diff --git a/src/Accuracy/Accuracy.cpp b/src/Accuracy/Accuracy.cpp
deleted file mode 100644
index 3dfbf94..0000000
--- a/src/Accuracy/Accuracy.cpp
+++ /dev/null
@@ -1,72 +0,0 @@
-#ifndef ACCURACY_HPP
-#define ACCURACY_HPP
-
-#include <vector>
-#include <numeric>
-#include <cmath>
-
-class Accuracy {
-private:
-    double accumulated_sum;
-    int accumulated_count;
-
-public:
-    Accuracy() : accumulated_sum(0.0), accumulated_count(0) {}
-
-    double calculate(const std::vector<int>& predictions, const std::vector<int>& y) {
-        std::vector<int> comparisons(predictions.size());
-        for (size_t i = 0; i < predictions.size(); ++i) {
-            comparisons[i] = compare(predictions[i], y[i]);
-        }
-
-        double accuracy = static_cast<double>(std::accumulate(comparisons.begin(), comparisons.end(), 0)) / predictions.size();
-
-        accumulated_sum += std::accumulate(comparisons.begin(), comparisons.end(), 0);
-        accumulated_count += predictions.size();
-
-        return accuracy;
-    }
-
-    double calculate_accumulated() {
-        double accuracy = static_cast<double>(accumulated_sum) / accumulated_count;
-        return accuracy;
-    }
-
-    void new_pass() {
-        accumulated_sum = 0.0;
-        accumulated_count = 0;
-    }
-
-    virtual int compare(int prediction, int ground_truth) {
-        return prediction == ground_truth ? 1 : 0;
-    }
-};
-
-class Accuracy_Categorical : public Accuracy {
-public:
-    int compare(int prediction, int ground_truth) override {
-        return prediction == ground_truth ? 1 : 0;
-    }
-};
-
-class Accuracy_Regression : public Accuracy {
-private:
-    double precision;
-
-public:
-    Accuracy_Regression() : precision(0.0) {}
-
-    void init(const std::vector<double>& y, bool reinit = false) {
-        if (precision == 0.0 || reinit) {
-            precision = std::sqrt(std::accumulate(y.begin(), y.end(), 0.0, [](double acc, double val) {
-                return acc + (val * val);
-            })) / 250.0;
-        }
-    }
-
-    int compare(int prediction, int ground_truth) override {
-        return std::abs(prediction - ground_truth) < precision ? 1 : 0;
-    }
-};
-
-#endif // ACCURACY_HPP
diff --git a/src/Accuracy/Accuracy.hpp b/src/Accuracy/Accuracy.hpp
deleted file mode 100644
index 58adfab..0000000
--- a/src/Accuracy/Accuracy.hpp
+++ /dev/null
@@ -1,36 +0,0 @@
-#ifndef ACCURACY_HPP
-#define ACCURACY_HPP
-
-#include <vector>
-#include <numeric>
-#include <cmath>
-
-class Accuracy {
-private:
-    double accumulated_sum;
-    int accumulated_count;
-
-public:
-    Accuracy();
-    double calculate(const std::vector<int>& predictions, const std::vector<int>& y);
-    double calculate_accumulated();
-    void new_pass();
-    virtual int compare(int prediction, int ground_truth);
-};
-
-class Accuracy_Categorical : public Accuracy {
-public:
-    int compare(int prediction, int ground_truth) override;
-};
-
-class Accuracy_Regression : public Accuracy {
-private:
-    double precision;
-
-public:
-    Accuracy_Regression();
-    void init(const std::vector<double>& y, bool reinit = false);
-    int compare(int prediction, int ground_truth) override;
-};
-
-#endif // ACCURACY_HPP
diff --git a/src/ActivationFunction/ActivationFunction.cpp b/src/ActivationFunction/ActivationFunction.cpp
deleted file mode 100644
index e405564..0000000
--- a/src/ActivationFunction/ActivationFunction.cpp
+++ /dev/null
@@ -1,15 +0,0 @@
-#include "ActivationFunction.hpp"
-
-void Activation_ReLU::forward(std::vector<double>& inputs, bool training) {
-    // Implementation
-}
-
-void Activation_ReLU::backward(std::vector<double>& dvalues) {
-    // Implementation
-}
-
-std::vector<double> Activation_ReLU::predictions(const std::vector<double>& outputs) {
-    // Implementation
-}
-
-// Implement similar member functions for other Activation classes
diff --git a/src/ActivationFunction/ActivationFunction.hpp b/src/ActivationFunction/ActivationFunction.hpp
deleted file mode 100644
index c943d66..0000000
--- a/src/ActivationFunction/ActivationFunction.hpp
