public async Task IntegrationSolvesXOrWithHyperbolicTangentActivationFunction()
{
var perceptron = await _errorBackPropagationTraining.TrainAsync(_trainingPatterns, errorMax : 0.1d, maxEpochs : 5000);
foreach (var trainingPattern in _trainingPatterns)
{
var xorResult = await perceptron.FireAsync(trainingPattern.InputValues);
xorResult.First().Should().BeApproximately(trainingPattern.IdealActivations.First(), 0.1d);
}
}
public async Task IntegrationSolvesXOrWithSoftmaxActivationFunction(bool oneHot)
{
_errorBackPropagationTraining = SetUpXOrTraining(bias: 0d, learningRate: 1d, momentum: 0.3d, slopeMultiplier: 1d, oneHot: oneHot);
var perceptron = await _errorBackPropagationTraining.TrainAsync(_trainingPatterns, errorMax : 0.01d, maxEpochs : 1500);
for (var t = 0; t < _trainingPatterns.Count; t++)
{
var trainingPattern = _trainingPatterns.ElementAt(t);
var xorResult = await perceptron.FireAsync(trainingPattern.InputValues);
xorResult.ElementAt(0).Should().BeApproximately(trainingPattern.IdealActivations.ElementAt(0), 0.1d, "Pattern " + t);
xorResult.ElementAt(1).Should().BeApproximately(trainingPattern.IdealActivations.ElementAt(1), 0.1d, "Pattern " + t);
}
}
public async Task IntegrationSolvesXOrWithReluActivationFunction()
{
var timer = new Stopwatch();
timer.Start();
var perceptron = await _errorBackPropagationTraining.TrainAsync(_trainingPatterns, errorMax : 0.01d, maxEpochs : 10000);
timer.Stop();
Console.WriteLine(timer.ElapsedMilliseconds);
for (var t = 0; t < _trainingPatterns.Count; t++)
{
var trainingPattern = _trainingPatterns.ElementAt(t);
var xorResult = await perceptron.FireAsync(trainingPattern.InputValues);
xorResult.ElementAt(0).Should().BeApproximately(trainingPattern.IdealActivations.ElementAt(0), 0.1d, "Pattern " + t);
}
}