
param_attr=paddle.attr.ParamAttr(name="_target_language_embedding"))
group_inputs.append(trg_embedding)
decoder = paddle.layer.recurrent_group(
name=decoder_group_name,
step=gru_decoder_without_attention,
input=group_inputs)
lbl = paddle.layer.data(
name="target_language_next_word",
type=paddle.data_type.integer_value_sequence(target_dict_dim))
cost = paddle.layer.classification_cost(input=decoder, label=lbl)
return cost
```
## Data Preparation
The data used in this example is from [WMT14] (http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/), which is a parallel corpus of French-to-English translation. Use [bitexts] (http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz) as training data, [dev + test data] (http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz) as validation and test data. PaddlePaddle has been packaged in the data set of the read interface, in the first run, the program will automatically complete the download. Users do not need to manually complete the relevant data preparation!
## Model Training and Testing
### Model Training
Starting the model training is very simple, just in the command line window to execute `python train.py`. The `train ()` function in the `train.py` script of the model training phase completes the following logic:
**a) Define the network, parse the network structure, initialize the model parameters.**
```python
# define the network topolgy.
cost = seq2seq_net(source_dict_dim, target_dict_dim)
parameters = paddle.parameters.create(cost)
```
**b) Set the training process optimization strategy. Define the training data to read `reader`**
```python
# define optimization method
optimizer = paddle.optimizer.RMSProp(
learning_rate=1e-3,
gradient_clipping_threshold=10.0,
regularization=paddle.optimizer.L2Regularization(rate=8e-4))
# define the trainer instance
trainer = paddle.trainer.SGD(
cost=cost, parameters=parameters, update_equation=optimizer)
# define data reader
wmt14_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(source_dict_dim), buf_size=8192),
batch_size=55)
```
**c) Define the event handle, print the training intermediate results, save the model snapshot**
```python
# define the event_handler callback
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if not event.batch_id % 100 and event.batch_id:
with gzip.open(
os.path.join(save_path,
"nmt_without_att_%05d_batch_%05d.tar.gz" %
event.pass_id, event.batch_id), "w") as f:
parameters.to_tar(f)
if event.batch_id and not event.batch_id % 10:
logger.info("Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics))
```
**d) Start training**
```python
# start training
trainer.train(
reader=wmt14_reader, event_handler=event_handler, num_passes=2)
```
The output sample is
```text
Pass 0, Batch 0, Cost 267.674663, {'classification_error_evaluator': 1.0}
.........
Pass 0, Batch 10, Cost 172.892294, {'classification_error_evaluator': 0.953895092010498}
.........
Pass 0, Batch 20, Cost 177.989329, {'classification_error_evaluator': 0.9052488207817078}
.........
Pass 0, Batch 30, Cost 153.633665, {'classification_error_evaluator': 0.8643803596496582}
.........
Pass 0, Batch 40, Cost 168.170543, {'classification_error_evaluator': 0.8348183631896973}
```
### Generate Translation Results
In PaddlePaddle, it is also easy to use translated models to generate translated text.
1. First of all, please modify the `generate.py` script` main` passed to the `generate` function parameters to choose which saved model to use. The default parameters are as follows:
```python
generate(
source_dict_dim=30000,
target_dict_dim=30000,
batch_size=20,
beam_size=3,
model_path="models/nmt_without_att_params_batch_00100.tar.gz")
```
2. In the terminal phase, execute the `python generate.py` command. The` generate () `in the script executes the following code:
**a) Load the test sample**
```python
# load data samples for generation
gen_creator = paddle.dataset.wmt14.gen(source_dict_dim)
gen_data = []
for item in gen_creator():
gen_data.append((item[0], ))
```
**b) Initialize the model, execute `infer ()` for each input sample to generate `beam search` translation results**
```python
beam_gen = seq2seq_net(source_dict_dim, target_dict_dim, True)
with gzip.open(init_models_path) as f:
parameters = paddle.parameters.Parameters.from_tar(f)
# prob is the prediction probabilities, and id is the prediction word.
beam_result = paddle.infer(
output_layer=beam_gen,
parameters=parameters,
input=gen_data,
field=['prob', 'id'])
```
**c) Next, load the source and target language dictionaries, convert the sentences represented by the `id` sequence into the original language and output the results.**
```python
beam_result = inferer.infer(input=test_batch, field=["prob", "id"])
gen_sen_idx = np.where(beam_result[1] == -1)[0]
assert len(gen_sen_idx) == len(test_batch) * beam_size
start_pos, end_pos = 1, 0
for i, sample in enumerate(test_batch):
print(" ".join([
src_dict[w] for w in sample[0][1:-1]
])) # skip the start and ending mark when print the source sentence
for j in xrange(beam_size):
end_pos = gen_sen_idx[i * beam_size + j]
print("%.4f\t%s" % (beam_result[0][i][j], " ".join(
trg_dict[w] for w in beam_result[1][start_pos:end_pos])))
start_pos = end_pos + 2
print("\n")
```
Set the width of the beam search to 3, enter a French sentence. Then it automatically generate the corresponding test data for the translation results, the output format is as follows:
```text
Elles connaissent leur entreprise mieux que personne .
-3.754819 They know their business better than anyone . <e>
-4.445528 They know their businesses better than anyone . <e>
-5.026885 They know their business better than anybody . <e>
```
- The first line of input for the source language.
- Second ~ beam_size + 1 line is the result of the `beam_size` translation generated by the column search
- the output of the same row is separated into two columns by "\ t", the first column is the log probability of the sentence, and the second column is the text of the translation result.
- the symbol `<s>` represents the beginning of the sentence, the symbol `<e>` indicates the end of a sentence, and if there is a word that is not included in the dictionary, it is replaced with the symbol `<unk>`.
So far, we have implemented a basic machine translation model using PaddlePaddle. We can see, PaddlePaddle provides a flexible and rich API. This enables users to easily choose and use a various complex network configuration. NMT itself is also a rapidly developing field, and many new ideas continue to emerge. This example is a basic implementation of NMT. Users can also implement more complex NMT models using PaddlePaddle.
## References
[1] Sutskever I, Vinyals O, Le Q V. [Sequence to Sequence Learning with Neural Networks] (https://arxiv.org/abs/1409.3215) [J]. 2014, 4: 3104-3112.
[2] Cho K, Van Merriënboer B, Gulcehre C, et al. [Learning phrase representations using RNN encoder-decoder for statistical machine translation (http://www.aclweb.org/anthology/D/D14/D14-1179 .pdf) [C]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 1724-1734.
[3] Bahdanau D, Cho K, Bengio Y. [Neural machine translation by exclusive learning to align and translate] (https://arxiv.org/abs/1409.0473) [C]. Proceedings of ICLR 2015, 2015
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# 场景文字识别 (STR, Scene Text Recognition)
## STR任务简介
许多场景图像中包含着丰富的文本信息，它们可以从很大程度上帮助人们去认知场景图像的内容及含义，因此场景图像中的文本识别对所在图像的信息获取具有极其重要的作用。同时，场景图像文字识别技术的发展也促进了一些新型应用的产生，例如：\[[1](#参考文献)\]通过使用深度学习模型来自动识别路牌中的文字，帮助街景应用获取更加准确的地址信息。
本例将演示如何用 PaddlePaddle 完成 **场景文字识别 (STR, Scene Text Recognition)** 。任务如下图所示，给定一张场景图片，`STR` 需要从中识别出对应的文字"keep"。
<p align="center">
<img src="./images/503.jpg"/><br/>
图 1. 输入数据示例 "keep"
</p>
## 使用 PaddlePaddle 训练与预测
### 安装依赖包
```bash
pip install -r requirements.txt
```
### 修改配置参数
`config.py` 脚本中包含了模型配置和训练相关的参数以及对应的详细解释，代码片段如下：
```python
class TrainerConfig(object):
# Whether to use GPU in training or not.
use_gpu = True
# The number of computing threads.
trainer_count = 1
# The training batch size.
batch_size = 10
...
class ModelConfig(object):
# Number of the filters for convolution group.
filter_num = 8
...
