Create Usage.md

Author: RandolphVI

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`.
+  --testing-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.
+  --filter-sizes            LIST    Filter sizes.                           Default is [3,4,5].
+  --num-filters             INT     Number of filters per filter size.      Default is 128.  
+  --pooling-size            INT     Pooling size.                           Default is 3.
+  --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-hops-dim      INT     Dim of Attention hops.                  Default is 30.
+  --fc-dim                  INT     Dim of FC neurons.                      Default is 512.
+  --dropout-rate            FLOAT   Dropout keep probability.               Default is 0.5.
+  --num-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 100.
+  --batch-size              INT     Batch size.                             Default is 256.
+  --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 500.
+  --l2-lambda               FLOAT   L2 regularization lambda.               Default is 0.0.
+  --checkpoint-steps        INT     How many steps to save model.           Default is 500.
+  --num-checkpoints         INT     Number of checkpoints to store.         Default is 500.
+```
+
+## Training
+
+The following commands train a model. (Use CNN for example)
+
+```bash
+$ python3 train_cnn.py
+```
+
+Training a model for a 200 epochs and set batch size as 128.
+
+```bash
+$ python3 train_cnn.py --epochs 200 --batch-size 128
+```
+
+In the beginning, you will see the program shows:
+
+![](https://live.staticflickr.com/65535/49726025868_da2759aaea_o.png)
+
+**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:
+
+```text
+.
+├── logs
+├── runs
+│   └── 1585814009 [a 10-digital format]
+│       ├── bestcheckpoints
+│       ├── checkpoints
+│       ├── embedding
+│       └── summaries
+├── test_cnn.py
+├── text_cnn.py
+└── train_cnn.py
+```
+
+**The programs name and identify the model by using the asctime (It should be 10-digital number, like 1585814009).** 
+
+## 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:
+
+![](https://live.staticflickr.com/65535/49726620511_f2e3abdfac_o.png)
+
+**And you need to input R for restore.**
+
+Then you will be asked to give the model name (a 10-digital format, like 1585814009):
+
+![](https://live.staticflickr.com/65535/49726066673_1732b92b96_o.png)
+
+And the model will continue training from the last time.
+
+## Test
+
+The following commands test a model.
+
+```bash
+$ python3 test_cnn.py
+```
+
+Then you will be asked to give the model name (a 10-digital format, like 1585814009):
+
+![](https://live.staticflickr.com/65535/49726643681_25f83b405e_o.png)
+
+And you can choose to use the best model or the latest model **(B for Best, L for Latest)**:
+
+![](https://live.staticflickr.com/65535/49726644721_b552318c16_o.png)
+
+Finally, you can get the `predictions.json` file under the `/outputs`  folder, it should be like:
+
+```text
+.
+├── graph
+├── logs
+├── output
+│   └── 1585814009
+│       └── predictions.json
+├── runs
+│   └── 1585814009
+│       ├── bestcheckpoints
+│       ├── checkpoints
+│       ├── embedding
+│       └── summaries
+├── test_cnn.py
+├── text_cnn.py
+└── train_cnn.py
+```
+