+++ /dev/null
@@ -1,50 +0,0 @@
-#ifndef ACTIVATIONFUNCTION_HPP
-#define ACTIVATIONFUNCTION_HPP
-
-#include <vector>
-
-class Activation_ReLU {
-public:
-    void forward(std::vector<double>& inputs, bool training);
-    void backward(std::vector<double>& dvalues);
-    std::vector<double> predictions(const std::vector<double>& outputs);
-private:
-    std::vector<double> inputs;
-    std::vector<double> output;
-    std::vector<double> dinputs;
-};
-
-class Activation_Softmax {
-public:
-    void forward(std::vector<double>& inputs, bool training);
-    void backward(std::vector<double>& dvalues);
-    std::vector<double> predictions(const std::vector<double>& outputs);
-private:
-    std::vector<double> inputs;
-    std::vector<double> output;
-    std::vector<double> dinputs;
-};
-
-class Activation_Sigmoid {
-public:
-    void forward(std::vector<double>& inputs, bool training);
-    void backward(std::vector<double>& dvalues);
-    std::vector<double> predictions(const std::vector<double>& outputs);
-private:
-    std::vector<double> inputs;
-    std::vector<double> output;
-    std::vector<double> dinputs;
-};
-
-class Activation_Linear {
-public:
-    void forward(std::vector<double>& inputs, bool training);
-    void backward(std::vector<double>& dvalues);
-    std::vector<double> predictions(const std::vector<double>& outputs);
-private:
-    std::vector<double> inputs;
-    std::vector<double> output;
-    std::vector<double> dinputs;
-};
-
-#endif // ACTIVATIONFUNCTION_HPP
diff --git a/src/Layers/Layers.cpp b/src/Layers/Layers.cpp
deleted file mode 100644
index c4df860..0000000
--- a/src/Layers/Layers.cpp
+++ /dev/null
@@ -1,56 +0,0 @@
-#include "Layers.hpp"
-#include <random>
-
-Layer_Dense::Layer_Dense(int n_inputs, int n_neurons,
-                double weight_regularizer_l1,
-                double weight_regularizer_l2,
-                double bias_regularizer_l1,
-                double bias_regularizer_l2)
-    : weight_regularizer_l1(weight_regularizer_l1),
-      weight_regularizer_l2(weight_regularizer_l2),
-      bias_regularizer_l1(bias_regularizer_l1),
-      bias_regularizer_l2(bias_regularizer_l2)
-{
-    // Initialize weights and biases
-    weights.resize(n_inputs, std::vector<double>(n_neurons));
-    for (auto& row : weights) {
-        for (double& weight : row) {
-            weight = 0.01 * (std::rand() / double(RAND_MAX));
-        }
-    }
-    biases.resize(1, std::vector<double>(n_neurons, 0.0));
-}
-
-void Layer_Dense::forward(const std::vector<double>& inputs, bool training) {
-    this->inputs = inputs;
-
-    // Forward pass implementation
-}
-
-void Layer_Dense::backward(std::vector<double>& dvalues) {
-    // Backward pass implementation
-}
-
-std::pair<std::vector<std::vector<double>>, std::vector<std::vector<double>>> Layer_Dense::get_parameters() const {
-    return std::make_pair(weights, biases);
-}
-
-void Layer_Dense::set_parameters(const std::vector<std::vector<double>>& weights, const std::vector<std::vector<double>>& biases) {
-    this->weights = weights;
-    this->biases = biases;
-}
-
-Layer_Dropout::Layer_Dropout(double rate) : rate(1 - rate) {}
-
-void Layer_Dropout::forward(const std::vector<double>& inputs, bool training) {    // Forward pass implementation
-}
-
-void Layer_Dropout::backward(std::vector<double>& dvalues) {
-    // Backward pass implementation
-}
-
-void Layer_Input::forward(const std::vector<double>& inputs, bool training) {
-    this->output = inputs;
-}
-
-// Implement similar member functions for other Layer classes
diff --git a/src/Loss/Loss.hpp b/src/Loss/Loss.hpp
deleted file mode 100644
index 1b13507..0000000
--- a/src/Loss/Loss.hpp
+++ /dev/null
@@ -1,49 +0,0 @@
-#ifndef LOSS_HPP
-#define LOSS_HPP
-
-#include <vector>
-#include <cmath>
-#include <iostream>
-#include "Layers/Layers.hpp"
-
-class Loss {
-public:
-    virtual double regularization_loss();
-    virtual double forward(const std::vector<double>& y_pred, const std::vector<double>& y_true);
-    virtual void backward(std::vector<double>& dvalues, const std::vector<double>& y_true);
