69 lines
1.6 KiB
C++
69 lines
1.6 KiB
C++
#include "matrices.h"
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#include <cassert>
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#include <math.h>
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#define assertm(exp, msg) assert((void(msg), exp))
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class Layer {
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public:
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Matrix input;
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Matrix weights;
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Matrix raw_output;
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Matrix activated_output;
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Matrix biases;
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static inline float Sigmoid(float);
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static inline float SigmoidPrime(float);
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inline Layer(int); // Number of neurons
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inline void Forward(); // Forward Pass with sigmoid
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inline void Forward(float (*activation)(float)); // Forward Pass with custom activation function
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inline void Feed(Matrix);
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// Constructors
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// Input size, Size
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Layer(int, int);
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};
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Layer::Layer(int input_size, int size){
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this->input = Matrix(input_size, 1);
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this->weights = Matrix(input_size, size);
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this->weights.Randomize(0.0F, 1.0F);
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// Z, A and B are the same size as A
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this->raw_output = this->input;
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this->activated_output = this->input;
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this->biases = this->input;
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this->biases.Randomize(0.0F, 1.0F);
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}
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void Layer::Feed(Matrix a){
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this->input = a;
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}
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float Layer::Sigmoid(float x){
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return 1 / (1 + exp(-x));
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}
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float Layer::SigmoidPrime(float x){
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float buffer = Layer::Sigmoid(x);
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return buffer * (1 - buffer);
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}
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void Layer::Forward(float (*activation)(float)){
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// Multiply inputs by weights
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// W x I + B = Z
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this->raw_output = this->input.Multiply(&this->weights).Add(&this->biases);
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// Now through activation function
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// A = F(Z)
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this->activated_output = this->raw_output.Function(activation);
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}
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void Layer::Forward(){
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this->Forward(&Layer::Sigmoid);
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} |