prob not working

Author: kemsig

include/TensorSANN/layers/DenseLayer.hpp

@@ -9,14 +9,15 @@
 
 namespace TensorSANN {
 
-class DenseLayer : TrainableLayer{
+class DenseLayer : public TrainableLayer{
 public:
     DenseLayer(size_t input_size, size_t output_size);
 
     Tensor forward(const Tensor &input) override;
 
     Tensor backward(const Tensor &output_grad) override;
 
+    void update_weights_biases(float learning_rate) override;
 };
 
 

include/TensorSANN/layers/TrainableLayer.hpp

@@ -9,25 +9,27 @@
 
 namespace TensorSANN {
 
-class TrainableLayer : Layer{
+class TrainableLayer : public Layer{
 public:
-    virtual Tensor forward(const Tensor &input);
+  virtual ~TrainableLayer() = default;
 
-    virtual Tensor backward(const Tensor &output_grad);
+  virtual Tensor forward(const Tensor &input);
 
-    Tensor weights() {return weights_;}
-    Tensor biases() {return biases_;}
-    const Tensor weights() const {return weights_;}
-    const Tensor biases() const {return biases_;}
+  virtual Tensor backward(const Tensor &output_grad);
 
-protected:
-    Tensor weights_;
-    Tensor biases_;
-  //  Tensor cachedInput_;
-};
+  virtual void update_weights_biases(float learning_rate);
+
+  Tensor weights() {return weights_;}
+  Tensor biases() {return biases_;}
+  const Tensor weights() const {return weights_;}
+  const Tensor biases() const {return biases_;}
 
 public:
-  bool isTrainable_ = true;
+  bool isTrainable_ = true; // note might be redudnat
+  Tensor weights_;
+  Tensor biases_;
+  Tensor cachedInput_;
 
+};
 
 }

src/layers/DenseLayer.cpp

@@ -3,8 +3,10 @@
 #include "TensorSANN/utils/Tensor.hpp"
 
 namespace TensorSANN{
-    DenseLayer::DenseLayer(size_t input_size, size_t output_size) : weights_(Tensor({input_size, output_size})), biases_(Tensor({output_size})) {
-        // normal distrubtion random that i got from chat gpt
+    DenseLayer::DenseLayer(size_t input_size, size_t output_size){
+        weights_ = Tensor({input_size, output_size});
+        biases_ = Tensor({output_size});
+
         std::random_device rd;
         std::mt19937 gen(rd());
         std::normal_distribution<float> dist(0.0f, 0.1f);
@@ -47,4 +49,10 @@ namespace TensorSANN{
         // return d_Z to continue back prop
         return d_Z;
     }
+
+    void DenseLayer::update_weights_biases(float learning_rate){
+        weights_ = weights_ - ((*(weights_.grad())) * learning_rate);
+        biases_ = biases_ - ((*(biases_.grad())) * learning_rate);
+
+    }
 }   // namespace TensorSANN

src/main.cpp

@@ -1,6 +1,7 @@
 #include <iostream>
 #include "TensorSANN/utils/Tensor.hpp"
 #include "TensorSANN/layers/DenseLayer.hpp"
+#include "TensorSANN/layers/TrainableLayer.hpp"
 #include "TensorSANN/activations/ReLU.hpp"
 #include "TensorSANN/optimizers/SGD.hpp"
 

src/optimizers/SGD.cpp

@@ -11,8 +11,11 @@ bool SGD::update(Layer &layer){
 
     // if it is then we can attempt to downcast;
     TrainableLayer *t_layer = dynamic_cast<TrainableLayer*>(&layer);
+    if (!t_layer) return false; // just in case downcast fails
+
+    // update weights
+    (*t_layer).update_weights_biases(learning_rate);
 
-    
     return true;
 }