* 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";
class Benchmark {
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();
}
}
