Sync public subset from Flux (private)

include/modules/neural_networks/activation_functions/ReLU.h

@@ -0,0 +1,28 @@
+#pragma once
+
+#include "./core/omp_config.h"
+
+#include "./utils/vector.h"
+#include "./utils/matrix.h"
+#include "./utils/random.h"
+
+
+namespace neural_networks{
+
+	template <typename T>
+	struct activation_ReLU{
+
+			utils::Matrix<T> outputs;
+
+	        void forward(utils::Matrix<T> inputs){
+			    outputs = numerics::max(inputs, T{0});
+			    //outputs.print();
+	        }
+
+
+
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/activation_functions/Softmax.h

@@ -0,0 +1,37 @@
+#pragma once
+
+#include "./core/omp_config.h"
+
+#include "./utils/vector.h"
+#include "./utils/matrix.h"
+
+#include "./numerics/max.h"
+#include "./numerics/matsubtract.h"
+#include "./numerics/exponential.h"
+#include "./numerics/matdiv.h"
+
+
+namespace neural_networks{
+
+	template <typename T>
+	struct activation_softmax{
+
+			utils::Matrix<T> exp_values;
+			utils::Matrix<T> probabilities;
+			utils::Matrix<T> outputs;
+
+	        void forward(const utils::Matrix<T> inputs){
+	
+
+			    exp_values = numerics::exponential(numerics::matsubtract(inputs, numerics::max(inputs, "rows"), "col"));
+			    probabilities = numerics::matdiv(exp_values, numerics::matsum(exp_values, "col"), "col");
+
+			    outputs = probabilities;
+
+	        }
+
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/datasets/spiral.h

@@ -0,0 +1,42 @@
+#pragma once
+
+#include "./core/omp_config.h"
+#include "./utils/matrix.h"
+#include "./utils/vector.h"
+#include "./utils/random.h"
+
+//#include <math.h>
+
+
+namespace neural_networks{
+
+		template <typename TX, typename Ty>
+		void create_spital_data(const uint64_t samples, const uint64_t classes, utils::Matrix<TX>& X, utils::Vector<Ty>& y) {
+
+			const uint64_t rows = samples*classes;
+			TX r, t;
+			uint64_t row_idx;
+
+
+			if ((rows != X.rows()) || (X.cols() != 2)){
+				X.resize(samples*classes, 2);
+			}
+			if (rows != y.size()){
+				y.resize(rows);
+			}
+
+			for (uint64_t i = 0; i < classes; ++i){
+				for (uint64_t j = 0; j < samples; ++j){
+					r = static_cast<TX>(j)/static_cast<TX>(samples);
+					t =  static_cast<TX>(i)*4.0 + (4.0+r);
+					row_idx = (i*samples) + j;
+
+					X(row_idx, 0) = r*std::cos(t*2.5) + utils::random(TX{-0.15}, TX{0.15});
+					X(row_idx, 1) = r*std::sin(t*2.5) + utils::random(TX{-0.15}, TX{0.15});
+					y[row_idx] = static_cast<Ty>(i);
+				}
+			}
+		}
+
+
+} // end namesoace NN

include/modules/neural_networks/layers/dense_layer.h

@@ -0,0 +1,42 @@
+#pragma once
+
+#include "./core/omp_config.h"
+
+#include "./utils/vector.h"
+#include "./utils/matrix.h"
+#include "./utils/random.h"
+
+
+namespace neural_networks{
+
+	template <typename T>
+	struct dense_layer{
+			utils::Matrix<T> weights;
+			utils::Vector<T> biases;
+			utils::Matrix<T> outputs;
+			
+			// Default Constructor
+			dense_layer() = default;
+
+			// Constructor
+	    	dense_layer(const uint64_t n_inputs, const uint64_t n_neurons){
+	        	
+	        	weights.random(n_inputs, n_neurons, -1, 1);
+	        	biases.resize(n_neurons, T{0});
+	        	//weights.print();
+	        	//outputs.resize()
+	        	
+	        }
+
+
+	        void forward(utils::Matrix<T> inputs){
+			    outputs = numerics::matadd(numerics::matmul_auto(inputs, (weights)), biases, "row");
+	        }
+
+
+
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/loss/Loss _CategoricalCrossentrophy.h

@@ -0,0 +1,34 @@
+#pragma once
+
+#include "./core/omp_config.h"
+
+#include "./utils/vector.h"
+#include "./utils/matrix.h"
+
+
+namespace neural_networks{
+
+	template <typename Td, typename Ti>
+	struct Loss{
+
+			utils::Matrix<Td> sample_losses;
+			Td data_losses;
+
+			virtual utils::Vector<Td> forward(const utils::Matrix<Td>& output, const utils::Matrix<Ti>& y) = 0;
+
+			Td calculate(const utils::Matrix<Td>& output, const utils::Matrix<Ti>& y){
+				// Calculate sample losses
+				sample_losses = forward(output, y);
+
+				// Calculate mean loss
+				data_losses = numerics::mean(sample_losses);
+				return data_losses;
+
+			}
+
+
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/loss/loss.h

@@ -0,0 +1,34 @@
+#pragma once
+
+#include "./core/omp_config.h"
+
+#include "./utils/vector.h"
+#include "./utils/matrix.h"
+
+
+namespace neural_networks{
+
+	template <typename Td, typename Ti>
+	struct Loss{
+
+			utils::Matrix<Td> sample_losses;
+			Td data_losses;
+
+			virtual utils::Vector<Td> forward(const utils::Matrix<Td>& output, const utils::Matrix<Ti>& y) = 0;
+
+			Td calculate(const utils::Matrix<Td>& output, const utils::Matrix<Ti>& y){
+				// Calculate sample losses
+				sample_losses = forward(output, y);
+
+				// Calculate mean loss
+				data_losses = numerics::mean(sample_losses);
+				return data_losses;
+
+			}
+
+
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/neural_networks.h

@@ -0,0 +1,12 @@
+// #include "./modules/neural_networks/neural_networks.h"
+#pragma once
+
+#include "datasets/spiral.h"
+
+#include "layers/dense_layer.h"
+
+
+#include "activation_functions/ReLU.h"
+#include "activation_functions/Softmax.h"
+
+#include "loss/loss.h"