a.predict_soft_return_val.js

// averaged scores across layer nets
{ '0': 3.7868791304080087,
  '1': 1.2131208695919908,
  BYTES_PER_ELEMENT: 8,
  get: [Function: get],
  set: [Function: set],
  slice: [Function: slice],
  subarray: [Function: subarray],
  buffer:
   { '0': 252,
     '1': 14,
     '2': 24,
     '3': 73,
     '4': 135,
     '5': 75,
     '6': 14,
     '7': 64,
     '8': 6,
     '9': 226,
     '10': 207,
     '11': 109,
     '12': 241,
     '13': 104,
     '14': 243,
     '15': 63,
     slice: [Function: slice],
     byteLength: 16 },
  length: 2,
  byteOffset: 0,
  byteLength: 16 }
  
  // stats object for prediction
{ maxi: 0,
  maxv: 3.7868791304080087,
  mini: 1,
  minv: 1.2131208695919908,
  dv: 2.573758260816018 }

lib.js

var maxmin = function(w) {
  if(w.length === 0) { return {}; } // ... ;s
  var maxv = w[0];
  var minv = w[0];
  var maxi = 0;
  var mini = 0;
  var n = w.length;
  for(var i=1;i<n;i++) {
    if(w[i] > maxv) { maxv = w[i]; maxi = i; }
    if(w[i] < minv) { minv = w[i]; mini = i; }
  }
  return {maxi: maxi, maxv: maxv, mini: mini, minv: minv, dv:maxv-minv};
}

// returns prediction scores for given test data point, as Vol
// uses an averaged prediction from the best ensemble_size models
// x is a Vol.
predict_soft: function(data) {
  // forward prop the best networks
  // and accumulate probabilities at last layer into a an output Vol
  var nv = Math.min(this.ensemble_size, this.evaluated_candidates.length);
  if(nv === 0) { return new convnetjs.Vol(0,0,0); } // not sure what to do here? we're not ready yet
  var xout, n;
  for(var j=0;j<nv;j++) {
    var net = this.evaluated_candidates[j].net;
    var x = net.forward(data);
    if(j===0) {
      xout = x;
      n = x.w.length;
    } else {
      // add it on
      for(var d=0;d<n;d++) {
        xout.w[d] += x.w[d];
      }
    }
  }
  // produce average
  for(var d=0;d<n;d++) {
    xout.w[d] /= n;
  }
  return xout;
},

predict: function(data) {
  var xout = this.predict_soft(data);
  if(xout.w.length !== 0) {
    var stats = maxmin(xout.w);
    var predicted_label = stats.maxi;
  } else {
    var predicted_label = -1; // error out
  }
  return predicted_label;

}