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));
}
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);
}
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);
}
