public void GetErrorsOutputSignal()
{
//Arrange
float[][] outputSignal = new float[][]
{
new float[] { 1 },
new float[] { 0 },
new float[] { 0 },
new float[] { 1 }
};
float[][] actualSignal = new float[][]
{
new float[] { 1 },
new float[] { 0 },
new float[] { 0 },
new float[] { 1 }
};
float[][] expected = new float[][]
{
new float[] { 0 },
new float[] { 0 },
new float[] { 0 },
new float[] { 0 }
};
//Act
var actual = NeuralMath.GetTotalLoss(outputSignal, actualSignal, LossFunc.MSE);
//Assert
for (int i = 0; i < expected.Length; i++)
{
for (int j = 0; j < expected[i].Length; j++)
{
Assert.AreEqual(expected[i][j], actual);
}
}
}
public float CalculateError(float[][] inputSignals, float[][] expectedSignals)
{
var _outputSignals = OnActivationNeuralNet(inputSignals).OutputsSignalsPrevLayer;
return(NeuralMath.GetTotalLoss(_outputSignals, expectedSignals, LossFunc));
}
