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First commit. Create MVS solution. Create sample class. Create functional tests.
This commit is contained in:
davidjacnogueira
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@@ -1,2 +1,6 @@
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# MLP
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# MLP
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Simple multilayer perceptron c++ implementation.
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Simple multilayer perceptron c++ implementation.
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David Nogueira, 2016.01.16
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src/Main.cpp
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//============================================================================
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// Name : Main.cpp
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// Author : David Nogueira
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//============================================================================
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#include "MLP.h"
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#include <stdio.h>
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#include <stdlib.h>
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#include <iostream>
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#include <sstream>
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#include <fstream>
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#include <vector>
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#include <algorithm>
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#include <cassert>
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void LearnAND() {
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std::cout << "Train AND function with mlp." << std::endl;
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std::vector<TrainingSample> training_set =
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{
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{{ 1, 0, 0 },{1,0}},
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{{ 1, 0, 1 },{1,0}},
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{{ 1, 1, 0 },{1,0}},
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{{ 1, 1, 1 },{0,1}}
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};
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MLP my_mlp(0.1, 100, 0.5);
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my_mlp.Train(training_set, 1, 1);
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assert(my_mlp.GetOutput({ 1, 0, 0 }) == 0);
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assert(my_mlp.GetOutput({ 1, 0, 1 }) == 0);
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assert(my_mlp.GetOutput({ 1, 1, 0 }) == 0);
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assert(my_mlp.GetOutput({ 1, 1, 1 }) == 1);
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std::cout << "Trained with success." << std::endl;
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std::cout << std::endl;
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}
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void LearnNAND() {
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std::cout << "Train NAND function with mlp." << std::endl;
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std::vector<TrainingSample> training_set =
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{
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{{ 1, 0, 0 },{0,1}},
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{{ 1, 0, 1 },{0,1}},
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{{ 1, 1, 0 },{0,1}},
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|
{{ 1, 1, 1 },{1,0}}
|
||||||
|
};
|
||||||
|
|
||||||
|
MLP my_mlp(0.1, 100, 0.5);
|
||||||
|
my_mlp.Train(training_set, 1, 1);
|
||||||
|
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 0);
|
||||||
|
std::cout << "Trained with success." << std::endl;
|
||||||
|
std::cout << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
void LearnOR() {
|
||||||
|
std::cout << "Train OR function with mlp." << std::endl;
|
||||||
|
|
||||||
|
std::vector<TrainingSample> training_set =
|
||||||
|
{
|
||||||
|
{{ 1, 0, 0 },{1,0}},
|
||||||
|
{{ 1, 0, 1 },{0,1}},
|
||||||
|
{{ 1, 1, 0 },{0,1}},
|
||||||
|
{{ 1, 1, 1 },{0,1}}
|
||||||
|
};
|
||||||
|
|
||||||
|
MLP my_mlp(0.1, 100, 0.5);
|
||||||
|
my_mlp.Train(training_set, 1, 1);
|
||||||
|
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 0);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 1);
|
||||||
|
std::cout << "Trained with success." << std::endl;
|
||||||
|
std::cout << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
void LearnNOR() {
|
||||||
|
std::cout << "Train NOR function with mlp." << std::endl;
|
||||||
|
|
||||||
|
