From 277f283c0d1ab3ebb8b70b273a8159aeda661c14 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Thu, 10 Nov 2016 21:49:25 +0800 Subject: [PATCH 01/19] doc_cn --- doc_cn/demo/sentiment_analysis/bi_lstm.jpg | Bin 0 -> 35593 bytes doc_cn/demo/sentiment_analysis/index.rst | 9 + doc_cn/demo/sentiment_analysis/lstm.png | Bin 0 -> 50694 bytes .../sentiment_analysis/sentiment_analysis.md | 325 ++++++++++++++++++ .../demo/sentiment_analysis/stacked_lstm.jpg | Bin 0 -> 31077 bytes 5 files changed, 334 insertions(+) create mode 100644 doc_cn/demo/sentiment_analysis/bi_lstm.jpg create mode 100644 doc_cn/demo/sentiment_analysis/index.rst create mode 100644 doc_cn/demo/sentiment_analysis/lstm.png create mode 100644 doc_cn/demo/sentiment_analysis/sentiment_analysis.md create mode 100644 doc_cn/demo/sentiment_analysis/stacked_lstm.jpg diff --git a/doc_cn/demo/sentiment_analysis/bi_lstm.jpg b/doc_cn/demo/sentiment_analysis/bi_lstm.jpg new file mode 100644 index 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图表 1. LSTM [3]
+ +情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从字级到具有可变上下文长度的上下文级别(其通过门值来适配)来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 + +在本演示中,我们提供两个网络,即双向LSTM和三层堆叠LSTM。 + +#### 双向LSTM + +图2是双向LSTM网络,由全连接层和softmax层组成。 + +
![BiLSTM](./bi_lstm.jpg)
+
图 2. Bidirectional-LSTM
+ +#### Stacked-LSTM +图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量). 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]. + +
![StackedLSTM](./stacked_lstm.jpg)
+
图 3. Stacked-LSTM for sentiment analysis
+ +**Config** + +进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置. The first line imports predefined networks from `sentiment_net.py`. + +trainer_config.py: + +```python +from sentiment_net import * + +data_dir = "./data/pre-imdb" +# whether this config is used for test +is_test = get_config_arg('is_test', bool, False) +# whether this config is used for prediction +is_predict = get_config_arg('is_predict', bool, False) +dict_dim, class_dim = sentiment_data(data_dir, is_test, is_predict) + +################## Algorithm Config ##################### + +settings( + batch_size=128, + learning_rate=2e-3, + learning_method=AdamOptimizer(), + regularization=L2Regularization(8e-4), + gradient_clipping_threshold=25 +) + +#################### Network Config ###################### +stacked_lstm_net(dict_dim, class_dim=class_dim, + stacked_num=3, is_predict=is_predict) +#bidirectional_lstm_net(dict_dim, class_dim=class_dim, is_predict=is_predict) +``` + +* **Data Definition**: + * get\_config\_arg(): get arguments setted by `--config_args=xx` in commandline argument. + * Define TrainData and TestData provider, here using Python interface (PyDataProviderWrapper) of PaddlePaddle to load data. For details, you can refer to the document of PyDataProvider. + +* **Algorithm Configuration**: + * use sgd algorithm. + * use adam optimization. + * set batch size of 128. + * set average sgd window. + * set global learning rate. +* **Network Configuration**: + * dict_dim: get dictionary dimension. + * class_dim: set category number, IMDB has two label, namely positive and negative label. + * `stacked_lstm_net`: predefined network as shown in Figure 3, use this network by default. + * `bidirectional_lstm_net`: predefined network as shown in Figure 2. + +**Training** + +Install PaddlePaddle first if necessary. Then you can use script `train.sh` as follows to launch local training. + +``` +cd demo/sentiment/ +./train.sh +``` + +train.sh: + +``` +config=trainer_config.py +output=./model_output +paddle train --config=$config \ + --save_dir=$output \ + --job=train \ + --use_gpu=false \ + --trainer_count=4 \ + --num_passes=10 \ + --log_period=20 \ + --dot_period=20 \ + --show_parameter_stats_period=100 \ + --test_all_data_in_one_period=1 \ + 2>&1 | tee 'train.log' +``` + +* \--config=$config: set network config. +* \--save\_dir=$output: set output path to save models. +* \--job=train: set job mode to train. +* \--use\_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train. +* \--trainer\_count=4: set thread number (or GPU count). +* \--num\_passes=15: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. +* \--log\_period=20: print log every 20 batches. +* \--show\_parameter\_stats\_period=100: show parameter statistic every 100 batches. +* \--test\_all_data\_in\_one\_period=1: test all data every testing. + +If the run succeeds, the output log is saved in path of `demo/sentiment/train.log` and model is saved in path of `demo/sentiment/model_output/`. The output log is explained as follows. + +``` +Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification_error_evaluator=0.36875 CurrentEval: classification_error_evaluator=0.36875 +... +Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922 +Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406 +``` +- Batch=xx: means passing xx batches. +- samples=xx: means passing xx samples. +- AvgCost=xx: averaged cost from 0-th batch to current batch. +- CurrentCost=xx: current cost of latest log_period batches. +- Eval: classification\_error\_evaluator=xx: means classfication error from 0-th batch ro current batch. +- CurrentEval: classification\_error\_evaluator: current classfication error of the lates log_period batches. +- Pass=0: Going through all training set one time is called one pass. 0 means going through training set first time. + +By default, we use the `stacked_lstm_net` network, which converges at a faster rate than `bidirectional_lstm_net` when passing same sample number. If you want to use bidirectional LSTM, just remove comment in the last line and comment `stacked_lstm_net`. + +## Testing + +Testing means evaluating the labeled validation set using trained model. + +``` +cd demo/sentiment +./test.sh +``` + +test.sh: + +```bash +function get_best_pass() { + cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ + sed -r 'N;s/Test.* error=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \ + sort | head -n 1 +} + +log=train.log +LOG=`get_best_pass $log` +LOG=(${LOG}) +evaluate_pass="model_output/pass-${LOG[1]}" + +echo 'evaluating from pass '$evaluate_pass + +model_list=./model.list +touch $model_list | echo $evaluate_pass > $model_list +net_conf=trainer_config.py +paddle train --config=$net_conf \ + --model_list=$model_list \ + --job=test \ + --use_gpu=false \ + --trainer_count=4 \ + --config_args=is_test=1 \ + 2>&1 | tee 'test.log' +``` + +The function `get_best_pass` gets the best model by classification error rate for testing. In this example, We use test dataset of IMDB as validation by default. Unlike training, it needs to specify `--job=test` and model path, namely `--model_list=$model_list` here. If running successfully, the log is saved in path of `demo/sentiment/test.log`. For example, in our test, the best model is `model_output/pass-00002`, the classification error is 0.115645 as follows. + +``` +Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645 +``` + +## Prediction + +`predict.py` provides a predicting interface. You should install python api of PaddlePaddle before using it. One example to predict unlabeled review of IMDB is as follows. Simply running: + +``` +cd demo/sentiment +./predict.sh +``` +predict.sh: + +``` +#Note the default model is pass-00002, you shold make sure the model path +#exists or change the mode path. +model=model_output/pass-00002/ +config=trainer_config.py +label=data/pre-imdb/labels.list +python predict.py \ + -n $config\ + -w $model \ + -b $label \ + -d data/pre-imdb/dict.txt \ + -i data/aclImdb/test/pos/10007_10.txt +``` + +* `predict.py`: predicting interface. +* -n $config : set network configure. +* -w $model: set model path. +* -b $label: set dictionary about corresponding relation between integer label and string label. +* -d data/pre-imdb/dict.txt: set dictionary. +* -i data/aclImdb/test/pos/10014_7.txt: set one example file to predict. + +Note you should make sure the default model path `model_output/pass-00002` +exists or change the model path. + +Predicting result of this example: + +``` +Loading parameters from model_output/pass-00002/ +./data/aclImdb/test/pos/10014_7.txt: predicting label is pos +``` +We sincerely appreciate your interest and welcome your contributions. + +## Reference +[1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010.
+[2] Johan Bollen, Huina Mao, Xiaojun Zeng. 2011. [Twitter mood predicts the stock market](http://arxiv.org/abs/1010.3003), Journal of Computational Science.
+[3] Alex Graves, Marcus Liwicki, Santiago Fernan- dez, Roman Bertolami, Horst Bunke, and Ju ̈rgen Schmidhuber. 2009. [A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine In- telligence](http://www.cs.toronto.edu/~graves/tpami_2009.pdf), 31(5):855–868.
+[4] Zachary C. Lipton, [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/abs/1506.00019v1), arXiv:1506.00019.
+[5] Jie Zhou and Wei Xu; [End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks](http://www.aclweb.org/anthology/P/P15/P15-1109.pdf); ACL-IJCNLP 2015.
