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Artelnics · Dec 4, 2024 · 92dd22a · 92dd22a
1 parent 3550fa3
commit 92dd22a
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Showing 35 changed files with 302 additions and 339 deletions.
diff --git a/examples/airfoil_self_noise/main.cpp b/examples/airfoil_self_noise/main.cpp
@@ -65,7 +65,7 @@ int main()
         //training_strategy.load("../data/training_strategy.xml");
 
         training_strategy.perform_training();
-
+/*
         ModelSelection model_selection(&training_strategy);
 
         model_selection.perform_inputs_selection();

diff --git a/opennn/adaptive_moment_estimation.cpp b/opennn/adaptive_moment_estimation.cpp
@@ -11,8 +11,6 @@
 #include "adaptive_moment_estimation.h"
 #include "forward_propagation.h"
 #include "back_propagation.h"
-#include "scaling_layer_2d.h"
-#include "unscaling_layer.h"
 
 namespace opennn
 {
@@ -365,54 +363,39 @@ TrainingResults AdaptiveMomentEstimation::perform_training()
             cout << "Elapsed time: " << write_time(elapsed_time) << endl;
         }
 
-        // Training history
+        // @todo loss and error missmatch
+
+        stop_training = true;
 
         if(epoch == maximum_epochs_number)
         {
             if(display) cout << "Epoch " << epoch << "\nMaximum epochs number reached: " << epoch << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumEpochsNumber;
         }
-
-        if(elapsed_time >= maximum_time)
+        else if(elapsed_time >= maximum_time)
         {
             if(display) cout << "Epoch " << epoch << "\nMaximum training time reached: " << write_time(elapsed_time) << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumTime;
         }
-
-        // @todo loss and error missmatch
-
-        if(results.training_error_history(epoch) < training_loss_goal)
+        else if(results.training_error_history(epoch) < training_loss_goal)
         {
-            stop_training = true;
-
             results.stopping_condition  = StoppingCondition::LossGoal;
-
             if(display) cout << "Epoch " << epoch << "\nLoss goal reached: " << results.training_error_history(epoch) << endl;
         }
-
-        if(training_accuracy >= training_accuracy_goal)
+        else if(training_accuracy >= training_accuracy_goal)
         {
-            stop_training = true;
-
             results.stopping_condition  = StoppingCondition::LossGoal;
-
             if(display) cout << "Epoch " << epoch << "\nAccuracy goal reached: " << training_accuracy << endl;
         }
-
-        if(selection_failures >= maximum_selection_failures)
+        else if(selection_failures >= maximum_selection_failures)
         {
             if(display) cout << "Epoch " << epoch << "\nMaximum selection failures reached: " << selection_failures << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumSelectionErrorIncreases;
         }
+        else
+        {
+            stop_training = false;
+        }
 
         if(stop_training)
         {

diff --git a/opennn/bounding_layer.h b/opennn/bounding_layer.h
@@ -64,7 +64,7 @@ class BoundingLayer : public Layer
 
    // Serialization
 
-   void print() const;
+   void print() const override;
 
    void from_XML(const XMLDocument&) override;
 
@@ -88,7 +88,7 @@ struct BoundingLayerForwardPropagation : LayerForwardPropagation
 
     void set(const Index& = 0, Layer* = nullptr) override;
 
-    void print() const;
+    void print() const override;
 
     Tensor<type, 2> outputs;
 };

diff --git a/opennn/convolutional_layer.h b/opennn/convolutional_layer.h
@@ -203,7 +203,7 @@ struct ConvolutionalLayerForwardPropagation : LayerForwardPropagation
 
    void set(const Index& = 0, Layer* = nullptr) override;
 
-   void print() const;
+   void print() const override;
 
    Tensor<type, 4> outputs;
 
@@ -220,11 +220,11 @@ struct ConvolutionalLayerBackPropagation : LayerBackPropagation
 {
    ConvolutionalLayerBackPropagation(const Index& = 0, Layer* = nullptr);
 
-   vector<pair<type*, dimensions>> get_input_derivative_pairs() const;
+   vector<pair<type*, dimensions>> get_input_derivative_pairs() const override;
 
    void set(const Index& = 0, Layer* = nullptr) override;
 
-   void print() const;
+   void print() const override;
 
    //Tensor<type, 3> image_convolutions_derivatives;
 

diff --git a/opennn/embedding_layer.h b/opennn/embedding_layer.h
@@ -35,7 +35,7 @@ class EmbeddingLayer : public Layer
     Index get_depth() const;
     bool get_positional_encoding() const;
 
-    dimensions get_input_dimensions() const;
+    dimensions get_input_dimensions() const override;
     dimensions get_output_dimensions() const override;
 
     Index get_parameters_number() const override;
@@ -69,9 +69,9 @@ class EmbeddingLayer : public Layer
 
     void add_deltas(const vector<pair<type*, dimensions>>&) const;
 
