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Machine learning and Matrix operation library by TypeScript.
Make sure NPM is installed, Switch to the project directory then execute the following command.
$ npm install infers@latest
Reference in project:
import { Matrix, BPNet } from 'infers'
Matrix transpose:
let m = new Matrix([
[1, 5, 0],
[2, 4 , -1],
[0, -2, 0]
])
m.T.print()
// Matrix 3x3 [
// 1, 2, 0,
// 5, 4, -2,
// 0, -1, 0,
// ]
BP neural network example of XOR, three-layer network:
let xs = new Matrix([[1, 0], [0, 1], [0, 0], [1, 1]])
let ys = new Matrix([[1], [1], [0], [0]])
let model = new BPNet([2, [6, 'Tanh'], [1, 'Sigmoid']], { rate: 0.1 })
model.fit(xs, ys, {
epochs: 5000, onEpoch: (epoch, loss) => {
if (epoch % 100 === 0) console.log('epoch:' + epoch, 'loss:', loss)
}
})
model.predict(xs).print()
// Matrix 4x1 [
// 0.9862025352830867,
// 0.986128496195502,
// 0.01443800549676924,
// 0.014425871504885788,
// ]
BP neural network example of addition, four-layer network:
let xs = new Matrix([[1, 4], [3, 2], [6, 5], [4, 7]])
let ys = new Matrix([[5], [5], [11], [11]])
let model = new BPNet([2, 6, 6, 1], { mode: 'bgd', rate: 0.01 })
model.fit(xs, ys, {
epochs: 500, onEpoch: (epoch, loss) => {
console.log('epoch:' + epoch, 'loss:', loss)
}
})
let xs2 = new Matrix([[5, 8], [22, 6], [-5, 9], [-5, -4]])
model.predict(xs2).print()
// Matrix 4x1 [
// 12.994745740521667,
// 27.99134620596921,
// 3.9987224114576856,
// -9.000000644547901,
// ]
RNN: Recurrent neural network example:
let trainData = ['hello rnn', 'good morning', 'I love 🍎!', 'I eat 🍊!']
let net = new RNN({ trainData })
net.fit({
epochs: 1500, onEpochs: (epoch, loss) => {
if (epoch % 10 === 0) console.log('epoch: ', epoch, 'loss: ', loss)
}
})
console.log(net.predict('I love'))
console.log(net.predict('I eat'))
console.log(net.predict('hel'))
console.log(net.predict('good'))
// 🍊!/n
// 🍎!/n
// lo rnn/n
// morning/n
[number, (number | [number, ActivationFunction]), ...(number | [number, ActivationFunction])[]]numbernumber'Sigmoid' | 'Relu' | 'Tanh' | 'Softmax'
'sgd' | 'bgd' | 'mbgd'
Different learning rates, iterations and network shapes are needed to deal with different problems, which need to be adjusted according to the cost function. Parameter optimization is also the process of model optimization.
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