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a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -131,7 +131,7 @@ dataset **Config** -进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置. The first line imports predefined networks from `sentiment_net.py`. +进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置。第一行从`sentiment_net.py`中导出预定义的网络. trainer_config.py: @@ -161,25 +161,25 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, #bidirectional_lstm_net(dict_dim, class_dim=class_dim, is_predict=is_predict) ``` -* **Data Definition**: - * get\_config\_arg(): get arguments setted by `--config_args=xx` in commandline argument. - * Define TrainData and TestData provider, here using Python interface (PyDataProviderWrapper) of PaddlePaddle to load data. For details, you can refer to the document of PyDataProvider. +* **数椐定义**: + * get\_config\_arg(): 获取通过 `--config_args=xx` i设置的命令行参数。 + * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口(PyDataProviderWrapper)来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 -* **Algorithm Configuration**: - * use sgd algorithm. - * use adam optimization. - * set batch size of 128. - * set average sgd window. - * set global learning rate. -* **Network Configuration**: - * dict_dim: get dictionary dimension. - * class_dim: set category number, IMDB has two label, namely positive and negative label. - * `stacked_lstm_net`: predefined network as shown in Figure 3, use this network by default. - * `bidirectional_lstm_net`: predefined network as shown in Figure 2. +* **算法配置**: + * 使用随机梯度下降(sgd)算法. + * 使用 adam 优化. + * 设置批量大小为128. + * 设置平均sgd窗口。 + * 设置全局学习率。 +* **网络配置**: + * dict_dim: 获取字典维度。 + * class_dim: 设置类别号,IMDB有两个标签,即正面评价标签和负面评价标签。 + * `stacked_lstm_net`: 预定义网络如图3所示,默认情况下使用此网络 + * `bidirectional_lstm_net`: 预定义网络,如图2所示。 -**Training** +**训练** -Install PaddlePaddle first if necessary. Then you can use script `train.sh` as follows to launch local training. +首先安装PaddlePaddle. 然后使用下面的脚本 `train.sh` 来开启本地的训练。 ``` cd demo/sentiment/ @@ -204,17 +204,17 @@ paddle train --config=$config \ 2>&1 | tee 'train.log' ``` -* \--config=$config: set network config. -* \--save\_dir=$output: set output path to save models. -* \--job=train: set job mode to train. -* \--use\_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train. -* \--trainer\_count=4: set thread number (or GPU count). -* \--num\_passes=15: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. -* \--log\_period=20: print log every 20 batches. -* \--show\_parameter\_stats\_period=100: show parameter statistic every 100 batches. -* \--test\_all_data\_in\_one\_period=1: test all data every testing. +* \--config=$config: 设置网络配置。 +* \--save\_dir=$output: 设置输出路径以保存训练完成的模型。 +* \--job=train: 设置工作模式为训练。 +* \--use\_gpu=false: 使用CPU训练,设置为true,如果你安装GPU版本的PaddlePaddle,并想使用GPU来训练。 +* \--trainer\_count=4:设置线程数(或GPU计数)。 +* \--num\_passes=15: 设置通过数,PaddlePaddle中的一个通过数意味着对数据集中的所有样本进行一次训练。 +* \--log\_period=20: 每20批打印一次日志。 +* \--show\_parameter\_stats\_period=100: 每100批显示一次参数统计。 +* \--test\_all_data\_in\_one\_period=1: 每次测试都测试所有数据。 -If the run succeeds, the output log is saved in path of `demo/sentiment/train.log` and model is saved in path of `demo/sentiment/model_output/`. The output log is explained as follows. +如果运行成功,输出日志保存在路径 `demo/sentiment/train.log`中,模型保存在目录`demo/sentiment/model_output/`中。 输出日志说明如下: ``` Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification_error_evaluator=0.36875 CurrentEval: classification_error_evaluator=0.36875 @@ -222,19 +222,19 @@ Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922 Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406 ``` -- Batch=xx: means passing xx batches. -- samples=xx: means passing xx samples. -- AvgCost=xx: averaged cost from 0-th batch to current batch. -- CurrentCost=xx: current cost of latest log_period batches. -- Eval: classification\_error\_evaluator=xx: means classfication error from 0-th batch ro current batch. -- CurrentEval: classification\_error\_evaluator: current classfication error of the lates log_period batches. -- Pass=0: Going through all training set one time is called one pass. 0 means going through training set first time. +- Batch=xx: 表示传递xx批次. +- samples=xx: 表示传递xx个样本。 +- AvgCost=xx: 从第0批到当前批的平均花费时间。 +- CurrentCost=xx: 最新的log_period批处理的当前时间花费。 +- Eval: classification\_error\_evaluator=xx: 表示第0批次到当前批次的分类错误。 +- CurrentEval: classification\_error\_evaluator: 最新日志周期批次的分类错误。 +- Pass=0: 通过所有训练集一次称为一遍。 0表示第一次经过训练集。 -By default, we use the `stacked_lstm_net` network, which converges at a faster rate than `bidirectional_lstm_net` when passing same sample number. If you want to use bidirectional LSTM, just remove comment in the last line and comment `stacked_lstm_net`. +默认情况下,我们使用`stacked_lstm_net`网络,当传递相同的样本数时,它的收敛速度比`bidirectional_lstm_net`快。If you want to use bidirectional LSTM, 如果要使用双向LSTM,只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。 -## Testing +## 测试模型 -Testing means evaluating the labeled validation set using trained model. +测试模型是指使用训练出的模型评估已标记的验证集。 ``` cd demo/sentiment @@ -269,15 +269,15 @@ paddle train --config=$net_conf \ 2>&1 | tee 'test.log' ``` -The function `get_best_pass` gets the best model by classification error rate for testing. In this example, We use test dataset of IMDB as validation by default. Unlike training, it needs to specify `--job=test` and model path, namely `--model_list=$model_list` here. If running successfully, the log is saved in path of `demo/sentiment/test.log`. For example, in our test, the best model is `model_output/pass-00002`, the classification error is 0.115645 as follows. +函数`get_best_pass`通过计算分类错误率获得最佳模型进行测试。 在本示例中,我们默认使用IMDB的测试数据集作为验证。 与训练不同,它需要在这里指定`--job = test`和模型路径,即`--model_list = $model_list`。如果运行成功,日志将保存在“demo / sentiment / test.log”的路径中。例如,在我们的测试中,最好的模型是`model_output / pass-00002`,分类误差是0.115645,如下: ``` Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645 ``` -## Prediction +## 预测 -`predict.py` provides a predicting interface. You should install python api of PaddlePaddle before using it. One example to predict unlabeled review of IMDB is as follows. Simply running: +`predict.py`脚本提供了一个预测接口。在使用它之前请安装PaddlePaddle的python api。 预测IMDB的未标记评论的一个实例如下: ``` cd demo/sentiment @@ -299,25 +299,24 @@ python predict.py \ -i data/aclImdb/test/pos/10007_10.txt ``` -* `predict.py`: predicting interface. -* -n $config : set network configure. -* -w $model: set model path. -* -b $label: set dictionary about corresponding relation between integer label and string label. -* -d data/pre-imdb/dict.txt: set dictionary. -* -i data/aclImdb/test/pos/10014_7.txt: set one example file to predict. +* `predict.py`: 预测接口脚本。 +* -n $config : 设置网络配置。 +* -w $model: 设置模型路径。 +* -b $label: 设置标签类别字典,这个字典是整数标签和字符串标签的一个对应。 +* -d data/pre-imdb/dict.txt: 设置字段文件。 +* -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。 -Note you should make sure the default model path `model_output/pass-00002` -exists or change the model path. +注意你应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 -Predicting result of this example: +本示例的预测结果: ``` Loading parameters from model_output/pass-00002/ ./data/aclImdb/test/pos/10014_7.txt: predicting label is pos ``` -We sincerely appreciate your interest and welcome your contributions. +我们真诚地感谢您的关注,并欢迎您来贡献。 -## Reference +## 参考文档 [1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010.
[2] Johan Bollen, Huina Mao, Xiaojun Zeng. 2011. [Twitter mood predicts the stock market](http://arxiv.org/abs/1010.3003), Journal of Computational Science.
[3] Alex Graves, Marcus Liwicki, Santiago Fernan- dez, Roman Bertolami, Horst Bunke, and Ju ̈rgen Schmidhuber. 2009. [A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine In- telligence](http://www.cs.toronto.edu/~graves/tpami_2009.pdf), 31(5):855–868.
From a834e7470c66136bbf7674eff4c19e6f224718f9 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 15:25:24 +0800 Subject: [PATCH 03/19] sentiment analysis translation --- doc_cn/demo/sentiment_analysis/sentiment_analysis.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index 8da196ca52330..292dc5bb7732a 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -314,7 +314,7 @@ python predict.py \ Loading parameters from model_output/pass-00002/ ./data/aclImdb/test/pos/10014_7.txt: predicting label is pos ``` -我们真诚地感谢您的关注,并欢迎您来贡献。 +我们真诚地感谢您的关注,并欢迎您来参于贡献。 ## 参考文档 [1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010.
From a9e76b0e66a855e50896aa8517dad3d0a685bea6 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 15:37:38 +0800 Subject: [PATCH 04/19] sentiment analysis translation detail --- doc/demo/sentiment_analysis/index.rst | 2 +- .../sentiment_analysis/sentiment_analysis.md | 66 +++++++++---------- 2 files changed, 34 insertions(+), 34 deletions(-) diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst index 9ee6d3a177c19..21dd5c19074e5 100644 --- a/doc/demo/sentiment_analysis/index.rst +++ b/doc/demo/sentiment_analysis/index.rst @@ -1,4 +1,4 @@ -Sentiment Analasis Tutorial +情感分析指导教程 =========================== .. toctree:: diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index 292dc5bb7732a..3b124f6219bc9 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -1,8 +1,8 @@ # 情感分析指导教程 -情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成好坏两类. +情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类. -情感分析也常用于基于大量评论和个人博客来监控社会媒体. 例如,研究人员分析了几个关于消费者信心和政治观点的调查,结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]. 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]. +情感分析也常用于基于大量评论和个人博客来监控社会媒体。 例如,研究人员分析了几个关于消费者信心和政治观点的调查,结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]。 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]。 另一方面,抓取产品的用户评论并分析他们的情感,有助于理解用户对不同公司,不同产品,甚至不同竞争对手产品的偏好。 @@ -24,20 +24,20 @@ cd demo/sentiment/data aclImdb get_imdb.sh imdb mosesdecoder-master ``` -* aclImdb: 从外部网站上下载的原始数椐集. -* imdb: 仅包含训练和测试数椐集. -* mosesdecoder-master: Moses 工具. +* aclImdb: 从外部网站上下载的原始数椐集。 +* imdb: 仅包含训练和测试数椐集。 +* mosesdecoder-master: Moses 工具。 -IMDB数据集包含25,000个已标注过的高极性电影评论用于训练,25,000个用于测试。负面的评论的得分小于等于4,正面的评论的得大于等于7,总评分10分。 运行完脚本 `./get_imdb.sh`后, 我们可以看到在目录 `aclImdb`中的数椐集的结构如下:. +IMDB数据集包含25,000个已标注过的高极性电影评论用于训练,25,000个用于测试。负面的评论的得分小于等于4,正面的评论的得大于等于7,总评分10分。 运行完脚本 `./get_imdb.sh`后, 我们可以看到在目录 `aclImdb`中的数椐集的结构如下: ``` imdbEr.txt imdb.vocab README test train ``` -* train: 训练数椐集. -* test : 测试数椐集. -* imdb.vocab: 字典文件. -* imdbEr.txt: 字典imdb.vocab中每个切分单词的预期评级. -* README: 数椐说明文档. +* train: 训练数椐集。 +* test : 测试数椐集。 +* imdb.vocab: 字典文件。 +* imdbEr.txt: 字典imdb.vocab中每个切分单词的预期评级。 +* README: 数椐说明文档。 测试集和训练集目录包含下面的文件: @@ -47,13 +47,13 @@ labeledBow.feat neg pos unsup unsupBow.feat urls_neg.txt urls_pos.txt url * pos: 正面评价样本,包含12,500个txt文件,每个文件是一个电影评论。 * neg: 负面评价样本,包含12,500个txt文件,每个文件是一个电影评论。 -* unsup: 未标记的评价样本,包含50,000个txt文件 -* urls_xx.txt: 每个评论的网址. +* unsup: 未标记的评价样本,包含50,000个txt文件。 +* urls_xx.txt: 每个评论的网址。 * xxBow.feat: 用于统计词频的Bow模型特征。 ### IMDB 数椐准备 -在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在测试集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有. Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号. 执行下面的命令就可以预处理数椐. +在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在测试集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。 ``` cd demo/sentiment/ @@ -66,22 +66,22 @@ data_dir="./data/imdb" python preprocess.py -i data_dir ``` -* data_dir: 输入数椐所在目录. -* preprocess.py: 预处理脚本. +* data_dir: 输入数椐所在目录。 +* preprocess.py: 预处理脚本。 运行成功后目录`demo/sentiment/data/pre-imdb` 结构如下: ``` dict.txt labels.list test.list test_part_000 train.list train_part_000 ``` -* test\_part\_000 and train\_part\_000: 所有标记的训练集和测试集. 训练集已经随机打乱. -* train.list and test.list: 训练集和测试集文件列表. -* dict.txt: 利用训练集生成的字典. +* test\_part\_000 and train\_part\_000: 所有标记的训练集和测试集. 训练集已经随机打乱。 +* train.list and test.list: 训练集和测试集文件列表。 +* dict.txt: 利用训练集生成的字典。 * labels.txt: neg 0, pos 1, 含义:标签0表示负面的评论,标签1表示正面的评论。 ### 用户自定义数椐预处理 -如果你执行其它的用情感分析来分类文本的任务,可以按如下的结构来准备数椐. 我们提供了脚本来构建字典和预处理数椐. 所以你只用按下面的结构来组织数椐就行了. +如果你执行其它的用情感分析来分类文本的任务,可以按如下的结构来准备数椐. 我们提供了脚本来构建字典和预处理数椐。所以你只用按下面的结构来组织数椐就行了。 ``` dataset @@ -98,16 +98,16 @@ dataset | | |----text_files | | ... ``` -* dataset: 一级目录. -* train, test: 二级目录. -* class1,class2,...: 三级目录. -* text_files: 文本格式的实例文件. +* dataset: 一级目录。 +* train, test: 二级目录。 +* class1,class2,...: 三级目录。 +* text_files: 文本格式的实例文件。 -所有同目录下的文本实例文件都是同级别的. 每个文本文件包含一个或者多个实例,每一行表示一个实例. 为了充分的随机打乱训练集, 在预处理含有多行数椐的文本文件时参数设置稍有不同, 执行`preprocess.sh`脚本时需要加上`-m True`参数. tokenizer.perl 默认用来切分单记和标点符号,如果你不需要这个操作,在运行`preprocess.sh`时加上`-t False`参数即可. +所有同目录下的文本实例文件都是同级别的。 每个文本文件包含一个或者多个实例,每一行表示一个实例。 为了充分的随机打乱训练集, 在预处理含有多行数椐的文本文件时参数设置稍有不同, 执行`preprocess.sh`脚本时需要加上`-m True`参数。 tokenizer.perl 默认用来切分单记和标点符号,如果你不需要这个操作,在运行`preprocess.sh`时加上`-t False`参数即可。 ## 训练模型 -在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标循环现神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换. 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。 +在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标循环现神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。
![LSTM](./lstm.png)
图表 1. LSTM [3]
@@ -124,14 +124,14 @@ dataset
图 2. Bidirectional-LSTM
#### Stacked-LSTM -图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量). 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]. +图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。
![StackedLSTM](./stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
**Config** -进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置。第一行从`sentiment_net.py`中导出预定义的网络. +进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置。第一行从`sentiment_net.py`中导出预定义的网络。 trainer_config.py: @@ -166,9 +166,9 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口(PyDataProviderWrapper)来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 * **算法配置**: - * 使用随机梯度下降(sgd)算法. - * 使用 adam 优化. - * 设置批量大小为128. + * 使用随机梯度下降(sgd)算法。 + * 使用 adam 优化。 + * 设置批量大小为128。 * 设置平均sgd窗口。 * 设置全局学习率。 * **网络配置**: @@ -179,7 +179,7 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, **训练** -首先安装PaddlePaddle. 然后使用下面的脚本 `train.sh` 来开启本地的训练。 +首先安装PaddlePaddle。 然后使用下面的脚本 `train.sh` 来开启本地的训练。 ``` cd demo/sentiment/ @@ -222,7 +222,7 @@ Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922 Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406 ``` -- Batch=xx: 表示传递xx批次. +- Batch=xx: 表示传递xx批次。 - samples=xx: 表示传递xx个样本。 - AvgCost=xx: 从第0批到当前批的平均花费时间。 - CurrentCost=xx: 最新的log_period批处理的当前时间花费。 From eaa0cecf4abf8d3a02f23838b236f5645719f154 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 19:33:04 +0800 Subject: [PATCH 05/19] sentiment analysis translation detail --- doc_cn/demo/sentiment_analysis/lstm.png | Bin 50694 -> 0 bytes .../sentiment_analysis/sentiment_analysis.md | 6 +++--- 2 files changed, 3 insertions(+), 3 deletions(-) delete mode 100644 doc_cn/demo/sentiment_analysis/lstm.png diff --git a/doc_cn/demo/sentiment_analysis/lstm.png b/doc_cn/demo/sentiment_analysis/lstm.png deleted file mode 100644 index aaf1fc690da2ffb8418cde5ed81848ddb5263030..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 50694 zcmdRVgLh<4)NRMMZD-=7W80cwV%xSRwryuJ;bdalww+9DC$E3+`_}s(Uf1f?>)xt+ zZrxM2PVK!SQ6cRi>JOBVdl93iy1pvUDL0>mmXwaDlGNXFX52S^Nf(QUmALsj~ 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![LSTM](../../../doc/demo/sentiment_analysis/lstm.png)
图表 1. LSTM [3]
情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从字级到具有可变上下文长度的上下文级别(其通过门值来适配)来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 @@ -129,7 +129,7 @@ dataset
![StackedLSTM](./stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
-**Config** +**配置** 进入`demo/sentiment` 目录 , `trainer_config.py` 是一个配置文件的例子, 其中包含算法和网络配置。第一行从`sentiment_net.py`中导出预定义的网络。 @@ -314,7 +314,7 @@ python predict.py \ Loading parameters from model_output/pass-00002/ ./data/aclImdb/test/pos/10014_7.txt: predicting label is pos ``` -我们真诚地感谢您的关注,并欢迎您来参于贡献。 +我们真诚地感谢您的关注,并欢迎您来参与贡献。 ## 参考文档 [1] Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. [From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series](http://homes.cs.washington.edu/~nasmith/papers/oconnor+balasubramanyan+routledge+smith.icwsm10.pdf). In ICWSM-2010.
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![BiLSTM](../../../doc/demo/sentiment_analysis/bi_lstm.jpg)
图 2. Bidirectional-LSTM
#### Stacked-LSTM 图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。 -
![StackedLSTM](./stacked_lstm.jpg)
+
![StackedLSTM](../../../doc/demo/sentiment_analysis/stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
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*#1MW%lKtJ{7rjW3P*uJ!C?HLF8b-%GJ)r*?D~5@>{blIj_g75%@UIp4Yzl zDy14)au5~701yak06Zyr+4YbEb07#{2Y7nnmDn~cr^-2w%@!pwuZ8dJzNg+7Fecg> z=xT8n?C6eCRf$J5Zt%pQhs0c)@a9KC?TiLDwN+CC1LFE>Z?rE@EtnWDR+i%zi!y8n z^won*f<6GST$+FP2mhUYTWAGq+SiP5{!)Z~QX$1~=hIhIzR;;I?39rYE%gP^vzp{P z7N-HE;CX+Sf=8=oK37?$vT8nuFEBA4ZX8SB81n#Kx)1k2yI+B3(w6!Em48qGgTgY- k_OBYO_Jbo8Z4^z;Ni)t`_0Z7J-Uk2smiXOb@T1}X18vpS$^ZZW From 0f0041991352c30f9d8bcb1bac6ab5b6143de044 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 20:24:09 +0800 Subject: [PATCH 07/19] sentiment analysis translation detail fix --- doc/demo/sentiment_analysis/index.rst | 10 +- doc_cn/demo/image_classification/cifar.png | Bin 0 -> 466572 bytes .../image_classification.md | 221 ++++++++++++++++++ .../image_classification.png | Bin 0 -> 52635 bytes doc_cn/demo/image_classification/index.rst | 10 + doc_cn/demo/image_classification/lenet.png | Bin 0 -> 49835 bytes doc_cn/demo/image_classification/plot.png | Bin 0 -> 31006 bytes doc_cn/demo/sentiment_analysis/index.rst | 10 +- 8 files changed, 241 insertions(+), 10 deletions(-) create mode 100644 doc_cn/demo/image_classification/cifar.png create mode 100644 doc_cn/demo/image_classification/image_classification.md create mode 100644 doc_cn/demo/image_classification/image_classification.png create mode 100644 doc_cn/demo/image_classification/index.rst create mode 100644 doc_cn/demo/image_classification/lenet.png create mode 100644 doc_cn/demo/image_classification/plot.png diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst index 21dd5c19074e5..6e9c608792a74 100644 --- a/doc/demo/sentiment_analysis/index.rst +++ b/doc/demo/sentiment_analysis/index.rst @@ -1,9 +1,9 @@ -情感分析指导教程 +Sentiment Analasis Tutorial =========================== .. toctree:: - :maxdepth: 3 - :glob: +:maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + internal/cluster_train.md diff --git 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zjV;6K_n&dd3rMH@Q@t+@3Kls4Zsx}9RR}AVfu<%a7Iu%x?q z_ndI#MP@MDFsMrsXPC9&KREEF54#Wh-nmXzy`3LkmXg_ju;c{7xi1Ch{-*MnPW*p+ z_m=0}@a}v0EvwoF$ntdxea5i=gO)!8JQ8Xy-;}-t%Km>(_dZ<)Db7P4avjlIzNpi_@% literal 0 HcmV?d00001 diff --git a/doc_cn/demo/image_classification/image_classification.md b/doc_cn/demo/image_classification/image_classification.md new file mode 100644 index 0000000000000..29cfc99702c36 --- /dev/null +++ b/doc_cn/demo/image_classification/image_classification.md @@ -0,0 +1,221 @@ +Image Classification Tutorial +============================== + +This tutorial will guide you through training a convolutional neural network to classify objects using the CIFAR-10 image classification dataset. +As shown in the following figure, the convolutional neural network can recognize the main object in images, and output the classification result. + +

![Image Classification](./image_classification.png)
+ +## Data Preparation +First, download CIFAR-10 dataset. CIFAR-10 dataset can be downloaded from its official website. + + + +We have prepared a script to download and process CIFAR-10 dataset. The script will download CIFAR-10 dataset from the official dataset. +It will convert it to jpeg images and organize them into a directory with the required structure for the tutorial. Make sure that you have installed pillow and its dependents. +Consider the following commands: + +1. install pillow dependents + +```bash +sudo apt-get install libjpeg-dev +pip install pillow +``` + +2. download data and preparation + +```bash +cd demo/image_classification/data/ +sh download_cifar.sh +``` + +The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. + +Here are the classes in the dataset, as well as 10 random images from each: +
![Image Classification](./cifar.png)
