passed reference to network, added assignment operator function to Sequential

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

@@ -90,6 +90,9 @@ class Sequential
   //! Copy constructor.
   Sequential(const Sequential&);
 
+  //! Copy assignment operator.
+  void operator = (const Sequential& layer);
+
   //! Destroy the Sequential object.
   ~Sequential();
 
@@ -229,6 +232,9 @@ class Sequential
   //! Locally-stored output height visitor.
   OutputHeightVisitor outputHeightVisitor;
 
+  //! Locally-stored copy visitor
+  CopyVisitor<CustomLayers...> copyVisitor;
+
   //! The input width.
   size_t width;
 

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

@@ -56,6 +56,27 @@ Sequential(const bool model, const bool ownsLayers) :
   // Nothing to do here.
 }
 
+template <typename InputDataType, typename OutputDataType, bool Residual,
+          typename... CustomLayers>
+void Sequential<InputDataType, OutputDataType, Residual, CustomLayers...>::
+operator = (const Sequential& layer)
+{
+  model = layer.model;
+  reset = layer.reset;
+  width = layer.width;
+  height = layer.height;
+  ownsLayers = layer.ownsLayers;
+  parameters = layer.parameters;
+  network.clear();
+  // Build new layers according to source network.
+  for (size_t i = 0; i < layer.network.size(); ++i)
+  {
+    this->network.push_back(boost::apply_visitor(copyVisitor,
+        layer.network[i]));
+  }
+}
+
+
 template <typename InputDataType, typename OutputDataType, bool Residual,
           typename... CustomLayers>
 Sequential<

src/mlpack/methods/reinforcement_learning/q_learning.hpp

@@ -78,7 +78,7 @@ class QLearning
    * @param environment Reinforcement learning task.
    */
   QLearning(TrainingConfig config,
-            NetworkType network,
+            NetworkType& network,
             PolicyType policy,
             ReplayType replayMethod,
             UpdaterType updater = UpdaterType(),
@@ -141,7 +141,7 @@ class QLearning
   TrainingConfig config;
 
   //! Locally-stored learning network.
-  NetworkType learningNetwork;
+  NetworkType& learningNetwork;
 
   //! Locally-stored target network.
   NetworkType targetNetwork;

src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp

@@ -31,13 +31,13 @@ QLearning<
   PolicyType,
   ReplayType
 >::QLearning(TrainingConfig config,
-             NetworkType network,
+             NetworkType& network,
              PolicyType policy,
              ReplayType replayMethod,
              UpdaterType updater,
              EnvironmentType environment):
     config(std::move(config)),
-    learningNetwork(std::move(network)),
+    learningNetwork(network),
     updater(std::move(updater)),
     #if ENS_VERSION_MAJOR >= 2
     updatePolicy(NULL),

src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp

@@ -39,59 +39,21 @@ template <
 class DuelingDQN
 {
  public:
-  /**
-   * Default constructor.
-   */
+  //! Default constructor.
   DuelingDQN()
-  { /* Nothing to do here. */ }
-
-  //! Copy constructor.
-  DuelingDQN(const DuelingDQN& model):
-      featureNetwork(model.featureNetwork),
-      advantageNetwork(model.advantageNetwork),
-      valueNetwork(model.valueNetwork),
-      actionValues(model.actionValues)
   {
-    std::cout << "copyConstructor" << '\n';
-
+    featureNetwork = new Sequential<>();
+    valueNetwork = new Sequential<>();
+    advantageNetwork = new Sequential<>();
     concat = new Concat<>(true);
-    concat.Add(&valueNetwork);
-    concat.Add(&advantageNetwork);
 
+    concat->Add(valueNetwork);
+    concat->Add(advantageNetwork);
     completeNetwork.Add(new IdentityLayer<>());
-    completeNetwork.Add(&featureNetwork);
-    completeNetwork.Add(&concat);
-    completeNetwork.Parameters() = model.completeNetwork.Parameters();
-  };
-
-  //! Move constructor.
-  DuelingDQN(DuelingDQN&& model):
-      featureNetwork(std::move(model.featureNetwork)),
-      advantageNetwork(std::move(model.advantageNetwork)),
-      valueNetwork(std::move(model.valueNetwork)),
-      actionValues(std::move(model.actionValues))
-  {
-    std::cout << "move Constructor" << '\n';
-
-    concat = new Concat<>(true);
-    concat.Add(&valueNetwork);
-    concat.Add(&advantageNetwork);
-
-    completeNetwork.Add(new IdentityLayer<>());
-    completeNetwork.Add(&featureNetwork);
-    completeNetwork.Add(&concat);
-    completeNetwork.Parameters() = model.completeNetwork.Parameters();
+    completeNetwork.Add(featureNetwork);
+    completeNetwork.Add(concat);
   }
 