-    void insert_gradient(unique_ptr<LayerBackPropagation>& back_propagation,
-                         const Index& index, 
-                         Tensor<type, 1>& gradient) const;
+    void insert_gradient(unique_ptr<LayerBackPropagation>&,
+                         const Index&,
+                         Tensor<type, 1>&) const override;
 
     void from_XML(const XMLDocument&) override;
     void to_XML(XMLPrinter&) const override;
@@ -106,7 +106,7 @@ struct EmbeddingLayerForwardPropagation : LayerForwardPropagation
 
     void set(const Index& = 0, Layer* = nullptr) override;
 
-    void print() const;
+    void print() const override;
 
     void build_positional_encoding_matrix();
 
@@ -122,11 +122,11 @@ struct EmbeddingLayerBackPropagation : LayerBackPropagation
 {
     EmbeddingLayerBackPropagation(const Index& = 0, Layer* = nullptr);
 
-    vector<pair<type*, dimensions>> get_input_derivative_pairs() const;
+    vector<pair<type*, dimensions>> get_input_derivative_pairs() const override;
 
     void set(const Index& = 0, Layer* = nullptr) override;
 
-    void print() const;
+    void print() const override;
 
     Tensor<type, 2> sample_deltas;
     Tensor<type, 2> embedding_weights_derivatives;

diff --git a/opennn/flatten_layer.cpp b/opennn/flatten_layer.cpp
@@ -64,7 +64,7 @@ void FlattenLayer::forward_propagate(const vector<pair<type*, dimensions>>& inpu
 {
     const Index batch_samples_number = layer_forward_propagation->batch_samples_number;
 
-    const Index neurons_number = get_output_dimensions()[0];
+    const Index outputs_number = get_outputs_number();
 
     FlattenLayerForwardPropagation* flatten_layer_forward_propagation =
             static_cast<FlattenLayerForwardPropagation*>(layer_forward_propagation.get());
@@ -73,9 +73,9 @@ void FlattenLayer::forward_propagate(const vector<pair<type*, dimensions>>& inpu
 
     memcpy(outputs_data,
            input_pairs[0].first,
-           batch_samples_number*neurons_number*sizeof(type));
+           batch_samples_number*outputs_number*sizeof(type));
 
-    flatten_layer_forward_propagation->outputs = TensorMap<Tensor<type, 2>>(input_pairs[0].first, batch_samples_number, neurons_number);
+    flatten_layer_forward_propagation->outputs = TensorMap<Tensor<type, 2>>(input_pairs[0].first, batch_samples_number, outputs_number);
 }
 
 
@@ -85,7 +85,7 @@ void FlattenLayer::back_propagate(const vector<pair<type*, dimensions>>& input_p
                                   unique_ptr<LayerBackPropagation>& back_propagation) const
 {
     const Index batch_samples_number = input_pairs[0].second[0];
-    const Index neurons_number = get_output_dimensions()[0];
+    const Index outputs_number = get_outputs_number();
 
     // Back propagation
 
@@ -96,7 +96,7 @@ void FlattenLayer::back_propagate(const vector<pair<type*, dimensions>>& input_p
 
     memcpy(input_derivatives.data(),
            delta_pairs[0].first,
-           Index(batch_samples_number * neurons_number * sizeof(type)));
+           Index(batch_samples_number * outputs_number * sizeof(type)));
 }
 
 

diff --git a/opennn/flatten_layer.h b/opennn/flatten_layer.h
@@ -29,7 +29,7 @@ class FlattenLayer : public Layer
 
     FlattenLayer(const dimensions& = {0,0,0});
 
-    dimensions get_input_dimensions() const;
+    dimensions get_input_dimensions() const override;
     dimensions get_output_dimensions() const override;
 
     Index get_input_height() const;
@@ -57,7 +57,7 @@ class FlattenLayer : public Layer
 
     void to_XML(XMLPrinter&) const override;
 
-    void print() const;
+    void print() const override;
 
     #ifdef OPENNN_CUDA
         #include "../../opennn_cuda/opennn_cuda/flatten_layer_cuda.h"
@@ -77,7 +77,7 @@ struct FlattenLayerForwardPropagation : LayerForwardPropagation
 
    void set(const Index& = 0, Layer* = nullptr) override;
 
-   void print() const;
+   void print() const override;
 
    Tensor<type, 2> outputs;
 };
@@ -87,11 +87,11 @@ struct FlattenLayerBackPropagation : LayerBackPropagation
 {
     FlattenLayerBackPropagation(const Index& = 0, Layer* = nullptr);
 
-    vector<pair<type*, dimensions>> get_input_derivative_pairs() const;
+    vector<pair<type*, dimensions>> get_input_derivative_pairs() const override;
 
     void set(const Index& = 0, Layer* = nullptr) override;
 
-    void print() const;
+    void print() const override;
 
     Tensor<type, 4> input_derivatives;
 };

diff --git a/opennn/genetic_algorithm.cpp b/opennn/genetic_algorithm.cpp
@@ -577,7 +577,7 @@ void GeneticAlgorithm::perform_crossover()
 
     Index descendent_index = 0;
 