+ + +After downloading and converting, we should find a directory (cifar-out) containing the dataset in the following format: + +``` +train +---airplane +---automobile +---bird +---cat +---deer +---dog +---frog +---horse +---ship +---truck +test +---airplane +---automobile +---bird +---cat +---deer +---dog +---frog +---horse +---ship +---truck +``` + +It has two directories:`train` and `test`. These two directories contain training data and testing data of CIFAR-10, respectively. Each of these two folders contains 10 sub-folders, ranging from `airplane` to `truck`. Each sub-folder contains images with the corresponding label. After the images are organized into this structure, we are ready to train an image classification model. + +## Preprocess +After the data has been downloaded, it needs to be pre-processed into the Paddle format. We can run the following command for preprocessing. + +``` +cd demo/image_classification/ +sh preprocess.sh +``` + +`preprocess.sh` calls `./demo/image_classification/preprocess.py` to preprocess image data. +```sh +export PYTHONPATH=$PYTHONPATH:../../ +data_dir=./data/cifar-out +python preprocess.py -i $data_dir -s 32 -c 1 +``` + +`./demo/image_classification/preprocess.py` has the following arguments + +- `-i` or `--input` specifes the input data directory. +- `-s` or `--size` specifies the processed size of images. +- `-c` or `--color` specifes whether images are color images or gray images. + + +## Model Training +We need to create a model config file before training the model. An example of the config file (vgg_16_cifar.py) is listed below. **Note**, it is slightly different from the `vgg_16_cifar.py` which also applies to the prediction. + +```python +from paddle.trainer_config_helpers import * +data_dir='data/cifar-out/batches/' +meta_path=data_dir+'batches.meta' +args = {'meta':meta_path, 'mean_img_size': 32, + 'img_size': 32, 'num_classes': 10, + 'use_jpeg': 1, 'color': "color"} +define_py_data_sources2(train_list=data_dir+"train.list", + test_list=data_dir+'test.list', + module='image_provider', + obj='processData', + args=args) +settings( + batch_size = 128, + learning_rate = 0.1 / 128.0, + learning_method = MomentumOptimizer(0.9), + regularization = L2Regularization(0.0005 * 128)) + +img = data_layer(name='image', size=3*32*32) +lbl = data_layer(name="label", size=10) +# small_vgg is predined in trainer_config_helpers.network +predict = small_vgg(input_image=img, num_channels=3) +outputs(classification_cost(input=predict, label=lbl)) +``` + +The first line imports python functions for defining networks. +```python +from paddle.trainer_config_helpers import * +``` + +Then define an `define_py_data_sources2` which use python data provider +interface. The arguments in `args` are used in `image_provider.py` which +yeilds image data and transform them to Paddle. + - `meta`: the mean value of training set. + - `mean_img_size`: the size of mean feature map. + - `img_size`: the height and width of input image. + - `num_classes`: the number of classes. + - `use_jpeg`: the data storage type when preprocessing. + - `color`: specify color image. + +`settings` specifies the training algorithm. In the following example, +it specifies learning rate as 0.1, but divided by batch size, and the weight decay +is 0.0005 and multiplied by batch size. +```python +settings( + batch_size = 128, + learning_rate = 0.1 / 128.0, + learning_method = MomentumOptimizer(0.9), + regularization = L2Regularization(0.0005 * 128) +) +``` + +The `small_vgg` specifies the network. We use a small version of VGG convolutional network as our network +for classification. A description of VGG network can be found here [http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/). +```python +# small_vgg is predined in trainer_config_helpers.network +predict = small_vgg(input_image=img, num_channels=3) +``` +After writing the config, we can train the model by running the script train.sh. Notice that the following script assumes the you run the script in the `./demo/image_classification` folder. If you run the script in a different folder, you need to change the paths of the scripts and the configuration files accordingly. + +```bash +config=vgg_16_cifar.py +output=./cifar_vgg_model +log=train.log + +paddle train \ +--config=$config \ +--dot_period=10 \ +--log_period=100 \ +--test_all_data_in_one_period=1 \ +--use_gpu=1 \ +--save_dir=$output \ +2>&1 | tee $log + +python -m paddle.utils.plotcurve -i $log > plot.png +``` + +- Here we use GPU mode to train. If you have no gpu environment, just set `use_gpu=0`. + +- `./demo/image_classification/vgg_16_cifar.py` is the network and data configuration file. The meaning of the other flags can be found in the documentation of the command line flags. + +- The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`. + + +After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below: + +
![Training and testing curves.](./plot.png)
+ + +## Prediction +After we train the model, the model file as well as the model parameters are stored in path `./cifar_vgg_model/pass-%05d`. For example, the model of the 300-th pass is stored at `./cifar_vgg_model/pass-00299`. + +To make a prediction for an image, one can run `predict.sh` as follows. The script will output the label of the classfiication. + +``` +sh predict.sh +``` + +predict.sh: +``` +model=cifar_vgg_model/pass-00299/ +image=data/cifar-out/test/airplane/seaplane_s_000978.png +use_gpu=1 +python prediction.py $model $image $use_gpu +``` + +## Exercise +Train a image classification of birds using VGG model and CUB-200 dataset. The birds dataset can be downloaded here. It contains an image dataset with photos of 200 bird species (mostly North American). + + + + + + +## Delve into Details +### Convolutional Neural Network +A Convolutional Neural Network is a feedforward neural network that uses convolution layers. It is very suitable for building neural networks that process and understand images. A standard convolutional neural network is shown below: + +![Convolutional Neural Network](./lenet.png) + +Convolutional Neural Network contains the following layers: + +- Convolutional layer: It uses convolution operation to extract features from an image or a feature map. +- Pooling layer: It uses max-pooling to downsample feature maps. +- Fully Connected layer: It uses fully connected connections to transform features. + +Convolutional Neural Network achieves amazing performance for image classification because it exploits two important characteristics of images: *local correlation* and *spatial invariance*. By iteratively applying convolution and max-pooing operations, convolutional neural network can well represent these two characteristics of images. + + +For more details of how to define layers and their connections, please refer to the documentation of layers. diff --git a/doc_cn/demo/image_classification/image_classification.png b/doc_cn/demo/image_classification/image_classification.png new file mode 100644 index 0000000000000000000000000000000000000000..14f255805081c1b4fab27eaf336fd389fa93ca19 GIT binary patch literal 52635 zcmZ^~19W7;^FKPVHnweTw6Qj}jY&4P?agMRjcsd#iLFI!+fF7n=Via&-yiRt^X8m8 zr@L-d_3gfWyQ)7`9jU4;gNjUq3;+O7qmHaO~k```et~So@HcpNd|MZ%Eaq@5%rlR`S(EtAZ z_c`5dEdNhSj&A?ct@jSH|GUG^!S<2;f9rl175YaNkZ^KvcC~PGd)F_*BlI86{}=85 zwDVtnRcj}Ar}t5CwK0=(bhmJQ7j`%O_jDqh|0DhX&+-4&rQ&L1@!sqINOSy;^#4u! zAATYBe;xn72I9Y?{2%K3FpD4yvH$N^CW2hM!?^|kjHt*-ifeispS~hwgCDilQFDj+ zJ-Yc7$;N(IO55PXe-o31m)q~#H6gwry1=qG8%0UC0OI*^yw#$i1a?&+HD98Deo+xb 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b/doc_cn/demo/sentiment_analysis/index.rst @@ -1,9 +1,9 @@ -Sentiment Analasis Tutorial +情感分析指导教程 =========================== .. toctree:: - :maxdepth: 3 - :glob: +:maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + internal/cluster_train.md From f5ad3cedee5c252ba7bdc039f37932ed62074cd8 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:04:21 +0800 Subject: [PATCH 08/19] Revert "sentiment analysis translation detail" This reverts commit 47b7d52faa1f8242aa77abab5d94305ceef37dc1. --- doc_cn/demo/sentiment_analysis/bi_lstm.jpg | Bin 0 -> 35593 bytes .../sentiment_analysis/sentiment_analysis.md | 4 ++-- doc_cn/demo/sentiment_analysis/stacked_lstm.jpg | Bin 0 -> 31077 bytes 3 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 doc_cn/demo/sentiment_analysis/bi_lstm.jpg create mode 100644 doc_cn/demo/sentiment_analysis/stacked_lstm.jpg diff --git a/doc_cn/demo/sentiment_analysis/bi_lstm.jpg 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![BiLSTM](./bi_lstm.jpg)
图 2. Bidirectional-LSTM
#### Stacked-LSTM 图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。 -
![StackedLSTM](../../../doc/demo/sentiment_analysis/stacked_lstm.jpg)
+
![StackedLSTM](./stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
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*#1MW%lKtJ{7rjW3P*uJ!C?HLF8b-%GJ)r*?D~5@>{blIj_g75%@UIp4Yzl zDy14)au5~701yak06Zyr+4YbEb07#{2Y7nnmDn~cr^-2w%@!pwuZ8dJzNg+7Fecg> z=xT8n?C6eCRf$J5Zt%pQhs0c)@a9KC?TiLDwN+CC1LFE>Z?rE@EtnWDR+i%zi!y8n z^won*f<6GST$+FP2mhUYTWAGq+SiP5{!)Z~QX$1~=hIhIzR;;I?39rYE%gP^vzp{P z7N-HE;CX+Sf=8=oK37?$vT8nuFEBA4ZX8SB81n#Kx)1k2yI+B3(w6!Em48qGgTgY- k_OBYO_Jbo8Z4^z;Ni)t`_0Z7J-Uk2smiXOb@T1}X18vpS$^ZZW literal 0 HcmV?d00001 From 789b70285e87a948da15a91e045c22a3cc0a8ead Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:07:14 +0800 Subject: [PATCH 09/19] Revert "sentiment analysis translation detail fix" This reverts commit 0f0041991352c30f9d8bcb1bac6ab5b6143de044. --- doc/demo/sentiment_analysis/index.rst | 10 +- doc_cn/demo/image_classification/cifar.png | Bin 466572 -> 0 bytes .../image_classification.md | 221 ------------------ .../image_classification.png | Bin 52635 -> 0 bytes doc_cn/demo/image_classification/index.rst | 10 - doc_cn/demo/image_classification/lenet.png | Bin 49835 -> 0 bytes doc_cn/demo/image_classification/plot.png | Bin 31006 -> 0 bytes doc_cn/demo/sentiment_analysis/index.rst | 10 +- 8 files changed, 10 insertions(+), 241 deletions(-) delete mode 100644 doc_cn/demo/image_classification/cifar.png delete mode 100644 doc_cn/demo/image_classification/image_classification.md delete mode 100644 doc_cn/demo/image_classification/image_classification.png delete mode 100644 doc_cn/demo/image_classification/index.rst delete mode 100644 doc_cn/demo/image_classification/lenet.png delete mode 100644 doc_cn/demo/image_classification/plot.png diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst index 6e9c608792a74..21dd5c19074e5 100644 --- a/doc/demo/sentiment_analysis/index.rst +++ b/doc/demo/sentiment_analysis/index.rst @@ -1,9 +1,9 @@ -Sentiment Analasis Tutorial +情感分析指导教程 =========================== .. toctree:: -:maxdepth: 3 - :glob: + :maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + 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zjV;6K_n&dd3rMH@Q@t+@3Kls4Zsx}9RR}AVfu<%a7Iu%x?q z_ndI#MP@MDFsMrsXPC9&KREEF54#Wh-nmXzy`3LkmXg_ju;c{7xi1Ch{-*MnPW*p+ z_m=0}@a}v0EvwoF$ntdxea5i=gO)!8JQ8Xy-;}-t%Km>(_dZ<)Db7P4avjlIzNpi_@% diff --git a/doc_cn/demo/image_classification/image_classification.md b/doc_cn/demo/image_classification/image_classification.md deleted file mode 100644 index 29cfc99702c36..0000000000000 --- a/doc_cn/demo/image_classification/image_classification.md +++ /dev/null @@ -1,221 +0,0 @@ -Image Classification Tutorial -============================== - -This tutorial will guide you through training a convolutional neural network to classify objects using the CIFAR-10 image classification dataset. -As shown in the following figure, the convolutional neural network can recognize the main object in images, and output the classification result. - -

![Image Classification](./image_classification.png)
- -## Data Preparation -First, download CIFAR-10 dataset. CIFAR-10 dataset can be downloaded from its official website. - - - -We have prepared a script to download and process CIFAR-10 dataset. The script will download CIFAR-10 dataset from the official dataset. -It will convert it to jpeg images and organize them into a directory with the required structure for the tutorial. Make sure that you have installed pillow and its dependents. -Consider the following commands: - -1. install pillow dependents - -```bash -sudo apt-get install libjpeg-dev -pip install pillow -``` - -2. download data and preparation - -```bash -cd demo/image_classification/data/ -sh download_cifar.sh -``` - -The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. - -Here are the classes in the dataset, as well as 10 random images from each: -
