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convnetjs-ts

This is port of convnetjs. Deep Learning in Javascript. Train Convolutional Neural Networks.

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convnetjs-ts

This is a porting from ConvNetJS. Now fully added type annotations and webpacked, so you can use this library in TypeScript for node.js and browser! Of course you can use it in JavaScript.

Install

Install

npm install convnetjs-ts

Import to your node project

JavaScript(ES6 or later/ TypeScript style)

import * as convnetjs from "convnetjs-ts"; 

JavaScript(ES5 style)

var convnetjs = require("convnetjs-ts");

for browser build .js file is here: node_modules/convnetjs-ts/build/convnet.js

but using webpack is recommended!

ConvNetJS

ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports:

  • Common Neural Network modules (fully connected layers, non-linearities)
  • Classification (SVM/Softmax) and Regression (L2) cost functions
  • Ability to specify and train Convolutional Networks that process images
  • An experimental Reinforcement Learning module, based on Deep Q Learning

For much more information, see the main page at convnetjs.com

Example Code

Here's a minimum example of defining a 2-layer neural network and training it on a single data point:

import * as convnetjs from "convnetjs-ts"; 

// species a 2-layer neural network with one hidden layer of 20 neurons
var layer_defs = [];
// input layer declares size of input. here: 2-D data
// ConvNetJS works on 3-Dimensional volumes (sx, sy, depth), but if you're not dealing with images
// then the first two dimensions (sx, sy) will always be kept at size 1
layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
layer_defs.push({type:'fc', num_neurons:20, activation:'relu'}); 
// declare the linear classifier on top of the previous hidden layer
layer_defs.push({type:'softmax', num_classes:10});

var net = new convnetjs.Net();
net.makeLayers(layer_defs);

// forward a random data point through the network
var x = new convnetjs.Vol([0.3, -0.5]);
var prob = net.forward(x); 

// prob is a Vol. Vols have a field .w that stores the raw data, and .dw that stores gradients
console.log('probability that x is class 0: ' + prob.w[0]); // prints 0.50101

var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, l2_decay:0.001});
trainer.train(x, 0); // train the network, specifying that x is class zero

var prob2 = net.forward(x);
console.log('probability that x is class 0: ' + prob2.w[0]);
// now prints 0.50374, slightly higher than previous 0.50101: the networks
// weights have been adjusted by the Trainer to give a higher probability to
// the class we trained the network with (zero)

and here is a small Convolutional Neural Network if you wish to predict on images:
TODO: convert function for Node.js

import * as convnetjs from "convnetjs-ts"; 
var layer_defs = [];
layer_defs.push({type:'input', out_sx:32, out_sy:32, out_depth:3}); // declare size of input
// output Vol is of size 32x32x3 here
layer_defs.push({type:'conv', sx:5, filters:16, stride:1, pad:2, activation:'relu'});
// the layer will perform convolution with 16 kernels, each of size 5x5.
// the input will be padded with 2 pixels on all sides to make the output Vol of the same size
// output Vol will thus be 32x32x16 at this point
layer_defs.push({type:'pool', sx:2, stride:2});
// output Vol is of size 16x16x16 here
layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
// output Vol is of size 16x16x20 here
layer_defs.push({type:'pool', sx:2, stride:2});
// output Vol is of size 8x8x20 here
layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
// output Vol is of size 8x8x20 here
layer_defs.push({type:'pool', sx:2, stride:2});
// output Vol is of size 4x4x20 here
layer_defs.push({type:'softmax', num_classes:10});
// output Vol is of size 1x1x10 here

net = new convnetjs.Net();
net.makeLayers(layer_defs);

// helpful utility for converting images into Vols is included
var x = convnetjs.img_to_vol(document.getElementById('some_image'))
var output_probabilities_vol = net.forward(x)

and a very simple Reinforce-Learning smaple(This code refer to this sample):

import { deepqlearn } from "convnetjs-ts";
const brainOpt = { start_learn_threshold: 100 };
const brain = new deepqlearn.Brain(3, 2, brainOpt); // 3 inputs, 2 possible outputs (0,1)
const state = [0, 0, 0];
for (let k = 0; k < 1000; k++) {
    const action = brain.forward(state); // returns index of chosen action
    const reward = action === 1 ? 1.0 : 0.0; //give a reward for action 1 (no matter what state is)
    brain.backward(reward); // <-- learning magic happens here
    state[Math.floor(Math.random() * 3)] = Math.random(); // change state
}
brain.epsilon_test_time = 0.0; // don't make any more random choices
brain.learning = false;
// get an optimal action from the learned policy
const input = [1, 1, 1];
const chosen_action = brain.forward(input);
console.log("chosen action after learning: " + chosen_action);
// tanh are their own layers. Softmax gets its own fully connected layer.
// this should all get desugared just fine.

Getting Started

A Getting Started tutorial is available on main page.

The full Documentation can also be found there.

TODO: release convnetjs-ts
See the releases page for this project to get the minified, compiled library, and a direct link to is also available below for convenience (but please host your own copy)

License

MIT

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