Binomial_CrossEnthophy

fixed rowwise/colswise mean/sum and implemented binomial_corssentrhopy. Next up is regression.

include/modules/neural_networks/activation_functions/Activation_Sigmoid.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include "core/omp_config.h"
+
+#include "utils/vector.h"
+#include "utils/matrix.h"
+
+#include "numerics/neg.h"
+#include "numerics/exp.h"
+#include "numerics/add.h"
+#include "numerics/div.h"
+#include "numerics/sub.h"
+#include "numerics/mul.h"
+
+namespace neural_networks{
+
+	template <typename T>
+	struct Activation_Sigmoid{
+
+		utils::Matrix<T> _inputs;
+		utils::Matrix<T> outputs;
+
+		utils::Matrix<T> dinputs;
+
+        void forward(const utils::Matrix<T>& inputs){
+        	_inputs = inputs;
+
+        	outputs = numerics::neg(inputs);
+        	outputs = numerics::exp(outputs);
+        	outputs = numerics::add(outputs, T{1});
+        	outputs = numerics::div(T{1}, outputs);
+        }
+        void backward(const utils::Matrix<T>& dvalues){
+        	
+        	dinputs = numerics::sub(T{1}, outputs);
+        	dinputs = numerics::mul(dvalues, dinputs);
+        	dinputs = numerics::mul(dinputs, outputs);
+
+
+
+        }
+	};
+
+
+
+} // end namespace neural_networks

include/modules/neural_networks/activation_functions/Activation_Softmax.h

@@ -29,7 +29,7 @@ namespace neural_networks{
 			    utils::Matrix<T> exp_values = numerics::exp(numerics::sub_colwise(inputs, numerics::max_rowwise(inputs)));
 
 			    // Normalize them for each sample
-			    utils::Matrix<T> probabilities = numerics::div_colwise(exp_values, numerics::sum_colwise(exp_values));
+			    utils::Matrix<T> probabilities = numerics::div_colwise(exp_values, numerics::sum_rowwise(exp_values));
 			    outputs = probabilities;
 	        }