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Add complete failure of a trained classifier

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This commit is contained in:
Shaunak Kishore
2015-09-12 22:42:06 -04:00
parent 22dc07aaa2
commit f95eb7ac86
2 changed files with 70 additions and 2 deletions
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20
lib/stroke_extractor.js
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@ -494,7 +494,7 @@ function extract_strokes(paths, endpoints, bridges, log) {
// Exports go below this fold.
this.get_glyph_render_data = function(glyph, manual_bridges) {
this.get_glyph_render_data = function(glyph, manual_bridges, classifier) {
var paths = orient_paths(split_path(glyph.path));
var endpoints = [];
for (var i = 0; i < paths.length; i++) {
@ -503,7 +503,7 @@ this.get_glyph_render_data = function(glyph, manual_bridges) {
}
}
var log = [];
var bridges = get_bridges(endpoints, hand_tuned_classifier);
var bridges = get_bridges(endpoints, classifier || hand_tuned_classifier);
var strokes = extract_strokes(
paths, endpoints, manual_bridges || bridges, log);
var expected = UNIHAN_STROKE_COUNTS[glyph.name];
@ -524,6 +524,20 @@ this.get_glyph_render_data = function(glyph, manual_bridges) {
};
}
this.check_classifier_on_glyph = function(glyph, classifier) {
assert(glyph.manual.verified);
var render = get_glyph_render_data(glyph, undefined, classifier);
function canonicalize(bridges) {
var result = [];
for (var i = 0; i < bridges.length; i++) {
result.push(Point.key(bridges[i].map(Point.key).sort()));
}
return result.sort();
}
return _.isEqual(canonicalize(render.bridges),
canonicalize(glyph.manual.bridges));
}
this.get_glyph_training_data = function(glyph) {
assert(glyph.manual.verified);
var paths = orient_paths(split_path(glyph.path));
@ -570,3 +584,5 @@ this.get_glyph_training_data = function(glyph) {
}
return result;
}
this.hand_tuned_classifier = hand_tuned_classifier;

52
server/training.js Normal file
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@ -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));
});