public void ActivateProducesCorrectResultNegativeValue(double activationValue, double expectedResult)
{
var activationFunction = TanHActivationFunction.Create();
var activationResult = activationFunction.Activate(activationValue);
DoubleAssertionHelpers.AssertWithPrecision(expectedResult, activationResult, 10);
}
public void DerviativeProducesCorrectResultNegativeValue(double inputValue, double expectedResult)
{
var activationFunction = TanHActivationFunction.Create();
var derivativeResult = activationFunction.Derivative(inputValue);
DoubleAssertionHelpers.AssertWithPrecision(expectedResult, derivativeResult, 10);
}
public void RunXORNeuralNetwork()
{
var activationFunctionSigmoid = new TanHActivationFunction( );
var learningRate = .05;
XORNetwork = new NeuralNetwork.NeuralNetwork(new int[] { 2, 3, 1 }, activationFunctionSigmoid, learningRate);
var random = new Random(( int )DateTime.Now.Ticks);
var pass = 0;
var fail = 0;
for (double i = 1; i < 100000; i++)
{
var j = ( int )(random.NextDouble( ) * 2);
var k = ( int )(random.NextDouble( ) * 2);
var inputs = new double[] { j, k };
var expectedOutputs = new double[] { j ^ k };
var actualOutputs = XORNetwork.RunNetwork(inputs);
XORNetwork.Train(inputs, expectedOutputs);
if (((j ^ k) == 1 && actualOutputs[0] > .55) || ((j ^ k) == 0 && actualOutputs[0] < .45))
{
pass++;
}
else
{
fail++;
};
//Console.WriteLine( $"{j} | {k} = {j | k}, {actualOutputs[ 0 ]}" );
if (i % 100 == 0)
{
Console.WriteLine($"Pass: {pass}, Fail: {fail}, {( double )pass*100 / ( pass + fail )}% ; ");
Console.WriteLine($"{j} ^ {k} = {j ^ k}, {actualOutputs[ 0 ]}");
if (fail == 0)
{
Console.WriteLine($"Learning complete, learned in {i}");
return;
}
pass = 0;
fail = 0;
}
}
}
