Starting on the model.h, but need to make layer structs and structs for loss and optimizers

include/modules/neural_networks/loss/Loss_MeanSquaredError.h

@@ -0,0 +1,58 @@
+#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 T>
+	struct Loss_MeanSquaredError : Loss<T>  {
+
+		utils::Matrix<T> dinputs;
+		utils::Matrix<T> y_true;
+
+		utils::Vector<T> sample_losses;
+
+	
+		utils::Vector<T> forward(const utils::Matrix<T>& y_pred, const utils::Matrix<T>& y_true) override{
+			
+			
+			// Calculate loss
+			sample_losses = numerics::mean_rowwise(numerics::pow(numerics::sub(y_true, y_pred), T{2}));
+
+
+			// Return losses
+			return sample_losses;
+		}
+		
+		void backward(const utils::Matrix<T>& dvalues, const utils::Matrix<T>& y_true) override{
+
+
+			// Number of samples
+			const T samples = static_cast<T> (dvalues.rows());
+			// Number of outputs in every sample
+			const T outputs = static_cast<T> (dvalues.cols());
+
+
+			// Gradient values
+			dinputs = numerics::mul(numerics::div(numerics::sub(y_true, dvalues), outputs), T{-2});
+			// Normalise gradient
+			dinputs = numerics::div(dinputs, samples);
+
+
+		}
+	};
+
+
+
+} // end namespace neural_networks