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parallel_training.cpp
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parallel_training.cpp
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// Software: Training Artificial Neural Network for MNIST database
// Author: Hy Truong Son
// Major: BSc. Computer Science
// Class: 2013 - 2016
// Institution: Eotvos Lorand University
// Email: [email protected]
// Website: http://people.inf.elte.hu/hytruongson/
// Copyright 2015 (c). All rights reserved.
#include<omp.h>
#include <iostream>
#include <fstream>
#include <cstring>
#include <string>
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <vector>
#include <set>
#include <iterator>
#include <algorithm>
using namespace std;
// Training image file name
const string training_image_fn = "mnist/train-images.idx3-ubyte";
// Training label file name
const string training_label_fn = "mnist/train-labels.idx1-ubyte";
// Weights file name
const string model_fn = "model-neural-network.dat";
// Report file name
const string report_fn = "training-report.dat";
// Number of training samples
const int nTraining = 6000;
// Image size in MNIST database
const int width = 28;
const int height = 28;
// n1 = Number of input neurons
// n2 = Number of hidden neurons
// n3 = Number of output neurons
// epochs = Number of iterations for back-propagation algorithm
// learning_rate = Learing rate
// momentum = Momentum (heuristics to optimize back-propagation algorithm)
// epsilon = Epsilon, no more iterations if the learning error is smaller than epsilon
const int n1 = width * height; // = 784, without bias neuron
const int n2 = 128;
const int n3 = 10; // Ten classes: 0 - 9
const int epochs = 512;
const double learning_rate = 1e-3;
const double momentum = 0.9;
const double epsilon = 1e-3;
// From layer 1 to layer 2. Or: Input layer - Hidden layer
double *w1[n1 + 1], *delta1[n1 + 1], *out1;
// From layer 2 to layer 3. Or; Hidden layer - Output layer
double *w2[n2 + 1], *delta2[n2 + 1], *in2, *out2, *theta2;
// Layer 3 - Output layer
double *in3, *out3, *theta3;
double expected[n3 + 1];
// Image. In MNIST: 28x28 gray scale images.
int d[width + 1][height + 1];
// File stream to read data (image, label) and write down a report
ifstream image;
ifstream label;
ofstream report;
// +--------------------+
// | About the software |
// +--------------------+
void about() {
// Details
cout << "**************************************************" << endl;
cout << "*** Training Neural Network for MNIST database ***" << endl;
cout << "**************************************************" << endl;
cout << endl;
cout << "No. input neurons: " << n1 << endl;
cout << "No. hidden neurons: " << n2 << endl;
cout << "No. output neurons: " << n3 << endl;
cout << endl;
cout << "No. iterations: " << epochs << endl;
cout << "Learning rate: " << learning_rate << endl;
cout << "Momentum: " << momentum << endl;
cout << "Epsilon: " << epsilon << endl;
cout << endl;
cout << "Training image data: " << training_image_fn << endl;
cout << "Training label data: " << training_label_fn << endl;
cout << "No. training sample: " << nTraining << endl << endl;
}
// +-----------------------------------+
// | Memory allocation for the network |
// +-----------------------------------+
void init_array() {
// Layer 1 - Layer 2 = Input layer - Hidden layer
#pragma omp parallel for
for (int i = 1; i <= n1; ++i) {
w1[i] = new double [n2 + 1];
delta1[i] = new double [n2 + 1];
}
out1 = new double [n1 + 1];
// Layer 2 - Layer 3 = Hidden layer - Output layer
#pragma omp parallel for
for (int i = 1; i <= n2; ++i) {
w2[i] = new double [n3 + 1];
delta2[i] = new double [n3 + 1];
}
in2 = new double [n2 + 1];
out2 = new double [n2 + 1];
theta2 = new double [n2 + 1];
// Layer 3 - Output layer
in3 = new double [n3 + 1];
out3 = new double [n3 + 1];
theta3 = new double [n3 + 1];
// Initialization for weights from Input layer to Hidden layer
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n1; ++i) {
for (int j = 1; j <= n2; ++j) {
int sign = rand() % 2;
// Another strategy to randomize the weights - quite good
// w1[i][j] = (double)(rand() % 10 + 1) / (10 * n2);
w1[i][j] = (double)(rand() % 6) / 10.0;
if (sign == 1) {
w1[i][j] = - w1[i][j];
}
}
}
// Initialization for weights from Hidden layer to Output layer
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
int sign = rand() % 2;
// Another strategy to randomize the weights - quite good
// w2[i][j] = (double)(rand() % 6) / 10.0;
w2[i][j] = (double)(rand() % 10 + 1) / (10.0 * n3);
if (sign == 1) {
w2[i][j] = - w2[i][j];
}
}
}
}
// +------------------+
// | Sigmoid function |
// +------------------+
double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}
// +------------------------------+
// | Forward process - Perceptron |
// +------------------------------+
void perceptron() {