-    virtual void remember_trainable_layers(const std::vector<Layer*>& trainable_layers);
-    virtual double calculate(const std::vector<double>& output, const std::vector<double>& y, bool include_regularization = false);
-    virtual double calculate_accumulated(bool include_regularization = false);
-    virtual void new_pass();
-
-private:
-    std::vector<Layer*> trainable_layers;
-    double accumulated_sum;
-    int accumulated_count;
-};
-
-class Loss_CategoricalCrossentropy : public Loss {
-public:
-    double forward(const std::vector<double>& y_pred, const std::vector<double>& y_true) override;
-    void backward(std::vector<double>& dvalues, const std::vector<double>& y_true) override;
-};
-
-class Loss_BinaryCrossentropy : public Loss {
-public:
-    double forward(const std::vector<double>& y_pred, const std::vector<double>& y_true) override;
-    void backward(std::vector<double>& dvalues, const std::vector<double>& y_true) override;
-};
-
-class Loss_MeanSquaredError : public Loss {
-public:
-    double forward(const std::vector<double>& y_pred, const std::vector<double>& y_true) override;
-    void backward(std::vector<double>& dvalues, const std::vector<double>& y_true) override;
-};
-
-class Loss_MeanAbsoluteError : public Loss {
-public:
-    double forward(const std::vector<double>& y_pred, const std::vector<double>& y_true) override;
-    void backward(std::vector<double>& dvalues, const std::vector<double>& y_true) override;
-};
-
-#endif // LOSS_HPP
diff --git a/src/Model/Model.cpp b/src/Model/Model.cpp
deleted file mode 100644
index 1cf4296..0000000
--- a/src/Model/Model.cpp
+++ /dev/null
@@ -1,73 +0,0 @@
-#include "Model/Model.hpp"
-#include "ActivationFunction/ActivationFunction.hpp"
-#include "Loss/Loss.hpp"
-#include "Optimizer/Optimizer.hpp"
-#include "Accuracy/Accuracy.hpp"
-#include "Layers/Layers.hpp"
-
-Model::Model() {}
-
-void Model::add(Layer& layer) {
-    layers.push_back(&layer);
-}
-
-void Model::set_loss(Loss& loss) {
-    this->loss = &loss;
-}
-
-void Model::set_optimizer(Optimizer& optimizer) {
-    this->optimizer = &optimizer;
-}
-
-void Model::set_accuracy(Accuracy& accuracy) {
-    this->accuracy = &accuracy;
-}
-
-void Model::finalize() {
-    // Implement finalize method
-}
-
-void Model::train(const std::vector<std::vector<double>>& X, const std::vector<std::vector<double>>& y,
-                  int epochs, int batch_size, int print_every, const std::vector<std::vector<double>>& validation_data) {
-    // Implement train method
-}
-
-void Model::evaluate(const std::vector<std::vector<double>>& X_val, const std::vector<std::vector<double>>& y_val, int batch_size) {
-    // Implement evaluate method
-}
-
-std::vector<std::vector<double>> Model::predict(const std::vector<std::vector<double>>& X, int batch_size) {
-    // Implement predict method
-}
-
-void Model::save_parameters(const std::string& path) {
-    // Implement save_parameters method
-}
-
-void Model::load_parameters(const std::string& path) {
-    // Implement load_parameters method
-}
-
-void Model::save(const std::string& path) {
-    // Implement save method
-}
-
-void Model::forward(const std::vector<std::vector<double>>& X, bool training) {
-    // Implement forward method
-}
-
-void Model::backward(const std::vector<std::vector<double>>& output, const std::vector<std::vector<double>>& y) {
-    // Implement backward method
-}
-
-std::vector<std::vector<std::vector<double>>> Model::get_parameters() {
-    // Implement get_parameters method
-}
-
-void Model::set_parameters(const std::vector<std::vector<std::vector<double>>>& parameters) {
-    // Implement set_parameters method
-}
-
-Model Model::load(const std::string& path) {
-    // Implement load method
-}
diff --git a/src/Model/Model.hpp b/src/Model/Model.hpp
deleted file mode 100644
index f77fe2e..0000000
--- a/src/Model/Model.hpp
+++ /dev/null
@@ -1,42 +0,0 @@
-#pragma once
-#include "ActivationFunction/ActivationFunction.hpp"
-#include "Loss/Loss.hpp"
-#include "Optimizer/Optimizer.hpp"