```
修改 `config.py` 脚本可以实现对参数的调整。例如，通过修改 `use_gpu` 参数来指定是否使用 GPU 进行训练。
### 模型训练
训练脚本 [./train.py](./train.py) 中设置了如下命令行参数：
```
Options:
--train_file_list_path TEXT The path of the file which contains path list
of train image files. [required]
--test_file_list_path TEXT The path of the file which contains path list
of test image files. [required]
--label_dict_path TEXT The path of label dictionary. If this parameter
is set, but the file does not exist, label
dictionay will be built from the training data
automatically. [required]
--model_save_dir TEXT The path to save the trained models (default:
'models').
--help Show this message and exit.
```
- `train_file_list` ：训练数据的列表文件，每行由图片的存储路径和对应的标记文本组成，格式为：
```
word_1.png, "PROPER"
word_2.png, "FOOD"
```
- `test_file_list` ：测试数据的列表文件，格式同上。
- `label_dict_path` ：训练数据中标记字典的存储路径，如果指定路径中字典文件不存在，程序会使用训练数据中的标记数据自动生成标记字典。
- `model_save_dir` ：模型参数的保存目录，默认为`./models`。
### 具体执行的过程：
1.从官方网站下载数据\[[2](#参考文献)\]（Task 2.3: Word Recognition (2013 edition)），会有三个文件: `Challenge2_Training_Task3_Images_GT.zip`、`Challenge2_Test_Task3_Images.zip` 和 `Challenge2_Test_Task3_GT.txt`。
分别对应训练集的图片和图片对应的单词、测试集的图片、测试数据对应的单词。然后执行以下命令，对数据解压并移动至目标文件夹：
```bash
mkdir -p data/train_data
mkdir -p data/test_data
unzip Challenge2_Training_Task3_Images_GT.zip -d data/train_data
unzip Challenge2_Test_Task3_Images.zip -d data/test_data
mv Challenge2_Test_Task3_GT.txt data/test_data
```
2.获取训练数据文件夹中 `gt.txt` 的路径 (data/train_data）和测试数据文件夹中`Challenge2_Test_Task3_GT.txt`的路径(data/test_data)。
3.执行如下命令进行训练：
```bash
python train.py \
--train_file_list_path 'data/train_data/gt.txt' \
--test_file_list_path 'data/test_data/Challenge2_Test_Task3_GT.txt' \
--label_dict_path 'label_dict.txt'
```
4.训练过程中，模型参数会自动备份到指定目录，默认会保存在 `./models` 目录下。
### 预测
预测部分由 `infer.py` 完成，使用的是最优路径解码算法，即：在每个时间步选择一个概率最大的字符。在使用过程中，需要在 `infer.py` 中指定具体的模型保存路径、图片固定尺寸、batch_size（默认为10）、标记词典路径和图片文件的列表文件。执行如下代码：
```bash
python infer.py \
--model_path 'models/params_pass_00000.tar.gz' \
--image_shape '173,46' \
--label_dict_path 'label_dict.txt' \
--infer_file_list_path 'data/test_data/Challenge2_Test_Task3_GT.txt'
```
即可进行预测。
### 其他数据集
- [SynthText in the Wild Dataset](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)(41G)
- [ICDAR 2003 Robust Reading Competitions](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions)
### 注意事项
- 由于模型依赖的 `warp CTC` 只有CUDA的实现，本模型只支持 GPU 运行。
- 本模型参数较多，占用显存比较大，实际执行时可以通过调节 `batch_size` 来控制显存占用。
- 本例使用的数据集较小，如有需要，可以选用其他更大的数据集\[[3](#参考文献)\]来训练模型。
## 参考文献
1. [Google Now Using ReCAPTCHA To Decode Street View Addresses](https://techcrunch.com/2012/03/29/google-now-using-recaptcha-to-decode-street-view-addresses/)
2. [Focused Scene Text](http://rrc.cvc.uab.es/?ch=2&com=introduction)
3. [SynthText in the Wild Dataset](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)
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# Scheduled Sampling
## 概述
序列生成任务的生成目标是在给定源输入的条件下，最大化目标序列的概率。训练时该模型将目标序列中的真实元素作为解码器每一步的输入，然后最大化下一个元素的概率。生成时上一步解码得到的元素被用作当前的输入，然后生成下一个元素。可见这种情况下训练阶段和生成阶段的解码器输入数据的概率分布并不一致。
Scheduled Sampling \[[1](#参考文献)\]是一种解决训练和生成时输入数据分布不一致的方法。在训练早期该方法主要使用目标序列中的真实元素作为解码器输入，可以将模型从随机初始化的状态快速引导至一个合理的状态。随着训练的进行，该方法会逐渐更多地使用生成的元素作为解码器输入，以解决数据分布不一致的问题。
标准的序列到序列模型中，如果序列前面生成了错误的元素，后面的输入状态将会收到影响，而该误差会随着生成过程不断向后累积。Scheduled Sampling以一定概率将生成的元素作为解码器输入，这样即使前面生成错误，其训练目标仍然是最大化真实目标序列的概率，模型会朝着正确的方向进行训练。因此这种方式增加了模型的容错能力。
## 算法简介
Scheduled Sampling主要应用在序列到序列模型的训练阶段，而生成阶段则不需要使用。
训练阶段解码器在最大化第$t$个元素概率时，标准序列到序列模型使用上一时刻的真实元素$y_{t-1}$作为输入。设上一时刻生成的元素为$g_{t-1}$，Scheduled Sampling算法会以一定概率使用$g_{t-1}$作为解码器输入。
设当前已经训练到了第$i$个mini-batch，Scheduled Sampling定义了一个概率$\epsilon_i$控制解码器的输入。$\epsilon_i$是一个随着$i$增大而衰减的变量，常见的定义方式有：
- 线性衰减：$\epsilon_i=max(\epsilon,k-c*i)$，其中$\epsilon$限制$\epsilon_i$的最小值，$k$和$c$控制线性衰减的幅度。
- 指数衰减：$\epsilon_i=k^i$，其中$0<k<1$，$k$控制着指数衰减的幅度。
- 反向Sigmoid衰减：$\epsilon_i=k/(k+exp(i/k))$，其中$k>1$，$k$同样控制衰减的幅度。
图1给出了这三种方式的衰减曲线，
<p align="center">
<img src="images/decay.jpg" width="50%" align="center"><br>
图1. 线性衰减、指数衰减和反向Sigmoid衰减的衰减曲线
</p>
如图2所示，在解码器的$t$时刻Scheduled Sampling以概率$\epsilon_i$使用上一时刻的真实元素$y_{t-1}$作为解码器输入，以概率$1-\epsilon_i$使用上一时刻生成的元素$g_{t-1}$作为解码器输入。从图1可知随着$i$的增大$\epsilon_i$会不断减小，解码器将不断倾向于使用生成的元素作为输入，训练阶段和生成阶段的数据分布将变得越来越一致。
<p align="center">
<img src="images/Scheduled_Sampling.jpg" width="50%" align="center"><br>
图2. Scheduled Sampling选择不同元素作为解码器输入示意图
</p>
## 模型实现
由于Scheduled Sampling是对序列到序列模型的改进，其整体实现框架与序列到序列模型较为相似。为突出本文重点，这里仅介绍与Scheduled Sampling相关的部分，完整的代码见`scheduled_sampling.py`。
首先导入需要的包，并定义控制衰减概率的类`RandomScheduleGenerator`，如下：
```python
import numpy as np
import math
class RandomScheduleGenerator:
"""
The random sampling rate for scheduled sampling algoithm, which uses devcayed
sampling rate.
