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| #include <cmath>
#include <ctime>
#include <cassert>
#include <fstream>
#include <iostream>
#include <algorithm>
#include <boost/array.hpp>
#include <boost/random.hpp>
#include <boost/cstdlib.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/matrix.hpp>
using namespace std;
using namespace boost;
using namespace boost::numeric;
const double learning_rate = 0.1; // define learning rate
const double lambda = 0.0; // define lambda for weight decay
const unsigned int mnbatch_sz = 1250; // define size of batch
const unsigned int epoc = -1; // define number of epoch
const unsigned int midsz = 200; // define number of hiden neurons
template <typename T = double>
struct sum {
sum(const T & init = T()) : value(init) { }
void operator()(const T & val) {
value += val;
}
T value;
};
struct tanh_nl {
template <typename T>
T operator()(const T & vec) {
T res(vec.size());
for (size_t i = 0, e = vec.size(); i != e; ++i) {
res(i) = tanh(vec(i));
}
return res;
}
};
struct softmax_nl {
template <typename T>
T operator()(const T & vec) {
T tmp(vec.size());
for (size_t i = 0, e = vec.size(); i != e; ++i) {
tmp(i) = exp(vec(i));
}
typename T::value_type exp_sum =
for_each(tmp.begin(), tmp.end(),
sum<typename T::value_type>(0)).value;
T res(vec.size());
for (size_t i = 0, e = vec.size(); i != e; ++i) {
res(i) = tmp(i) / exp_sum;
}
return res;
}
};
template <int Dim, int NbClass>
class MLP_tanh_softmax
{
public:
typedef ublas::vector<double> mlp_vec;
typedef ublas::matrix<double> mlp_mat;
typedef tuple<mlp_mat, mlp_mat, // W1, W2
mlp_vec, mlp_vec> mlp_params; // b1, b2
typedef array<mlp_vec, 4> fprop_vecs;
#define PARAMS_SIZE mlp_mat(midsz, Dim), mlp_mat(NbClass, midsz), \
mlp_vec(midsz), mlp_vec(NbClass)
#define INIT_MATS(x) do { \
get<0>((x)) = ublas::zero_matrix<double>(midsz, Dim); \
get<1>((x)) = ublas::zero_matrix<double>(NbClass, midsz); \
} while(false)
#define INIT_VECS(x) do { \
get<2>((x)) = ublas::zero_vector<double>(midsz); \
get<3>((x)) = ublas::zero_vector<double>(NbClass); \
} while(false)
MLP_tanh_softmax()
{
reset();
}
void reset()
{
params = mlp_params(PARAMS_SIZE);
mt19937 rng(static_cast<boost::uint32_t>(time(0)));
// Initialisation aléatoire des paramêtres de la couche cachée dans ]-1/Dim, 1/Dim[
// Où Dim est la taille des vecteurs d'entrée
double ini = 1.0 / sqrt(double(Dim));
uniform_real<> dist1(-ini, ini);
variate_generator<mt19937&, uniform_real<> > rand1(rng, dist1);
for (mlp_mat::array_type::iterator
it = get<0>(params).data().begin(),
end = get<0>(params).data().end();
it != end; ++it) {
*it = rand1();
}
// Initialisation aléatoire des paramêtres de la couche de sortie dans ]-1/midsz, 1/midsz[
//Où midsz est la taille de la couche cachée
ini = 1.0 / sqrt(double(midsz));
uniform_real<> dist2(-ini, ini);
variate_generator<mt19937&, uniform_real<> > rand2(rng, dist2);
for (mlp_mat::array_type::iterator
it = get<1>(params).data().begin(),
end = get<1>(params).data().end();
it != end; ++it) {
*it = rand2();
}
// Initialisation des biais à 0
INIT_VECS(params);
}
int test(const mlp_vec & vec)
{
fprop_vecs tmp;
forward_prop(vec, tmp);
return distance(tmp[3].begin(),
max_element(tmp[3].begin(), tmp[3].end()));
}
int test(const vector<mlp_vec> & test_set,
const vector<int> & classes,
size_t first, size_t size)
{
int res = 0;
for (size_t i = first, e = first + size; i != e; ++i) {
int pred = test(test_set[i]);
if (pred == classes[i])
++res;
}
return res;
}
// Entraine le réseau de neurones par mini batch jusqu'à ce que l'on trouve un minimum local
// (supposé atteind lorsque l'entrainement stagne pour stop_count epoques)
// L'ensemble de données est coupé en deux partie (chacune composés d'éléments contigues dans l'ensemble)
// La partie d'entrainement, sur laquel sera effectuée la rétropropagation, et la partie validation
// avec laquel on teste les performances (et donc l'évolution itérative).