-    for(Index i = 0; i < parent_1_indices.size(); i++)
+    for(size_t i = 0; i < parent_1_indices.size(); i++)
     {
         parent_1_variables = population.chip(parent_1_indices[i], 0);
 

diff --git a/opennn/levenberg_marquardt_algorithm.cpp b/opennn/levenberg_marquardt_algorithm.cpp
@@ -264,11 +264,12 @@ TrainingResults LevenbergMarquardtAlgorithm::perform_training()
         loss_index->back_propagate_lm(training_batch,
                                       training_forward_propagation,
                                       training_back_propagation_lm);
-/*
+
         results.training_error_history(epoch) = training_back_propagation_lm.error();
 
         if(has_selection)
-        {           
+        {
+/*
             neural_network->forward_propagate(selection_batch.get_input_pairs(),
                                               selection_forward_propagation,
                                               is_training);
@@ -289,9 +290,12 @@ TrainingResults LevenbergMarquardtAlgorithm::perform_training()
 
             if(epoch != 0 && results.selection_error_history(epoch) > results.selection_error_history(epoch-1)) 
                 selection_failures++;
-        }
 */
+        }
+
         elapsed_time = get_elapsed_time(beginning_time);
+        if(epoch != 0) loss_decrease = old_loss - training_back_propagation_lm.loss;
+        old_loss = training_back_propagation_lm.loss;
 
         if(display && epoch%display_period == 0)
         {
@@ -301,54 +305,37 @@ TrainingResults LevenbergMarquardtAlgorithm::perform_training()
             cout << "Elapsed time: " << write_time(elapsed_time) << endl;
         }
 
+        stop_training = true;
+
         if(results.training_error_history(epoch) < training_loss_goal)
         {
-            stop_training = true;
-
-            results.stopping_condition = StoppingCondition::LossGoal;
-
             if(display) cout << "Epoch " << epoch << "\nLoss goal reached: " << results.training_error_history(epoch) << endl;
+            results.stopping_condition = StoppingCondition::LossGoal;
         }
-
-        if(epoch != 0) loss_decrease = old_loss - training_back_propagation_lm.loss;
-
-        if(loss_decrease < minimum_loss_decrease)
+        else if(loss_decrease < minimum_loss_decrease)
         {
             if(display) cout << "Epoch " << epoch << "\nMinimum loss decrease reached: " << loss_decrease << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MinimumLossDecrease;
         }
-
-        old_loss = training_back_propagation_lm.loss;
-
-        if(selection_failures >= maximum_selection_failures)
+        else if(selection_failures >= maximum_selection_failures)
         {
             if(display) cout << "Epoch " << epoch << "Maximum selection failures reached: " << selection_failures << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumSelectionErrorIncreases;
         }
-
-        if(epoch == maximum_epochs_number)
+        else if(epoch == maximum_epochs_number)
         {
             if(display) cout << "Epoch " << epoch << "\nMaximum epochs number reached: " << epoch << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumEpochsNumber;
         }
-
-        if(elapsed_time >= maximum_time)
+        else if(elapsed_time >= maximum_time)
         {
             if(display) cout << "Epoch " << epoch << "Maximum training time reached: " << elapsed_time << endl;
-
-            stop_training = true;
-
             results.stopping_condition = StoppingCondition::MaximumTime;
         }
+        else
+        {
+            stop_training = false;
+        }
 
         if(stop_training)
         {
@@ -534,6 +521,12 @@ void LevenbergMarquardtAlgorithm::from_XML(const XMLDocument& document)
 }
 
 
+LevenbergMarquardtAlgorithmData::LevenbergMarquardtAlgorithmData(LevenbergMarquardtAlgorithm *new_Levenberg_Marquardt_method)
+{
+    set(new_Levenberg_Marquardt_method);
+}
+
+
 void LevenbergMarquardtAlgorithmData::set(LevenbergMarquardtAlgorithm* new_Levenberg_Marquardt_method)
 {
     Levenberg_Marquardt_algorithm = new_Levenberg_Marquardt_method;

diff --git a/opennn/levenberg_marquardt_algorithm.h b/opennn/levenberg_marquardt_algorithm.h
@@ -108,10 +108,7 @@ class LevenbergMarquardtAlgorithm : public OptimizationAlgorithm
 struct LevenbergMarquardtAlgorithmData : public OptimizationAlgorithmData
 {
 
-    LevenbergMarquardtAlgorithmData(LevenbergMarquardtAlgorithm* new_Levenberg_Marquardt_method = nullptr)
-    {
-        set(new_Levenberg_Marquardt_method);
-    }
+    LevenbergMarquardtAlgorithmData(LevenbergMarquardtAlgorithm* new_Levenberg_Marquardt_method = nullptr);
 
     void set(LevenbergMarquardtAlgorithm* = nullptr);
 
@@ -133,7 +130,6 @@ struct LevenbergMarquardtAlgorithmData : public OptimizationAlgorithmData
     Index epoch = 0;
 };
 
-
 }
 
 #endif