-#include "Accuracy/Accuracy.hpp"
-#include "Layers/Layers.hpp"
-
-class Model {
-private:
-    Loss* loss;
-    Optimizer* optimizer;
-    Accuracy* accuracy;
-    std::vector<Layer*> layers;
-
-public:
-    Model();
-
-    void add(Layer& layer);
-    void set_loss(Loss& loss);
-    void set_optimizer(Optimizer& optimizer);
-    void set_accuracy(Accuracy& accuracy);
-    void finalize();
-
-    void train(const std::vector<std::vector<double>>& X, const std::vector<std::vector<double>>& y,
-               int epochs, int batch_size, int print_every, const std::vector<std::vector<double>>& validation_data);
-
-    void evaluate(const std::vector<std::vector<double>>& X_val, const std::vector<std::vector<double>>& y_val, int batch_size);
-
-    std::vector<std::vector<double>> predict(const std::vector<std::vector<double>>& X, int batch_size);
-
-    void save_parameters(const std::string& path);
-    void load_parameters(const std::string& path);
-    void save(const std::string& path);
-
-    void forward(const std::vector<std::vector<double>>& X, bool training);
-    void backward(const std::vector<std::vector<double>>& output, const std::vector<std::vector<double>>& y);
-
-    std::vector<std::vector<std::vector<double>>> get_parameters();
-    void set_parameters(const std::vector<std::vector<std::vector<double>>>& parameters);
-
-    static Model load(const std::string& path);
-};
diff --git a/src/Optimizer/Optimizer.cpp b/src/Optimizer/Optimizer.cpp
deleted file mode 100644
index 5bb9584..0000000
--- a/src/Optimizer/Optimizer.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-#include "Optimizer/Optimizer.hpp"
-#include "Layers/Layers.hpp"
-
-Optimizer_SGD::Optimizer_SGD(double learning_rate, double decay, double momentum)
-    : learning_rate(learning_rate),
-      current_learning_rate(learning_rate),
-      decay(decay),
-      iterations(0),
-      momentum(momentum) {}
-
-void Optimizer_SGD::pre_update_params() {
-    // pre_update_params implementation
-}
-
-void Optimizer_SGD::update_params(Layer& layer) {
-    // update_params implementation for any Layer
-}
-
-void Optimizer_SGD::post_update_params() {
-    iterations++;
-}
-
-Optimizer_Adagrad::Optimizer_Adagrad(double learning_rate, double decay, double epsilon)
-    : learning_rate(learning_rate),
-      current_learning_rate(learning_rate),
-      decay(decay),
-      iterations(0),
-      epsilon(epsilon) {}
-
-void Optimizer_Adagrad::pre_update_params() {
-    // pre_update_params implementation
-}
-
-void Optimizer_Adagrad::update_params(Layer& layer) {
-    // update_params implementation for any Layer
-}
-
-void Optimizer_Adagrad::post_update_params() {
-    iterations++;
-}
-
-// Similar implementations for Optimizer_RMSprop and Optimizer_Adam
-
diff --git a/src/Optimizer/Optimizer.hpp b/src/Optimizer/Optimizer.hpp
deleted file mode 100644
index 2143edc..0000000
--- a/src/Optimizer/Optimizer.hpp
+++ /dev/null
@@ -1,45 +0,0 @@
-#pragma once
-#include "Layers/Layers.hpp"
-
-class Optimizer {
-public:
-    virtual void pre_update_params() = 0;
-    virtual void update_params(Layer& layer) = 0;
-    virtual void post_update_params() = 0;
-    // Other common members and methods
-};
-
-class Optimizer_SGD : public Optimizer {
-public:
-    Optimizer_SGD(double learning_rate, double decay, double momentum);
-
-    void pre_update_params() override;
-    void update_params(Layer& layer) override;
-    void post_update_params() override;
-
-private:
-    double learning_rate;
-    double current_learning_rate;
-    double decay;
-    int iterations;
-    double momentum;
-};
-
-class Optimizer_Adagrad : public Optimizer {
-public:
-    Optimizer_Adagrad(double learning_rate, double decay, double epsilon);
-
-    void pre_update_params() override;
-    void update_params(Layer& layer) override;
-    void post_update_params() override;
-
-private:
-    double learning_rate;
-    double current_learning_rate;
-    double decay;
-    int iterations;
-    double epsilon;
-};
-
-// Similar declarations for Optimizer_RMSprop and Optimizer_Adam
-