public void SetUp()
{
_trainingPatterns = new List <TrainingPattern>
{
new TrainingPattern(new [] { -1d, -1d }, new [] { -1d }),
new TrainingPattern(new [] { -1d, 1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, -1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, 1d }, new [] { -1d }),
};
_errorBackPropagationTraining = XOrSetUp.SetUpXOrTrainingSingleFold <HyperbolicTangentUnitActivationTraining>(bias: Math.E, learningRate: 0.1d);
}
public void SetUp()
{
_trainingPatterns = new List <TrainingPattern>
{
new TrainingPattern(new [] { 0d, 0d }, new [] { 0d }),
new TrainingPattern(new [] { 1d, 0d }, new [] { 1d }),
new TrainingPattern(new [] { 0d, 1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, 1d }, new [] { 0d }),
};
_errorBackPropagationTraining = XOrSetUp.SetUpXOrTrainingSingleFold <SigmoidUnitActivationTraining>(bias: 0.5d, batch: true);
}
public void SetUp()
{
_trainingPatterns = new List <TrainingPattern>
{
new TrainingPattern(new [] { -1d, -1d }, new [] { -1d }),
new TrainingPattern(new [] { -1d, 1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, -1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, 1d }, new [] { -1d }),
};
_errorBackPropagationTraining = XOrSetUp.SetUpXOrTrainingSingleFold <BipolarUnitActivationTraining>(bias: 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 void SetUp()
{
_trainingPatterns = new List <TrainingPattern>
{
new TrainingPattern(new [] { 0d, 0d }, new [] { 0d }),
new TrainingPattern(new [] { 1d, 0d }, new [] { 1d }),
new TrainingPattern(new [] { 0d, 1d }, new [] { 1d }),
new TrainingPattern(new [] { 1d, 1d }, new [] { 0d }),
};
var inventoryAndChaining = new ErrorBackPropagationBuilder()
.With.ANewLayerOfInputUnits(2)
.ConnectedTo.ANewLayerOfHiddenUnits(3).With.UnitActivation <SigmoidUnitActivationTraining>()
.ConnectedTo.ANewLayerOfOutputUnits(1).With.OutputUnitActivation <ReluUnitActivationTraining>();
_errorBackPropagationTraining = XOrSetUp.SetUpXOrTraining(inventoryAndChaining, learningRate: 0.8d);
}