"""
...
```
下面将分别定义类`RandomScheduleGenerator`的`__init__`、`getScheduleRate`和`processBatch`三个方法。
`__init__`方法对类进行初始化，其`schedule_type`参数指定了使用哪种衰减方式，可选的方式有`constant`、`linear`、`exponential`和`inverse_sigmoid`。`constant`指对所有的mini-batch使用固定的$\epsilon_i$，`linear`指线性衰减方式，`exponential`表示指数衰减方式，`inverse_sigmoid`表示反向Sigmoid衰减。`__init__`方法的参数`a`和`b`表示衰减方法的参数，需要在验证集上调优。`self.schedule_computers`将衰减方式映射为计算$\epsilon_i$的函数。最后一行根据`schedule_type`将选择的衰减函数赋给`self.schedule_computer`变量。
```python
def __init__(self, schedule_type, a, b):
"""
schduled_type: is the type of the decay. It supports constant, linear,
exponential, and inverse_sigmoid right now.
a: parameter of the decay (MUST BE DOUBLE)
b: parameter of the decay (MUST BE DOUBLE)
"""
self.schedule_type = schedule_type
self.a = a
self.b = b
self.data_processed_ = 0
self.schedule_computers = {
"constant": lambda a, b, d: a,
"linear": lambda a, b, d: max(a, 1 - d / b),
"exponential": lambda a, b, d: pow(a, d / b),
"inverse_sigmoid": lambda a, b, d: b / (b + math.exp(d * a / b)),
}
assert (self.schedule_type in self.schedule_computers)
self.schedule_computer = self.schedule_computers[self.schedule_type]
```
`getScheduleRate`根据衰减函数和已经处理的数据量计算$\epsilon_i$。
```python
def getScheduleRate(self):
"""
Get the schedule sampling rate. Usually not needed to be called by the users
"""
return self.schedule_computer(self.a, self.b, self.data_processed_)
```
`processBatch`方法根据概率值$\epsilon_i$进行采样，得到`indexes`，`indexes`中每个元素取值为`0`的概率为$\epsilon_i$，取值为`1`的概率为$1-\epsilon_i$。`indexes`决定了解码器的输入是真实元素还是生成的元素，取值为`0`表示使用真实元素，取值为`1`表示使用生成的元素。
```python
def processBatch(self, batch_size):
"""
Get a batch_size of sampled indexes. These indexes can be passed to a
MultiplexLayer to select from the grouth truth and generated samples
from the last time step.
"""
rate = self.getScheduleRate()
numbers = np.random.rand(batch_size)
indexes = (numbers >= rate).astype('int32').tolist()
self.data_processed_ += batch_size
return indexes
```
Scheduled Sampling需要在序列到序列模型的基础上增加一个输入`true_token_flag`，以控制解码器输入。
```python
true_token_flags = paddle.layer.data(
name='true_token_flag',
type=paddle.data_type.integer_value_sequence(2))
```
这里还需要对原始reader进行封装，增加`true_token_flag`的数据生成器。下面以线性衰减为例说明如何调用上面定义的`RandomScheduleGenerator`产生`true_token_flag`的输入数据。
```python
schedule_generator = RandomScheduleGenerator("linear", 0.75, 1000000)
def gen_schedule_data(reader):
"""
Creates a data reader for scheduled sampling.
Output from the iterator that created by original reader will be
appended with "true_token_flag" to indicate whether to use true token.
:param reader: the original reader.
:type reader: callable
:return: the new reader with the field "true_token_flag".
:rtype: callable
"""
def data_reader():
for src_ids, trg_ids, trg_ids_next in reader():
yield src_ids, trg_ids, trg_ids_next, \
[0] + schedule_generator.processBatch(len(trg_ids) - 1)
return data_reader
```
这段代码在原始输入数据（即源序列元素`src_ids`、目标序列元素`trg_ids`和目标序列下一个元素`trg_ids_next`）后追加了控制解码器输入的数据。由于解码器第一个元素是序列开始符，因此将追加的数据第一个元素设置为`0`，表示解码器第一步始终使用真实目标序列的第一个元素（即序列开始符）。
训练时`recurrent_group`每一步调用的解码器函数如下：
```python
def gru_decoder_with_attention_train(enc_vec, enc_proj, true_word,
true_token_flag):
"""
The decoder step for training.
:param enc_vec: the encoder vector for attention
:type enc_vec: LayerOutput
:param enc_proj: the encoder projection for attention
:type enc_proj: LayerOutput
:param true_word: the ground-truth target word
:type true_word: LayerOutput
:param true_token_flag: the flag of using the ground-truth target word
:type true_token_flag: LayerOutput
:return: the softmax output layer
:rtype: LayerOutput
"""
decoder_mem = paddle.layer.memory(
name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
context = paddle.networks.simple_attention(
encoded_sequence=enc_vec,
encoded_proj=enc_proj,
decoder_state=decoder_mem)
gru_out_memory = paddle.layer.memory(
name='gru_out', size=target_dict_dim)
generated_word = paddle.layer.max_id(input=gru_out_memory)
generated_word_emb = paddle.layer.embedding(
input=generated_word,
size=word_vector_dim,
param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
current_word = paddle.layer.multiplex(
input=[true_token_flag, true_word, generated_word_emb])
with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
decoder_inputs += paddle.layer.full_matrix_projection(input=context)
decoder_inputs += paddle.layer.full_matrix_projection(
input=current_word)
gru_step = paddle.layer.gru_step(
name='gru_decoder',
input=decoder_inputs,
output_mem=decoder_mem,
size=decoder_size)
with paddle.layer.mixed(
name='gru_out',
size=target_dict_dim,
bias_attr=True,
act=paddle.activation.Softmax()) as out:
out += paddle.layer.full_matrix_projection(input=gru_step)
return out
```
该函数使用`memory`层`gru_out_memory`记忆上一时刻生成的元素，根据`gru_out_memory`选择概率最大的词语`generated_word`作为生成的词语。`multiplex`层会在真实元素`true_word`和生成的元素`generated_word`之间做出选择，并将选择的结果作为解码器输入。`multiplex`层使用了三个输入，分别为`true_token_flag`、`true_word`和`generated_word_emb`。对于这三个输入中每个元素，若`true_token_flag`中的值为`0`，则`multiplex`层输出`true_word`中的相应元素；若`true_token_flag`中的值为`1`，则`multiplex`层输出`generated_word_emb`中的相应元素。
## 参考文献
[1] Bengio S, Vinyals O, Jaitly N, et al. [Scheduled sampling for sequence prediction with recurrent neural networks](http://papers.nips.cc/paper/5956-scheduled-sampling-for-sequence-prediction-with-recurrent-neural-networks)//Advances in Neural Information Processing Systems. 2015: 1171-1179.
