Adding some changes in implementation

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

@@ -82,8 +82,7 @@ template<typename InputDataType,
 class Linear;
 
 template<typename InputDataType,
-         typename OutputDataType,
-         typename RegularizerType>
+         typename OutputDataType>
 class RBF;
 
 template<typename InputDataType,
@@ -216,7 +215,7 @@ using MoreTypes = boost::variant<
         Subview<arma::mat, arma::mat>*,
         VRClassReward<arma::mat, arma::mat>*,
         VirtualBatchNorm<arma::mat, arma::mat>*,
-        RBF<arma::mat, arma::mat, NoRegularizer>*
+        RBF<arma::mat, arma::mat>*
 >;
 
 template <typename... CustomLayers>

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

@@ -37,8 +37,7 @@ namespace ann /** Artificial Neural Network. */ {
 
 template <
     typename InputDataType = arma::mat,
-    typename OutputDataType = arma::mat,
-    typename RegularizerType = NoRegularizer
+    typename OutputDataType = arma::mat
 >
 class RBF
 {
@@ -55,8 +54,7 @@ class RBF
    */
   RBF(const size_t inSize,
       const size_t outSize,
-      arma::mat& centres,
-      RegularizerType regularizer = RegularizerType());
+      arma::mat& centres);
 
   /**
    * Reset the layer parameter.
@@ -101,9 +99,6 @@ class RBF
   //! Modify the output parameter.
   OutputDataType& OutputParameter() { return outputParameter; }
   //! Get the parameters.
-  OutputDataType const& Parameters() const { return weights; }
-  //! Modify the parameters.
-  OutputDataType& Parameters() { return weights; }
 
   //! Get the input parameter.
   InputDataType const& InputParameter() const { return inputParameter; }
@@ -121,9 +116,6 @@ class RBF
   //! Modify the delta.
   OutputDataType& Delta() { return delta; }
 
-  //! Modify the bias weights of the layer.
-  arma::mat& Sigmas() { return sigmas; }
-
   /**
    * Serialize the layer.
    */
@@ -149,9 +141,6 @@ class RBF
   //! Locally-stored the output distances of the shape.
   OutputDataType distances;
 
-  //! Locally-stored weight object.
-  OutputDataType weights;
-
   //! Locally-stored reset parameter used to initialize the layer once.
   bool reset;
 
@@ -161,8 +150,6 @@ class RBF
   //! Locally-stored number of output units.
   size_t outSize;
 
-  //! Locally-stored regularizer object.
-  RegularizerType regularizer;
 }; // class RBF
 
 } // namespace ann

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

@@ -17,9 +17,8 @@
 namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
-RBF<InputDataType, OutputDataType, RegularizerType>::RBF() :
+template<typename InputDataType, typename OutputDataType>
+RBF<InputDataType, OutputDataType>::RBF() :
     inSize(0),
     outSize(0),
     reset(false)
@@ -27,32 +26,26 @@ RBF<InputDataType, OutputDataType, RegularizerType>::RBF() :
   // Nothing to do here.
 }
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
-RBF<InputDataType, OutputDataType, RegularizerType>::RBF(
+template<typename InputDataType, typename OutputDataType>
+RBF<InputDataType, OutputDataType>::RBF(
     const size_t inSize,
     const size_t outSize,
-    arma::mat& centres,
-    RegularizerType regularizer) :
+    arma::mat& centres) :
     inSize(inSize),
     outSize(outSize),
     centres(centres),
-    regularizer(regularizer),
     reset(false)
 {
-  weights.set_size(outSize * inSize + outSize, 1);
 }
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
-void RBF<InputDataType, OutputDataType, RegularizerType>::Reset()
+template<typename InputDataType, typename OutputDataType>
+void RBF<InputDataType, OutputDataType>::Reset()
 {
 }
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
+template<typename InputDataType, typename OutputDataType>
 template<typename eT>
-void RBF<InputDataType, OutputDataType, RegularizerType>::Forward(
+void RBF<InputDataType, OutputDataType>::Forward(
     const arma::Mat<eT>& input,
     arma::Mat<eT>& output)
 {
@@ -73,17 +66,12 @@ void RBF<InputDataType, OutputDataType, RegularizerType>::Forward(
                                  2), 0), 0.5).t();
   }
 
-  sigmas = arma::mean(distances, 1);
-  arma::mat betas = 1 / 2 * arma::pow(sigmas, 2);
-  distances = arma::pow(distances, 2);
-  distances = distances.each_col() % betas;
-  output = arma::exp(-1 * distances);
+  output = distances;
 }
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
+template<typename InputDataType, typename OutputDataType>
 template<typename eT>
-void RBF<InputDataType, OutputDataType, RegularizerType>::Backward(
+void RBF<InputDataType, OutputDataType>::Backward(
     const arma::Mat<eT>& /* input */,
     const arma::Mat<eT>& gy,
     arma::Mat<eT>& g)
@@ -91,21 +79,15 @@ void RBF<InputDataType, OutputDataType, RegularizerType>::Backward(
   g = centres.t() * gy;
 }
 
-template<typename InputDataType, typename OutputDataType,
-    typename RegularizerType>
+template<typename InputDataType, typename OutputDataType>
 template<typename Archive>
-void RBF<InputDataType, OutputDataType, RegularizerType>::serialize(
+void RBF<InputDataType, OutputDataType>::serialize(
     Archive& ar,
     const unsigned int /* version */)
 {
   ar & BOOST_SERIALIZATION_NVP(distances);
   ar & BOOST_SERIALIZATION_NVP(sigmas);
   ar & BOOST_SERIALIZATION_NVP(centres);
-
-  // This is inefficient, but we have to allocate this memory so that
-  // WeightSetVisitor gets the right size.
-  if (Archive::is_loading::value)
-    weights.set_size(outSize * inSize + outSize, 1);
 }
 
 } // namespace ann

src/mlpack/tests/feedforward_network_test.cpp

@@ -687,7 +687,7 @@ BOOST_AUTO_TEST_CASE(RBFNetworkTest)
   model.Add<Linear<> >(10, 2);
   model1.Add<LogSoftMax<> >();
   // Vanilla neural net with logistic activation function.
-  TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2);
+  TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.3);
 }
 
 BOOST_AUTO_TEST_SUITE_END();