神经网络的JavaScript实现：ConvNetJS
jopen
10年前
ConvNetJS是神经网络的一个JavaScript实现，可以让你在浏览器中训练深度网络。目前看来，它最重要的用途是帮助Deep Learning 初学者更快、更直观的理解算法。它当前支持：
- 常见的神经网络模块（全连接层，非线性）
- 分类（SVM/ SOFTMAX）和回归（L2）的成本函数
- 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
- 实验强化学习模块，基于深Q学习。
示例
Interactively regress toy 1-D data
Classify MNIST digits with a Convolutional Neural Network
示例代码
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)