84 lines
No EOL
2.4 KiB
C++
Executable file
84 lines
No EOL
2.4 KiB
C++
Executable file
#ifndef ANNCLASSES_H
|
|
#define ANNCLASSES_H
|
|
|
|
#include <forward_list>
|
|
#include <list>
|
|
#include <vector>
|
|
|
|
|
|
enum Activ
|
|
{
|
|
RELU, TANH, SIGMOID, LINEAR, SOFTMAX
|
|
};
|
|
|
|
class Neuron
|
|
{
|
|
public:
|
|
Neuron(int prev_layer_size); //prev_layer_size = number of weights
|
|
void set_bias(float value);
|
|
float get_bias();
|
|
void set_nth_weight(int n, float value);
|
|
float get_nth_weight(int n);
|
|
float get_weighted_sum();
|
|
void set_activated_output(float value);
|
|
float get_activated_output();
|
|
void set_derror(float value);
|
|
float get_derror();
|
|
void activate(std::forward_list<Neuron>::iterator &prev_layer_it, Activ activ_function=LINEAR);
|
|
private:
|
|
std::forward_list<float> weights;
|
|
float bias;
|
|
float weighted_sum;
|
|
float activated_output;
|
|
float derror;
|
|
};
|
|
|
|
|
|
class Network
|
|
{
|
|
public:
|
|
Network(int n_layers, int n_neurons);
|
|
Network(const std::vector<int> &n_neurons, Activ h_activ=RELU, Activ o_activ=SIGMOID);
|
|
|
|
int get_neurons_number();
|
|
|
|
bool train(const std::vector<std::vector<float>> &inputs, const std::vector<std::vector<float>> &targets, float learning_rate=0.001, int n_episodes=30, int batch_size=32);
|
|
|
|
std::vector<float> predict(const std::vector<std::vector<float>> &inputs, bool as_raw=true);
|
|
void print();
|
|
|
|
//to be deleted
|
|
//bool forward(const std::vector<float> &input, const std::vector<float> &target);
|
|
//bool set_errors(const std::vector<float> &target);
|
|
//bool backward(float learning_rate);
|
|
private:
|
|
std::list<std::forward_list<Neuron>> layers;
|
|
int neurons_number;
|
|
Activ h_activ;
|
|
Activ o_activ;
|
|
|
|
bool forward(const std::vector<float> &input, const std::vector<float> &target);
|
|
bool set_errors(const std::vector<float> &target);
|
|
bool backward(float learning_rate);
|
|
};
|
|
|
|
|
|
class Tools
|
|
{
|
|
public:
|
|
static void activate_randomness();
|
|
static float get_random(float mini, float maxi);
|
|
//Activation functions and their derivatives
|
|
static float activation_function(Activ activ, float value);
|
|
static float activation_function_derivative(Activ activ, float value);
|
|
//float activation_function(Activ activ, float value);
|
|
//float activation_function_derivative(Activ activ, float value);
|
|
private:
|
|
float relu(float value);
|
|
float sigmoid(float value);
|
|
float relu_derivative(float value);
|
|
float sigmoid_derivative(float value);
|
|
float tanh_derivative(float value);
|
|
};
|
|
|
|
#endif |