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# 命名实体识别
以下是本例的简要目录结构及说明：
```text
.
├── data # 存储运行本例所依赖的数据
│   ├── download.sh
├── images # README 文档中的图片
├── index.html
├── infer.py # 测试脚本
├── network_conf.py # 模型定义
├── reader.py # 数据读取接口
├── README.md # 文档
├── train.py # 训练脚本
└── utils.py # 定义同样的函数
```
## 简介
命名实体识别（Named Entity Recognition，NER）又称作“专名识别”，是指识别文本中具有特定意义的实体，主要包括人名、地名、机构名、专有名词等，是自然语言处理研究的一个基础问题。NER任务通常包括实体边界识别、确定实体类别两部分，可以将其作为序列标注问题解决。
序列标注可以分为Sequence Classification、Segment Classification和Temporal Classification三类[[1](#参考文献)]，本例只考虑Segment Classification，即对输入序列中的每个元素在输出序列中给出对应的标签。对于NER任务，由于需要标识边界，一般采用[BIO标注方法](http://book.paddlepaddle.org/07.label_semantic_roles/)定义的标签集，如下是一个NER的标注结果示例：
<p align="center">
<img src="images/ner_label_ins.png" width="80%" align="center"/><br/>
图1. BIO标注方法示例
</p>
根据序列标注结果可以直接得到实体边界和实体类别。类似的，分词、词性标注、语块识别、[语义角色标注](http://book.paddlepaddle.org/07.label_semantic_roles/index.cn.html)等任务都可通过序列标注来解决。使用神经网络模型解决问题的思路通常是：前层网络学习输入的特征表示，网络的最后一层在特征基础上完成最终的任务；对于序列标注问题，通常：使用基于RNN的网络结构学习特征，将学习到的特征接入CRF完成序列标注。实际上是将传统CRF中的线性模型换成了非线性神经网络。沿用CRF的出发点是：CRF使用句子级别的似然概率，能够更好的解决标记偏置问题[[2](#参考文献)]。本例也将基于此思路建立模型。虽然，这里以NER任务作为示例，但所给出的模型可以应用到其他各种序列标注任务中。
由于序列标注问题的广泛性，产生了[CRF](http://book.paddlepaddle.org/07.label_semantic_roles/index.cn.html)等经典的序列模型，这些模型大多只能使用局部信息或需要人工设计特征。随着深度学习研究的发展，循环神经网络（Recurrent Neural Network，RNN等 序列模型能够处理序列元素之间前后关联问题，能够从原始输入文本中学习特征表示，而更加适合序列标注任务，更多相关知识可参考PaddleBook中[语义角色标注](https://github.com/PaddlePaddle/book/blob/develop/07.label_semantic_roles/README.cn.md)一课。
## 模型详解
NER任务的输入是"一句话"，目标是识别句子中的实体边界及类别，我们参照论文\[[2](#参考文献)\]仅对原始句子进行了一些简单的预处理工作：将每个词转换为小写，并将原词是否大写另作为一个特征，共同作为模型的输入。模型如图2所示，工作流程如下：
1. 构造输入
- 输入1是句子序列，采用one-hot方式表示
- 输入2是大写标记序列，标记了句子中每一个词是否是大写，采用one-hot方式表示；
2. one-hot方式的句子序列和大写标记序列通过词表，转换为实向量表示的词向量序列；
3. 将步骤2中的2个词向量序列作为双向RNN的输入，学习输入序列的特征表示，得到新的特性表示序列；
4. CRF以步骤3中模型学习到的特征为输入，以标记序列为监督信号，实现序列标注。
<p align="center">
<img src="images/ner_network.png" width="40%" align="center"/><br/>
图2. NER 模型网络结构图
</p>
## 数据说明
在本例中，我们以 [CoNLL 2003 NER任务](http://www.clips.uantwerpen.be/conll2003/ner/)为例，原始Reuters数据由于版权原因需另外申请免费下载，请大家按照原网站说明获取。
+ 我们仅在`data`目录下的`train`和`test`文件中放置少数样本用以示例输入数据格式。
+ 本例依赖数据还包括
1. 输入文本的词典
2. 为词典中的词语提供预训练好的词向量
2. 标记标签的词典
标记标签词典已附在`data`目录中，对应于`data/target.txt`文件。输入文本的词典以及词典中词语的预训练的词向量来自：[Stanford CS224d](http://cs224d.stanford.edu/)课程作业。**为运行本例，请首先在`data`目录下运行`download.sh`脚本下载输入文本的词典和预训练的词向量。** 完成后会将这两个文件一并放入`data`目录下，输入文本的词典和预训练的词向量分别对应：`data/vocab.txt`和`data/wordVectors.txt`这两个文件。
CoNLL 2003原始数据格式如下：
```
U.N. NNP I-NP I-ORG
official NN I-NP O
Ekeus NNP I-NP I-PER
heads VBZ I-VP O
for IN I-PP O
Baghdad NNP I-NP I-LOC
. . O O
```
- 第一列为原始句子序列
- 第二、三列分别为词性标签和句法分析中的语块标签，本例不使用
- 第四列为采用了 I-TYPE 方式表示的NER标签
- I-TYPE 和 BIO 方式的主要区别在于语块开始标记的使用上，I-TYPE只有在出现相邻的同类别实体时对后者使用B标记，其他均使用I标记），句子之间以空行分隔。
我们在`reader.py`脚本中完成对原始数据的处理以及读取，主要包括下面几个步骤:
1. 从原始数据文件中抽取出句子和标签，构造句子序列和标签序列；
2. 将 I-TYPE 表示的标签转换为 BIO 方式表示的标签；
3. 将句子序列中的单词转换为小写，并构造大写标记序列；
4. 依据词典获取词对应的整数索引。
预处理完成后，一条训练样本包含3个部分作为神经网络的输入信息用于训练：（1）句子序列；（2）首字母大写标记序列；（3）标注序列，下表是一条训练样本的示例：
| 句子序列 | 大写标记序列 | 标注序列 |
|---|---|---|
| u.n. | 1 | B-ORG |
| official | 0 | O |
| ekeus | 1 | B-PER |
| heads | 0 | O |
| for | 0 | O |
| baghdad | 1 | B-LOC |
| . | 0 | O |
## 运行
### 编写数据读取接口
自定义数据读取接口只需编写一个 Python 生成器实现从原始输入文本中解析一条训练样本的逻辑。[reader.py](./reader.py) 中的`data_reader`函数实现了读取原始数据返回类型为： `paddle.data_type.integer_value_sequence`的 3 个输入（分别对应：词语在字典的序号、是否为大写、标注结果在字典中的序号）给`network_conf.ner_net`中定义的 3 个 `data_layer` 的功能。
### 训练
1. 运行 `sh data/download.sh`
2. 修改 `train.py` 的 `main` 函数，指定数据路径
```python
main(
train_data_file='data/train',
test_data_file='data/test',
vocab_file='data/vocab.txt',
target_file='data/target.txt',
emb_file='data/wordVectors.txt')
```
3. 运行命令 `python train.py` ，**需要注意：直接运行使用的是示例数据，请替换真实的标记数据。**
```text
commandline: --use_gpu=False --trainer_count=1
Initing parameters..