#pragma omp parallel for
for (int i = 1; i <= n2; ++i) {
in2[i] = 0.0;
}
#pragma omp parallel for
for (int i = 1; i <= n3; ++i) {
in3[i] = 0.0;
}
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n1; ++i) {
for (int j = 1; j <= n2; ++j) {
in2[j] += out1[i] * w1[i][j];
}
}
#pragma omp parallel for
for (int i = 1; i <= n2; ++i) {
out2[i] = sigmoid(in2[i]);
}
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
in3[j] += out2[i] * w2[i][j];
}
}
#pragma omp parallel for
for (int i = 1; i <= n3; ++i) {
out3[i] = sigmoid(in3[i]);
}
}
// +---------------+
// | Norm L2 error |
// +---------------+
double square_error(){
double res = 0.0;
#pragma omp parallel for reduction(+:res)
for (int i = 1; i <= n3; ++i) {
res += (out3[i] - expected[i]) * (out3[i] - expected[i]);
}
res *= 0.5;
return res;
}
// +----------------------------+
// | Back Propagation Algorithm |
// +----------------------------+
void back_propagation() {
double sum;
#pragma omp parallel for
for (int i = 1; i <= n3; ++i) {
theta3[i] = out3[i] * (1 - out3[i]) * (expected[i] - out3[i]);
}
for (int i = 1; i <= n2; ++i) {
sum = 0.0;
#pragma omp parallel for reduction(+:sum)
for (int j = 1; j <= n3; ++j) {
sum += w2[i][j] * theta3[j];
}
theta2[i] = out2[i] * (1 - out2[i]) * sum;
}
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
delta2[i][j] = (learning_rate * theta3[j] * out2[i]) + (momentum * delta2[i][j]);
w2[i][j] += delta2[i][j];
}
}
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n1; ++i) {
for (int j = 1 ; j <= n2 ; j++ ) {
delta1[i][j] = (learning_rate * theta2[j] * out1[i]) + (momentum * delta1[i][j]);
w1[i][j] += delta1[i][j];
}
}
}
// +-------------------------------------------------+
// | Learning process: Perceptron - Back propagation |
// +-------------------------------------------------+
int learning_process() {
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n1; ++i) {
for (int j = 1; j <= n2; ++j) {
delta1[i][j] = 0.0;
}
}
#pragma omp parallel for collapse(2)
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
delta2[i][j] = 0.0;
}
}
for (int i = 1; i <= epochs; ++i) {
perceptron();
back_propagation();
if (square_error() < epsilon) {
return i;
}
}
return epochs;
}
// +--------------------------------------------------------------+
// | Reading input - gray scale image and the corresponding label |
// +--------------------------------------------------------------+
void input() {
// Reading image
char number;
for (int j = 1; j <= height; ++j) {
for (int i = 1; i <= width; ++i) {
image.read(&number, sizeof(char));
if (number == 0) {
d[i][j] = 0;
} else {
d[i][j] = 1;
}
}
}
// cout << "Image:" << endl;
for (int j = 1; j <= height; ++j) {
for (int i = 1; i <= width; ++i) {
// cout << d[i][j];
}
// cout << endl;
}
for (int j = 1; j <= height; ++j) {
for (int i = 1; i <= width; ++i) {
int pos = i + (j - 1) * width;
out1[pos] = d[i][j];
}
}
// Reading label
label.read(&number, sizeof(char));
for (int i = 1; i <= n3; ++i) {
expected[i] = 0.0;
}
expected[number + 1] = 1.0;
// cout << "Label: " << (int)(number) << endl;
}
// +------------------------+
// | Saving weights to file |
// +------------------------+
void write_matrix(string file_name) {
ofstream file(file_name.c_str(), ios::out);
// Input layer - Hidden layer
for (int i = 1; i <= n1; ++i) {
for (int j = 1; j <= n2; ++j) {
file << w1[i][j] << " ";
}
file << endl;
}
// Hidden layer - Output layer
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
file << w2[i][j] << " ";
}
file << endl;
}
file.close();
}
// +--------------+
// | Main Program |
// +--------------+
int main(int argc, char *argv[]) {
about();
report.open(report_fn.c_str(), ios::out);
image.open(training_image_fn.c_str(), ios::in | ios::binary); // Binary image file
label.open(training_label_fn.c_str(), ios::in | ios::binary ); // Binary label file
// Reading file headers
char number;
for (int i = 1; i <= 16; ++i) {
image.read(&number, sizeof(char));
}
for (int i = 1; i <= 8; ++i) {
label.read(&number, sizeof(char));
}
// Neural Network Initialization
init_array();
double t1 = omp_get_wtime();
for (int sample = 1; sample <= nTraining; ++sample) {
// cout << "Sample " << sample << endl;
// Getting (image, label)
input();
// Learning process: Perceptron (Forward procedure) - Back propagation
int nIterations = learning_process();
// Write down the squared error
//cout << "No. iterations: " << nIterations << endl;
//printf("Error: %0.6lf\n\n", square_error());
report << "Sample " << sample << ": No. iterations = " << nIterations << ", Error = " << square_error() << endl;
// Save the current network (weights)
if (sample % 100 == 0) {
cout << "Saving the network to " << model_fn << " file." << endl;
write_matrix(model_fn);
}
}
double t2 = omp_get_wtime();
cout<<(t2-t1);
// Save the final network
write_matrix(model_fn);
report.close();
image.close();
label.close();
return 0;
}