static const unsigned stop_count = 5;
void train(const vector<mlp_vec> & data_set,
const vector<int> & classes,
size_t train_sz, size_t valid_sz,
ostream & ostr)
{
bool min_found = false; //< At least local one
double actual_min = 100.0;
unsigned int pos = 0, count = 0, egal_count = 0;
unsigned int i = 0;
while (i < epoc) {
if (pos == 0) { // One more epoc
static double last_error = 100.0;
ostr << "Iteration " << count++ << endl;
int tst = 0;
double error_percent = 0.0;
if (valid_sz != 0) {
tst = test(data_set, classes, 0, train_sz);
error_percent =
100.0 * (train_sz - tst) / double(train_sz);
ostr << "Erreur Entrainement : "
<< error_percent << endl;
tst = test(data_set, classes,
train_sz, valid_sz);
error_percent =
100.0 * (valid_sz - tst) / double(valid_sz);
ostr << "Erreur Validation : "
<< error_percent << endl;
ostr << endl;
}
if (last_error == error_percent) {
if (min_found && ++egal_count == stop_count)
break;
} else {
if (error_percent <= actual_min) {
min_found = false;
actual_min = 100.0;
}
if (error_percent > last_error) {
min_found = true;
actual_min = last_error;
}
egal_count = 0;
last_error = error_percent;
}
}
int mnbatch_size = min(mnbatch_sz, train_sz - pos);
mlp_params grad = calc_grad(data_set, classes,
pos, mnbatch_size);
get<0>(params) -= learning_rate * get<0>(grad);
get<1>(params) -= learning_rate * get<1>(grad);
get<2>(params) -= learning_rate * get<2>(grad);
get<3>(params) -= learning_rate * get<3>(grad);
if ((pos += mnbatch_size) > train_sz) {
++i;
pos = 0;
}
}
}
void dump(ostream & ostr)
{
ostr << "W1:\t" << get<0>(params)
<< "\nW2:\t" << get<1>(params)
<< "\nb1:\t" << get<2>(params)
<< "\nb2:\t" << get<3>(params)
<< endl;
}
private:
mlp_params calc_grad(const vector<mlp_vec> & train_set,
const vector<int> & classes,
size_t first,
unsigned int mnbatch_size)
{
mlp_params grad(PARAMS_SIZE);
INIT_MATS(grad); INIT_VECS(grad);
for (unsigned int i = 0; i < mnbatch_size; ++i) {
int index = first + i;
fprop_vecs tmp;
forward_prop(train_set[index], tmp);
mlp_params tmp_grad;
back_prop(train_set[index],
classes[index],
tmp, tmp_grad);
get<0>(grad) += get<0>(tmp_grad);
get<1>(grad) += get<1>(tmp_grad);
get<2>(grad) += get<2>(tmp_grad);
get<3>(grad) += get<3>(tmp_grad);
}
get<0>(grad) /= double(mnbatch_size);
get<1>(grad) /= double(mnbatch_size);
get<2>(grad) /= double(mnbatch_size);
get<3>(grad) /= double(mnbatch_size);
return grad;
}
void forward_prop(const mlp_vec & vec, fprop_vecs & res)
{
fprop_vecs ret;
ret[0] = prod(get<0>(params), vec) + get<2>(params);
ret[1] = mid_func(ret[0]);
ret[2] = prod(get<1>(params), ret[1]) + get<3>(params);
ret[3] = out_func(ret[2]);
#if !(defined NDEBUG)
cout << "Input vector:\t" << vec << endl;
cout << "Middle activ:\t" << ret[0] << endl;
cout << "Middle w/ nl:\t" << ret[1] << endl;
cout << "Output activ:\t" << ret[2] << endl;
cout << "Output w/ nl:\t" << ret[3] << endl;
#endif
swap(ret, res);
}
void back_prop(const mlp_vec & vec, int clas,
const fprop_vecs & fp_vecs,
mlp_params & res)
{
mlp_params ret(PARAMS_SIZE);
for (size_t i = 0, e = get<3>(ret).size(); i != e; ++i) {
// Caca beurk, mais évite un branchement... :-)
get<3>(ret)[i] = fp_vecs[3][i] - double(i == clas);
}
for (size_t i = 0, e = get<1>(ret).size1(); i != e; ++i) {
for (size_t j = 0, f = get<1>(ret).size2(); j != f; ++j) {
get<1>(ret)(i,j) = get<3>(ret)[i] * fp_vecs[1][j] +
2 * lambda * get<1>(params)(i,j);
}
}
mlp_vec dcdhs(fp_vecs[1].size());
for (size_t i = 0, e = dcdhs.size(); i != e; ++i) {
double sum = 0.0;
for (size_t j = 0, f = get<1>(params).size1(); j != f; ++j) {
sum += get<3>(ret)[j] * get<1>(params)(j,i);
}
dcdhs[i] = sum;
}
for (size_t i = 0, e = get<2>(ret).size(); i != e; ++i) {
get<2>(ret)[i] = dcdhs[i] *
(1 - fp_vecs[1][i] * fp_vecs[1][i]);
}
for (size_t i = 0, e = get<0>(ret).size1(); i != e; ++i) {
for (size_t j = 0, f = get<0>(ret).size2(); j != f; ++j) {
get<0>(ret)(i,j) = get<2>(ret)[i] * vec[j] +
2 * lambda * get<0>(params)(i,j);
}
}
swap(ret, res);
}
#undef PARAMS_SIZE
#undef INIT_MATS
#undef INIT_VECS
mlp_params params; // mlp_mat W1; mlp_mat W2; mlp_vec b1; mlp_vec b2;
tanh_nl mid_func;
softmax_nl out_func;
};
int main()
{
// Vecteurs de dimmension 784, 10 classes
typedef MLP_tanh_softmax<784, 10> MLPts;
MLPts mlp_ts = MLPts();
// Loading MNIST
cout << "Loading... "; cout.flush();
vector<MLPts::mlp_vec> data;
vector<int> classes;
ifstream train("mnist.txt");
size_t i = 0, count = 0;
double tmp;
while (train >> tmp && count < 30000) {
if (i == 784) {
classes.push_back(int(tmp));
i = 0;
++count;
continue;
} else if (i == 0) {
data.push_back(MLPts::mlp_vec(784));
}
data.back()[i++] = tmp;
}
cout << "Loaded. (" << data.size() << " items)" << endl;
ofstream output("res.txt");
mlp_ts.train(data, classes, 25000, 5000, output);
output << endl;
mlp_ts.dump(output);
return exit_success;
} |
[AI Code]Réseau de neurones: perceptron multi-couche en C++
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