From fb49c794b2ae3e31a20b9eb29ae5501672799573 Mon Sep 17 00:00:00 2001
From: LeLeLeLeto <leleto.fr@gmail.com>
Date: Tue, 31 Dec 2024 17:21:57 +0100
Subject: [PATCH] Added layer backpropagation

---
 layer.h | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/layer.h b/layer.h
index 529cc71..8294d13 100644
--- a/layer.h
+++ b/layer.h
@@ -15,12 +15,20 @@ class Layer {
     Matrix activated_output;
     Matrix biases;
 
+    // Planning for back propagation
+    // Each layer needs the derivative of Z with respect to W, derivative of A with respect to Z and derivative of loss with respect to A
+    // Let's call them dzw, daz and dca
+    Matrix daz;
+
     static inline float Sigmoid(float);
     static inline float SigmoidPrime(float);
 
     inline void Forward(); // Forward Pass with sigmoid
     inline void Forward(float (*activation)(float)); // Forward Pass with custom activation function
 
+    inline void BackPropagate(Matrix);
+    inline void BackPropagate(Matrix, Matrix, float (*activation)(float)); // To backpropagate, we need the derivative of loss with respect to A and the derivative of used activation function
+
     inline void Feed(Matrix);
 
     // Constructors
@@ -29,6 +37,31 @@ class Layer {
     Layer();
 };
 
+void Layer::BackPropagate(Matrix dzw, Matrix dca, float (*derivative)(float)){
+    // Calculate daz ; derivative of activation function
+    this->daz = this->activated_output.Function(derivative);
+    // this->daz.Print("daz");
+
+    // We need to transpose dzw and extend down
+    // dzw.Print("dzw");
+    dzw = dzw.Transpose().ExtendDown(dca.values.size());
+    // dzw.Print("dzw extended transposed");
+
+    Matrix dcw = this->daz.Hadamard(&dca).ExtendRight(this->input.values.size());
+    // dcw.Print("daz . dca");
+    dcw = dcw.Hadamard(&dzw);
+    // dcw.Print("daz . dca . dzw : DCW");
+
+    // this->weights.Print("weights");
+
+    // Apply dcw to weights
+    float learning_rate = 0.1F;
+    Matrix reduced_dcw = dcw.Multiply(learning_rate);
+    // We SUBSTRACT the derivative of loss with respect to the weights.
+    this->weights = this->weights.Substract(&reduced_dcw);
+    // this->weights.Print("New weights");
+}
+
 Layer::Layer(){
 
 }