TMVA:

* removed debug messages
* fixed dummy layer in TDeepNet::Backward that producing segfault.
* added initialization to zero in TCpuMatrix
* set batch size to 1 in GetMvaValue in MethodDL

tmva/tmva/inc/TMVA/DNN/CNN/ConvLayer.h

@@ -207,7 +207,6 @@ TConvLayer<Architecture_t>::~TConvLayer()
 template <typename Architecture_t>
 auto TConvLayer<Architecture_t>::Forward(std::vector<Matrix_t> &input, bool applyDropout) -> void
 {
-   std::cout << "Conv Layer Forward" << std::endl;
    for (size_t i = 0; i < this->GetBatchSize(); i++) {
 
       if (applyDropout && (this->GetDropoutProbability() != 1.0)) {
@@ -233,7 +232,6 @@ auto TConvLayer<Architecture_t>::Backward(std::vector<Matrix_t> &gradients_backw
                                           const std::vector<Matrix_t> &activations_backward,
                                           std::vector<Matrix_t> &inp1, std::vector<Matrix_t> &inp2) -> void
 {
-  std::cout << "Conv Layer Backward" << std::endl;
    Architecture_t::ConvLayerBackward(
       gradients_backward, this->GetWeightGradientsAt(0), this->GetBiasGradientsAt(0), this->GetDerivatives(),
       this->GetActivationGradients(), this->GetWeightsAt(0), activations_backward, this->GetBatchSize(),

tmva/tmva/inc/TMVA/DNN/CNN/MaxPoolLayer.h

@@ -170,7 +170,6 @@ TMaxPoolLayer<Architecture_t>::~TMaxPoolLayer()
 template <typename Architecture_t>
 auto TMaxPoolLayer<Architecture_t>::Forward(std::vector<Matrix_t> &input, bool applyDropout) -> void
 {
-   std::cout << "Max Pool Layer Forward" << std::endl;
    for (size_t i = 0; i < this->GetBatchSize(); i++) {
 
       if (applyDropout && (this->GetDropoutProbability() != 1.0)) {
@@ -189,7 +188,6 @@ auto TMaxPoolLayer<Architecture_t>::Backward(std::vector<Matrix_t> &gradients_ba
                                              const std::vector<Matrix_t> &activations_backward,
                                              std::vector<Matrix_t> &inp1, std::vector<Matrix_t> &inp2) -> void
 {
-   std::cout << "Max Pool Layer Backward" << std::endl;
    Architecture_t::MaxPoolLayerBackward(gradients_backward, this->GetActivationGradients(), indexMatrix,
                                         this->GetBatchSize(), this->GetDepth(), this->GetNLocalViews());
 }

tmva/tmva/inc/TMVA/DNN/DeepNet.h

@@ -872,8 +872,8 @@ auto TDeepNet<Architecture_t, Layer_t>::Backward(std::vector<Matrix_t> &input, c
 
    std::vector<Matrix_t> dummy;
    for (size_t i = 0; i < input.size(); i++) {
-      //dummy.emplace_back(input[i].GetNrows(), input[i].GetNcols());
-      dummy.emplace_back(0, 0);
+      dummy.emplace_back(input[i].GetNrows(), input[i].GetNcols());
+      // dummy.emplace_back(0, 0);
     }
    fLayers[0]->Backward(dummy, input, inp1, inp2);
 }

tmva/tmva/src/DNN/Architectures/Cpu/CpuMatrix.cxx

@@ -24,6 +24,11 @@ TCpuMatrix<AReal>::TCpuMatrix(size_t nRows, size_t nCols)
     : fBuffer(nRows * nCols), fNCols(nCols), fNRows(nRows)
 {
    Initialize();
+   for (size_t j = 0; j < fNCols; j++) {
+      for (size_t i = 0; i < fNRows; i++) {
+         (*this)(i, j) = 0;
+      }
+   }
 }
 
 //____________________________________________________________________________

tmva/tmva/src/DNN/Architectures/Cpu/Propagation.cxx

@@ -273,8 +273,6 @@ template <typename AFloat>
 void TCpu<AFloat>::CalculateConvBiasGradients(TCpuMatrix<AFloat> &biasGradients, std::vector<TCpuMatrix<AFloat>> &df,
                                               size_t batchSize, size_t depth, size_t nLocalViews)
 {
-   std::cout << "Calculate Conv Bias Gradients method call" << std::endl;
-
    for (size_t i = 0; i < depth; i++) {
       AFloat sum = 0;
       for (size_t j = 0; j < nLocalViews; j++) {

tmva/tmva/src/MethodDL.cxx

@@ -1254,6 +1254,12 @@ void MethodDL::TrainCpu()
 Double_t MethodDL::GetMvaValue(Double_t * /*errLower*/, Double_t * /*errUpper*/)
 {
 #ifdef DNNCPU
+   // set the batch size to 1 for the evaluation
+   fNet->SetBatchSize(1);
+   // set also in the layers
+   auto &layers = fNet->GetLayers();
+   for (auto &l : layers) l->SetBatchSize(1);
+
    using Architecture_t = DNN::TCpu<Double_t>;
    using Matrix_t = typename Architecture_t::Matrix_t;