Init parameters done.
Pass 0, Batch 0, Cost 41.430110, {'ner_chunk.precision': 0.01587301678955555, 'ner_chunk.F1-score': 0.028368793427944183, 'ner_chunk.recall': 0.13333334028720856, 'error': 0.939393937587738}
Test with Pass 0, Batch 0, {'ner_chunk.precision': 0.0, 'ner_chunk.F1-score': 0.0, 'ner_chunk.recall': 0.0, 'error': 0.16260161995887756}
```
### 预测
1. 修改 [infer.py](./infer.py) 的 `main` 函数，指定：需要测试的模型的路径、测试数据、字典文件，预测标记文件的路径，默认参数如下：
```python
infer(
model_path="models/params_pass_0.tar.gz",
batch_size=2,
test_data_file="data/test",
vocab_file="data/vocab.txt",
target_file="data/target.txt")
```
2. 在终端运行 `python infer.py`，开始测试，会看到如下预测结果（以下为训练500个pass所得模型的部分预测结果）：
```text
cricket O
- O
leicestershire B-ORG
take O
over O
at O
top O
after O
innings O
victory O
. O
london B-LOC
1996-08-30 O
west B-MISC
indian I-MISC
all-rounder O
phil B-PER
simmons I-PER
took O
four O
```
输出分为两列，以“\t” 分隔，第一列是输入的词语，第二列是标记结果。多条输入序列之间以空行分隔。
## 参考文献
1. Graves A. [Supervised Sequence Labelling with Recurrent Neural Networks](http://www.cs.toronto.edu/~graves/preprint.pdf)[J]. Studies in Computational Intelligence, 2013, 385.
2. Collobert R, Weston J, Bottou L, et al. [Natural Language Processing (Almost) from Scratch](http://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf)[J]. Journal of Machine Learning Research, 2011, 12(1):2493-2537.
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# Single Shot MultiBox Detector (SSD) Object Detection
## Introduction
Single Shot MultiBox Detector (SSD) is one of the new and enhanced detection algorithms detecting objects in images [ 1 ]. SSD algorithm is characterized by rapid detection and high detection accuracy. PaddlePaddle has an integrated SSD algorithm! This example demonstrates how to use the SSD model in PaddlePaddle for object detection. We first provide a brief introduction to the SSD principle. Then we describe how to train, evaluate and test on the PASCAL VOC data set, and finally on how to use SSD on custom data set.
## SSD Architecture
SSD uses a convolutional neural network to achieve end-to-end detection. The term "End-to-end" is used because it uses the input as the original image and the output for the test results, without the use of external tools or processes for feature extraction. One popular model of SSD is VGG16 [ 2 ]. SSD differs from VGG16 network model as in following.
1. The final fc6, fc7 full connection layer into a convolution layer, convolution layer parameters through the original fc6, fc7 parameters obtained.
2. Change the parameters of the pool5 layer from 2x2-s2 (kernel size 2x2, stride size to 2) to 3x3-s1-p1 (kernel size is 3x3, stride size is 1, padding size is 1).
3. The initial layers are composed of conv4\_3、conv7、conv8\_2、conv9\_2、conv10\_2, and pool11 layers. The main purpose of the priorbox layer is to generate a series of rectangular candidates based on the input feature map. A more detailed introduction to SSD can be found in the paper\[[1](#References)\]。
Below is the overall structure of the model (300x300)
<p align="center">
<img src="images/ssd_network.png" width="900" height="250" hspace='10'/> <br/>
图1. SSD网络结构
</p>
Each box in the figure represents a convolution layer, and the last two rectangles represent the summary of each convolution layer output and the post-processing phase. Specifically, the network will output a set of candidate rectangles in the prediction phase. Each rectangle contains two types of information: the position and the category score. The network produces thousands of predictions at various scales and aspect ratios before performing non-maximum suppression, resulting in a handful of final tags.
## Example Overview
This example contains the following files:
<table>
<caption>Table 1. Directory structure</caption>
<tr><th>File</th><th>Description</th></tr>
<tr><td>train.py</td><td>Training script</td></tr>
<tr><td>eval.py</td><td>Evaluation</td></tr>
<tr><td>infer.py</td><td>Prediction using the trained model</tr>
<tr><td>visual.py</td><td>Visualization of the test results</td></tr>
<tr><td>image_util.py</td><td>Image preprocessing required common function</td></tr>
<tr><td>data_provider.py</td><td>Data processing scripts, generate training, evaluate or detect the required data</td></tr>
<tr><td>config/pascal_voc_conf.py</td><td> Neural network hyperparameter configuration file</td></tr>
<tr><td>data/label_list</td><td>Label list</td></tr>
<tr><td>data/prepare_voc_data.py</td><td>Prepare training PASCAL VOC data list</td></tr>
</table>
The training phase requires pre-processing of the data, including clipping, sampling, etc. This is done in ```image_util.py``` and ```data_provider.py```.```config/vgg_config.py```. ```data/prepare_voc_data.py``` is used to generate a list of files, including the training set and test set, the need to use the user to download and extract data, the default use of VOC2007 and VOC2012.
## PASCAL VOC Data set
### Data Preparation
First download the data set. VOC2007\[[3](#References)\] contains both training and test data set, and VOC2012\[[4](#References)\] contains only training set. Downloaded data are stored in ```data/VOCdevkit/VOC2007``` and ```data/VOCdevkit/VOC2012```. Next, run ```data/prepare_voc_data.py``` to generate ```trainval.txt``` and ```test.txt```. The relevant function is as following:
```python
def prepare_filelist(devkit_dir, years, output_dir):
trainval_list = []
test_list = []
for year in years:
trainval, test = walk_dir(devkit_dir, year)
trainval_list.extend(trainval)
test_list.extend(test)
random.shuffle(trainval_list)
with open(osp.join(output_dir, 'trainval.txt'), 'w') as ftrainval:
for item in trainval_list:
ftrainval.write(item[0] + ' ' + item[1] + '\n')
with open(osp.join(output_dir, 'test.txt'), 'w') as ftest:
for item in test_list:
ftest.write(item[0] + ' ' + item[1] + '\n')
```
The data in ```trainval.txt``` will look like:
```
VOCdevkit/VOC2007/JPEGImages/000005.jpg VOCdevkit/VOC2007/Annotations/000005.xml
VOCdevkit/VOC2007/JPEGImages/000007.jpg VOCdevkit/VOC2007/Annotations/000007.xml
VOCdevkit/VOC2007/JPEGImages/000009.jpg VOCdevkit/VOC2007/Annotations/000009.xml
```
The first field is the relative path of the image file, and the second field is the relative path of the corresponding label file.
### To Use Pre-trained Model
We also provide a pre-trained model using VGG-16 with good performance. To use the model, download the file http://paddlepaddle.bj.bcebos.com/model_zoo/detection/ssd_model/vgg_model.tar.gz, and place it as ```vgg/vgg_model.tar.gz```。
### Training
Next, run ```python train.py``` to train the model. Note that this example only supports the CUDA GPU environment, and can not be trained using only CPU. This is mainly because the training is very slow using CPU only.
