ニューラルネットワークのハッカーガイド。 第2章：機械学習。 サポートベクターネットワーク（SVM）トレーニング

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 //   5  (x,y,a,b,c),     //            var Circuit = function() { //     this.mulg0 = new multiplyGate(); this.mulg1 = new multiplyGate(); this.addg0 = new addGate(); this.addg1 = new addGate(); }; Circuit.prototype = { forward: function(x,y,a,b,c) { this.ax = this.mulg0.forward(a, x); // a*x this.by = this.mulg1.forward(b, y); // b*y this.axpby = this.addg0.forward(this.ax, this.by); // a*x + b*y this.axpbypc = this.addg1.forward(this.axpby, c); // a*x + b*y + c return this.axpbypc; }, backward: function(gradient_top) { //    this.axpbypc.grad = gradient_top; this.addg1.backward(); //    axpby  c this.addg0.backward(); //    ax  by this.mulg1.backward(); //    b  y this.mulg0.backward(); //    a  x } } 

 // SVM  var SVM = function() { //     this.a = new Unit(1.0, 0.0); this.b = new Unit(-2.0, 0.0); this.c = new Unit(-1.0, 0.0); this.circuit = new Circuit(); }; SVM.prototype = { forward: function(x, y) { // ,  x  y   this.unit_out = this.circuit.forward(x, y, this.a, this.b, this.c); return this.unit_out; }, backward: function(label) { //   +1  -1 //    a,b,c this.a.grad = 0.0; this.b.grad = 0.0; this.c.grad = 0.0; //     ,     var pull = 0.0; if(label === 1 && this.unit_out.value < 1) { pull = 1; //    :   } if(label === -1 && this.unit_out.value > -1) { pull = -1; //       ,   } this.circuit.backward(pull); //    x,y,a,b,c //     :       this.a.grad += -this.a.value; this.b.grad += -this.b.value; }, learnFrom: function(x, y, label) { this.forward(x, y); //   ( .value   ) this.backward(label); //   ( .grad   ) this.parameterUpdate(); //     }, parameterUpdate: function() { var step_size = 0.01; this.a.value += step_size * this.a.grad; this.b.value += step_size * this.b.grad; this.c.value += step_size * this.c.grad; } }; 

 var data = []; var labels = []; data.push([1.2, 0.7]); labels.push(1); data.push([-0.3, -0.5]); labels.push(-1); data.push([3.0, 0.1]); labels.push(1); data.push([-0.1, -1.0]); labels.push(-1); data.push([-1.0, 1.1]); labels.push(-1); data.push([2.1, -3]); labels.push(1); var svm = new SVM(); // ,     var evalTrainingAccuracy = function() { var num_correct = 0; for(var i = 0; i < data.length; i++) { var x = new Unit(data[i][0], 0.0); var y = new Unit(data[i][1], 0.0); var true_label = labels[i]; // ,       var predicted_label = svm.forward(x, y).value > 0 ? 1 : -1; if(predicted_label === true_label) { num_correct++; } } return num_correct / data.length; }; //   for(var iter = 0; iter < 400; iter++) { //      var i = Math.floor(Math.random() * data.length); var x = new Unit(data[i][0], 0.0); var y = new Unit(data[i][1], 0.0); var label = labels[i]; svm.learnFrom(x, y, label); if(iter % 25 == 0) { //  10 ... console.log('training accuracy at iter ' + iter + ': ' + evalTrainingAccuracy()); } } 

 training accuracy at iteration 0: 0.3333333333333333 training accuracy at iteration 25: 0.3333333333333333 training accuracy at iteration 50: 0.5 training accuracy at iteration 75: 0.5 training accuracy at iteration 100: 0.3333333333333333 training accuracy at iteration 125: 0.5 training accuracy at iteration 150: 0.5 training accuracy at iteration 175: 0.5 training accuracy at iteration 200: 0.5 training accuracy at iteration 225: 0.6666666666666666 training accuracy at iteration 250: 0.6666666666666666 training accuracy at iteration 275: 0.8333333333333334 training accuracy at iteration 300: 1 training accuracy at iteration 325: 1 training accuracy at iteration 350: 1 training accuracy at iteration 375: 1 

 var a = 1, b = -2, c = -1; //   for(var iter = 0; iter < 400; iter++) { //      var i = Math.floor(Math.random() * data.length); var x = data[i][0]; var y = data[i][1]; var label = labels[i]; //   var score = a*x + b*y + c; var pull = 0.0; if(label === 1 && score < 1) pull = 1; if(label === -1 && score > -1) pull = -1; //      var step_size = 0.01; a += step_size * (x * pull - a); // -a     b += step_size * (y * pull - b); // -b     c += step_size * (1 * pull); }