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Fix wrong matrix sizes in layer constructor

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This commit is contained in:
LeLeLeLeto 2024-12-31 02:08:13 +01:00
parent 627679252f
commit db04387314
1 changed files with 17 additions and 13 deletions
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30
layer.h
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@ -1,3 +1,6 @@
#ifndef LAYER_H_
#define LAYER_H_
#include "matrices.h" #include "matrices.h"
#include <cassert> #include <cassert>
#include <math.h> #include <math.h>
@ -15,8 +18,6 @@ class Layer {
static inline float Sigmoid(float); static inline float Sigmoid(float);
static inline float SigmoidPrime(float); static inline float SigmoidPrime(float);
inline Layer(int); // Number of neurons
inline void Forward(); // Forward Pass with sigmoid inline void Forward(); // Forward Pass with sigmoid
inline void Forward(float (*activation)(float)); // Forward Pass with custom activation function inline void Forward(float (*activation)(float)); // Forward Pass with custom activation function
@ -29,21 +30,22 @@ class Layer {
}; };
Layer::Layer(){ Layer::Layer(){
} }
Layer::Layer(int input_size, int size){ Layer::Layer(int input_size, int size){
this->input = Matrix(input_size, 1); this->input = Matrix(input_size, 1);
this->weights = Matrix(input_size, size); // Every neuron has a weight for every input
this->weights.Randomize(0.0F, 1.0F); this->weights = Matrix(size, input_size);
this->weights.Randomize(-1.0F, 1.0F);
// Z, A and B are the same size as A this->raw_output = Matrix(size, 1);
this->raw_output = this->input; this->activated_output = this->raw_output;
this->activated_output = this->input;
this->biases = this->input; // One bias per neuron
this->biases.Randomize(0.0F, 1.0F); this->biases = Matrix(size, 1);
this->biases.Randomize(-1.0F, 1.0F);
} }
void Layer::Feed(Matrix a){ void Layer::Feed(Matrix a){
@ -60,9 +62,9 @@ float Layer::SigmoidPrime(float x){
} }
void Layer::Forward(float (*activation)(float)){ void Layer::Forward(float (*activation)(float)){
// Multiply inputs by weights // Multiply weight matrix by input matrix
// W x I + B = Z // W x I + B = Z
this->raw_output = this->input.Multiply(&this->weights).Add(&this->biases); this->raw_output = this->weights.Multiply(&this->input).Add(&this->biases);
// Now through activation function // Now through activation function
// A = F(Z) // A = F(Z)
@ -71,4 +73,6 @@ void Layer::Forward(float (*activation)(float)){
void Layer::Forward(){ void Layer::Forward(){
this->Forward(&Layer::Sigmoid); this->Forward(&Layer::Sigmoid);
} }
#endif