![Image Classification](./cifar.png)
- - -After downloading and converting, we should find a directory (cifar-out) containing the dataset in the following format: - -``` -train ----airplane ----automobile ----bird ----cat ----deer ----dog ----frog ----horse ----ship ----truck -test ----airplane ----automobile ----bird ----cat ----deer ----dog ----frog ----horse ----ship ----truck -``` - -It has two directories:`train` and `test`. These two directories contain training data and testing data of CIFAR-10, respectively. Each of these two folders contains 10 sub-folders, ranging from `airplane` to `truck`. Each sub-folder contains images with the corresponding label. After the images are organized into this structure, we are ready to train an image classification model. - -## Preprocess -After the data has been downloaded, it needs to be pre-processed into the Paddle format. We can run the following command for preprocessing. - -``` -cd demo/image_classification/ -sh preprocess.sh -``` - -`preprocess.sh` calls `./demo/image_classification/preprocess.py` to preprocess image data. -```sh -export PYTHONPATH=$PYTHONPATH:../../ -data_dir=./data/cifar-out -python preprocess.py -i $data_dir -s 32 -c 1 -``` - -`./demo/image_classification/preprocess.py` has the following arguments - -- `-i` or `--input` specifes the input data directory. -- `-s` or `--size` specifies the processed size of images. -- `-c` or `--color` specifes whether images are color images or gray images. - - -## Model Training -We need to create a model config file before training the model. An example of the config file (vgg_16_cifar.py) is listed below. **Note**, it is slightly different from the `vgg_16_cifar.py` which also applies to the prediction. - -```python -from paddle.trainer_config_helpers import * -data_dir='data/cifar-out/batches/' -meta_path=data_dir+'batches.meta' -args = {'meta':meta_path, 'mean_img_size': 32, - 'img_size': 32, 'num_classes': 10, - 'use_jpeg': 1, 'color': "color"} -define_py_data_sources2(train_list=data_dir+"train.list", - test_list=data_dir+'test.list', - module='image_provider', - obj='processData', - args=args) -settings( - batch_size = 128, - learning_rate = 0.1 / 128.0, - learning_method = MomentumOptimizer(0.9), - regularization = L2Regularization(0.0005 * 128)) - -img = data_layer(name='image', size=3*32*32) -lbl = data_layer(name="label", size=10) -# small_vgg is predined in trainer_config_helpers.network -predict = small_vgg(input_image=img, num_channels=3) -outputs(classification_cost(input=predict, label=lbl)) -``` - -The first line imports python functions for defining networks. -```python -from paddle.trainer_config_helpers import * -``` - -Then define an `define_py_data_sources2` which use python data provider -interface. The arguments in `args` are used in `image_provider.py` which -yeilds image data and transform them to Paddle. - - `meta`: the mean value of training set. - - `mean_img_size`: the size of mean feature map. - - `img_size`: the height and width of input image. - - `num_classes`: the number of classes. - - `use_jpeg`: the data storage type when preprocessing. - - `color`: specify color image. - -`settings` specifies the training algorithm. In the following example, -it specifies learning rate as 0.1, but divided by batch size, and the weight decay -is 0.0005 and multiplied by batch size. -```python -settings( - batch_size = 128, - learning_rate = 0.1 / 128.0, - learning_method = MomentumOptimizer(0.9), - regularization = L2Regularization(0.0005 * 128) -) -``` - -The `small_vgg` specifies the network. We use a small version of VGG convolutional network as our network -for classification. A description of VGG network can be found here [http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/). -```python -# small_vgg is predined in trainer_config_helpers.network -predict = small_vgg(input_image=img, num_channels=3) -``` -After writing the config, we can train the model by running the script train.sh. Notice that the following script assumes the you run the script in the `./demo/image_classification` folder. If you run the script in a different folder, you need to change the paths of the scripts and the configuration files accordingly. - -```bash -config=vgg_16_cifar.py -output=./cifar_vgg_model -log=train.log - -paddle train \ ---config=$config \ ---dot_period=10 \ ---log_period=100 \ ---test_all_data_in_one_period=1 \ ---use_gpu=1 \ ---save_dir=$output \ -2>&1 | tee $log - -python -m paddle.utils.plotcurve -i $log > plot.png -``` - -- Here we use GPU mode to train. If you have no gpu environment, just set `use_gpu=0`. - -- `./demo/image_classification/vgg_16_cifar.py` is the network and data configuration file. The meaning of the other flags can be found in the documentation of the command line flags. - -- The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`. - - -After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below: - -
![Training and testing curves.](./plot.png)
- - -## Prediction -After we train the model, the model file as well as the model parameters are stored in path `./cifar_vgg_model/pass-%05d`. For example, the model of the 300-th pass is stored at `./cifar_vgg_model/pass-00299`. - -To make a prediction for an image, one can run `predict.sh` as follows. The script will output the label of the classfiication. - -``` -sh predict.sh -``` - -predict.sh: -``` -model=cifar_vgg_model/pass-00299/ -image=data/cifar-out/test/airplane/seaplane_s_000978.png -use_gpu=1 -python prediction.py $model $image $use_gpu -``` - -## Exercise -Train a image classification of birds using VGG model and CUB-200 dataset. The birds dataset can be downloaded here. It contains an image dataset with photos of 200 bird species (mostly North American). - - - - - - -## Delve into Details -### Convolutional Neural Network -A Convolutional Neural Network is a feedforward neural network that uses convolution layers. It is very suitable for building neural networks that process and understand images. A standard convolutional neural network is shown below: - -![Convolutional Neural Network](./lenet.png) - -Convolutional Neural Network contains the following layers: - -- Convolutional layer: It uses convolution operation to extract features from an image or a feature map. -- Pooling layer: It uses max-pooling to downsample feature maps. -- Fully Connected layer: It uses fully connected connections to transform features. - -Convolutional Neural Network achieves amazing performance for image classification because it exploits two important characteristics of images: *local correlation* and *spatial invariance*. By iteratively applying convolution and max-pooing operations, convolutional neural network can well represent these two characteristics of images. - - -For more details of how to define layers and their connections, please refer to the documentation of layers. diff --git a/doc_cn/demo/image_classification/image_classification.png b/doc_cn/demo/image_classification/image_classification.png deleted file mode 100644 index 14f255805081c1b4fab27eaf336fd389fa93ca19..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 52635 zcmZ^~19W7;^FKPVHnweTw6Qj}jY&4P?agMRjcsd#iLFI!+fF7n=Via&-yiRt^X8m8 zr@L-d_3gfWyQ)7`9jU4;gNjUq3;+O7qmHaO~k```et~So@HcpNd|MZ%Eaq@5%rlR`S(EtAZ z_c`5dEdNhSj&A?ct@jSH|GUG^!S<2;f9rl175YaNkZ^KvcC~PGd)F_*BlI86{}=85 zwDVtnRcj}Ar}t5CwK0=(bhmJQ7j`%O_jDqh|0DhX&+-4&rQ&L1@!sqINOSy;^#4u! zAATYBe;xn72I9Y?{2%K3FpD4yvH$N^CW2hM!?^|kjHt*-ifeispS~hwgCDilQFDj+ zJ-Yc7$;N(IO55PXe-o31m)q~#H6gwry1=qG8%0UC0OI*^yw#$i1a?&+HD98Deo+xb 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-1,9 +1,9 @@ -情感分析指导教程 +Sentiment Analasis Tutorial =========================== .. toctree:: -:maxdepth: 3 - :glob: + :maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + internal/cluster_train.md From 2fe61ceced83d9a14a42c046eb2e5663271c12ed Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:18:26 +0800 Subject: [PATCH 10/19] sentiment analysis translation fix errors --- doc/demo/sentiment_analysis/index.rst | 10 +++++----- doc_cn/demo/sentiment_analysis/bi_lstm.jpg | Bin 35593 -> 0 bytes doc_cn/demo/sentiment_analysis/index.rst | 10 +++++----- .../sentiment_analysis/sentiment_analysis.md | 4 ++-- doc_cn/demo/sentiment_analysis/stacked_lstm.jpg | Bin 31077 -> 0 bytes 5 files changed, 12 insertions(+), 12 deletions(-) delete mode 100644 doc_cn/demo/sentiment_analysis/bi_lstm.jpg delete mode 100644 doc_cn/demo/sentiment_analysis/stacked_lstm.jpg diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst 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a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index ea4f8a7e0edfe..a65f3f164ce0e 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -120,13 +120,13 @@ dataset 图2是双向LSTM网络,由全连接层和softmax层组成。 -

![BiLSTM](./bi_lstm.jpg)
+
![BiLSTM](../../../doc/demo/sentiment_analysis/bi_lstm.jpg)
图 2. Bidirectional-LSTM
#### Stacked-LSTM 图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。 -
![StackedLSTM](./stacked_lstm.jpg)
+
![StackedLSTM](../../../doc/demo/sentiment_analysis/stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
