ENH: update AE tests with new Shark API

Modules/Learning/DimensionalityReductionLearning/include/otbAutoencoderModel.txx

@@ -44,7 +44,7 @@
 #include <shark/Algorithms/StoppingCriteria/MaxIterations.h> //A simple stopping criterion that stops after a fixed number of iterations
 #include <shark/Algorithms/StoppingCriteria/TrainingProgress.h> //Stops when the algorithm seems to converge, Tracks the progress of the training error over a period of time
 
-#include <shark/Algorithms/GradientDescent/SteepestDescent.h>
+#include <shark/Algorithms/GradientDescent/Adam.h>
 #if defined(__GNUC__) || defined(__clang__)
 #pragma GCC diagnostic pop
 #endif
@@ -167,12 +167,12 @@ AutoencoderModel<TInputValue,NeuronType>
   shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
   shark::SquaredLoss<shark::RealVector> loss;
   //~ shark::ErrorFunction error(trainSet, &model, &loss);
-  shark::ErrorFunction error(trainSet, &net, &loss);
+  shark::ErrorFunction<> error(trainSet, &net, &loss);
 
-  shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+  shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
   error.setRegularizer(m_Regularization[layer_index],&regularizer);
 
-  shark::IRpropPlusFull optimizer;
+  shark::Adam<> optimizer;
   error.init();
   optimizer.init(error);
 
@@ -221,11 +221,11 @@ void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(
   shark::SquaredLoss<shark::RealVector> loss;
   //~ shark::SparseAutoencoderError error(trainSet,&net, &loss, m_Rho[layer_index], m_Beta[layer_index]);
   // SparseAutoencoderError doesn't exist anymore, for now use a plain ErrorFunction
-  shark::ErrorFunction error(trainSet, &net, &loss);
+  shark::ErrorFunction<> error(trainSet, &net, &loss);
   
-  shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+  shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
   error.setRegularizer(m_Regularization[layer_index],&regularizer);
-  shark::IRpropPlusFull optimizer;
+  shark::Adam<> optimizer;
   error.init();
   optimizer.init(error);
 
@@ -269,11 +269,11 @@ AutoencoderModel<TInputValue,NeuronType>
   shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);
   shark::SquaredLoss<shark::RealVector> loss;
 
-  shark::ErrorFunction error(trainSet, &net, &loss);
-  shark::TwoNormRegularizer regularizer(error.numberOfVariables());
+  shark::ErrorFunction<> error(trainSet, &net, &loss);
+  shark::TwoNormRegularizer<> regularizer(error.numberOfVariables());
   error.setRegularizer(m_Regularization[0],&regularizer);
 
-  shark::IRpropPlusFull optimizer;
+  shark::Adam<> optimizer;
   error.init();
   optimizer.init(error);
   otbMsgDevMacro(<<"Error before training : " << optimizer.solution().value);