Swooping git litter

src/mlpack/methods/ann/rnn.hpp

@@ -205,7 +205,11 @@ class RNN
   //! Serialize the model.
   template<typename Archive>
   void Serialize(Archive& ar, const unsigned int /* version */);
-
+        
+  //! Get the maximum number of steps to backpropagate through time (BPTT).
+  size_t Rho() const { return rho; }
+  //! Modify the maximum number of steps to backpropagate through time (BPTT).
+  size_t& Rho() { return rho; }
  private:
   // Helper functions.
   /**
@@ -255,6 +259,9 @@ class RNN
   //! Number of steps to backpropagate through time (BPTT).
   size_t rho;
 
+  //! Number of steps to backpropagate through time (BPTT) at the previous step.
+  size_t prevRho;
+
   //! Instantiated outputlayer used to evaluate the network.
   OutputLayerType outputLayer;
 

src/mlpack/methods/ann/rnn_impl.hpp

@@ -22,11 +22,8 @@
 #include "visitor/deterministic_set_visitor.hpp"
 #include "visitor/gradient_set_visitor.hpp"
 #include "visitor/gradient_visitor.hpp"
-<<<<<<< bba7704723aca043c4076008cca07eff8267671b
-=======
 #include "visitor/weight_set_visitor.hpp"
 #include "visitor/rho_set_visitor.hpp"
->>>>>>> Fixed LSTM baseline bug
 
 namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
@@ -39,14 +36,8 @@ RNN<OutputLayerType, InitializationRuleType>::RNN(
     OutputLayerType outputLayer,
     InitializationRuleType initializeRule) :
     rho(rho),
-<<<<<<< bba7704723aca043c4076008cca07eff8267671b
     outputLayer(std::move(outputLayer)),
     initializeRule(std::move(initializeRule)),
-=======
-    prevRho(0),
-    outputLayer(outputLayer),
-    initializeRule(initializeRule),
->>>>>>> Fixed LSTM baseline bug
     inputSize(0),
     outputSize(0),
     targetSize(0),