Binomial_CrossEnthophy

fixed rowwise/colswise mean/sum and implemented binomial_corssentrhopy. Next up is regression.
2026-05-22 10:11:43 +02:00
parent eb0a49591e
commit cb65174cf4
21 changed files with 894 additions and 159 deletions
@@ -0,0 +1,81 @@
+#pragma once
+
+#include "core/omp_config.h"
+
+#include "utils/vector.h"
+#include "utils/matrix.h"
+#include "utils/matcast.h"
+
+#include "numerics/clip.h"
+#include "numerics/log.h"
+#include "numerics/sub.h"
+
+#include "Loss.h"
+
+
+namespace neural_networks{
+
+	template <typename Td, typename Ti>
+	struct Loss_BinaryCrossentropy : Loss<Td, Ti>  {
+
+		utils::Matrix<Td> dinputs;
+		utils::Matrix<Td> y_true;
+
+	
+		utils::Vector<Td> forward(const utils::Matrix<Td>& y_pred, const utils::Matrix<Ti>& y_true) override{
+			
+			this->y_true = utils::matcast<Td, Ti>(y_true);
+
+			// Clip daa to prevent division by 0
+			// Clip both sides not to drag mean towards any value
+			utils::Matrix<Td> y_pred_clipped = numerics::clip(y_pred, Td{1e-7}, Td{1.0} - Td{1e-7});
+
+			// Calculate sample-wise loss
+			utils::Matrix<Td> sample_losses_temp = numerics::log(numerics::sub(Td{1}, y_pred_clipped));
+			sample_losses_temp = numerics::mul(sample_losses_temp, numerics::sub(Td{1}, this->y_true));
+			sample_losses_temp = numerics::add(sample_losses_temp, numerics::mul(this->y_true, numerics::log(y_pred_clipped)));
+			sample_losses_temp = numerics::neg(sample_losses_temp);
+
+
+
+
+
+			utils::Vector<Td> sample_losses = numerics::mean_rowwise(sample_losses_temp);
+
+			// Return losses
+			return sample_losses;
+		}
+		
+		void backward(const utils::Matrix<Td>& dvalues, const utils::Matrix<Ti>& y_true) override{
+
+				/*std::cout << "BCE backward y_true: "
+				          << y_true.rows() << " x " << y_true.cols()
+				          << std::endl;*/
+
+
+			// Number of samples
+			const Td samples = static_cast<Td> (this->y_true.rows());
+			// Number of outputs in every sample
+			const Td outputs = static_cast<Td> (dvalues.cols());
+
+			// Clip data to prevent division by 0
+			// Clip both sides to not drag mean towards any value
+			utils::Matrix<Td> clipped_dvalues = numerics::clip(dvalues, Td{1e-7}, Td{1.0} - Td{1e-7});
+
+
+			// Calculate gradient
+			dinputs = numerics::div(numerics::neg(numerics::sub(numerics::div(this->y_true, clipped_dvalues), numerics::div(numerics::sub(Td{1}, this->y_true), numerics::sub(Td{1}, clipped_dvalues)))), outputs);
+			// Normalize gradients
+			dinputs = numerics::div(dinputs, samples);
+
+			/*
+			std::cout << "BCE backward dinputs: "
+          << dinputs.rows() << " x " << dinputs.cols()
+          << std::endl;*/
+
+		}
+	};
+
+
+
+} // end namespace neural_networks