masasron/deepnet

DeepNet is simple node based cli tool for creating machine learning classifiers you can use on the web.

DeepNet

DeepNet is simple node based cli tool for creating machine learning classifiers you can use on the web.

root$ node deepnet/src/cli.js -h

Usage: cli [options] [command]


  Options:

    -V, --version  output the version number
    -h, --help     output usage information


  Commands:

    train [options] <file>
    make-dataset [options] <positive_dataset_file> <negative_dataset_file>
    predict [options] <model> <test-data>

Train

root$ node deepnet/src/cli.js train -h

  Usage: train [options] <file>



  Options:

    -t, --test-dataset-percentage <n>  percentage of datasets to keep for testing (default: 25)
    -n, --name <value>                 choose a name for your model (default: model-1518027472621)
    -s, --save-period <n>              save model every <n> iterations (default: 20000)
    -v, --vectorize <f>                automatically vectorize strings from training data (default: true)
    -l, --learning-rate <f>            network learning rate (default: 0.1)
    -e, --error-threshold <f>          minimum error threshold (default: 0.005)
    -y, --hidden-layers <n>            number of hidden layers (default: 6)
    -i, --iterations <n>               maximum number of iterations (default: 20000)
    -p, --log-period <n>               log progress every <n> iterations (default: 25)
    -g, --log <b>                      log traning progress (default: true)
    -r, --randomize <b>                randomize dataset (default: true)
    -a, --activation <activation>      activation function (default: sigmoid)
    -h, --help                         output usage information

The train command require a JSON dataset file in the format below.
You may use the make-dataset helper command to generate this file.

[
  {
    "input": [0.1,0.2,0.3],
    "output": [0.6]
  },
  {
    "input": [0.1,0,0],
    "output": [0.1]
  }
]

Make Dataset

root$ node deepnet/src/cli.js make-dataset -h

  Usage: make-dataset [options] <positive_dataset_file> <negative_dataset_file>


  Options:

    -n, --name <f>       choose a dataset name (default: dataset-1518028193094)
    -v, --vectorize <f>  automatically vectorize strings (default: true)
    -h, --help           output usage information

Predict

You can use the predict command to load an existing model.

root$ node deepnetsrc/cli.js predict -h

  Usage: predict [options] <model> <test-data>


  Options:

    <model>      path to the model .bin file
    <test-data>  test data as string (if -, read from stdin)
    -h, --help   output usage information