-  DuelingDQN& operator = (DuelingDQN model)
-  {
-    std::swap(this->completeNetwork, model.completeNetwork);
-    std::swap(featureNetwork, model.featureNetwork);
-    std::swap(valueNetwork, model.valueNetwork);
-    std::swap(advantageNetwork, model.advantageNetwork);
-    return *this;
-  };
-
   /**
    * Construct an instance of DuelingDQN class.
    *
@@ -107,26 +69,26 @@ class DuelingDQN
       completeNetwork(EmptyLoss<>(), GaussianInitialization(0, 0.001))
   {
     featureNetwork = new Sequential<>();
-    featureNetwork.Add(new Linear<>(inputDim, h1));
-    featureNetwork.Add(new ReLULayer<>());
+    featureNetwork->Add(new Linear<>(inputDim, h1));
+    featureNetwork->Add(new ReLULayer<>());
 
     valueNetwork = new Sequential<>();
-    valueNetwork.Add(new Linear<>(h1, h2));
-    valueNetwork.Add(new ReLULayer<>());
-    valueNetwork.Add(new Linear<>(h2, 1));
+    valueNetwork->Add(new Linear<>(h1, h2));
+    valueNetwork->Add(new ReLULayer<>());
+    valueNetwork->Add(new Linear<>(h2, 1));
 
     advantageNetwork = new Sequential<>();
-    advantageNetwork.Add(new Linear<>(h1, h2));
-    advantageNetwork.Add(new ReLULayer<>());
-    advantageNetwork.Add(new Linear<>(h2, outputDim));
+    advantageNetwork->Add(new Linear<>(h1, h2));
+    advantageNetwork->Add(new ReLULayer<>());
+    advantageNetwork->Add(new Linear<>(h2, outputDim));
 
     concat = new Concat<>(true);
-    concat.Add(&valueNetwork);
-    concat.Add(&advantageNetwork);
+    concat->Add(valueNetwork);
+    concat->Add(advantageNetwork);
 
     completeNetwork.Add(new IdentityLayer<>());
-    completeNetwork.Add(&featureNetwork);
-    completeNetwork.Add(&concat);
+    completeNetwork.Add(featureNetwork);
+    completeNetwork.Add(concat);
     this->ResetParameters();
   }
 
@@ -139,14 +101,22 @@ class DuelingDQN
       valueNetwork(std::move(valueNetwork))
   {
     concat = new Concat<>(true);
-    concat.Add(&valueNetwork);
-    concat.Add(&advantageNetwork);
+    concat->Add(valueNetwork);
+    concat->Add(advantageNetwork);
     completeNetwork.Add(new IdentityLayer<>());
-    completeNetwork.Add(&featureNetwork);
-    completeNetwork.Add(&concat);
+    completeNetwork.Add(featureNetwork);
+    completeNetwork.Add(concat);
     this->ResetParameters();
   }
 
+  //! Copy assignment operator.
+  void operator = (const DuelingDQN& model)
+  {
+    *valueNetwork = *model.valueNetwork;
+    *advantageNetwork = *model.advantageNetwork;
+    *featureNetwork = *model.featureNetwork;
+  }
+
   /**
    * Predict the responses to a given set of predictors. The responses will
    * reflect the output of the given output layer as returned by the
@@ -222,16 +192,16 @@ class DuelingDQN
   CompleteNetworkType completeNetwork;
 
   //! Locally-stored concat network.
-  Concat<> concat;
+  Concat<>* concat;
 
   //! Locally-stored feature network.
-  FeatureNetworkType featureNetwork;
+  FeatureNetworkType* featureNetwork;
 
   //! Locally-stored advantage network.
-  AdvantageNetworkType advantageNetwork;
+  AdvantageNetworkType* advantageNetwork;
 
   //! Locally-stored value network.
-  ValueNetworkType valueNetwork;
+  ValueNetworkType* valueNetwork;
 
   //! Locally-stored actionValues of the network.
   arma::mat actionValues;