var net; // declared outside -> global variable in window scope
function start() {
var layer_defs = [];
// input layer of size 1x1x2 (all volumes are 3D)
layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
// some fully connected layers
layer_defs.push({type:'fc', num_neurons:20, activation:'relu'});
layer_defs.push({type:'fc', num_neurons:20, activation:'relu'});
// a softmax classifier predicting probabilities for two classes: 0,1
layer_defs.push({type:'softmax', num_classes:2});
// create a net out of it
var net = new convnetjs.Net();
net.makeLayers(layer_defs);
// the network always works on Vol() elements. These are essentially
// simple wrappers around lists, but also contain gradients and dimensions
// line below will create a 1x1x2 volume and fill it with 0.5 and -1.3
var x = new convnetjs.Vol([0.5, -1.3],[0.5, -1.3]);
var x = new convnetjs.Vol([0],[1]);
var probability_volume = net.forward(x);
console.log('probability that x is class 0: ' + probability_volume.w);
// prints 0.50101
var trainer = new convnetjs.Trainer(net, {learning_rate:0.01, l2_decay:0.001});
trainer.train(x, [[0],[1]]);
var net; // declared outside -> global variable in window scope function start() { var layer_defs = []; // input layer of size 1x1x2 (all volumes are 3D) layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2}); // some fully connected layers layer_defs.push({type:'fc', num_neurons:20, activation:'relu'}); layer_defs.push({type:'fc', num_neurons:20, activation:'relu'}); // a softmax classifier predicting probabilities for two classes: 0,1 layer_defs.push({type:'softmax', num_classes:2});
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