```python
paddle.init(use_gpu=True, trainer_count=4)
data_args = data_provider.Settings(
data_dir='./data',
label_file='label_list',
resize_h=cfg.IMG_HEIGHT,
resize_w=cfg.IMG_WIDTH,
mean_value=[104,117,124])
train(train_file_list='./data/trainval.txt',
dev_file_list='./data/test.txt',
data_args=data_args,
init_model_path='./vgg/vgg_model.tar.gz')
```
Below is a description about this script:
1. Call ```paddle.init``` with 4 GPUs.
2. ```data_provider.Settings()``` is to pass configuration parameters. For ```config/vgg_config.py``` setting，300x300 is a typical configuration for both the accuracy and efficiency. It can be extended to 512x512 by modifying the configuration file.
3. In ```train()```执 function, ```train_file_list``` specifies the training data list, and ```dev_file_list``` specifies the evaluation data list, and ```init_model_path``` specifies the pre-training model location.
4. During the training process will print some log information, each training a batch will output the current number of rounds, the current batch cost and mAP (mean Average Precision. Each training pass will be saved a model to the default saved directory ```checkpoints``` (Need to be created in advance).
The following shows the SDD300x300 in the VOC data set.
<p align="center">
<img src="images/SSD300x300_map.png" hspace='10'/> <br/>
图2. SSD300x300 mAP收敛曲线
</p>
### Model Assessment
Next, run ```python eval.py``` to evaluate the model.
```python
paddle.init(use_gpu=True, trainer_count=4) # use 4 gpus
data_args = data_provider.Settings(
data_dir='./data',
label_file='label_list',
resize_h=cfg.IMG_HEIGHT,
resize_w=cfg.IMG_WIDTH,
mean_value=[104, 117, 124])
eval(
eval_file_list='./data/test.txt',
batch_size=4,
data_args=data_args,
model_path='models/pass-00000.tar.gz')
```
### Obejct Detection
Run ```python infer.py``` to perform the object detection using the trained model.
```python
infer(
eval_file_list='./data/infer.txt',
save_path='infer.res',
data_args=data_args,
batch_size=4,
model_path='models/pass-00000.tar.gz',
threshold=0.3)
```
Here ```eval_file_list``` specified image path list, ```save_path``` specifies directory to save the prediction result.
```text
VOCdevkit/VOC2007/JPEGImages/006936.jpg 12 0.997844 131.255611777 162.271582842 396.475315094 334.0
VOCdevkit/VOC2007/JPEGImages/006936.jpg 14 0.998557 229.160234332 49.5991278887 314.098775387 312.913876176
VOCdevkit/VOC2007/JPEGImages/006936.jpg 14 0.372522 187.543615699 133.727034628 345.647156239 327.448492289
...
```
一共包含4个字段，以tab分割，第一个字段是检测图像路径，第二字段为检测矩形框内类别，第三个字段是置信度，第四个字段是4个坐标值（以空格分割）。
Below is the example after running ```python visual.py``` to visualize the model result. The default visualization of the image saved in the ```./visual_res```.
<p align="center">
<img src="images/vis_1.jpg" height=150 width=200 hspace='10'/>
<img src="images/vis_2.jpg" height=150 width=200 hspace='10'/>
<img src="images/vis_3.jpg" height=150 width=100 hspace='10'/>
<img src="images/vis_4.jpg" height=150 width=200 hspace='10'/> <br />
Figure 3. SSD300x300 Visualization Example
</p>
## To Use Custo Data set
In PaddlePaddle, using the custom data set to train SSD model is also easy! Just input the format that ```train.txt``` can understand. Below is a recommended structure to input for ```train.txt```.
```text
image00000_file_path image00000_annotation_file_path
image00001_file_path image00001_annotation_file_path
image00002_file_path image00002_annotation_file_path
...
```
The first column is for the image file path, and the second column for the corresponding marked data file path. In the case of using xml file format, ```data_provider.py``` can be used to process the data as follows.
```python
bbox_labels = []
root = xml.etree.ElementTree.parse(label_path).getroot()
for object in root.findall('object'):
bbox_sample = []
# start from 1
bbox_sample.append(float(settings.label_list.index(
object.find('name').text)))
bbox = object.find('bndbox')
difficult = float(object.find('difficult').text)
bbox_sample.append(float(bbox.find('xmin').text)/img_width)
bbox_sample.append(float(bbox.find('ymin').text)/img_height)
bbox_sample.append(float(bbox.find('xmax').text)/img_width)
bbox_sample.append(float(bbox.find('ymax').text)/img_height)
bbox_sample.append(difficult)
bbox_labels.append(bbox_sample)
```
Now the marked data（e.g. image00000\_annotation\_file\_path）is as follows:
```text
label1 xmin1 ymin1 xmax1 ymax1
label2 xmin2 ymin2 xmax2 ymax2
...
```
Here each row corresponds to an object for 5 fields. The first is for the label (note the background 0, need to be numbered from 1), and the remaining four are for the coordinates.
```python
bbox_labels = []
with open(label_path) as flabel:
for line in flabel:
bbox_sample = []
bbox = [float(i) for i in line.strip().split()]
label = bbox[0]
bbox_sample.append(label)
bbox_sample.append(bbox[1]/float(img_width))
bbox_sample.append(bbox[2]/float(img_height))
bbox_sample.append(bbox[3]/float(img_width))
bbox_sample.append(bbox[4]/float(img_height))
bbox_sample.append(0.0)
bbox_labels.append(bbox_sample)
```
Another important thing is to change the size of the image and the size of the object to change the configuration of the network structure. Use ```config/vgg_config.py``` to create the custom configuration file. For more details, please refer to \[[1](#References)\]。
## References
1. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. [SSD: Single shot multibox detector](https://arxiv.org/abs/1512.02325). European conference on computer vision. Springer, Cham, 2016.
2. Simonyan, Karen, and Andrew Zisserman. [Very deep convolutional networks for large-scale image recognition](https://arxiv.org/abs/1409.1556). arXiv preprint arXiv:1409.1556 (2014).
3. [The PASCAL Visual Object Classes Challenge 2007](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html)
4. [Visual Object Classes Challenge 2012 (VOC2012)](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html)
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# 文本分类
以下是本例目录包含的文件以及对应说明:
```text
.