std::vector<TrainingSample> training_set =
|
||||||
|
{
|
||||||
|
{{ 1, 0, 0 },{0,1}},
|
||||||
|
{{ 1, 0, 1 },{1,0}},
|
||||||
|
{{ 1, 1, 0 },{1,0}},
|
||||||
|
{{ 1, 1, 1 },{1,0}}
|
||||||
|
};
|
||||||
|
|
||||||
|
MLP my_mlp(0.1, 100, 0.5);
|
||||||
|
my_mlp.Train(training_set, 1, 1);
|
||||||
|
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 0 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0, 1 }) == 0);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 0 }) == 0);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1, 1 }) == 0);
|
||||||
|
std::cout << "Trained with success." << std::endl;
|
||||||
|
std::cout << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
void LearnNOT() {
|
||||||
|
std::cout << "Train NOT function with mlp." << std::endl;
|
||||||
|
|
||||||
|
std::vector<TrainingSample> training_set =
|
||||||
|
{
|
||||||
|
{{ 1, 0},{0,1}},
|
||||||
|
{{ 1, 1},{1,1}}
|
||||||
|
};
|
||||||
|
|
||||||
|
MLP my_mlp(0.1, 100, 0.5);
|
||||||
|
my_mlp.Train(training_set, 1, 1);
|
||||||
|
|
||||||
|
assert(my_mlp.GetOutput({ 1, 0 }) == 1);
|
||||||
|
assert(my_mlp.GetOutput({ 1, 1 }) == 0);
|
||||||
|
std::cout << "Trained with success." << std::endl;
|
||||||
|
std::cout << std::endl;
|
||||||
|
}
|
||||||
|
|
||||||
|
int main() {
|
||||||
|
LearnAND();
|
||||||
|
LearnNAND();
|
||||||
|
LearnOR();
|
||||||
|
LearnNOR();
|
||||||
|
LearnNOT();
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
45
src/Sample.h
Normal file
45
src/Sample.h
Normal file
@@ -0,0 +1,45 @@
|
|||||||
|
#ifndef TRAININGSAMPLE_H
|
||||||
|
#define TRAININGSAMPLE_H
|
||||||
|
|
||||||
|
#include <stdlib.h>
|
||||||
|
#include <vector>
|
||||||
|
|
||||||
|
class Sample {
|
||||||
|
public:
|
||||||
|
Sample(const std::vector<double> & input_vector) {
|
||||||
|
|
||||||
|
m_input_vector = input_vector;
|
||||||
|
}
|
||||||
|
std::vector<double> & input_vector() {
|
||||||
|
return m_input_vector;
|
||||||
|
}
|
||||||
|
uint32_t GetInputVectorSize() const {
|
||||||
|
return m_input_vector.size();
|
||||||
|
}
|
||||||
|
void AddBiasValue(double bias_value) {
|
||||||
|
m_input_vector.insert(m_input_vector.begin(), bias_value);
|
||||||
|
}
|
||||||
|
protected:
|
||||||
|
std::vector<double> m_input_vector;
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
class TrainingSample : public Sample {
|
||||||
|
public:
|
||||||
|
TrainingSample(const std::vector<double> & input_vector,
|
||||||
|
const std::vector<double> & output_vector) :
|
||||||
|
Sample(input_vector) {
|
||||||
|
m_output_vector = output_vector;
|
||||||
|
}
|
||||||
|
std::vector<double> & output_vector() {
|
||||||
|
return m_output_vector;
|
||||||
|
}
|
||||||
|
uint32_t GetOutputVectorSize() const {
|
||||||
|
return m_output_vector.size();
|
||||||
|
}
|
||||||
|
protected:
|
||||||
|
std::vector<double> m_output_vector;
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
#endif // TRAININGSAMPLE_H
|
||||||
62
src/Utils.h
Normal file
62
src/Utils.h
Normal file
@@ -0,0 +1,62 @@
|
|||||||
|
#ifndef UTILS_H
|
||||||
|
#define UTILS_H
|
||||||
|
|
||||||
|
#include <stdlib.h>
|
||||||
|
#include <math.h>
|
||||||
|
#include <numeric>
|
||||||
|
#include <chrono>
|
||||||
|
#ifdef _WIN32
|
||||||
|
#include <time.h>
|
||||||
|
#else
|
||||||
|
#include <sys/time.h>
|
||||||
|
#endif
|
||||||
|
|
||||||
|
namespace utils {
|
||||||
|
|
||||||
|
struct gen_rand {
|
||||||
|
double factor;
|
||||||
|
public:
|
||||||
|
gen_rand(double r = 1.0) : factor(r / RAND_MAX) {}
|
||||||
|
double operator()() {
|
||||||
|
return rand() * factor;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
inline double sigmoid(double x) {
|
||||||
|
//Typical sigmoid function created from input x
|
||||||
|
//param x: input value
|
||||||
|
//return: sigmoided value
|
||||||
|
return 1 / (1 + exp(-x));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Derivative of sigmoid function
|
||||||
|
inline double deriv_sigmoid(double x) {
|
||||||
|
return sigmoid(x)*(1 - sigmoid(x));
|
||||||
|
};
|
||||||
|
|
||||||
|
class Chronometer {
|
||||||
|
public:
|
||||||
|
Chronometer() {
|
||||||
|
time_span = std::chrono::steady_clock::duration::zero();
|
||||||
|
};
|
||||||
|
virtual ~Chronometer() {};
|
||||||
|
|
||||||
|
void GetTime() {
|
||||||
|
clock_begin = std::chrono::steady_clock::now();
|
||||||
|
}
|
||||||
|
void StopTime() {
|
||||||
|
std::chrono::steady_clock::time_point clock_end = std::chrono::steady_clock::now();
|
||||||
|
time_span += clock_end - clock_begin;
|
||||||
|
}
|
||||||
|
//Return elapsed time in seconds
|
||||||
|
double GetElapsedTime() {
|
||||||
|
return double(time_span.count()) *
|
||||||
|
std::chrono::steady_clock::period::num / std::chrono::steady_clock::period::den;
|
||||||
|
}
|
||||||
|
protected:
|
||||||
|
std::chrono::steady_clock::time_point clock_begin;
|
||||||
|
std::chrono::steady_clock::duration time_span;
|
||||||
|
};
|
||||||
|
|
||||||
|
}
|
||||||
|
#endif // UTILS_H
|
||||||
Reference in New Issue
Block a user