Add complete failure of a trained classifier

server/training.js

@@ -0,0 +1,52 @@
+function evaluate(glyphs, classifier) {
+  var num_correct = 0;
+  for (var i = 0; i < glyphs.length; i++) {
+    if (check_classifier_on_glyph(glyphs[i], classifier)) {
+      num_correct += 1;
+    }
+  }
+  return num_correct/glyphs.length;
+}
+
+Meteor.startup(function() {
+  var glyphs = Glyphs.find({'manual.verified': true}).fetch();
+  var glyphs = glyphs.slice(0, 100);
+  console.log('Hand-tuned accuracy:', evaluate(glyphs, hand_tuned_classifier));
+
+  var training_data = [];
+  var training_labels = [];
+  for (var i = 0; i < glyphs.length; i++) {
+    var glyph_data = get_glyph_training_data(glyphs[i]);
+    for (var j = 0; j < glyph_data.length; j++) {
+      training_data.push(glyph_data[j]);
+    }
+  }
+  console.log('Got ' + training_data.length + ' rows of training data.');
+
+  var input = new convnetjs.Vol(1, 1, 8);
+  var net = new convnetjs.Net();
+  net.makeLayers([
+    {type: 'input', out_sx: 1, out_sy: 1, out_depth: 8},
+    {type: 'fc', num_neurons: 8, activation: 'tanh'},
+    {type: 'fc', num_neurons: 8, activation: 'tanh'},
+    {type: 'regression', num_neurons: 1},
+  ]);
+  var trainer = new convnetjs.Trainer(
+      net, {method: 'adadelta', l2_decay: 0.001, batch_size: 10});
+  for (var iteration = 0; iteration < 1; iteration++) {
+    for (var i = 0; i < training_data.length; i++) {
+      assert(input.w.length === training_data[i][0].length);
+      input.w = training_data[i][0];
+      trainer.train(input, training_data[i][1]);
+    }
+    console.log('Completed iteration:', iteration);
+  }
+  console.log('Trained neural network.');
+
+  function net_classifier(features) {
+    assert(input.w.length === features.length);
+    input.w = features;
+    return net.forward(input).w[0];
+  }
+  console.log('Neural-net accuracy:', evaluate(glyphs, net_classifier));
+});