public void learn(Matrix target)
{
Matrix outputError = MatrixMath.Subtraction(target, this.learningOut); // this is the error of my output layer will use a f**k ton of math for next steps
// This is the gradient from output to
Matrix dSigmoidOutput = dsigmoid(learningOut);
//dsig rows: 4 Col: 1 outputError Rows: 4 Col: 1
Matrix temp = MatrixMath.HMultiplication(dSigmoidOutput, outputError);
Matrix gradient = MatrixMath.Multiplication(temp, this.lr);
// Debug.Log(gradient.getColumns() + " " + gradient.getRows() + " " + temp.getColumns() + " " + temp.getRows());
Matrix hiddenTranspose = MatrixMath.Transpose(hiddenLearn);
Matrix weightHODeltas = MatrixMath.Multiplication(gradient, hiddenTranspose);
//hiddenLearn.Print();
this.weightH2 = MatrixMath.Addition(this.weightH2, weightHODeltas);
Matrix weightH2Transpose = MatrixMath.Transpose(weightH2);
Matrix hiddenErrors = MatrixMath.Multiplication(weightH2Transpose, outputError);
//calcl hidden gradient
Matrix dSigmoidHidden = dsigmoid(hiddenLearn);
Matrix hiddenTemp = MatrixMath.HMultiplication(dSigmoidHidden, hiddenErrors);
Matrix hiddenGradient = MatrixMath.Multiplication(hiddenTemp, this.lr);
//clac input to hidden deltas
Matrix inputTranspose = MatrixMath.Transpose(input);
Matrix weight1Delta = MatrixMath.Multiplication(hiddenGradient, inputTranspose);
this.weightH1 = MatrixMath.Addition(this.weightH1, weight1Delta);
// this.weightH1.Print();
}
public Matrix feedforward(Matrix inputs)
{
this.input = inputs;
Matrix hidden = MatrixMath.Multiplication(weightH1, inputs);
Matrix hOut = MatrixMath.Addition(hidden, bias1);
Matrix hiddenOut = activationFunction(hidden);
Matrix output = MatrixMath.Multiplication(weightH2, hiddenOut);
Matrix oOut = MatrixMath.Addition(output, bias2);
Matrix outputOut = activationFunction(output);
learningOut = outputOut;
hiddenLearn = hidden;
outputOut.Print();
Debug.Log("");
return(outputOut);
}