DeepIntelligence
diff --git a/‎include/ActivationFunc.h‎
Lines changed: 26 additions & 2 deletions b/‎include/ActivationFunc.h‎
Lines changed: 26 additions & 2 deletions
diff --git a/‎include/DeepLearning.pb.h‎
Lines changed: 63 additions & 117 deletions b/‎include/DeepLearning.pb.h‎
Lines changed: 63 additions & 117 deletions
diff --git a/‎include/MultiLayerPerceptron.h‎
Lines changed: 1 addition & 10 deletions b/‎include/MultiLayerPerceptron.h‎
Lines changed: 1 addition & 10 deletions
@@ -1,10 +1,34 @@
 #pragma once
-#include <memory>
-#include <armadillo>
+#include "common.h"
+
 
 namespace NeuralNet{
 	enum ActivationType {softmax, sigmoid, linear, tanh, ReLU};
 
+	inline ActivationType GetActivationType(DeepLearning::NeuralNetParameter_ActivationType type){
+		switch (type) {
+			case DeepLearning::NeuralNetParameter_ActivationType_sigmoid:
+				return sigmoid;
+				break;
+			case DeepLearning::NeuralNetParameter_ActivationType_linear:
+				return linear;
+				break;
+			case DeepLearning::NeuralNetParameter_ActivationType_tanh:
+				return tanh;
+				break;						
+			case DeepLearning::NeuralNetParameter_ActivationType_softmax:
+				return softmax;
+				break;		
+			case DeepLearning::NeuralNetParameter_ActivationType_ReLU:
+				return ReLU;
+				break;		
+			default:
+				std::cerr << "invalid activation type" << std::endl;
+				exit(1);
+				break;
+		}
+	}
+	
 	inline void ApplyActivation(std::shared_ptr<arma::mat> output, ActivationType actType){
     std::shared_ptr<arma::mat> &p=output;
     arma::mat maxVal = arma::max(*p,0);
 
@@ -27,34 +27,25 @@ namespace NeuralNet {
         void vectoriseWeight(arma::vec &x);
         void calLoss(std::shared_ptr<arma::mat> delta);
         virtual void forward();
-	virtual void setTrainingSamples(std::shared_ptr<arma::mat> X, std::shared_ptr<arma::mat> Y);
-        virtual void applyUpdates(std::vector<std::shared_ptr<arma::mat>>);
+       	virtual void applyUpdates(std::vector<std::shared_ptr<arma::mat>>);
         virtual void calGradient();
-        virtual std::vector<std::shared_ptr<arma::mat>> netGradients();
         virtual double getLoss();
         virtual void save(std::string filename);
         virtual void load(std::string filename);
         virtual std::shared_ptr<arma::mat> netOutput() {
             return netOutput_;
         }
     private:
-        DeepLearning::NeuralNetParameter neuralNetPara;
         int numLayers;
         int numInstance;
         bool testGrad;
         double error;
         /**the collection of Base layers*/
         std::vector<BaseLayer> layers;
-        /**training data, input and label*/
-        std::shared_ptr<arma::mat> trainingX;
-        std::shared_ptr<arma::mat> trainingY;
         /* dimension parameters for each layer*/
         std::vector<int> dimensions;
         /* network output*/
         std::shared_ptr<arma::mat> netOutput_;
-        /* network gradients*/
-        std::vector<std::shared_ptr<arma::mat>> netGradVector;
-
         int totalDim;
 
     };