Exemplo n.º 1
0
        public async Task TestCancel()
        {
            IInputLayer inputLayer  = new InputLayer(() => new InputNode(), 2, new Bias());
            var         innerLayer  = new Layer(() => new Neuron(new Logistic(0.888)), 3, new Bias());
            var         outputLayer = new Layer(new Neuron(new Logistic(0.777)));

            var network = new Network
            {
                InputLayer  = inputLayer,
                OutputLayer = outputLayer
            };

            network.AddInnerLayer(innerLayer);

            var generator = new EachToEachSynapseGenerator(new Random());

            generator.Generate(network, inputLayer, innerLayer);
            generator.Generate(network, innerLayer, outputLayer);


            var samples = new List <ILearningSample>
            {
                new LearningSample(new double[] { 0, 1 }, new double[] { 1 }),
                new LearningSample(new double[] { 1, 0 }, new double[] { 1 }),
                new LearningSample(new double[] { 0, 0 }, new double[] { 0 }),
                new LearningSample(new double[] { 1, 1 }, new double[] { 0 })
            };

            var strategy = new BackpropagationStrategy();
            var settings = new LearningSettings {
                EpochRepeats = 20000
            };
            var learning = new Learning <Network, ILearningSample>(network, strategy, settings);

            var cts  = new CancellationTokenSource();
            var task = Task.Run(async() =>
            {
                await Task.Delay(1000);
                cts.Cancel();
            });

            await Assert.ThrowsAsync <OperationCanceledException>(async() => await learning.Learn(samples, cts.Token));
        }
Exemplo n.º 2
0
        public async Task TestTeachLite()
        {
            var inputLayer  = new InputLayer(new InputNode(), new Bias());
            var innerLayer  = new Layer(new Neuron(new Rectifier()));
            var outputLayer = new Layer(new Neuron(new Rectifier()));

            var network = new Network
            {
                InputLayer  = inputLayer,
                OutputLayer = outputLayer
            };

            network.AddInnerLayer(innerLayer);

            var generator = new EachToEachSynapseGenerator(new Random());

            generator.Generate(network, inputLayer, innerLayer);
            generator.Generate(network, innerLayer, outputLayer);

            var samples = new List <ILearningSample>
            {
                new LearningSample(new double[] { 0 }, new double[] { 1 }),
            };

            var strategy = new BackpropagationStrategy();
            var settings = new LearningSettings
            {
                EpochRepeats        = 10000,
                InitialTheta        = THETA,
                ThetaFactorPerEpoch = epoch => 0.9995
            };
            var learning = new Learning <Network, ILearningSample>(network, strategy, settings);

            await learning.Learn(samples);

            await network.Input(new double[] { 1 });

            var output = (await network.Output()).First();

            Assert.True(Math.Abs(output) < DELTA);
        }
Exemplo n.º 3
0
        public async Task TestTeachXor()
        {
            IInputLayer inputLayer  = new InputLayer(() => new InputNode(), 2, new Bias());
            var         innerLayer  = new Layer(() => new Neuron(new Logistic(0.888)), 3, new Bias());
            var         outputLayer = new Layer(new Neuron(new Logistic(0.777)));

            var network = new Network
            {
                InputLayer  = inputLayer,
                OutputLayer = outputLayer
            };

            network.AddInnerLayer(innerLayer);

            foreach (var layer in network.Layers)
            {
                foreach (var node in layer.Nodes)
                {
                    node.OnResultCalculated += (n, v) =>
                    {
                        Debug.WriteLine($"{n}: {v}");

                        return(Task.CompletedTask);
                    };
                }
            }

            var generator = new EachToEachSynapseGenerator(new Random());

            generator.Generate(network, inputLayer, innerLayer);
            generator.Generate(network, innerLayer, outputLayer);

            var samples = new List <ILearningSample>
            {
                new LearningSample(new double[] { 0, 1 }, new double[] { 1 }),
                new LearningSample(new double[] { 1, 0 }, new double[] { 1 }),
                new LearningSample(new double[] { 0, 0 }, new double[] { 0 }),
                new LearningSample(new double[] { 1, 1 }, new double[] { 0 })
            };

            await network.Input(new double[] { 1, 0 });

            var beforeLearning = network.LastCalculatedValue.First();

            var strategy = new BackpropagationStrategy();
            var settings = new LearningSettings
            {
                EpochRepeats        = 10000,
                InitialTheta        = THETA,
                ThetaFactorPerEpoch = epoch => 0.9999,
                ShuffleEveryEpoch   = true
            };
            var learning = new Learning <Network, ILearningSample>(network, strategy, settings);
            await learning.Learn(samples);

            await network.Input(new double[] { 1, 0 });

            var afterLearning = (await network.Output()).First();

            Assert.True(beforeLearning < afterLearning);

            await network.Input(new double[] { 1, 0 });

            var output = (await network.Output()).First();

            Assert.True(Math.Abs(1 - output) < DELTA);

            await network.Input(new double[] { 1, 1 });

            output = (await network.Output()).First();
            Assert.True(Math.Abs(0 - output) < DELTA);

            await network.Input(new double[] { 0, 0 });

            output = (await network.Output()).First();
            Assert.True(Math.Abs(0 - output) < DELTA);

            await network.Input(new double[] { 0, 1 });

            output = (await network.Output()).First();
            Assert.True(Math.Abs(1 - output) < DELTA);
        }