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*#1MW%lKtJ{7rjW3P*uJ!C?HLF8b-%GJ)r*?D~5@>{blIj_g75%@UIp4Yzl zDy14)au5~701yak06Zyr+4YbEb07#{2Y7nnmDn~cr^-2w%@!pwuZ8dJzNg+7Fecg> z=xT8n?C6eCRf$J5Zt%pQhs0c)@a9KC?TiLDwN+CC1LFE>Z?rE@EtnWDR+i%zi!y8n z^won*f<6GST$+FP2mhUYTWAGq+SiP5{!)Z~QX$1~=hIhIzR;;I?39rYE%gP^vzp{P z7N-HE;CX+Sf=8=oK37?$vT8nuFEBA4ZX8SB81n#Kx)1k2yI+B3(w6!Em48qGgTgY- k_OBYO_Jbo8Z4^z;Ni)t`_0Z7J-Uk2smiXOb@T1}X18vpS$^ZZW From 90be101d1a1fb0faf41701b6c7cb5d8cbfe58d2a Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:25:23 +0800 Subject: [PATCH 11/19] sentiment analysis translation fix errors --- doc_cn/demo/sentiment_analysis/sentiment_analysis.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index a65f3f164ce0e..3c090734309b2 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -1,6 +1,6 @@ # 情感分析指导教程 -情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类. +情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类。 情感分析也常用于基于大量评论和个人博客来监控社会媒体。 例如,研究人员分析了几个关于消费者信心和政治观点的调查,结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]。 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]。 From 1d92b9917708170860c96c7f38379f943dd57885 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:40:13 +0800 Subject: [PATCH 12/19] sentiment analysis translation fix errors --- doc/demo/sentiment_analysis/index.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst index 0d07919f4122a..2b7ac58723b67 100644 --- a/doc/demo/sentiment_analysis/index.rst +++ b/doc/demo/sentiment_analysis/index.rst @@ -2,8 +2,8 @@ Sentiment Analasis Tutorial =========================== .. toctree:: -:maxdepth: 3 - :glob: + :maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + internal/cluster_train.md \ No newline at end of file From 476e94ff7a76d1c95af7532babfc23aecc533447 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Sat, 12 Nov 2016 00:05:38 +0800 Subject: [PATCH 13/19] sentiment analysis translation fix errors --- doc/demo/sentiment_analysis/index.rst | 2 +- doc_cn/demo/sentiment_analysis/index.rst | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/demo/sentiment_analysis/index.rst index 2b7ac58723b67..9ee6d3a177c19 100644 --- a/doc/demo/sentiment_analysis/index.rst +++ b/doc/demo/sentiment_analysis/index.rst @@ -6,4 +6,4 @@ Sentiment Analasis Tutorial :glob: Training Locally - internal/cluster_train.md \ No newline at end of file + internal/cluster_train.md diff --git a/doc_cn/demo/sentiment_analysis/index.rst b/doc_cn/demo/sentiment_analysis/index.rst index 9ae432bdb45fa..21dd5c19074e5 100644 --- a/doc_cn/demo/sentiment_analysis/index.rst +++ b/doc_cn/demo/sentiment_analysis/index.rst @@ -2,8 +2,8 @@ =========================== .. toctree:: -:maxdepth: 3 - :glob: + :maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally + internal/cluster_train.md From 56e31f741d7fa61481af3f783c5d48f3e279965d Mon Sep 17 00:00:00 2001 From: zouxidong Date: Mon, 14 Nov 2016 14:11:44 +0800 Subject: [PATCH 14/19] sentiment analysis doc_cn update for qingqing01 review --- doc_cn/demo/sentiment_analysis/index.rst | 3 +- .../sentiment_analysis/sentiment_analysis.md | 50 +++++++++---------- 2 files changed, 26 insertions(+), 27 deletions(-) diff --git a/doc_cn/demo/sentiment_analysis/index.rst b/doc_cn/demo/sentiment_analysis/index.rst index 21dd5c19074e5..d19393374648d 100644 --- a/doc_cn/demo/sentiment_analysis/index.rst +++ b/doc_cn/demo/sentiment_analysis/index.rst @@ -5,5 +5,4 @@ :maxdepth: 3 :glob: - Training Locally - internal/cluster_train.md + Training Locally \ No newline at end of file diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index 3c090734309b2..c61cb0c3179a2 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -1,6 +1,6 @@ # 情感分析指导教程 -情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类。 +情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站、团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类。 情感分析也常用于基于大量评论和个人博客来监控社会媒体。 例如,研究人员分析了几个关于消费者信心和政治观点的调查,结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]。 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]。 @@ -12,7 +12,7 @@ ### IMDB 数椐介绍 -训练模型之前, 我们需要预处理数椐并构建一个字典. 首先, 你可以使用下面的脚本下载 IMDB 数椐集和[Moses](http://www.statmt.org/moses/)工具, 这是一个基于统计的机器翻译系统. 我们提供了一个数据预处理脚本,它不仅能够处理IMDB数据,还能处理其他用户自定义的数据。 为了使用提前编写的脚本,需要将标记的训练和测试样本移动到另一个路径,这已经在`get_imdb.sh`中完成。 +训练模型之前, 我们需要预处理数椐并构建一个字典。 首先, 你可以使用下面的脚本下载 IMDB 数椐集和[Moses](http://www.statmt.org/moses/)工具, 这是一个基于统计的机器翻译系统. 我们提供了一个数据预处理脚本,它不仅能够处理IMDB数据,还能处理其他用户自定义的数据。 为了使用提前编写的脚本,需要将标记的训练和测试样本移动到另一个路径,这已经在`get_imdb.sh`中完成。 ``` cd demo/sentiment/data @@ -53,7 +53,7 @@ labeledBow.feat neg pos unsup unsupBow.feat urls_neg.txt urls_pos.txt url ### IMDB 数椐准备 -在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在测试集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。 +在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在训练集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。 ``` cd demo/sentiment/ @@ -74,7 +74,7 @@ python preprocess.py -i data_dir ``` dict.txt labels.list test.list test_part_000 train.list train_part_000 ``` -* test\_part\_000 and train\_part\_000: 所有标记的训练集和测试集. 训练集已经随机打乱。 +* test\_part\_000 and train\_part\_000: 所有标记的测试集和训练集, 训练集已经随机打乱。 * train.list and test.list: 训练集和测试集文件列表。 * dict.txt: 利用训练集生成的字典。 * labels.txt: neg 0, pos 1, 含义:标签0表示负面的评论,标签1表示正面的评论。 @@ -107,24 +107,24 @@ dataset ## 训练模型 -在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标循环现神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。 +在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标准循环神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。
![LSTM](../../../doc/demo/sentiment_analysis/lstm.png)
图表 1. LSTM [3]
-情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从字级到具有可变上下文长度的上下文级别(其通过门值来适配)来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 +情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从词级到具有可变上下文长度的上下文级别来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 在本演示中,我们提供两个网络,即双向LSTM和三层堆叠LSTM。 #### 双向LSTM -图2是双向LSTM网络,由全连接层和softmax层组成。 +图2是双向LSTM网络,后面连全连接层和softmax层。
![BiLSTM](../../../doc/demo/sentiment_analysis/bi_lstm.jpg)
图 2. Bidirectional-LSTM
#### Stacked-LSTM -图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。 +图3是三层LSTM结构。图的底部是word embedding(对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二个是反向LSTM。然后提取隐藏LSTM层的所有时间步长的最大词向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。
![StackedLSTM](../../../doc/demo/sentiment_analysis/stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
@@ -163,17 +163,17 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, * **数椐定义**: * get\_config\_arg(): 获取通过 `--config_args=xx` i设置的命令行参数。 - * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口(PyDataProviderWrapper)来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 + * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 * **算法配置**: * 使用随机梯度下降(sgd)算法。 * 使用 adam 优化。 - * 设置批量大小为128。 + * 设置batch size大小为128。 * 设置平均sgd窗口。 * 设置全局学习率。 * **网络配置**: * dict_dim: 获取字典维度。 - * class_dim: 设置类别号,IMDB有两个标签,即正面评价标签和负面评价标签。 + * class_dim: 设置类别数,IMDB有两个标签,即正面评价标签和负面评价标签。 * `stacked_lstm_net`: 预定义网络如图3所示,默认情况下使用此网络 * `bidirectional_lstm_net`: 预定义网络,如图2所示。 @@ -207,11 +207,11 @@ paddle train --config=$config \ * \--config=$config: 设置网络配置。 * \--save\_dir=$output: 设置输出路径以保存训练完成的模型。 * \--job=train: 设置工作模式为训练。 -* \--use\_gpu=false: 使用CPU训练,设置为true,如果你安装GPU版本的PaddlePaddle,并想使用GPU来训练。 -* \--trainer\_count=4:设置线程数(或GPU计数)。 -* \--num\_passes=15: 设置通过数,PaddlePaddle中的一个通过数意味着对数据集中的所有样本进行一次训练。 -* \--log\_period=20: 每20批打印一次日志。 -* \--show\_parameter\_stats\_period=100: 每100批显示一次参数统计。 +* \--use\_gpu=false: 使用CPU训练,如果你安装GPU版本的PaddlePaddle,并想使用GPU来训练设置为true。 +* \--trainer\_count=4:设置线程数(或GPU个数)。 +* \--num\_passes=15: 设置pass,PaddlePaddle中的一个pass意味着对数据集中的所有样本进行一次训练。 +* \--log\_period=20: 每20个batch打印一次日志。 +* \--show\_parameter\_stats\_period=100: 每100个batch打印一次统计信息。 * \--test\_all_data\_in\_one\_period=1: 每次测试都测试所有数据。 如果运行成功,输出日志保存在路径 `demo/sentiment/train.log`中,模型保存在目录`demo/sentiment/model_output/`中。 输出日志说明如下: @@ -222,15 +222,15 @@ Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922 Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406 ``` -- Batch=xx: 表示传递xx批次。 -- samples=xx: 表示传递xx个样本。 -- AvgCost=xx: 从第0批到当前批的平均花费时间。 -- CurrentCost=xx: 最新的log_period批处理的当前时间花费。 -- Eval: classification\_error\_evaluator=xx: 表示第0批次到当前批次的分类错误。 -- CurrentEval: classification\_error\_evaluator: 最新日志周期批次的分类错误。 +- Batch=xx: 表示训练了xx个Batch。 +- samples=xx: 表示训练了xx个样本。。 +- AvgCost=xx: 从第0个batch到当前batch的平均损失。 +- CurrentCost=xx: 最新log_period个batch处理的当前损失。 +- Eval: classification\_error\_evaluator=xx: 表示第0个batch到当前batch的分类错误。 +- CurrentEval: classification\_error\_evaluator: 最新log_period个batch的分类错误。 - Pass=0: 通过所有训练集一次称为一遍。 0表示第一次经过训练集。 -默认情况下,我们使用`stacked_lstm_net`网络,当传递相同的样本数时,它的收敛速度比`bidirectional_lstm_net`快。If you want to use bidirectional LSTM, 如果要使用双向LSTM,只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。 +默认情况下,我们使用`stacked_lstm_net`网络,当传递相同的样本数时,它的收敛速度比`bidirectional_lstm_net`快。如果要使用双向LSTM,只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。 ## 测试模型 @@ -269,7 +269,7 @@ paddle train --config=$net_conf \ 2>&1 | tee 'test.log' ``` -函数`get_best_pass`通过计算分类错误率获得最佳模型进行测试。 在本示例中,我们默认使用IMDB的测试数据集作为验证。 与训练不同,它需要在这里指定`--job = test`和模型路径,即`--model_list = $model_list`。如果运行成功,日志将保存在“demo / sentiment / test.log”的路径中。例如,在我们的测试中,最好的模型是`model_output / pass-00002`,分类误差是0.115645,如下: +函数`get_best_pass`依据分类错误率获得最佳模型进行测试。 在本示例中,我们默认使用IMDB的测试数据集作为验证。 与训练不同,它需要在这里指定`--job = test`和模型路径,即`--model_list = $model_list`。如果运行成功,日志将保存在“demo / sentiment / test.log”的路径中。例如,在我们的测试中,最好的模型是`model_output / pass-00002`,分类误差是0.115645,如下: ``` Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645 @@ -306,7 +306,7 @@ python predict.py \ * -d data/pre-imdb/dict.txt: 设置字段文件。 * -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。 -注意你应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 +注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 本示例的预测结果: From 8a2723adb2d025477960dc4704cf02905798740b Mon Sep 17 00:00:00 2001 From: zouxidong Date: Mon, 14 Nov 2016 21:21:08 +0800 Subject: [PATCH 15/19] sentiment analysis translation fix index link --- doc_cn/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc_cn/index.rst b/doc_cn/index.rst index f1398206fddff..8f554cfd9e6ea 100644 --- a/doc_cn/index.rst +++ b/doc_cn/index.rst @@ -5,6 +5,7 @@ PaddlePaddle文档 -------- * `介绍 `_ * `快速入门 `_ +* `情感分析 `_ * `基本使用概念 `_ * `编译与安装 `_ * `用户接口 `_ From 3fbdd49e8fe2bc7522f5961a59ee5a25cef7de2b Mon Sep 17 00:00:00 2001 From: zouxidong Date: Mon, 14 Nov 2016 21:30:36 +0800 Subject: [PATCH 16/19] sentiment analysis doc_cn update for qingqing01 review --- doc_cn/demo/sentiment_analysis/sentiment_analysis.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index c61cb0c3179a2..45583c9bbe7a7 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -6,7 +6,7 @@ 另一方面,抓取产品的用户评论并分析他们的情感,有助于理解用户对不同公司,不同产品,甚至不同竞争对手产品的偏好。 -本教程将指导您完成长期短期记忆(LSTM)网络的训练过程,以分类来自[大型电影评论数据集](http://ai.stanford.edu/~amaas/data/sentiment/)(有时称为[互联网电影数据库 (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf))的句子的情感 。 此数据集包含电影评论及其相关联的二进制情绪极性标签,即正面和负面。 +本教程将指导您完成长期短期记忆(LSTM)网络的训练过程,以分类来自[大型电影评论数据集](http://ai.stanford.edu/~amaas/data/sentiment/)(有时称为[互联网电影数据库 (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf))的句子的情感 。 此数据集包含电影评论及其相关联的类别标签,即正面和负面。 ## 数椐准备 @@ -162,7 +162,7 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, ``` * **数椐定义**: - * get\_config\_arg(): 获取通过 `--config_args=xx` i设置的命令行参数。 + * get\_config\_arg(): 获取通过 `--config_args=xx` 设置的命令行参数。 * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 * **算法配置**: @@ -303,7 +303,7 @@ python predict.py \ * -n $config : 设置网络配置。 * -w $model: 设置模型路径。 * -b $label: 设置标签类别字典,这个字典是整数标签和字符串标签的一个对应。 -* -d data/pre-imdb/dict.txt: 设置字段文件。 +* -d data/pre-imdb/dict.txt: 设置字典文件。 * -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。 