more project strucutre

Author: kemsig

include/TensorSANN/activations/ReLU.hpp

@@ -9,12 +9,13 @@ namespace TensorSANN {
 
 class ReLU : Layer{
 public:
-
-
     Tensor forward(const Tensor &input) override;
 
     Tensor backward(const Tensor &grad_output) override;
-};
 
+protected:
+    bool isTrainable_ = false;
+
+};
 
 }

include/TensorSANN/layers/DenseLayer.hpp

@@ -1,6 +1,6 @@
 #pragma once
 
-#include "TensorSANN/layers/Layer.hpp"
+#include "TensorSANN/layers/TrainableLayer.hpp"
 #include "TensorSANN/utils/Tensor.hpp"
 #include <memory>
 #include <random>
@@ -9,23 +9,14 @@
 
 namespace TensorSANN {
 
-class DenseLayer : Layer{
+class DenseLayer : TrainableLayer{
 public:
     DenseLayer(size_t input_size, size_t output_size);
 
     Tensor forward(const Tensor &input) override;
 
     Tensor backward(const Tensor &output_grad) override;
 
-    Tensor weights() {return weights_;}
-    Tensor biases() {return biases_;}
-    const Tensor weights() const {return weights_;}
-    const Tensor biases() const {return biases_;}
-
-protected:
-    Tensor weights_;
-    Tensor biases_;
-  //  Tensor cachedInput_;
 };
 
 

include/TensorSANN/layers/Layer.hpp

@@ -19,7 +19,10 @@ class Layer{
 protected:
     // Tensor output_;
     Tensor cachedInput_;
-};
 
 
+public:
+    bool isTrainable_ = false;
+};
+
 }

include/TensorSANN/layers/TrainableLayer.hpp

@@ -0,0 +1,33 @@
+#pragma once
+
+#include "TensorSANN/layers/Layer.hpp"
+#include "TensorSANN/utils/Tensor.hpp"
+#include <memory>
+#include <random>
+#include <vector>
+#include <string>
+
+namespace TensorSANN {
+
+class TrainableLayer : Layer{
+public:
+    virtual Tensor forward(const Tensor &input);
+
+    virtual Tensor backward(const Tensor &output_grad);
+
+    Tensor weights() {return weights_;}
+    Tensor biases() {return biases_;}
+    const Tensor weights() const {return weights_;}
+    const Tensor biases() const {return biases_;}
+
+protected:
+    Tensor weights_;
+    Tensor biases_;
+  //  Tensor cachedInput_;
+};
+
+public:
+  bool isTrainable_ = true;
+
+
+}

include/TensorSANN/optimizers/Optimizer.hpp

@@ -8,7 +8,8 @@ namespace TensorSANN{
 
 class Optimizer{
 public:
-    virtual void update(std::vector<Layer> & layers);
+    virtual ~Optimizer() = default;
+    virtual bool update(Layer & layer);
 };
 
 }// namespace TensorSANN

include/TensorSANN/optimizers/SGD.hpp

@@ -11,7 +11,7 @@ class SGD : Optimizer{
 public:
     SGD(float learning_rate);
 
-    void update(std::vector<Layer> & layers) override;
+    bool update(Layer & layer) override;
 
 private:
     float learning_rate;

src/main.cpp

@@ -2,9 +2,11 @@
 #include "TensorSANN/utils/Tensor.hpp"
 #include "TensorSANN/layers/DenseLayer.hpp"
 #include "TensorSANN/activations/ReLU.hpp"
+#include "TensorSANN/optimizers/SGD.hpp"
 
 int main() {
     std::cout << "Hello world!" << std::endl;
+    TensorSANN::SGD optimizer = TensorSANN::SGD(0.01f);
 
     std::vector<size_t> shape1 = {2, 3};  // 2x3 tensor
     TensorSANN::Tensor tensor_1(shape1);

src/optimizers/SGD.cpp

@@ -1,13 +1,19 @@
 #include "TensorSANN/optimizers/SGD.hpp"
+#include "TensorSANN/layers/TrainableLayer.hpp"
 
 namespace TensorSANN{
 
 SGD::SGD(float learning_rate) : learning_rate(learning_rate) {}
 
-void SGD::update(std::vector<Layer> &layers){
-    for (auto& layer : layers){
-       // for (size_t i = 0; i < )
-    }
+bool SGD::update(Layer &layer){
+    // check if trainable
+    if (!layer.isTrainable_) return false;
+
+    // if it is then we can attempt to downcast;
+    TrainableLayer *t_layer = dynamic_cast<TrainableLayer*>(&layer);
+
+    
+    return true;
 }
 
 }