├── images # 文档中的图片
│   ├── cnn_net.png
│   └── dnn_net.png
├── index.html # 文档
├── infer.py # 预测脚本
├── network_conf.py # 本例中涉及的各种网络结构均定义在此文件中，若进一步修改模型结构，请查看此文件
├── reader.py # 读取数据接口，若使用自定义格式的数据，请查看此文件
├── README.md # 文档
├── run.sh # 训练任务运行脚本，直接运行此脚本，将以默认参数开始训练任务
├── train.py # 训练脚本
└── utils.py # 定义通用的函数，例如：打印日志、解析命令行参数、构建字典、加载字典等
```
## 简介
文本分类任务根据给定一条文本的内容，判断该文本所属的类别，是自然语言处理领域的一项重要的基础任务。[PaddleBook](https://github.com/PaddlePaddle/book) 中的[情感分类](https://github.com/PaddlePaddle/book/blob/develop/06.understand_sentiment/README.cn.md)一课，正是一个典型的文本分类任务，任务流程如下：
1. 收集电影评论网站的用户评论数据。
2. 清洗，标记。
3. 模型设计。
4. 模型学习效果评估。
训练好的分类器能够**自动判断**新出现的用户评论的情感是正面还是负面，在舆情监控、营销策划、产品品牌价值评估等任务中，能够起到重要作用。以上过程也是我们去完成一个新的文本分类任务需要遵循的常规流程。可以看到，深度学习方法的巨大优势体现在：**免除复杂的特征的设计，只需要对原始文本进行基础的清理、标注即可**。
[PaddleBook](https://github.com/PaddlePaddle/book) 中的[情感分类](https://github.com/PaddlePaddle/book/blob/develop/06.understand_sentiment/README.cn.md)介绍了一个较为复杂的栈式双向 LSTM 模型，循环神经网络在一些需要理解语言语义的复杂任务中有着明显的优势，但计算量大，通常对调参技巧也有着更高的要求。在对计算时间有一定限制的任务中，也会考虑其它模型。除了计算时间的考量，更重要的一点：**模型选择往往是机器学习任务成功的基础**。机器学习任务的目标始终是提高泛化能力，也就是对未知的新的样本预测的能力：
1. 简单模型拟合能力不足，无法精确拟合训练样本，更加无法期待模型能够准确地预测没有出现在训练样本集中的未知样本，这就是**欠拟合**问题。
2. 然而，过于复杂的模型轻松“记忆”了训练样本集中的每一个样本，但对于没有出现在训练样本集中的未知样本却毫无识别能力，这就是**过拟合**问题。
"No Free Lunch (NFL)" 是机器学习任务基本原则之一：没有任何一种模型是天生优于其他模型的。模型的设计和选择建立在了解不同模型特性的基础之上，但同时也是一个多次实验评估的过程。在本例中，我们继续向大家介绍几种最常用的文本分类模型，它们的能力和复杂程度不同，帮助大家对比学习这些模型学习效果之间的差异，针对不同的场景选择使用。
## 模型详解
`network_conf.py` 中包括以下模型：
1. `fc_net`： DNN 模型，是一个非序列模型。使用基本的全连接结构。
2. `convolution_net`：浅层 CNN 模型，是一个基础的序列模型，能够处理变长的序列输入，提取一个局部区域之内的特征。
我们以情感分类任务为例，简单说明序列模型和非序列模型之间的差异。情感分类是一项常见的文本分类任务，模型自动判断文本中表现出的情感是正向还是负向。以句子 "The apple is not bad" 为例，"not bad" 是决定这个句子情感的关键：
- 对于 DNN 模型来说，只知道句子中有一个 "not" 和一个 "bad"，两者之间的顺序关系在输入网络时已丢失，网络不再有机会学习序列之间的顺序信息。
- CNN 模型接受文本序列作为输入，保留了 "not bad" 之间的顺序信息。
两者各自的一些特点简单总结如下：
1. DNN 的计算量可以远低于 CNN / RNN 模型，在对响应时间有要求的任务中具有优势。
2. DNN 刻画的往往是频繁词特征，潜在会受到分词错误的影响，但对一些依赖关键词特征也能做的不错的任务：如 Spam 短信检测，依然是一个有效的模型。
3. 在大多数需要一定语义理解（例如，借助上下文消除语义中的歧义）的文本分类任务上，以 CNN / RNN 为代表的序列模型的效果往往好于 DNN 模型。
### 1. DNN 模型
**DNN 模型结构入下图所示：**
<p align="center">
<img src="images/dnn_net.png" width = "90%" align="center"/><br/>
图1. 本例中的 DNN 文本分类模型
</p>
在 PaddlePaddle 实现该 DNN 结构的代码见 `network_conf.py` 中的 `fc_net` 函数，模型主要分为如下几个部分：
- **词向量层**：为了更好地表示不同词之间语义上的关系，首先将词语转化为固定维度的向量。训练完成后，词与词语义上的相似程度可以用它们的词向量之间的距离来表示，语义上越相似，距离越近。关于词向量的更多信息请参考PaddleBook中的[词向量](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)一节。
- **最大池化层**：最大池化在时间序列上进行，池化过程消除了不同语料样本在单词数量多少上的差异，并提炼出词向量中每一下标位置上的最大值。经过池化后，词向量层输出的向量序列被转化为一条固定维度的向量。例如，假设最大池化前向量的序列为`[[2,3,5],[7,3,6],[1,4,0]]`，则最大池化的结果为：`[7,4,6]`。
- **全连接隐层**：经过最大池化后的向量被送入两个连续的隐层，隐层之间为全连接结构。
- **输出层**：输出层的神经元数量和样本的类别数一致，例如在二分类问题中，输出层会有2个神经元。通过Softmax激活函数，输出结果是一个归一化的概率分布，和为1，因此第$i$个神经元的输出就可以认为是样本属于第$i$类的预测概率。
该 DNN 模型默认对输入的语料进行二分类（`class_dim=2`），embedding（词向量）维度默认为28（`emd_dim=28`），两个隐层均使用Tanh激活函数（`act=paddle.activation.Tanh()`）。需要注意的是，该模型的输入数据为整数序列，而不是原始的单词序列。事实上，为了处理方便，我们一般会事先将单词根据词频顺序进行 id 化，即将词语转化成在字典中的序号。
### 2. CNN 模型
**CNN 模型结构如下图所示：**
<p align="center">
<img src="images/cnn_net.png" width = "90%" align="center"/><br/>
图2. 本例中的 CNN 文本分类模型
</p>
通过 PaddlePaddle 实现该 CNN 结构的代码见 `network_conf.py` 中的 `convolution_net` 函数，模型主要分为如下几个部分:
- **词向量层**：与 DNN 中词向量层的作用一样，将词语转化为固定维度的向量，利用向量之间的距离来表示词之间的语义相关程度。如图2所示，将得到的词向量定义为行向量，再将语料中所有的单词产生的行向量拼接在一起组成矩阵。假设词向量维度为5，句子 “The cat sat on the read mat” 含 7 个词语，那么得到的矩阵维度为 7*5。关于词向量的更多信息请参考 PaddleBook 中的[词向量](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)一节。
- **卷积层**： 文本分类中的卷积在时间序列上进行，即卷积核的宽度和词向量层产出的矩阵一致，卷积沿着矩阵的高度方向进行。卷积后得到的结果被称为“特征图”（feature map）。假设卷积核的高度为 $h$，矩阵的高度为 $N$，卷积的步长为 1，则得到的特征图为一个高度为 $N+1-h$ 的向量。可以同时使用多个不同高度的卷积核，得到多个特征图。
- **最大池化层**: 对卷积得到的各个特征图分别进行最大池化操作。由于特征图本身已经是向量，因此这里的最大池化实际上就是简单地选出各个向量中的最大元素。各个最大元素又被拼接在一起，组成新的向量，显然，该向量的维度等于特征图的数量，也就是卷积核的数量。举例来说，假设我们使用了四个不同的卷积核，卷积产生的特征图分别为：`[2,3,5]`、`[8,2,1]`、`[5,7,7,6]` 和 `[4,5,1,8]`，由于卷积核的高度不同，因此产生的特征图尺寸也有所差异。分别在这四个特征图上进行最大池化，结果为：`[5]`、`[8]`、`[7]`和`[8]`，最后将池化结果拼接在一起，得到`[5,8,7,8]`。
- **全连接与输出层**：将最大池化的结果通过全连接层输出，与 DNN 模型一样，最后输出层的神经元个数与样本的类别数量一致，且输出之和为 1。
CNN 网络的输入数据类型和 DNN 一致。PaddlePaddle 中已经封装好的带有池化的文本序列卷积模块：`paddle.networks.sequence_conv_pool`，可直接调用。该模块的 `context_len` 参数用于指定卷积核在同一时间覆盖的文本长度，即图 2 中的卷积核的高度。`hidden_size` 用于指定该类型的卷积核的数量。本例代码默认使用了 128 个大小为 3 的卷积核和 128 个大小为 4 的卷积核，这些卷积的结果经过最大池化和结果拼接后产生一个 256 维的向量，向量经过一个全连接层输出最终的预测结果。
## 使用 PaddlePaddle 内置数据运行
### 如何训练
在终端中执行 `sh run.sh` 以下命令， 将以 PaddlePaddle 内置的情感分类数据集：`paddle.dataset.imdb` 直接运行本例，会看到如下输入：
```text
Pass 0, Batch 0, Cost 0.696031, {'__auc_evaluator_0__': 0.47360000014305115, 'classification_error_evaluator': 0.5}
Pass 0, Batch 100, Cost 0.544438, {'__auc_evaluator_0__': 0.839249312877655, 'classification_error_evaluator': 0.30000001192092896}
Pass 0, Batch 200, Cost 0.406581, {'__auc_evaluator_0__': 0.9030032753944397, 'classification_error_evaluator': 0.2199999988079071}
