Fixing 'rho' issue in RNN

src/mlpack/methods/ann/layer/gru.hpp

@@ -26,6 +26,7 @@
 #define MLPACK_METHODS_ANN_LAYER_GRU_HPP
 
 #include <list>
+#include <limits>
 
 #include <mlpack/prereqs.hpp>
 
@@ -66,7 +67,8 @@ class GRU
    * @param outSize The number of output units.
    * @param rho Maximum number of steps to backpropagate through time (BPTT).
    */
-  GRU(const size_t inSize, const size_t outSize, const size_t rho);
+  GRU(const size_t inSize, const size_t outSize, const size_t rho =
+      std::numeric_limits<size_t>::max());
 
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function

src/mlpack/methods/ann/layer/lstm.hpp

@@ -15,6 +15,8 @@
 
 #include <mlpack/prereqs.hpp>
 
+#include <limits>
+
 #include "../visitor/delta_visitor.hpp"
 #include "../visitor/output_parameter_visitor.hpp"
 
@@ -54,7 +56,8 @@ class LSTM
    * @param outSize The number of output units.
    * @param rho Maximum number of steps to backpropagate through time (BPTT).
    */
-  LSTM(const size_t inSize, const size_t outSize, const size_t rho);
+  LSTM(const size_t inSize, const size_t outSize, const size_t rho =
+      std::numeric_limits<size_t>::max());
 
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function

src/mlpack/methods/ann/rnn.hpp

@@ -210,11 +210,6 @@ 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.
   /**

src/mlpack/methods/ann/rnn_impl.hpp

@@ -24,7 +24,6 @@
 #include "visitor/gradient_set_visitor.hpp"
 #include "visitor/gradient_visitor.hpp"
 #include "visitor/weight_set_visitor.hpp"
-#include "visitor/rho_set_visitor.hpp"
 
 namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
@@ -184,15 +183,6 @@ template<typename OutputLayerType, typename InitializationRuleType>
 void RNN<OutputLayerType, InitializationRuleType>::SinglePredict(
     const arma::mat& predictors, arma::mat& results)
 {
-  if (prevRho != rho)
-  {
-    for (size_t i = 1; i < network.size(); ++i)
-      boost::apply_visitor(RhoSetVisitor(rho), network[i]);
-
-    inputSize = predictors.n_elem / rho;
-    prevRho = rho;
-  }
-
   for (size_t seqNum = 0; seqNum < rho; ++seqNum)
   {
     currentInput = predictors.rows(seqNum * inputSize,
@@ -225,13 +215,10 @@ double RNN<OutputLayerType, InitializationRuleType>::Evaluate(
   arma::mat target = arma::mat(responses.colptr(i), responses.n_rows,
       1, false, true);
 
-  if (prevRho != rho)
+  if (!inputSize)
   {
-    for (size_t i = 1; i < network.size(); ++i)
-      boost::apply_visitor(RhoSetVisitor(rho), network[i]);
     inputSize = input.n_elem / rho;
     targetSize = target.n_elem / rho;
-    prevRho = rho;
   }
 
   ResetCells();

src/mlpack/methods/ann/visitor/CMakeLists.txt

@@ -35,12 +35,12 @@ set(SOURCES
   parameters_set_visitor_impl.hpp
   parameters_visitor.hpp
   parameters_visitor_impl.hpp
+  reset_cell_visitor.hpp
+  reset_cell_visitor_impl.hpp
   reset_visitor.hpp
   reset_visitor_impl.hpp
   reward_set_visitor.hpp
   reward_set_visitor_impl.hpp
-  rho_set_visitor.hpp
-  rho_set_visitor_impl.hpp
   save_output_parameter_visitor.hpp
   save_output_parameter_visitor_impl.hpp
   set_input_height_visitor.hpp