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Drop neural net and trained classifier

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This commit is contained in:
Shaunak Kishore
2015-09-23 23:39:33 -04:00
parent b3606087b9
commit 3682b75835
6 changed files with 1 additions and 2242 deletions
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21
lib/classifier.js
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@ -1,21 +0,0 @@
"use strict";
this.combined_classifier = undefined;
Meteor.startup(function() {
var weight = 0.8;
var dimensions = 8;
var input = new convnetjs.Vol(1, 1, dimensions);
var net = new convnetjs.Net();
net.fromJSON(TRAINED_NEURAL_NET);
function net_classifier(features) {
input.w = features;
var softmax = net.forward(input).w;
return softmax[1] - softmax[0];
}
combined_classifier = function(features) {
return hand_tuned_classifier(features) + weight*net_classifier(features);
}
});

22
lib/external/convnet/1.1.0/LICENSE vendored
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@ -1,22 +0,0 @@
The MIT License
Copyright (c) 2014 Andrej Karpathy
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

2115
lib/external/convnet/1.1.0/convnet.js vendored
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3
lib/net.js
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4
lib/stroke_extractor.js
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@ -540,7 +540,7 @@ this.get_glyph_render_data = function(glyph, manual_bridges, classifier) {
}
}
var log = [];
var bridges = get_bridges(endpoints, classifier || combined_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];
@ -626,5 +626,3 @@ this.get_glyph_training_data = function(glyph) {
}
return result;
}
this.hand_tuned_classifier = hand_tuned_classifier;

78
server/training.js
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@ -1,78 +0,0 @@
"use strict";
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;
}
function train_neural_net() {
var glyphs = Glyphs.find({'manual.verified': true}).fetch();
var sample = _.sample(glyphs, 400);
console.log('Hand-tuned accuracy:', evaluate(sample, hand_tuned_classifier));
var training_data = [];
for (var i = 0; i < glyphs.length; i++) {
var glyph_data = get_glyph_training_data(glyphs[i]);
var positive_data = glyph_data.filter(function(x) { return x[1] > 0; });
var negative_data = glyph_data.filter(function(x) { return x[1] === 0; });
if (positive_data.length > negative_data.length) {
positive_data = _.sample(positive_data, negative_data.length);
} else {
negative_data = _.sample(negative_data, positive_data.length);
}
glyph_data = negative_data.concat(positive_data);
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 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: 'softmax', num_classes: 2},
]);
var trainer = new convnetjs.Trainer(
net, {method: 'adadelta', l2_decay: 0.001, batch_size: 10});
var input = new convnetjs.Vol(1, 1, 8);
for (var iteration = 0; iteration < 10; iteration++) {
var loss = 0;
var round_data = _.sample(training_data, 4000);
for (var i = 0; i < round_data.length; i++) {
assert(input.w.length === round_data[i][0].length);
input.w = round_data[i][0];
var stats = trainer.train(input, round_data[i][1]);
assert(!isNaN(stats.loss))
loss += stats.loss;
}
console.log('Iteration', iteration, 'mean loss:', loss/round_data.length);
}
console.log('Trained neural network:', JSON.stringify(net.toJSON()));
function net_classifier(features) {
assert(input.w.length === features.length);
input.w = features;
var softmax = net.forward(input).w;
assert(softmax.length === 2);
return softmax[1] - softmax[0];
}
console.log('Neural-net accuracy:', evaluate(sample, net_classifier));
function combined_classifier(weight) {
return function(features) {
return hand_tuned_classifier(features) + weight*net_classifier(features);
}
}
var weights = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1];
for (var i = 0; i < weights.length; i++) {
console.log('Weight', weights[i], 'combined accuracy:',
evaluate(sample, combined_classifier(weights[i])));
}
}