Test at Pass 0, {'__auc_evaluator_0__': 0.9289745092391968, 'classification_error_evaluator': 0.14927999675273895}
```
日志每隔 100 个 batch 输出一次，输出信息包括：（1）Pass 序号；（2）Batch 序号；（3）依次输出当前 Batch 上评估指标的评估结果。评估指标在配置网络拓扑结构时指定，在上面的输出中，输出了训练样本集之的 AUC 以及错误率指标。
### 如何预测
训练结束后模型默认存储在当前工作目录下，在终端中执行 `python infer.py` ，预测脚本会加载训练好的模型进行预测。
- 默认加载使用 `paddle.data.imdb.train` 训练一个 Pass 产出的 DNN 模型对 `paddle.dataset.imdb.test` 进行测试
会看到如下输出：
```text
positive 0.9275 0.0725 previous reviewer <unk> <unk> gave a much better <unk> of the films plot details than i could what i recall mostly is that it was just so beautiful in every sense emotionally visually <unk> just <unk> br if you like movies that are wonderful to look at and also have emotional content to which that beauty is relevant i think you will be glad to have seen this extraordinary and unusual work of <unk> br on a scale of 1 to 10 id give it about an <unk> the only reason i shy away from 9 is that it is a mood piece if you are in the mood for a really artistic very romantic film then its a 10 i definitely think its a mustsee but none of us can be in that mood all the time so overall <unk>
negative 0.0300 0.9700 i love scifi and am willing to put up with a lot scifi <unk> are usually <unk> <unk> and <unk> i tried to like this i really did but it is to good tv scifi as <unk> 5 is to star trek the original silly <unk> cheap cardboard sets stilted dialogues cg that doesnt match the background and painfully onedimensional characters cannot be overcome with a scifi setting im sure there are those of you out there who think <unk> 5 is good scifi tv its not its clichéd and <unk> while us viewers might like emotion and character development scifi is a genre that does not take itself seriously <unk> star trek it may treat important issues yet not as a serious philosophy its really difficult to care about the characters here as they are not simply <unk> just missing a <unk> of life their actions and reactions are wooden and predictable often painful to watch the makers of earth know its rubbish as they have to always say gene <unk> earth otherwise people would not continue watching <unk> <unk> must be turning in their <unk> as this dull cheap poorly edited watching it without <unk> breaks really brings this home <unk> <unk> of a show <unk> into space spoiler so kill off a main character and then bring him back as another actor <unk> <unk> all over again
```
输出日志每一行是对一条样本预测的结果，以 `\t` 分隔，共 3 列，分别是：（1）预测类别标签；（2）样本分别属于每一类的概率，内部以空格分隔；（3）输入文本。
## 使用自定义数据训练和预测
### 如何训练
1. 数据组织
假设有如下格式的训练数据：每一行为一条样本，以 `\t` 分隔，第一列是类别标签，第二列是输入文本的内容，文本内容中的词语以空格分隔。以下是两条示例数据：
```
positive PaddlePaddle is good
negative What a terrible weather
```
2. 编写数据读取接口
自定义数据读取接口只需编写一个 Python 生成器实现**从原始输入文本中解析一条训练样本**的逻辑。以下代码片段实现了读取原始数据返回类型为： `paddle.data_type.integer_value_sequence`（词语在字典的序号）和 `paddle.data_type.integer_value`（类别标签）的 2 个输入给网络中定义的 2 个 `data_layer` 的功能。
```python
def train_reader(data_dir, word_dict, label_dict):
def reader():
UNK_ID = word_dict["<UNK>"]
word_col = 0
lbl_col = 1
for file_name in os.listdir(data_dir):
with open(os.path.join(data_dir, file_name), "r") as f:
for line in f:
line_split = line.strip().split("\t")
word_ids = [
word_dict.get(w, UNK_ID)
for w in line_split[word_col].split()
]
yield word_ids, label_dict[line_split[lbl_col]]
return reader
```
- 关于 PaddlePaddle 中 `data_layer` 接受输入数据的类型，以及数据读取接口对应该返回数据的格式，请参考 [input-types](http://www.paddlepaddle.org/release_doc/0.9.0/doc_cn/ui/data_provider/pydataprovider2.html#input-types) 一节。
- 以上代码片段详见本例目录下的 `reader.py` 脚本，`reader.py` 同时提供了读取测试数据的全部代码。
接下来，只需要将数据读取函数 `train_reader` 作为参数传递给 `train.py` 脚本中的 `paddle.batch` 接口即可使用自定义数据接口读取数据，调用方式如下：
```python
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.train_reader(train_data_dir, word_dict, lbl_dict),
buf_size=1000),
batch_size=batch_size)
```
3. 修改命令行参数
- 如果将数据组织成示例数据的同样的格式，只需在 `run.sh` 脚本中修改 `train.py` 启动参数，指定 `train_data_dir` 参数，可以直接运行本例，无需修改数据读取接口 `reader.py`。
- 执行 `python train.py --help` 可以获取`train.py` 脚本各项启动参数的详细说明，主要参数如下：
- `nn_type`：选择要使用的模型，目前支持两种：“dnn” 或者 “cnn”。
- `train_data_dir`：指定训练数据所在的文件夹，使用自定义数据训练，必须指定此参数，否则使用`paddle.dataset.imdb`训练，同时忽略`test_data_dir`，`word_dict`，和 `label_dict` 参数。
- `test_data_dir`：指定测试数据所在的文件夹，若不指定将不进行测试。
- `word_dict`：字典文件所在的路径，若不指定，将从训练数据根据词频统计，自动建立字典。
- `label_dict`：类别标签字典，用于将字符串类型的类别标签，映射为整数类型的序号。
- `batch_size`：指定多少条样本后进行一次神经网络的前向运行及反向更新。
- `num_passes`：指定训练多少个轮次。
### 如何预测
1. 修改 `infer.py` 中以下变量，指定使用的模型、指定测试数据。
```python
model_path = "dnn_params_pass_00000.tar.gz" # 指定模型所在的路径
nn_type = "dnn" # 指定测试使用的模型
test_dir = "./data/test" # 指定测试文件所在的目录
word_dict = "./data/dict/word_dict.txt" # 指定字典所在的路径
label_dict = "./data/dict/label_dict.txt" # 指定类别标签字典的路径
```
2. 在终端中执行 `python infer.py`。
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