merge conflict

blank/main.cpp

@@ -37,8 +37,6 @@ int main()
    {
         cout << "Blank\n";
 
-
-
         cout << "Bye!" << endl;
 
         return 0;

examples/airfoil_self_noise/main.cpp

@@ -61,7 +61,7 @@ int main()
         testing_analysis.print_goodness_of_fit_analysis();
 
         // Save results
-
+        
         neural_network.save("../data/neural_network.xml");
         neural_network.save_expression_c("../data/airfoil_self_noise.c");
 
@@ -77,7 +77,7 @@ int main()
 //      const Tensor<type, 2> outputs = new_neural_network.calculate_outputs(inputs);
 
 //        cout << outputs << endl;
-
+        
         cout << "Good bye!" << endl;       
 
         return 0;

opennn/batch.cpp

@@ -80,7 +80,7 @@ void Batch::perform_augmentation() const
 
         if(random_rotation_minimum != 0 && random_rotation_maximum != 0)
         {
-            const type angle = (random_rotation_minimum < random_rotation_maximum)
+            const type angle = random_rotation_minimum < random_rotation_maximum
                              ? random_rotation_minimum + type(rand())
                              : random_rotation_maximum;
 
@@ -89,7 +89,7 @@ void Batch::perform_augmentation() const
 
         if(random_rescaling_minimum != 0 && random_rescaling_maximum != 0)
         {
-            const type rescaling = (random_rescaling_minimum < random_rescaling_maximum)
+            const type rescaling = random_rescaling_minimum < random_rescaling_maximum
                                  ? random_rescaling_minimum + type(rand())
                                  : random_rescaling_maximum;
 
@@ -98,7 +98,7 @@ void Batch::perform_augmentation() const
 
         if(random_horizontal_translation_minimum != 0 && random_horizontal_translation_maximum != 0)
         {
-            const type translation = (random_horizontal_translation_minimum < random_rescaling_maximum)
+            const type translation = random_horizontal_translation_minimum < random_rescaling_maximum
                                    ? random_horizontal_translation_minimum + type(rand())
                                    : random_rescaling_maximum;
 

opennn/bounding_layer.cpp

@@ -515,7 +515,7 @@ void BoundingLayer::write_XML(tinyxml2::XMLPrinter& file_stream) const
     {
         file_stream.OpenElement("Item");
 
-        file_stream.PushAttribute("Index", static_cast<unsigned>(i+1));
+        file_stream.PushAttribute("Index", unsigned(i+1));
 
         // Lower bound
 

opennn/config.h

@@ -24,7 +24,7 @@
 #include <algorithm>
 #include <execution>
 
-#define OPENNN_CUDA
+//#define OPENNN_CUDA
 #ifdef OPENNN_CUDA
 
 #include "../../opennn_cuda/CudaOpennn/kernel.cuh"
@@ -45,20 +45,20 @@
 
 namespace opennn
 {
-    //using namespace std;
+    using namespace std;
     using namespace Eigen;
 
     using type = float; 
 
-    using dimensions = std::vector<Index>;
+    using dimensions = vector<Index>;
 
     //using execution_policy = std::execution::par;
 
     template<typename Base, typename T>
-    inline bool is_instance_of(const T* ptr) {
+    inline bool is_instance_of(const T* ptr)
+    {
         return dynamic_cast<const Base*>(ptr) != nullptr;
     }
-
 }
 
 

opennn/convolutional_layer.cpp

@@ -467,8 +467,8 @@ void ConvolutionalLayer::back_propagate(const Tensor<pair<type*, dimensions>, 1>
 
     Tensor<type, 1>& biases_derivatives = convolutional_layer_back_propagation->biases_derivatives;
 
-    Eigen::array<Eigen::Index, 4> offsets;
-    Eigen::array<Eigen::Index, 4> extents;
+    Eigen::array<Index, 4> offsets;
+    Eigen::array<Index, 4> extents;
 
     // Synaptic weights derivatives
 

opennn/correlations.cpp

@@ -466,7 +466,7 @@ pair<Tensor<type, 2>, Tensor<type, 2>> filter_missing_values_matrix_matrix(const
 Tensor<type, 2> get_correlation_values(const Tensor<Correlation, 2>& correlations)
 {
     Tensor<type, 2> values;
-    /**
+    /*
     const Index rows_number = correlations.dimension(0);
     const Index raw_variables_number = correlations.dimension(1);
     Tensor<type, 2> values(rows_number, raw_variables_number);
@@ -922,7 +922,7 @@ Correlation logistic_correlation_vector_vector_spearman(const ThreadPoolDevice*
     correlation.r = linear_correlation(thread_pool_device, outputs.reshape(vector), targets.reshape(vector)).r;
 
     const type z_correlation = r_correlation_to_z_correlation(correlation.r);
-/**
+/*
     const Tensor<type, 1> confidence_interval_z = confidence_interval_z_correlation(z_correlation, inputs_dimensions(0));
 
     correlation.lower_confidence = z_correlation_to_r_correlation(confidence_interval_z(0));

opennn/cross_entropy_error.cpp

@@ -167,9 +167,6 @@ void CrossEntropyError::calculate_binary_output_delta(const Batch& batch,
     const ProbabilisticLayerForwardPropagation* probabilistic_layer_forward_propagation
             = static_cast<ProbabilisticLayerForwardPropagation*>(forward_propagation.layers(last_trainable_layer_index));
 
-    ProbabilisticLayerBackPropagation* probabilistic_layer_back_propagation
-            = static_cast<ProbabilisticLayerBackPropagation*>(back_propagation.neural_network.layers(last_trainable_layer_index));
-
     const Tensor<type, 2>& outputs = probabilistic_layer_forward_propagation->outputs;
 
     // Back propagation
@@ -189,9 +186,6 @@ void CrossEntropyError::calculate_multiple_output_delta(const Batch& batch,
 {
     const Index last_trainable_layer_index = neural_network->get_last_trainable_layer_index();
 
-    ProbabilisticLayerBackPropagation* probabilistic_layer_back_propagation
-            = static_cast<ProbabilisticLayerBackPropagation*>(back_propagation.neural_network.layers(last_trainable_layer_index));
-
     const Index batch_samples_number = batch.get_batch_samples_number();
 
     const pair<type*, dimensions> targets_pair = batch.get_targets_pair();