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
- A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations)
- 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
- Convolutional Neural Network on MNIST digits
- Convolutional Neural Network on CIFAR-10
- Toy 2D data
- Toy 1D regression
- Training an Autoencoder on MNIST digits
- Deep Q Learning Reinforcement Learning demo
- Image Regression ("Painting")
- Comparison of SGD/Adagrad/Adadelta on MNIST
- MagicNet classifying arbitary CSV data demo
Here's a minimum example of defining a 2-layer neural network and training it on a single data point:
// 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:
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)A Getting Started tutorial is available on main page.
The full Documentation can also be found there.
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)
If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. The compilation script simply concatenates files in src/ and then minifies the result.
The compilation is done using an ant task: it compiles build/convnet.js by concatenating the source files in src/ and then minifies the result into build/convnet-min.js. Make sure you have ant installed (on Ubuntu you can simply sudo apt-get install it), then cd into compile/ directory and run:
$ ant -lib yuicompressor-2.4.8.jar -f build.xml
The output files will be in build/
The library is also available on node.js:
- Install it:
$ npm install convnetjs - Use it:
var convnetjs = require("convnetjs");
MIT