@piman51277/backprop
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1.0.0 • Public • Published

backprop

An optimized implementation of the backpropagation algorithm in Typescript

Example Usage

import { TrainingData, Net } from "./Net";

//example dataset
const XOR: TrainingData = [
  [[0, 0], [0]],
  [[0, 1], [1]],
  [[1, 0], [1]],
  [[1, 1], [0]],
];

//create a net with random weights/biases
const net = Net.create(2, 1, 3, 1);

//train the net
const trainConfig = {
  gamma: 0.1, //learning rate of weights
  gamma_b: 0.1, //learning rate of biases
  momentum: 0.1, //momentum of weights/biases
  batchSize: 1, //batch size
};

console.log(net.errorDataset(XOR)); //print the average error of the net
net.train(XOR, 100000, trainConfig); //train the net for 100000 epochs
console.log(net.errorDataset(XOR)); //print the new average error of the net

Documentation

Table of Contents

Net

Net.prototype

Misc

Net

Net(options)

source | TOC
Constructor for the Net class.

Arguments

  • options {NetOptions} - The weights and biases of the net.

Net.create(inputs, outputs, hidden, hiddenLayers)

source | TOC
Creates a new net with the given dimensions.

Arguments

  • inputs {number}: The size of the input layer.
  • outputs {number}: The size of the output layer.
  • hidden {number}: The size of the hidden layer.
  • hiddenLayers {number}: The number of hidden layers.

Returns

{Net}: An instance of the Net class, with random weights and biases.

Net.mergeGradients(gradients)

source | TOC
Average several gradients into one.

Arguments

  • gradients {Gradient[]}: The gradients to average.

Returns

{Gradient}: The averaged gradient.

Net.prototype

Net.prototype.eval(input)

source | TOC
Run a forward pass of the net using the given input.

Arguments

  • input {number[]}: The input to the net.

Returns

{number[][]}: The state of the net after the input has been passed.

Net.prototype.gradient(input, expected)

source | TOC
Calculate the required gradient for a given test case

Arguments

  • input {number[]}: The input to the net.
  • expected {number[]}: The expected output of the net.

Returns

{Gradient}: The gradient of the net.

Net.prototype.apply(gradient [, gamma=0.1, gamma_b=0.1])

source | TOC
Apply a gradient to the net.

Arguments

  • gradient {Gradient[]}: The gradient to apply.
  • gamma {number}: [Optional] The learning rate of the weights.
  • gamma_b {number}: [Optional] The learning rate of the biases.

Net.prototype.train(dataset, epochs, options)

source | TOC
Train the net using the given dataset.

Arguments

  • dataset {TrainingData[]}: The dataset to train the net on.
  • epochs {number}: The number of epochs to train the net for.
  • options {TrainingOptions}: The options for training.

Net.prototype.error(input, expected)

source | TOC
Calculate the error of the net for a given test case using MSE

Arguments

  • input {number[]}: The input to the net.
  • expected {number[]}: The expected output of the net.

Returns

{number}: The error of the net.

Net.prototype.errorDataset(dataset)

source | TOC
Calculate the average error of the net for a given dataset.

Arguments

Returns

{number}: The average error of the net.

Net.prototype.debug()

source | TOC
Prints the state of the net to the console.

Format

Weights:
<Layer0 Weight0>, <Layer0 Weight1>, ..., <Layer0 WeightN>
<Layer1 Weight0>, <Layer1 Weight1>, ..., <Layer1 WeightN>
...
<LayerM Weight0>, <LayerM Weight1>, ..., <LayerM WeightN>
Biases:
<Layer0 Bias0>, <Layer0 Bias1>, ..., <Layer0 BiasN>
<Layer1 Bias0>, <Layer1 Bias1>, ..., <Layer1 BiasN>
...
<LayerM Bias0>, <LayerM Bias1>, ..., <LayerM BiasN>

Misc

NetOptions

{
  weights: number[][];
  biases: number[][];
  dimensions: number[];
};

An object describing the weights and biases of a net.

Gradient

[
    number[][], //bias gradient
    number[][] //weight gradient
]

An object describing the gradient of a net.

TrainingData

[
    [number[], number[]], //input, expected output
    [number[], number[]],
    [number[], number[]],
    ...
]

An array of training data.

TrainingOptions

{
  gamma?: number; //learning rate of the weights
  gamma_b?: number; //learning rate of the biases
  momentum?: number; //momentum of the weights/biases
  batchSize?: number; //the size of the batch
}

Notes

Weight Configuration

Weights are internally stored as a 1D array, grouped by origin node. This is so the weights can be read sequentially, increasing the performance of the network.

Example

A diagram of a simple net

For this net, the weights would be stored as such:

[
  [0.4, 0.8, 0.52, 0.3], //Layer 0
  [0.1, 0.63, 0.48, 0.2], //Layer 1
];

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