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Usage.md

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Usage

Options

Input and output options

  --train-file              STR    Training file.      		Default is `data/Train_sample.json`.
  --validation-file         STR    Validation file.      	Default is `data/Validation_sample.json`.
  --test-file               STR    Testing file.       		Default is `data/Test_sample.json`.
  --word2vec-file           STR    Word2vec model file.		Default is `data/word2vec_100.model`.

Model option

  --pad-seq-len             INT     Padding Sequence length of data.                    Depends on data.
  --embedding-type          INT     The embedding type.                                 Default is 1.
  --embedding-dim           INT     Dim of character embedding.                         Default is 100.
  --lstm-dim                INT     Dim of LSTM neurons.                                Default is 256.
  --lstm-layers             INT     Number of LSTM layers.                              Defatul is 1.
  --attention-dim           INT     Dim of Attention neurons.                           Default is 200.
  --attention-penalization  BOOL    Use attention penalization or not.                  Default is True.
  --fc-dim                  INT     Dim of FC neurons.                                  Default is 512.
  --dropout-rate            FLOAT   Dropout keep probability.                           Default is 0.5.
  --alpha                   FLOAT   Weight of global part in loss cal.                  Default is 0.5.
  --num-classes-list        LIST    Each number of labels in hierarchical structure.    Depends on data.
  --total-classes           INT     Total number of labels.                             Depends on data.
  --topK                    INT     Number of top K prediction classes.                 Default is 5.
  --threshold               FLOAT   Threshold for prediction classes.                   Default is 0.5.

Training option

  --epochs                  INT     Number of epochs.                       Default is 20.
  --batch-size              INT     Batch size.                             Default is 32.
  --learning-rate           FLOAT   Adam learning rate.                     Default is 0.001.
  --decay-rate              FLOAT   Rate of decay for learning rate.        Default is 0.95.
  --decay-steps             INT     How many steps before decy lr.          Default is 500.
  --evaluate-steps          INT     How many steps to evluate val set.      Default is 50.
  --l2-lambda               FLOAT   L2 regularization lambda.               Default is 0.0.
  --checkpoint-steps        INT     How many steps to save model.           Default is 50.
  --num-checkpoints         INT     Number of checkpoints to store.         Default is 10.

Training

The following commands train the model.

$ python3 train_harnn.py

Training a model for a 30 epochs and set batch size as 256.

$ python3 train_harnn.py --epochs 30 --batch-size 256

In the beginning, you will see the program shows:

You need to choose Training or Restore. (T for Training and R for Restore)

After training, you will get the /log and /run folder.

  • /log folder saves the log info file.
  • /run folder saves the checkpoints.

It should be like this:

.
├── logs
├── runs
│   └── 1586077936 [a 10-digital format]
│       ├── bestcheckpoints
│       ├── checkpoints
│       ├── embedding
│       └── summaries
├── test_harnn.py
├── text_harnn.py
└── train_harnn.py

The programs name and identify the model by using the asctime (It should be 10-digital number, like 1586077936).

Restore

When your model stops training for some reason and you want to restore training, you can:

In the beginning, you will see the program shows:

And you need to input R for restore.

Then you will be asked to give the model name (a 10-digital format, like 1586077936):

And the model will continue training from the last time.

Test

The following commands test the model.

$ python3 test_harnn.py

Then you will be asked to give the model name (a 10-digital format, like 1586077936):

And you can choose to use the best model or the latest model (B for Best, L for Latest):

Finally, you can get the predictions.json file under the /outputs folder, it should be like:

.
├── graph
├── logs
├── output
│   └── 1586077936
│       └── predictions.json
├── runs
│   └── 1586077936
│       ├── bestcheckpoints
│       ├── checkpoints
│       ├── embedding
│       └── summaries
├── test_harnn.py
├── text_harnn.py
└── train_harnn.py