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https://github.com/karpathy/convnetjs

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
https://github.com/karpathy/convnetjs

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Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

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README 

        

# 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](http://convnetjs.com)



**Note**: I am not actively maintaining ConvNetJS anymore because I simply don't have time. I think the npm repo might not work at this point.



## Online Demos

- [Convolutional Neural Network on MNIST digits](http://cs.stanford.edu/~karpathy/convnetjs/demo/mnist.html)

- [Convolutional Neural Network on CIFAR-10](http://cs.stanford.edu/~karpathy/convnetjs/demo/cifar10.html)

- [Toy 2D data](http://cs.stanford.edu/~karpathy/convnetjs/demo/classify2d.html)

- [Toy 1D regression](http://cs.stanford.edu/~karpathy/convnetjs/demo/regression.html)

- [Training an Autoencoder on MNIST digits](http://cs.stanford.edu/~karpathy/convnetjs/demo/autoencoder.html)

- [Deep Q Learning Reinforcement Learning demo](http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html)

- [Image Regression ("Painting")](http://cs.stanford.edu/~karpathy/convnetjs/demo/image_regression.html)

- [Comparison of SGD/Adagrad/Adadelta on MNIST](http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html)



## Example Code



Here's a minimum example of defining a **2-layer neural network** and training

it on a single data point:



```javascript

// 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:



```javascript

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)

```



## Getting Started

A [Getting Started](http://cs.stanford.edu/people/karpathy/convnetjs/started.html) tutorial is available on main page.



The full [Documentation](http://cs.stanford.edu/people/karpathy/convnetjs/docs.html) 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)



- [convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet.js)

- [convnet-min.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet-min.js)



## Compiling the library from src/ to build/

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/`

## Use in Node

The library is also available on *node.js*:



1. Install it: `$ npm install convnetjs`

2. Use it: `var convnetjs = require("convnetjs");`



## License

MIT