예제 #1
0
        public override double Forward(Matrix Actual, Matrix Expected, MatrixData data, int layerCount)
        {
            double error = 0.0;

            if (Actual.rows != Expected.rows || Actual.cols != Expected.cols)
            {
                throw new MatrixException("Actual does not have the same size as Expected");
            }

            double regularizationValue = 0.0;

            for (int i = 0; i < layerCount; i++)
            {
                regularizationValue = RegularizationFunction.CalculateNorm(data.Data["W" + i.ToString()]);
            }

            for (int i = 0; i < Actual.rows; i++)
            {
                for (int j = 0; j < Actual.cols; j++)
                {
                    error += Expected[i, j] * Math.Log(Expected[i, j] / Actual[i, j]);
                }
            }

            error += regularizationValue;

            BatchCost += error;
            return(error);
        }
예제 #2
0
파일: Output.cs 프로젝트: n1arash/Vortex
        public override Matrix Forward(Matrix inputs)
        {
            Params["W"].InMap(MutationFunction.Mutate);

            // Calculate Regularization Value On W and B
            RegularizationValue = (float)RegularizationFunction.CalculateNorm(Params["W"]);

            // Calculate Feed Forward Operation
            Params["X"] = inputs;
            Params["Z"] = Params["W"] * Params["X"] + Params["B"];
            Params["A"] = ActivationFunction.Forward(Params["Z"]);
            return(Params["A"]);
        }
예제 #3
0
        public override Matrix Backward(Matrix Actual, Matrix Expected, MatrixData data, int layerCount)
        {
            double error = 0.0;

            if (Actual.rows != Expected.rows || Actual.cols != Expected.cols)
            {
                throw new MatrixException("Actual Matrix does not have the same size as The Expected Matrix");
            }

            double regularizationValue = 0.0;

            for (int i = 0; i < layerCount; i++)
            {
                regularizationValue = RegularizationFunction.CalculateNorm(data.Data["W" + i.ToString()]);
            }

            for (int i = 0; i < Actual.rows; i++)
            {
                for (int j = 0; j < Actual.cols; j++)
                {
                    error += Math.Pow((Actual[i, j] - Expected[i, j]), 2);
                }
            }

            error /= Tao;

            error = Math.Exp(error);

            error *= Tao;

            Matrix gradMatrix = Actual.Duplicate();

            for (int i = 0; i < Actual.rows; i++)
            {
                for (int j = 0; j < Actual.cols; j++)
                {
                    gradMatrix[i, j] = (Actual[i, j] - Expected[i, j]) * error;
                }
            }

            return(gradMatrix);
        }