convnetjs_XOR.js

// http://cs.stanford.edu/people/karpathy/convnetjs//demo/classify2d.html

(function xor_data(){
    data = [];
    labels = [];
    data.push([0  ,  0 ]); labels.push(0);
    data.push([1  ,  -1 ]); labels.push(0);
    data.push([0  ,  -1 ]); labels.push(1);
    data.push([1  ,  0 ]); labels.push(1);
    N = labels.length;
})()

// small network

layer_defs = [];
layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
layer_defs.push({type:'fc', num_neurons:2, activation: 'relu'});
layer_defs.push({type:'softmax', num_classes: 2});

net = new convnetjs.Net();
net.makeLayers(layer_defs);

trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.05, momentum:0.1, batch_size:4, l2_decay:0.001});

// inspect, index 1
net.layers

// orig network

layer_defs = [];
layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
layer_defs.push({type:'fc', num_neurons:6, activation: 'tanh'});
layer_defs.push({type:'fc', num_neurons:2, activation: 'tanh'});
layer_defs.push({type:'softmax', num_classes:2});

net = new convnetjs.Net();
net.makeLayers(layer_defs);

trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, momentum:0.1, batch_size:10, l2_decay:0.001});