diff options
Diffstat (limited to 'third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js')
| -rw-r--r-- | third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js | 171 |
1 files changed, 171 insertions, 0 deletions
diff --git a/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js b/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js new file mode 100644 index 0000000000..bd6ccd3e3a --- /dev/null +++ b/third_party/webkit/PerformanceTests/ARES-6/ml/benchmark.js @@ -0,0 +1,171 @@ +/* + * Copyright (C) 2017 Apple Inc. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY APPLE INC. ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +"use strict"; + +let currentTime; +if (this.performance && performance.now) + currentTime = function() { return performance.now() }; +else if (this.preciseTime) + currentTime = function() { return preciseTime() * 1000; }; +else + currentTime = function() { return +new Date(); }; + +class MLBenchmark { + constructor() { } + + runIteration() + { + let Matrix = MLMatrix; + let ACTIVATION_FUNCTIONS = FeedforwardNeuralNetworksActivationFunctions; + + function run() { + + let it = (name, f) => { + f(); + }; + + function assert(b) { + if (!b) + throw new Error("Bad"); + } + + var functions = Object.keys(ACTIVATION_FUNCTIONS); + + it('Training the neural network with XOR operator', function () { + var trainingSet = new Matrix([[0, 0], [0, 1], [1, 0], [1, 1]]); + var predictions = [false, true, true, false]; + + for (var i = 0; i < functions.length; ++i) { + var options = { + hiddenLayers: [4], + iterations: 40, + learningRate: 0.3, + activation: functions[i] + }; + var xorNN = new FeedforwardNeuralNetwork(options); + + xorNN.train(trainingSet, predictions); + var results = xorNN.predict(trainingSet); + } + }); + + it('Training the neural network with AND operator', function () { + var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]]; + var predictions = [[1, 0], [1, 0], [1, 0], [0, 1]]; + + for (var i = 0; i < functions.length; ++i) { + var options = { + hiddenLayers: [3], + iterations: 75, + learningRate: 0.3, + activation: functions[i] + }; + var andNN = new FeedforwardNeuralNetwork(options); + andNN.train(trainingSet, predictions); + + var results = andNN.predict(trainingSet); + } + }); + + it('Export and import', function () { + var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]]; + var predictions = [0, 1, 1, 1]; + + for (var i = 0; i < functions.length; ++i) { + var options = { + hiddenLayers: [4], + iterations: 40, + learningRate: 0.3, + activation: functions[i] + }; + var orNN = new FeedforwardNeuralNetwork(options); + orNN.train(trainingSet, predictions); + + var model = JSON.parse(JSON.stringify(orNN)); + var networkNN = FeedforwardNeuralNetwork.load(model); + + var results = networkNN.predict(trainingSet); + } + }); + + it('Multiclass clasification', function () { + var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]]; + var predictions = [2, 0, 1, 0]; + + for (var i = 0; i < functions.length; ++i) { + var options = { + hiddenLayers: [4], + iterations: 40, + learningRate: 0.5, + activation: functions[i] + }; + var nn = new FeedforwardNeuralNetwork(options); + nn.train(trainingSet, predictions); + + var result = nn.predict(trainingSet); + } + }); + + it('Big case', function () { + var trainingSet = [[1, 1], [1, 2], [2, 1], [2, 2], [3, 1], [1, 3], [1, 4], [4, 1], + [6, 1], [6, 2], [6, 3], [6, 4], [6, 5], [5, 5], [4, 5], [3, 5]]; + var predictions = [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], + [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]; + for (var i = 0; i < functions.length; ++i) { + var options = { + hiddenLayers: [20], + iterations: 60, + learningRate: 0.01, + activation: functions[i] + }; + var nn = new FeedforwardNeuralNetwork(options); + nn.train(trainingSet, predictions); + + var result = nn.predict([[5, 4]]); + + assert(result[0][0] < result[0][1]); + } + }); + } + + run(); + } +} + +function runBenchmark() +{ + const numIterations = 60; + + let before = currentTime(); + + let benchmark = new Benchmark(); + + for (let iteration = 0; iteration < numIterations; ++iteration) + benchmark.runIteration(); + + let after = currentTime(); + return after - before; +} |