@nlpjs/neural
v4.25.0
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Neural Network
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NeuralNetwork
Introduction
NeuralNetwork is the class for an NLU Neural Network, able to train a classifier and then classify into intents. This package is part of the NLP.js suite.
Installing
NeuralNetwork is a class of the package @nlpjs/neural, that you can install via NPM:
npm install @nlpjs/neural
Corpus Format
For training the classifier you need a corpus. The corpus format is an array of objects where each object contains an input and output, where the input is an object with the features and the output is an object with the intents:
[
{
"input": { "who": 1, "are": 1, "you": 1 },
"output": { "who": 1 }
},
{
"input": { "say": 1, "about": 1, "you": 1 },
"output": { "who": 1 }
},
{
"input": { "why": 1, "are": 1, "you": 1, "here": 1 },
"output": { "who": 1 }
},
{
"input": { "who": 1, "developed": 1, "you": 1 },
"output": { "developer": 1 }
},
{
"input": { "who": 1, "is": 1, "your": 1, "developer": 1 },
"output": { "developer": 1 }
},
{
"input": { "who": 1, "do": 1, "you": 1, "work": 1, "for": 1 },
"output": { "developer": 1 }
},
{
"input": { "when": 1, "is": 1, "your": 1, "birthday": 1 },
"output": { "birthday": 1 }
},
{
"input": { "when": 1, "were": 1, "you": 1, "borned": 1 },
"output": { "birthday": 1 }
},
{
"input": { "date": 1, "of": 1, "your": 1, "birthday": 1 },
"output": { "birthday": 1 }
}
]
Example of use
The file corpus.json should contain the corpus shown in the Corpus Format section for this example. This will train this corpus and run the input equivalent to the sentence "when birthday". The result is each intent with the score for this intent.
const { NeuralNetwork } = require('@nlpjs/neural');
const corpus = require('./corpus.json');
const net = new NeuralNetwork();
net.train(corpus);
console.log(net.run({ when: 1, birthday: 1 }));
// { who: 0, developer: 0, birthday: 0.7975805386427789 }
Exporting trained model to JSON and importing
You can export the model to a json with the toJSON method, and import a model from a json with fromJSON method:
const { NeuralNetwork } = require('@nlpjs/neural');
const corpus = require('./corpus.json');
let net = new NeuralNetwork();
net.train(corpus);
const exported = net.toJSON();
net = new NeuralNetwork();
net.fromJSON(exported);
console.log(net.run({ when: 1, birthday: 1 }));
Options
There are several options that you can customize:
- iterations: maximum number of iterations (epochs) that the neural network can run. By default this is 20000.
- errorThresh: minimum error threshold, if the loss is lower than this number, then the training ends. By default this is 0.00005.
- deltaErrorThresh: minimum delta error threshold, this is, the difference between the current and the last errors. If the delta error threshold is lower than this number, then the training ends. By default this is 0.000001.
- learningRate: learning rate for the neural network. By default this is 0.6.
- momentum: momentum for the gradient descent optimization. By default this is 0.5.
- alpha: Multiplicator or alpha factor for the ReLu activation function. By default this is 0.07.
- log: If is false then no log happens, if is true then there is log in console. Also a function can be provided, and will receive two parameters: the status and the elapsed time of the last epoch. By default this is false.
Example of how to provide parameters:
const { NeuralNetwork } = require('@nlpjs/neural');
const corpus = require('./corpus.json');
const net = new NeuralNetwork({ learningRate: 0.01, log: true });
net.train(corpus);
console.log(net.run({ when: 1, birthday: 1 }));
// Epoch 2382 loss 0.0013668740975184709 time 0ms
// { who: 0, developer: 0, birthday: 0.8050273840765896 }
Contributing
You can read the guide of how to contribute at Contributing.
Contributors
Made with contributors-img.
Code of Conduct
You can read the Code of Conduct at Code of Conduct.
Who is behind it?
This project is developed by AXA Group Operations Spain S.A.
If you need to contact us, you can do it at the email [email protected]
License
Copyright (c) AXA Group Operations Spain S.A.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.