Improved status

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@562 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/Rbm.cpp

@@ -11,7 +11,6 @@
  * Created on 21. Oktober 2019, 21:28
  */
 
-#include <streambuf>
 #include "Rbm.hpp"
 #include "noise.h"
 
@@ -34,9 +33,8 @@ Rbm::~Rbm()
 {
 }
 
-void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize, const Params& params, IRbmListener* pListener)
+void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize, const Params& params, IListener* pListener)
 {
-	size_t i;
 	size_t epoch;
 	size_t gibbs;
 
@@ -53,13 +51,13 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 	arma::mat momentum_bias_v(arma::zeros(1, m_w.n_rows));
 	arma::mat momentum_bias_h(arma::zeros(1, m_w.n_cols));
 	arma::mat penalty_weights = arma::zeros(m_w.n_rows, m_w.n_cols);
-	double L1 = 0;
-	double L2 = 0;
 	
-	double progress = 0;
+	Status status;
+	
+	status.progress = 0;
 	while (trainingSizeRemain)
 	{
-		std::cout << "trainingSizeRemain: " << trainingSizeRemain << std::endl;
+		status.trainingSizeRemain = trainingSizeRemain;
 		size_t toSlice = std::min(miniBatchSize, trainingSizeRemain);
 		arma::mat miniBatch = batch.rows(batchRowIndex, batchRowIndex+toSlice-1);
 		trainingSizeRemain -= toSlice;
@@ -75,14 +73,7 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 
 		for (epoch=0; epoch < numEpochs; epoch++)
 		{
-			if (pListener)
-			{
-				if(!pListener->onProgress())
-				{
-					break;
-				}
-			}
-			
+
 			// Create hidden layer base on training data
 			if (params.m_doSampleBatch)
 			{
@@ -156,26 +147,33 @@ void Rbm::train(const arma::mat& batch, size_t numEpochs, size_t miniBatchSize,
 				}
 			}
 			
-			L1 = accu(abs(m_w));
-			L2 = accu(m_w % m_w);
+			status.L1 = accu(abs(m_w));
+			status.L2 = accu(m_w % m_w);
 			momentum_bias_v = params.m_momentum*momentum_bias_v + grad_bias_v;
 			momentum_bias_h = params.m_momentum*momentum_bias_h + grad_bias_h;
-			momentum_weights = params.m_momentum*momentum_weights + grad_weight - L2*penalty_weights;
+			momentum_weights = params.m_momentum*momentum_weights + grad_weight - status.L2*penalty_weights;
 			
 			m_bv += learning_rate*momentum_bias_v;
 			m_bh += learning_rate*momentum_bias_h;
 			m_w += learning_rate*momentum_weights;
 			
-			progress += dProgress;
-
+			status.progress += dProgress;
+			
 		} // Number of epochs
 			
+		status.epoch = epoch;
 		arma::mat diffErr = miniBatch - vis_probs;
 		arma::mat diffErr_squared = diffErr % diffErr;
-		double err = accu(diffErr_squared);
-		std::cout << "error (per mini batch) = " << err << std::endl;
-		std::cout << "L1                     = " << L1 << std::endl;
-		std::cout << "L2                     = " << L2 << std::endl;
+		status.err = accu(diffErr_squared);
+
+		if (pListener)
+		{
+			if(!pListener->onProgress(status))
+			{
+				break;
+			}
+		}
+		
 	} // number of mini batches
 }
 

source/Rbm.hpp

@@ -16,23 +16,11 @@
 
 #include <armadillo>
 #include "noise.h"
-class IRbmListener
-{
-public:
-	
-	virtual ~IRbmListener()
-	{
 
-	}
-
-	bool onProgress()
-	{
-		return true;
-	}
-};
 class Rbm
 {
 public:
+	
 	struct Params
 	{
 		Params()
@@ -45,7 +33,7 @@ public:
 		, m_numGibbs(1)
 		{
 		}
-		
+
 		double m_weightDecay;
 		double m_learningRate;
 		double m_momentum;
@@ -55,11 +43,33 @@ public:
 		size_t m_numGibbs;
 	};
 
+	struct Status
+	{
+		size_t epoch;
+		size_t trainingSizeRemain;
+		double progress;
+		double err;
+		double L1;
+		double L2;
+	};
+
+	class IListener
+	{
+	public:
+
+		IListener() {}
+		virtual ~IListener() {}
+		virtual bool onProgress(const Status &status)
+		{
+			return true;
+		}
+	};
+
 	Rbm(size_t numHidden, size_t numVisible);
 	Rbm(const Rbm& orig);
 	virtual ~Rbm();
 
-	void train(arma::mat const &batch, size_t numEpochs, size_t sizeMiniBatch, Params const &params, IRbmListener *pListener);
+	void train(arma::mat const &batch, size_t numEpochs, size_t sizeMiniBatch, Params const &params, IListener *pListener);
 	arma::mat sample(arma::mat const &src);
 	static arma::mat probsLogistic(arma::mat const &src);
 	arma::mat toHidden(const arma::mat &v);

source/main.cpp

@@ -1,8 +1,28 @@
+#include <streambuf>
 #include <cstdio>
 #include <cmath>
 #include <armadillo>
 #include "Rbm.hpp"
 
+class RbmListener : public Rbm::IListener
+{
+	public:
+	RbmListener() {}
+	virtual ~RbmListener() {}
+		
+	bool onProgress(const Rbm::Status &status)
+	{
+		std::cout << "Progress :          " << 100*status.progress << " %" << std::endl;
+		std::cout << "epoch :             " << status.epoch << std::endl;
+		std::cout << "trainingSizeRemain: " << status.trainingSizeRemain << std::endl;
+		std::cout << "error (per mini batch) = " << status.err << std::endl;
+		std::cout << "L1                     = " << status.L1 << std::endl;
+		std::cout << "L2                     = " << status.L2 << std::endl;
+		
+		return true;
+	}
+};
+
 int main()
 {
 	printf("Hallo, Welt!\n");
@@ -31,10 +51,12 @@ int main()
 	printf("Z.n_elem = %d\n", (int)Z.n_elem);
 	//    +------>
 
+	
+	RbmListener statusDisplay;
 	Rbm::Params params;
 	Rbm rbm(28*28, 64);
-	arma::mat batch = arma::randu(1000, 28*28);
-	rbm.train(batch, 1000, 100, params, nullptr);
+	arma::mat batch = arma::randu(100, 28*28);
+	rbm.train(batch, 100, 10, params, &statusDisplay);
 	
 //	arma::mat v = arma::randu(28*28, 1);
 //	arma::mat h = rbm.toHidden(v);