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The `process`function yield 7 lists which are six features and labels.
The `process`function yield 9 lists which are 8 features and label.
### Neural Network Config
`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure.
Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
### Run Training
The script for training is `train.sh`, user just need to execute:
@ -115,27 +120,36 @@ The content in `train.sh`:
```
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--save_dir=./output \
--trainer_count=4 \
--log_period=10 \
--num_passes=500 \
--use_gpu=false \
--show_parameter_stats_period=10 \
--num_passes=10000 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
```
- \--config=./db_lstm.py : network config file.
- \--save_di=./output: output path to save models.
- \--trainer_count=4 : set thread number (or GPU count).
- \--log_period=10 : print log every 20 batches.
- \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
- \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
- \--test_all_data_in_one_period=1: test all data in every testing.
After training, the models will be saved in directory `output`.
- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
- \--log_period=500: print log every 20 batches.
- \--trainer_count=1: set thread number (or GPU count).
- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
- \--save_dir=./output: output path to save models.
- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
- \--average_test_period=10000000: do test on average parameter every average_test_period batches
- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
- \--test_all_data_in_one_period=1: test all data in one period
After training, the models will be saved in directory `output`. Our training curve is as following:
<center>
</center>
### Run testing
The script for testing is `test.sh`, user just need to execute:
@ -155,6 +169,7 @@ paddle train \
- \--model_list=$model_list.list: model list file
- \--job=test: indicate the test job
- \--config_args=is_test=1: flag to indicate test
- \--test_all_data_in_one_period=1: test all data in 1 period
### Run prediction
@ -166,11 +181,13 @@ The script for prediction is `predict.sh`, user just need to execute:
In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
```
python predict.py
-c $config_file
-w $model_path
-l $label_file
-d $dict_file
-i $input_file
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file \
-o $output_file
```
`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.
@ -6,7 +6,7 @@ Sentiment analysis is also used to monitor social media based on large amount of
On the other hand, grabbing the user comments of products and analyzing their sentiment are useful to understand user preferences for companies, products, even competing products.
This tutorial will guide you through the process of training a Long Short Term Memory (LSTM) Network to classify the sentiment of sentences from [Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/), sometimes known as the [Internet Movie Database (IMDB)](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf). This dataset contains movie reviews along with their associated binary sentiment polarity labels, namely positive and negative. So randomly guessing yields 50% accuracy.
This tutorial will guide you through the process of training a Long Short Term Memory (LSTM) Network to classify the sentiment of sentences from [Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/), sometimes known as the Internet Movie Database (IMDB). This dataset contains movie reviews along with their associated binary sentiment polarity labels, namely positive and negative. So randomly guessing yields 50% accuracy.
## Data Preparation
@ -39,7 +39,7 @@ imdbEr.txt imdb.vocab README test train
* imdbEr.txt: expected rating for each token in imdb.vocab.
* README: data documentation.
Both train and test set directory contains:
The file in train set directory is as follows. The test set also contains them except `unsup` and `urls_unsup.txt`.
* 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.
* Define data provider, here using Python interface to load data. For details, you can refer to the document of PyDataProvider2.
* **Algorithm Configuration**:
* use sgd algorithm.
* use adam optimization.
* set batch size of 128.
* set average sgd window.
* set global learning rate.
* use adam optimization.
* set average sgd window.
* set L2 regularization.
* set gradient clipping threshold.
* **Network Configuration**:
* dict_dim: get dictionary dimension.
* class_dim: set category number, IMDB has two label, namely positive and negative label.
* dict_dim: dictionary dimension.
* class_dim: category number, IMDB has two label, namely positive and negative label.
* `stacked_lstm_net`: predefined network as shown in Figure 3, use this network by default.
* `bidirectional_lstm_net`: predefined network as shown in Figure 2.
The C++ class of the layer implements the initialization, forward, and backward part of the layer. The fully connected layer is at :code:`paddle/gserver/layers/FullyConnectedLayer.h` and :code:`paddle/gserver/layers/FullyConnectedLayer.cpp`. We list simplified version of the code below.
It needs to derive the base class :code:`paddle::BaseLayer`, and it needs to override the following functions:
It needs to derive the base class :code:`paddle::Layer`, and it needs to override the following functions:
- constructor and destructor.
- :code:`init` function. It is used to initialize the parameters and settings.
liaogang
9 years ago
