From 3c21b35626a162ed057ed7b064c6dd31a527ab8f Mon Sep 17 00:00:00 2001
From: rubenareces <rubenareces@artelnics.com>
Date: Tue, 10 Dec 2024 12:14:28 +0100
Subject: [PATCH] clean

---
 .github/workflows/build.yml |  3 +--
 examples/mnist/main.cpp     | 14 +++++++-------
 opennn/neural_network.cpp   | 14 +++++++-------
 opennn/pooling_layer.cpp    |  4 +---
 4 files changed, 16 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 1bb1b43c9..a0e43f086 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -32,5 +32,4 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           SONAR_TOKEN: ${{ secrets.SONARCLOUD_TOKEN }}
         run: |
-          sonar-scanner \
-            -Dsonar.cfamily.compile-commands="build_wrapper_output_directory/build-wrapper-dump.json"
\ No newline at end of file
+          sonar-scanner --define sonar.cfamily.compile-commands="${{ env.BUILD_WRAPPER_OUT_DIR }}/compile_commands.json"
\ No newline at end of file
diff --git a/examples/mnist/main.cpp b/examples/mnist/main.cpp
index 2a4d7646f..502e08b80 100644
--- a/examples/mnist/main.cpp
+++ b/examples/mnist/main.cpp
@@ -30,30 +30,30 @@ int main()
         const Index channels = 1;
         const Index targets = 2;
 
-        //ImageDataSet image_data_set(samples_number, {image_height, image_width, channels}, {targets});
+        ImageDataSet image_data_set(samples_number, {image_height, image_width, channels}, {targets});
 
-        //image_data_set.set_image_data_random();
+        image_data_set.set_image_data_random();
 
-        //image_data_set.set(DataSet::SampleUse::Training);
+        image_data_set.set(DataSet::SampleUse::Training);
 
-        ImageDataSet image_data_set(0,{0,0,0},{0});
+        //ImageDataSet image_data_set(0,{0,0,0},{0});
 
         //image_data_set.set_data_path("data");
         //image_data_set.set_data_path("C:/mnist/train");
-        image_data_set.set_data_path("C:/binary_mnist");
+        //image_data_set.set_data_path("C:/binary_mnist");
         //image_data_set.set_data_path("C:/Users/Roberto Lopez/Documents/opennn/examples/mnist/data");
         //image_data_set.set_data_path("C:/melanoma_dataset_bmp");
         //image_data_set.set_data_path("C:/melanoma_dataset_bmp_small"); 
         //image_data_set.set_data_path("C:/melanoma_supersmall");
         //image_data_set.set_input_dimensions({24,24,1});
 
-        image_data_set.read_bmp();
+        //image_data_set.read_bmp();
 
         // Neural network
         
         NeuralNetwork neural_network(NeuralNetwork::ModelType::ImageClassification,
             image_data_set.get_input_dimensions(),
-            { 16 },
+            { 1 },
             image_data_set.get_target_dimensions());
 
         //neural_network.print();
diff --git a/opennn/neural_network.cpp b/opennn/neural_network.cpp
index e3b02f2c4..ffae6d23e 100644
--- a/opennn/neural_network.cpp
+++ b/opennn/neural_network.cpp
@@ -424,17 +424,17 @@ void NeuralNetwork::set_image_classification(const dimensions& input_dimensions,
         const dimensions convolution_stride_dimensions = { 1, 1 };
         const ConvolutionalLayer::ConvolutionType convolution_type = ConvolutionalLayer::ConvolutionType::Valid;
 
-        add_layer(make_unique<ConvolutionalLayer>(get_output_dimensions(),
-                                                  kernel_dimensions,
-                                                  ConvolutionalLayer::ActivationFunction::RectifiedLinear,
-                                                  convolution_stride_dimensions,
-                                                  convolution_type,
-                                                  "convolutional_layer_" + to_string(i+1)));
+        //add_layer(make_unique<ConvolutionalLayer>(get_output_dimensions(),
+        //                                          kernel_dimensions,
+        //                                          ConvolutionalLayer::ActivationFunction::RectifiedLinear,
+        //                                          convolution_stride_dimensions,
+        //                                          convolution_type,
+        //                                          "convolutional_layer_" + to_string(i+1)));
 
         const dimensions pool_dimensions = { 2, 2 };
         const dimensions pooling_stride_dimensions = { 2, 2 };
         const dimensions padding_dimensions = { 0, 0 };
-        const PoolingLayer::PoolingMethod pooling_method = PoolingLayer::PoolingMethod::AveragePooling;
+        const PoolingLayer::PoolingMethod pooling_method = PoolingLayer::PoolingMethod::MaxPooling;
 
         add_layer(make_unique<PoolingLayer>(get_output_dimensions(),
                                             pool_dimensions,
diff --git a/opennn/pooling_layer.cpp b/opennn/pooling_layer.cpp
index 9244edff6..cd6e9e52c 100644
--- a/opennn/pooling_layer.cpp
+++ b/opennn/pooling_layer.cpp
@@ -255,7 +255,6 @@ void PoolingLayer::set_pooling_method(const string& new_pooling_method)
 }
 
 
-
 void PoolingLayer::forward_propagate(const vector<pair<type*, dimensions>>& input_pairs,
                                      unique_ptr<LayerForwardPropagation>& layer_forward_propagation,
                                      const bool& is_training)
@@ -351,8 +350,7 @@ void PoolingLayer::forward_propagate_max_pooling(const Tensor<type, 4>& inputs,
 
     const Eigen::array<Index, 2> reshape_dimensions = { pool_size, output_size };
 
-    #pragma omp parallel for
-
+#pragma omp parallel for
     for (Index batch_index = 0; batch_index < batch_samples_number; batch_index++)
     {
         const Tensor<type, 2> patches_flat = image_patches.chip(batch_index, 0)