feat: getSlug function

src/NeuralNet/Network.cpp

@@ -55,6 +55,18 @@ std::shared_ptr<Layer> Network::getOutputLayer() const {
   return this->layers[this->layers.size() - 1];
 }
 
+std::string Network::getSlug() const {
+  std::string slug;
+
+  for (int l = 0; l < this->layers.size(); l++) {
+    Layer &cLayer = *this->layers[l];
+    slug += cLayer.getSlug() + "-";
+  }
+
+  slug.pop_back();  // remove last "-"
+  return slug;
+}
+
 double Network::train(std::vector<std::vector<double>> X, std::vector<double> y,
                       int epochs,
                       std::vector<std::shared_ptr<Callback>> callbacks,

src/NeuralNet/Network.hpp

@@ -80,6 +80,14 @@ class Network : public Model {
    */
   size_t getNumLayers() const;
 
+  /**
+   * @brief Get the slug of the network based on it's architecture
+   *
+   * @return A string representing the combined slug of the different components
+   * in the Network
+   */
+  std::string getSlug() const;
+
   /**
    * @brief This method will Train the model with the given inputs and labels
    *

src/NeuralNet/layers/Dense.hpp

@@ -49,6 +49,13 @@ class Dense : public Layer {
     return;
   };
 
+  /**
+   * @brief Dense layer slug
+   */
+  std::string getSlug() const override {
+    return slug + std::to_string(nNeurons);
+  }
+
   /**
    * @brief This method is used to feed the inputs to the layer
    *
@@ -80,6 +87,7 @@ class Dense : public Layer {
   friend class Network;
 
   double bias;
+  std::string slug = "dns";
   Eigen::MatrixXd biases;
   WEIGHT_INIT weightInit;
   Eigen::MatrixXd weights;

src/NeuralNet/layers/Dropout.hpp

@@ -36,6 +36,13 @@ class Dropout : public Layer {
     this->scaleRate = 1.0 / (1.0 - rate);
   };
 
+  /**
+   * @brief Dropout layer slug
+   */
+  std::string getSlug() const override {
+    return slug + removeTrailingZeros(std::to_string(rate));
+  }
+
   /**
    * @brief This method is used to feed the inputs to the layer
    *
@@ -49,6 +56,7 @@ class Dropout : public Layer {
 
  private:
   std::vector<std::tuple<int, int>> coordinates;
+  std::string slug = "do";
 
   // non-public serialization
   friend class cereal::access;

src/NeuralNet/layers/Flatten.hpp

@@ -28,6 +28,11 @@ class Flatten : public Layer {
     type = LayerType::FLATTEN;
   };
 
+  /**
+   * @brief Flatten layer's slug
+   */
+  std::string getSlug() const override { return slug; }
+
   /**
    * @brief This method flattens a 3D vector into a 2D Eigen::MatrixXd
    *
@@ -72,6 +77,7 @@ class Flatten : public Layer {
   friend class cereal::access;
 
   std::tuple<int, int> inputShape;
+  std::string slug = "fltn";
 
   template <class Archive>
   void serialize(Archive &ar) {

src/NeuralNet/layers/Layer.hpp

@@ -43,6 +43,13 @@ class Layer {
    */
   int getNumNeurons() const { return nNeurons; };
 
+  /**
+   * @brief The slug of the layers (name + main parameter value)
+   *
+   * @return the slug
+   */
+  virtual std::string getSlug() const = 0;
+
   /**
    * @brief Method to print layer's outputs
    */

src/NeuralNet/utils/Functions.hpp

@@ -359,4 +359,16 @@ static void signalHandler(int signum) {
   exit(signum);
 };
 
+/* STRING OPERATIONS  */
+
+static std::string removeTrailingZeros(std::string str) {
+  str.erase(str.find_last_not_of('0') + 1, std::string::npos);
+
+  if (str.back() == '.') {
+    str.pop_back();
+  }
+
+  return str;
+}
+
 }  // namespace NeuralNet