A minimalist deep learning library in Javascript using WebGL + asm.js. Runs in your browser.
Currently it is a proof-of-concept (inference only). Note: Convolution is buggy when memories overlap.
The WebGL backend is powered by weblas: https://github.com/waylonflinn/weblas.
https://withablink.coding.me/goPolicyNet/ : a weiqi (baduk, go) policy network in AlphaGo style:
const N = 19;
const NN = N * N;
const nFeaturePlane = 8;
const nFilter = 128;
const x = new BlinkArray();
x.Init('weblas');
x.nChannel = nFeaturePlane;
x.data = new Float32Array(nFeaturePlane * NN);
for (var i = 0; i < NN; i++)
x.data[5 * NN + i] = 1; // set feature plane for empty board
// pre-act residual network with 6 residual blocks
const bak = new Float32Array(nFilter * NN);
x.Convolution(nFilter, 3);
x.CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak).CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak).CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak).CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak).CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak).CopyTo(bak);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.BatchNorm().ReLU().Convolution(nFilter, 3);
x.Add(bak);
x.BatchNorm().ReLU().Convolution(1, 1).Softmax();
<script src='weblas.js' type='text/javascript'></script>
<script src='BlinkDL.js' type='text/javascript'></script>
- Convolution (3x3_pad_1 and 1x1), BatchNorm, ReLU, Softmax
- Pooling layer
- FC layer
- Strided convolution
- Transposed convolution
- Webworker and async
- Faster inference with weblas pipeline, WebGPU, WebAssembly
- Memory manager
- Training