- Rbm: fixed parameter import

git-svn-id: http://moon:8086/svn/software/trunk/projects/Rbm@607 b431acfa-c32f-4a4a-93f1-934dc6c82436

source/Rbm.cpp

@@ -43,16 +43,16 @@ void Rbm::weightsInit(double stddev, double mu)
 
 }
 
-void Rbm::fromJson(Json::Value params)
+void Rbm::fromJson(Json::Value rbm)
 {
+	std::cout << "Importing Rbm" << std::endl;
+	m_params.fromJson(rbm["params"]);
 }
 
 Json::Value Rbm::toJson() const
 {
 	std::cout << "Exporting Rbm" << std::endl;
 	Json::Value rbm;
-	rbm["numVisible"] = m_bv.n_elem;
-	rbm["numHidden"] = m_bh.n_elem;
 	rbm["params"] = m_params.toJson();
 	return rbm;
 }

source/Rbm.hpp

@@ -60,6 +60,7 @@ public:
 
 		void fromJson(Json::Value params)
 		{
+			std::cout << "Importing Rbm::Params" << std::endl;
 			weightDecay = params["weightDecay"].asDouble();
 			learningRate = params["learningRate"].asDouble();
 			momentum = params["momentum"].asDouble();

source/Stack.cpp

@@ -85,8 +85,9 @@ bool Stack::load()
 		int numVisibleX = layer["numVisibleX"].asInt();
 		int numVisibleY = layer["numVisibleY"].asInt();
 		int numHidden = layer["numHidden"].asInt();
-		
+		Layer *pLayer = new Layer(layername, i, numVisibleX, numVisibleY, numHidden);
-		addLayer(new Layer(layername, i, numVisibleX, numVisibleY, numHidden));
+		pLayer->fromJson(layer["rbm"]);
+		addLayer(pLayer);
 	}
 	
 	return true;

source/main.cpp

@@ -89,20 +89,29 @@ int main()
 	size_t numTraining = batch.n_rows;
 	printf("Loaded %d training samples\n", (int)numTraining);
 
-#if 1
+#if 0
 	int i = 0;
 	Layer *lowerLayer = new Layer("Layer", i, 16, 16, 8);
 	stack.addLayer(lowerLayer);
 	
-	for (++i; i < 1; i++)
+	for (++i; i < 4; i++)
 	{
 		Layer *layer = new Layer("Layer", i, lowerLayer->bh().n_elem, 1, lowerLayer->bh().n_elem >> 1);
 		lowerLayer = layer;
 		stack.addLayer(layer);
 	}
 
-	Layer *layer = stack.getLayer(0);
+	stack.getLayer(0)->params().learningRate = 0.04;
-	layer->params().learningRate = 0.02;
+	stack.getLayer(0)->params().numEpochs = 1000;
+
+	stack.getLayer(1)->params().learningRate = 0.03;
+	stack.getLayer(1)->params().numEpochs = 500;
+
+	stack.getLayer(2)->params().learningRate = 0.02;
+	stack.getLayer(2)->params().numEpochs = 250;
+
+	stack.getLayer(3)->params().learningRate = 0.01;
+	stack.getLayer(3)->params().numEpochs = 125;
 
 	// Save project
 	stack.save();
@@ -127,6 +136,7 @@ int main()
 	// Save weights
 	stack.saveWeights();
 
+	Layer *layer = stack.getLayer(0);
 	arma::mat v = arma::randu(numTraining, layer->bv().n_elem);
 	arma::mat h = layer->toHiddenProbs(v);
 	arma::mat r = layer->toVisibleProbs(h);