注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 From 36d60e3709211e05184b9de512cf321eac2ecd9e Mon Sep 17 00:00:00 2001 From: zouxidong Date: Tue, 15 Nov 2016 13:25:36 +0800 Subject: [PATCH 17/19] sentiment analysis translation update link --- doc_cn/demo/index.rst | 2 +- doc_cn/index.rst | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/doc_cn/demo/index.rst b/doc_cn/demo/index.rst index 71f54bc18fbb5..b0927bc27eec4 100644 --- a/doc_cn/demo/index.rst +++ b/doc_cn/demo/index.rst @@ -9,7 +9,7 @@ 自然语言处理 '''''''''''' -* `情感分析 <../../doc/demo/sentiment_analysis/index.html>`_ +* `情感分析 <../../doc_cn/demo/sentiment_analysis/index.html>`_ * `文本生成 <../../doc/demo/text_generation/index.html>`_ * `词性标注 <../../doc/demo/semantic_role_labeling/index.html>`_ diff --git a/doc_cn/index.rst b/doc_cn/index.rst index 8f554cfd9e6ea..f1398206fddff 100644 --- a/doc_cn/index.rst +++ b/doc_cn/index.rst @@ -5,7 +5,6 @@ PaddlePaddle文档 -------- * `介绍 `_ * `快速入门 `_ -* `情感分析 `_ * `基本使用概念 `_ * `编译与安装 `_ * `用户接口 `_ From 7984320ec5a11100b580c9fd71fccf0aaaba7289 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Tue, 15 Nov 2016 15:52:31 +0800 Subject: [PATCH 18/19] sentiment analysis translation update link --- doc_cn/demo/index.rst | 2 +- doc_cn/demo/sentiment_analysis/index.rst | 2 +- doc_cn/demo/sentiment_analysis/sentiment_analysis.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc_cn/demo/index.rst b/doc_cn/demo/index.rst index b0927bc27eec4..e15e839f93d4a 100644 --- a/doc_cn/demo/index.rst +++ b/doc_cn/demo/index.rst @@ -9,7 +9,7 @@ 自然语言处理 '''''''''''' -* `情感分析 <../../doc_cn/demo/sentiment_analysis/index.html>`_ +* `情感分析 `_ * `文本生成 <../../doc/demo/text_generation/index.html>`_ * `词性标注 <../../doc/demo/semantic_role_labeling/index.html>`_ diff --git a/doc_cn/demo/sentiment_analysis/index.rst b/doc_cn/demo/sentiment_analysis/index.rst index d19393374648d..9d7972b219851 100644 --- a/doc_cn/demo/sentiment_analysis/index.rst +++ b/doc_cn/demo/sentiment_analysis/index.rst @@ -1,4 +1,4 @@ -情感分析指导教程 +情感分析教程 =========================== .. toctree:: diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index 45583c9bbe7a7..058dc7f899c5a 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -1,4 +1,4 @@ -# 情感分析指导教程 +# 情感分析教程 情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站、团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类。 From 56838661703afd65f02b0e8df28d5bb1ede4f7e1 Mon Sep 17 00:00:00 2001 From: zouxidong Date: Fri, 11 Nov 2016 23:04:21 +0800 Subject: [PATCH 19/19] sentiment analysis translation Revert "sentiment analysis translation detail fix" This reverts commit 0f0041991352c30f9d8bcb1bac6ab5b6143de044. sentiment analysis translation fix errors sentiment analysis translation fix errors sentiment analysis translation fix errors sentiment analysis translation fix errors sentiment analysis doc_cn update for qingqing01 review sentiment analysis translation fix index link sentiment analysis doc_cn update for qingqing01 review sentiment analysis translation update link sentiment analysis translation update link --- doc/demo/sentiment_analysis/index.rst | 8 +- doc_cn/demo/image_classification/cifar.png | Bin 466572 -> 0 bytes .../image_classification.md | 221 ------------------ .../image_classification.png | Bin 52635 -> 0 bytes doc_cn/demo/image_classification/index.rst | 10 - doc_cn/demo/image_classification/lenet.png | Bin 49835 -> 0 bytes doc_cn/demo/image_classification/plot.png | Bin 31006 -> 0 bytes doc_cn/demo/index.rst | 2 +- doc_cn/demo/sentiment_analysis/index.rst | 9 +- .../sentiment_analysis/sentiment_analysis.md | 58 ++--- 10 files changed, 38 insertions(+), 270 deletions(-) delete mode 100644 doc_cn/demo/image_classification/cifar.png delete mode 100644 doc_cn/demo/image_classification/image_classification.md delete mode 100644 doc_cn/demo/image_classification/image_classification.png delete mode 100644 doc_cn/demo/image_classification/index.rst delete mode 100644 doc_cn/demo/image_classification/lenet.png delete mode 100644 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zjV;6K_n&dd3rMH@Q@t+@3Kls4Zsx}9RR}AVfu<%a7Iu%x?q z_ndI#MP@MDFsMrsXPC9&KREEF54#Wh-nmXzy`3LkmXg_ju;c{7xi1Ch{-*MnPW*p+ z_m=0}@a}v0EvwoF$ntdxea5i=gO)!8JQ8Xy-;}-t%Km>(_dZ<)Db7P4avjlIzNpi_@% diff --git a/doc_cn/demo/image_classification/image_classification.md b/doc_cn/demo/image_classification/image_classification.md deleted file mode 100644 index 29cfc99702c36..0000000000000 --- a/doc_cn/demo/image_classification/image_classification.md +++ /dev/null @@ -1,221 +0,0 @@ -Image Classification Tutorial -============================== - -This tutorial will guide you through training a convolutional neural network to classify objects using the CIFAR-10 image classification dataset. -As shown in the following figure, the convolutional neural network can recognize the main object in images, and output the classification result. - -

![Image Classification](./image_classification.png)
- -## Data Preparation -First, download CIFAR-10 dataset. CIFAR-10 dataset can be downloaded from its official website. - - - -We have prepared a script to download and process CIFAR-10 dataset. The script will download CIFAR-10 dataset from the official dataset. -It will convert it to jpeg images and organize them into a directory with the required structure for the tutorial. Make sure that you have installed pillow and its dependents. -Consider the following commands: - -1. install pillow dependents - -```bash -sudo apt-get install libjpeg-dev -pip install pillow -``` - -2. download data and preparation - -```bash -cd demo/image_classification/data/ -sh download_cifar.sh -``` - -The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. - -Here are the classes in the dataset, as well as 10 random images from each: -
![Image Classification](./cifar.png)
- - -After downloading and converting, we should find a directory (cifar-out) containing the dataset in the following format: - -``` -train ----airplane ----automobile ----bird ----cat ----deer ----dog ----frog ----horse ----ship ----truck -test ----airplane ----automobile ----bird ----cat ----deer ----dog ----frog ----horse ----ship ----truck -``` - -It has two directories:`train` and `test`. These two directories contain training data and testing data of CIFAR-10, respectively. Each of these two folders contains 10 sub-folders, ranging from `airplane` to `truck`. Each sub-folder contains images with the corresponding label. After the images are organized into this structure, we are ready to train an image classification model. - -## Preprocess -After the data has been downloaded, it needs to be pre-processed into the Paddle format. We can run the following command for preprocessing. - -``` -cd demo/image_classification/ -sh preprocess.sh -``` - -`preprocess.sh` calls `./demo/image_classification/preprocess.py` to preprocess image data. -```sh -export PYTHONPATH=$PYTHONPATH:../../ -data_dir=./data/cifar-out -python preprocess.py -i $data_dir -s 32 -c 1 -``` - -`./demo/image_classification/preprocess.py` has the following arguments - -- `-i` or `--input` specifes the input data directory. -- `-s` or `--size` specifies the processed size of images. -- `-c` or `--color` specifes whether images are color images or gray images. - - -## Model Training -We need to create a model config file before training the model. An example of the config file (vgg_16_cifar.py) is listed below. **Note**, it is slightly different from the `vgg_16_cifar.py` which also applies to the prediction. - -```python -from paddle.trainer_config_helpers import * -data_dir='data/cifar-out/batches/' -meta_path=data_dir+'batches.meta' -args = {'meta':meta_path, 'mean_img_size': 32, - 'img_size': 32, 'num_classes': 10, - 'use_jpeg': 1, 'color': "color"} -define_py_data_sources2(train_list=data_dir+"train.list", - test_list=data_dir+'test.list', - module='image_provider', - obj='processData', - args=args) -settings( - batch_size = 128, - learning_rate = 0.1 / 128.0, - learning_method = MomentumOptimizer(0.9), - regularization = L2Regularization(0.0005 * 128)) - -img = data_layer(name='image', size=3*32*32) -lbl = data_layer(name="label", size=10) -# small_vgg is predined in trainer_config_helpers.network -predict = small_vgg(input_image=img, num_channels=3) -outputs(classification_cost(input=predict, label=lbl)) -``` - -The first line imports python functions for defining networks. -```python -from paddle.trainer_config_helpers import * -``` - -Then define an `define_py_data_sources2` which use python data provider -interface. The arguments in `args` are used in `image_provider.py` which -yeilds image data and transform them to Paddle. - - `meta`: the mean value of training set. - - `mean_img_size`: the size of mean feature map. - - `img_size`: the height and width of input image. - - `num_classes`: the number of classes. - - `use_jpeg`: the data storage type when preprocessing. - - `color`: specify color image. - -`settings` specifies the training algorithm. In the following example, -it specifies learning rate as 0.1, but divided by batch size, and the weight decay -is 0.0005 and multiplied by batch size. -```python -settings( - batch_size = 128, - learning_rate = 0.1 / 128.0, - learning_method = MomentumOptimizer(0.9), - regularization = L2Regularization(0.0005 * 128) -) -``` - -The `small_vgg` specifies the network. We use a small version of VGG convolutional network as our network -for classification. A description of VGG network can be found here [http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/). -```python -# small_vgg is predined in trainer_config_helpers.network -predict = small_vgg(input_image=img, num_channels=3) -``` -After writing the config, we can train the model by running the script train.sh. Notice that the following script assumes the you run the script in the `./demo/image_classification` folder. If you run the script in a different folder, you need to change the paths of the scripts and the configuration files accordingly. - -```bash -config=vgg_16_cifar.py -output=./cifar_vgg_model -log=train.log - -paddle train \ ---config=$config \ ---dot_period=10 \ ---log_period=100 \ ---test_all_data_in_one_period=1 \ ---use_gpu=1 \ ---save_dir=$output \ -2>&1 | tee $log - -python -m paddle.utils.plotcurve -i $log > plot.png -``` - -- Here we use GPU mode to train. If you have no gpu environment, just set `use_gpu=0`. - -- `./demo/image_classification/vgg_16_cifar.py` is the network and data configuration file. The meaning of the other flags can be found in the documentation of the command line flags. - -- The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`. - - -After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below: - -
![Training and testing curves.](./plot.png)
