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deepnet
Advanced tools
Readme
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>
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]
}
]
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
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
FAQs
Fast deep learning in node.
We found that deepnet demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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