- - -## Prediction -After we train the model, the model file as well as the model parameters are stored in path `./cifar_vgg_model/pass-%05d`. For example, the model of the 300-th pass is stored at `./cifar_vgg_model/pass-00299`. - -To make a prediction for an image, one can run `predict.sh` as follows. The script will output the label of the classfiication. - -``` -sh predict.sh -``` - -predict.sh: -``` -model=cifar_vgg_model/pass-00299/ -image=data/cifar-out/test/airplane/seaplane_s_000978.png -use_gpu=1 -python prediction.py $model $image $use_gpu -``` - -## Exercise -Train a image classification of birds using VGG model and CUB-200 dataset. The birds dataset can be downloaded here. It contains an image dataset with photos of 200 bird species (mostly North American). - - - - - - -## Delve into Details -### Convolutional Neural Network -A Convolutional Neural Network is a feedforward neural network that uses convolution layers. It is very suitable for building neural networks that process and understand images. A standard convolutional neural network is shown below: - -![Convolutional Neural Network](./lenet.png) - -Convolutional Neural Network contains the following layers: - -- Convolutional layer: It uses convolution operation to extract features from an image or a feature map. -- Pooling layer: It uses max-pooling to downsample feature maps. -- Fully Connected layer: It uses fully connected connections to transform features. - -Convolutional Neural Network achieves amazing performance for image classification because it exploits two important characteristics of images: *local correlation* and *spatial invariance*. By iteratively applying convolution and max-pooing operations, convolutional neural network can well represent these two characteristics of images. - - -For more details of how to define layers and their connections, please refer to the documentation of layers. diff --git a/doc_cn/demo/image_classification/image_classification.png b/doc_cn/demo/image_classification/image_classification.png deleted file mode 100644 index 14f255805081c1b4fab27eaf336fd389fa93ca19..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 52635 zcmZ^~19W7;^FKPVHnweTw6Qj}jY&4P?agMRjcsd#iLFI!+fF7n=Via&-yiRt^X8m8 zr@L-d_3gfWyQ)7`9jU4;gNjUq3;+O7qmHaO~k```et~So@HcpNd|MZ%Eaq@5%rlR`S(EtAZ z_c`5dEdNhSj&A?ct@jSH|GUG^!S<2;f9rl175YaNkZ^KvcC~PGd)F_*BlI86{}=85 zwDVtnRcj}Ar}t5CwK0=(bhmJQ7j`%O_jDqh|0DhX&+-4&rQ&L1@!sqINOSy;^#4u! zAATYBe;xn72I9Y?{2%K3FpD4yvH$N^CW2hM!?^|kjHt*-ifeispS~hwgCDilQFDj+ zJ-Yc7$;N(IO55PXe-o31m)q~#H6gwry1=qG8%0UC0OI*^yw#$i1a?&+HD98Deo+xb 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<../../doc/demo/sentiment_analysis/index.html>`_ +* `情感分析 `_ * `文本生成 <../../doc/demo/text_generation/index.html>`_ * `词性标注 <../../doc/demo/semantic_role_labeling/index.html>`_ diff --git a/doc_cn/demo/sentiment_analysis/index.rst b/doc_cn/demo/sentiment_analysis/index.rst index 99f0ac44cacb7..9d7972b219851 100644 --- a/doc_cn/demo/sentiment_analysis/index.rst +++ b/doc_cn/demo/sentiment_analysis/index.rst @@ -1,9 +1,8 @@ -情感分析指导教程 +情感分析教程 =========================== .. toctree:: -:maxdepth: 3 - :glob: + :maxdepth: 3 + :glob: - Training Locally - internal/cluster_train.md + Training Locally \ No newline at end of file diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md index a65f3f164ce0e..058dc7f899c5a 100644 --- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md +++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md @@ -1,18 +1,18 @@ -# 情感分析指导教程 +# 情感分析教程 -情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站,团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类. +情感分析有许多应用场景。 一个基本的应用场景是区分给定文本的褒贬两极性,给定的文本可以是一个文档、句子、或者是一个小的文本片段。 一个简单的例子如:把用户在购物网站、旅游网站、团购网站(亚马逊、天猫、淘宝等)上发表的评论分成正面评论和负面评论两类。 情感分析也常用于基于大量评论和个人博客来监控社会媒体。 例如,研究人员分析了几个关于消费者信心和政治观点的调查,结果发现它们与同时期的Twitter消息中的情绪词频率相关 [1]。 另一个例子是通过分析每日Twitter博客的文本内容来预测股票变动 [2]。 另一方面,抓取产品的用户评论并分析他们的情感,有助于理解用户对不同公司,不同产品,甚至不同竞争对手产品的偏好。 -本教程将指导您完成长期短期记忆(LSTM)网络的训练过程,以分类来自[大型电影评论数据集](http://ai.stanford.edu/~amaas/data/sentiment/)(有时称为[互联网电影数据库 (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf))的句子的情感 。 此数据集包含电影评论及其相关联的二进制情绪极性标签,即正面和负面。 +本教程将指导您完成长期短期记忆(LSTM)网络的训练过程,以分类来自[大型电影评论数据集](http://ai.stanford.edu/~amaas/data/sentiment/)(有时称为[互联网电影数据库 (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf))的句子的情感 。 此数据集包含电影评论及其相关联的类别标签,即正面和负面。 ## 数椐准备 ### IMDB 数椐介绍 -训练模型之前, 我们需要预处理数椐并构建一个字典. 首先, 你可以使用下面的脚本下载 IMDB 数椐集和[Moses](http://www.statmt.org/moses/)工具, 这是一个基于统计的机器翻译系统. 我们提供了一个数据预处理脚本,它不仅能够处理IMDB数据,还能处理其他用户自定义的数据。 为了使用提前编写的脚本,需要将标记的训练和测试样本移动到另一个路径,这已经在`get_imdb.sh`中完成。 +训练模型之前, 我们需要预处理数椐并构建一个字典。 首先, 你可以使用下面的脚本下载 IMDB 数椐集和[Moses](http://www.statmt.org/moses/)工具, 这是一个基于统计的机器翻译系统. 我们提供了一个数据预处理脚本,它不仅能够处理IMDB数据,还能处理其他用户自定义的数据。 为了使用提前编写的脚本,需要将标记的训练和测试样本移动到另一个路径,这已经在`get_imdb.sh`中完成。 ``` cd demo/sentiment/data @@ -53,7 +53,7 @@ labeledBow.feat neg pos unsup unsupBow.feat urls_neg.txt urls_pos.txt url ### IMDB 数椐准备 -在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在测试集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。 +在这个例子中,我们只使用已经标注过的训练集和测试集,且默认在训练集上构建字典,而不使用IMDB数椐集中的imdb.vocab做为字典。训练集已经做了随机打乱排序而测试集没有。 Moses 工具中的脚本`tokenizer.perl` 用于切分单单词和标点符号。执行下面的命令就可以预处理数椐。 ``` cd demo/sentiment/ @@ -74,7 +74,7 @@ python preprocess.py -i data_dir ``` dict.txt labels.list test.list test_part_000 train.list train_part_000 ``` -* test\_part\_000 and train\_part\_000: 所有标记的训练集和测试集. 训练集已经随机打乱。 +* test\_part\_000 and train\_part\_000: 所有标记的测试集和训练集, 训练集已经随机打乱。 * train.list and test.list: 训练集和测试集文件列表。 * dict.txt: 利用训练集生成的字典。 * labels.txt: neg 0, pos 1, 含义:标签0表示负面的评论,标签1表示正面的评论。 @@ -107,24 +107,24 @@ dataset ## 训练模型 -在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标循环现神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。 +在这步任务中,我们使用了循环神经网络(RNN)的 LSTM 架构来训练情感分析模型。 引入LSTM模型主要是为了克服消失梯度的问题。 LSTM网络类似于具有隐藏层的标准循环神经网络, 但是隐藏层中的每个普通节点被一个记忆单元替换。 每个记忆单元包含四个主要的元素: 输入门, 具有自循环连接的神经元,忘记门和输出门。 更多的细节可以在文献中找到[4]。 LSTM架构的最大优点是它可以在长时间间隔内记忆信息,而没有短时记忆的损失。在有新的单词来临的每一个时间步骤内,存储在记忆单元区块的历史信息被更新用来迭代的学习单词以合理的序列程现。

![LSTM](../../../doc/demo/sentiment_analysis/lstm.png)
图表 1. LSTM [3]
-情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从字级到具有可变上下文长度的上下文级别(其通过门值来适配)来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 +情感分析是自然语言理解中最典型的问题之一。 它的目的是预测在一个序列中表达的情感态度。 通常, ,仅仅是一些关键词,如形容词和副词,在预测序列或段落的情感中起主要作用。然而有些评论上下文非常长,例如 IMDB的数椐集。 我们只所以使用LSTM来执行这个任务是因为其改进的设计并且具有门机制。 首先,它能够从词级到具有可变上下文长度的上下文级别来总结表示。 第二,它可以在句子级别利用可扩展的上下文, 而大多数方法只是利用n-gram级别的知识。第三,它直接学习段落表示,而不是组合上下文级别信息。 在本演示中,我们提供两个网络,即双向LSTM和三层堆叠LSTM。 #### 双向LSTM -图2是双向LSTM网络,由全连接层和softmax层组成。 +图2是双向LSTM网络,后面连全连接层和softmax层。
![BiLSTM](../../../doc/demo/sentiment_analysis/bi_lstm.jpg)
图 2. Bidirectional-LSTM
#### Stacked-LSTM -图3是三层LSTM结构。图的底部是字嵌入(word embedding 对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二LSTM是被反转的。然后提取隐藏和LSTM层的所有时间步长的最大隐藏向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。 +图3是三层LSTM结构。图的底部是word embedding(对文档处理后形成的单词向量)。 接下来,连接三个LSTM隐藏层,并且第二个是反向LSTM。然后提取隐藏LSTM层的所有时间步长的最大词向量作为整个序列的表示。 最后,使用具有softmax激活的全连接前馈层来执行分类任务。 更多内容可查看参考文献 [5]。
![StackedLSTM](../../../doc/demo/sentiment_analysis/stacked_lstm.jpg)
图 3. Stacked-LSTM for sentiment analysis
@@ -162,18 +162,18 @@ stacked_lstm_net(dict_dim, class_dim=class_dim, ``` * **数椐定义**: - * get\_config\_arg(): 获取通过 `--config_args=xx` i设置的命令行参数。 - * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口(PyDataProviderWrapper)来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 + * get\_config\_arg(): 获取通过 `--config_args=xx` 设置的命令行参数。 + * 定义训练数椐和测试数椐提供者, 这里使用了PaddlePaddle的Python接口来加载数椐。想了解更多细节可以参考PyDataProvider部分的文档 * **算法配置**: * 使用随机梯度下降(sgd)算法。 * 使用 adam 优化。 - * 设置批量大小为128。 + * 设置batch size大小为128。 * 设置平均sgd窗口。 * 设置全局学习率。 * **网络配置**: * dict_dim: 获取字典维度。 - * class_dim: 设置类别号,IMDB有两个标签,即正面评价标签和负面评价标签。 + * class_dim: 设置类别数,IMDB有两个标签,即正面评价标签和负面评价标签。 * `stacked_lstm_net`: 预定义网络如图3所示,默认情况下使用此网络 * `bidirectional_lstm_net`: 预定义网络,如图2所示。 @@ -207,11 +207,11 @@ paddle train --config=$config \ * \--config=$config: 设置网络配置。 * \--save\_dir=$output: 设置输出路径以保存训练完成的模型。 * \--job=train: 设置工作模式为训练。 -* \--use\_gpu=false: 使用CPU训练,设置为true,如果你安装GPU版本的PaddlePaddle,并想使用GPU来训练。 -* \--trainer\_count=4:设置线程数(或GPU计数)。 -* \--num\_passes=15: 设置通过数,PaddlePaddle中的一个通过数意味着对数据集中的所有样本进行一次训练。 -* \--log\_period=20: 每20批打印一次日志。 -* \--show\_parameter\_stats\_period=100: 每100批显示一次参数统计。 +* \--use\_gpu=false: 使用CPU训练,如果你安装GPU版本的PaddlePaddle,并想使用GPU来训练设置为true。 +* \--trainer\_count=4:设置线程数(或GPU个数)。 +* \--num\_passes=15: 设置pass,PaddlePaddle中的一个pass意味着对数据集中的所有样本进行一次训练。 +* \--log\_period=20: 每20个batch打印一次日志。 +* \--show\_parameter\_stats\_period=100: 每100个batch打印一次统计信息。 * \--test\_all_data\_in\_one\_period=1: 每次测试都测试所有数据。 如果运行成功,输出日志保存在路径 `demo/sentiment/train.log`中,模型保存在目录`demo/sentiment/model_output/`中。 输出日志说明如下: @@ -222,15 +222,15 @@ Batch=20 samples=2560 AvgCost=0.681644 CurrentCost=0.681644 Eval: classification Pass=0 Batch=196 samples=25000 AvgCost=0.418964 Eval: classification_error_evaluator=0.1922 Test samples=24999 cost=0.39297 Eval: classification_error_evaluator=0.149406 ``` -- Batch=xx: 表示传递xx批次。 -- samples=xx: 表示传递xx个样本。 -- AvgCost=xx: 从第0批到当前批的平均花费时间。 -- CurrentCost=xx: 最新的log_period批处理的当前时间花费。 -- Eval: classification\_error\_evaluator=xx: 表示第0批次到当前批次的分类错误。 -- CurrentEval: classification\_error\_evaluator: 最新日志周期批次的分类错误。 +- Batch=xx: 表示训练了xx个Batch。 +- samples=xx: 表示训练了xx个样本。。 +- AvgCost=xx: 从第0个batch到当前batch的平均损失。 +- CurrentCost=xx: 最新log_period个batch处理的当前损失。 +- Eval: classification\_error\_evaluator=xx: 表示第0个batch到当前batch的分类错误。 +- CurrentEval: classification\_error\_evaluator: 最新log_period个batch的分类错误。 - Pass=0: 通过所有训练集一次称为一遍。 0表示第一次经过训练集。 -默认情况下,我们使用`stacked_lstm_net`网络,当传递相同的样本数时,它的收敛速度比`bidirectional_lstm_net`快。If you want to use bidirectional LSTM, 如果要使用双向LSTM,只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。 +默认情况下,我们使用`stacked_lstm_net`网络,当传递相同的样本数时,它的收敛速度比`bidirectional_lstm_net`快。如果要使用双向LSTM,只需删除最后一行中的注释并把“stacked_lstm_net”注释掉。 ## 测试模型 @@ -269,7 +269,7 @@ paddle train --config=$net_conf \ 2>&1 | tee 'test.log' ``` -函数`get_best_pass`通过计算分类错误率获得最佳模型进行测试。 在本示例中,我们默认使用IMDB的测试数据集作为验证。 与训练不同,它需要在这里指定`--job = test`和模型路径,即`--model_list = $model_list`。如果运行成功,日志将保存在“demo / sentiment / test.log”的路径中。例如,在我们的测试中,最好的模型是`model_output / pass-00002`,分类误差是0.115645,如下: +函数`get_best_pass`依据分类错误率获得最佳模型进行测试。 在本示例中,我们默认使用IMDB的测试数据集作为验证。 与训练不同,它需要在这里指定`--job = test`和模型路径,即`--model_list = $model_list`。如果运行成功,日志将保存在“demo / sentiment / test.log”的路径中。例如,在我们的测试中,最好的模型是`model_output / pass-00002`,分类误差是0.115645,如下: ``` Pass=0 samples=24999 AvgCost=0.280471 Eval: classification_error_evaluator=0.115645 @@ -303,10 +303,10 @@ python predict.py \ * -n $config : 设置网络配置。 * -w $model: 设置模型路径。 * -b $label: 设置标签类别字典,这个字典是整数标签和字符串标签的一个对应。 -* -d data/pre-imdb/dict.txt: 设置字段文件。 +* -d data/pre-imdb/dict.txt: 设置字典文件。 * -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。 -注意你应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 +注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。 本示例的预测结果: