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#include <iostream>
#include <math.h>
#include "HiddenLayer.h"
#include "dA.h"
#include "LogisticRegression.h"
#include "SdA.h"
using namespace std;
double uniform(double min, double max) {
return rand() / (RAND_MAX + 1.0) * (max - min) + min;
}
int binomial(int n, double p) {
if(p < 0 || p > 1) return 0;
int c = 0;
double r;
for(int i=0; i<n; i++) {
r = rand() / (RAND_MAX + 1.0);
if (r < p) c++;
}
return c;
}
double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}
// SdA
SdA::SdA(int size, int n_i, int *hls, int n_o, int n_l) {
int input_size;
N = size;
n_ins = n_i;
hidden_layer_sizes = hls;
n_outs = n_o;
n_layers = n_l;
sigmoid_layers = new HiddenLayer*[n_layers];
dA_layers = new dA*[n_layers];
// construct multi-layer
for(int i=0; i<n_layers; i++) {
if(i == 0) {
input_size = n_ins;
} else {
input_size = hidden_layer_sizes[i-1];
}
// construct sigmoid_layer
sigmoid_layers[i] = new HiddenLayer(N, input_size, hidden_layer_sizes[i], NULL, NULL);
// construct dA_layer
dA_layers[i] = new dA(N, input_size, hidden_layer_sizes[i],\
sigmoid_layers[i]->W, sigmoid_layers[i]->b, NULL);
}
// layer for output using LogisticRegression
log_layer = new LogisticRegression(N, hidden_layer_sizes[n_layers-1], n_outs);
}
SdA::~SdA() {
delete log_layer;
for(int i=0; i<n_layers; i++) {
delete sigmoid_layers[i];
delete dA_layers[i];
}
delete[] sigmoid_layers;
delete[] dA_layers;
}
void SdA::pretrain(int *input, double lr, double corruption_level, int epochs) {
int *layer_input;
int prev_layer_input_size;
int *prev_layer_input;
int *train_X = new int[n_ins];
for(int i=0; i<n_layers; i++) { // layer-wise
for(int epoch=0; epoch<epochs; epoch++) { // training epochs
for(int n=0; n<N; n++) { // input x1...xN
// initial input
for(int m=0; m<n_ins; m++) train_X[m] = input[n * n_ins + m];
// layer input
for(int l=0; l<=i; l++) {
if(l == 0) {
layer_input = new int[n_ins];
for(int j=0; j<n_ins; j++) layer_input[j] = train_X[j];
} else {
if(l == 1) prev_layer_input_size = n_ins;
else prev_layer_input_size = hidden_layer_sizes[l-2];
prev_layer_input = new int[prev_layer_input_size];
for(int j=0; j<prev_layer_input_size; j++) prev_layer_input[j] = layer_input[j];
delete[] layer_input;
layer_input = new int[hidden_layer_sizes[l-1]];
sigmoid_layers[l-1]->sample_h_given_v(prev_layer_input, layer_input);
delete[] prev_layer_input;
}
}
dA_layers[i]->train(layer_input, lr, corruption_level);
}
}
}
delete[] train_X;
delete[] layer_input;
}
void SdA::finetune(int *input, int *label, double lr, int epochs) {
int *layer_input;
int prev_layer_input_size;
int *prev_layer_input;
int *train_X = new int[n_ins];
int *train_Y = new int[n_outs];
for(int epoch=0; epoch<epochs; epoch++) {
for(int n=0; n<N; n++) { // input x1...xN
// initial input
for(int m=0; m<n_ins; m++) train_X[m] = input[n * n_ins + m];
for(int m=0; m<n_outs; m++) train_Y[m] = label[n * n_outs + m];
// layer input
for(int i=0; i<n_layers; i++) {
if(i == 0) {
prev_layer_input = new int[n_ins];
for(int j=0; j<n_ins; j++) prev_layer_input[j] = train_X[j];
} else {
prev_layer_input = new int[hidden_layer_sizes[i-1]];
for(int j=0; j<hidden_layer_sizes[i-1]; j++) prev_layer_input[j] = layer_input[j];
delete[] layer_input;
}
layer_input = new int[hidden_layer_sizes[i]];
sigmoid_layers[i]->sample_h_given_v(prev_layer_input, layer_input);
delete[] prev_layer_input;
}
log_layer->train(layer_input, train_Y, lr);
}
// lr *= 0.95;
}
delete[] layer_input;
delete[] train_X;
delete[] train_Y;
}
void SdA::predict(int *x, double *y) {
double *layer_input;
int prev_layer_input_size;
double *prev_layer_input;
double linear_output;
prev_layer_input = new double[n_ins];
for(int j=0; j<n_ins; j++) prev_layer_input[j] = x[j];
// layer activation
for(int i=0; i<n_layers; i++) {
layer_input = new double[sigmoid_layers[i]->n_out];
for(int k=0; k<sigmoid_layers[i]->n_out; k++) {
linear_output = 0.0;
for(int j=0; j<sigmoid_layers[i]->n_in; j++) {
linear_output += sigmoid_layers[i]->W[k][j] * prev_layer_input[j];
}
linear_output += sigmoid_layers[i]->b[k];
layer_input[k] = sigmoid(linear_output);
}
delete[] prev_layer_input;
if(i < n_layers-1) {
prev_layer_input = new double[sigmoid_layers[i]->n_out];
for(int j=0; j<sigmoid_layers[i]->n_out; j++) prev_layer_input[j] = layer_input[j];
delete[] layer_input;
}
}
for(int i=0; i<log_layer->n_out; i++) {
y[i] = 0;
for(int j=0; j<log_layer->n_in; j++) {
y[i] += log_layer->W[i][j] * layer_input[j];
}
y[i] += log_layer->b[i];
}
log_layer->softmax(y);
delete[] layer_input;
}
// HiddenLayer
HiddenLayer::HiddenLayer(int size, int in, int out, double **w, double *bp) {
N = size;
n_in = in;
n_out = out;
if(w == NULL) {
W = new double*[n_out];
for(int i=0; i<n_out; i++) W[i] = new double[n_in];
double a = 1.0 / n_in;
for(int i=0; i<n_out; i++) {
for(int j=0; j<n_in; j++) {
W[i][j] = uniform(-a, a);
}
}
} else {
W = w;
}
if(bp == NULL) {
b = new double[n_out];
} else {
b = bp;
}
}
HiddenLayer::~HiddenLayer() {
for(int i=0; i<n_out; i++) delete W[i];
delete[] W;
delete[] b;
}
double HiddenLayer::output(int *input, double *w, double b) {
double linear_output = 0.0;
for(int j=0; j<n_in; j++) {
linear_output += w[j] * input[j];
}
linear_output += b;
return sigmoid(linear_output);
}
void HiddenLayer::sample_h_given_v(int *input, int *sample) {
for(int i=0; i<n_out; i++) {
sample[i] = binomial(1, output(input, W[i], b[i]));
}
}
// dA
dA::dA(int size, int n_v, int n_h, double **w, double *hb, double *vb) {
N = size;
n_visible = n_v;
n_hidden = n_h;
if(w == NULL) {
W = new double*[n_hidden];
for(int i=0; i<n_hidden; i++) W[i] = new double[n_visible];
double a = 1.0 / n_visible;
for(int i=0; i<n_hidden; i++) {
for(int j=0; j<n_visible; j++) {
W[i][j] = uniform(-a, a);
}
}
} else {
W = w;
}
if(hb == NULL) {
hbias = new double[n_hidden];
for(int i=0; i<n_hidden; i++) hbias[i] = 0;
} else {
hbias = hb;
}
if(vb == NULL) {
vbias = new double[n_visible];
for(int i=0; i<n_visible; i++) vbias[i] = 0;
} else {
vbias = vb;
}
}
dA::~dA() {
// for(int i=0; i<n_hidden; i++) delete[] W[i];
// delete[] W;
// delete[] hbias;
delete[] vbias;
}
void dA::get_corrupted_input(int *x, int *tilde_x, double p) {
for(int i=0; i<n_visible; i++) {
if(x[i] == 0) {
tilde_x[i] = 0;
} else {
tilde_x[i] = binomial(1, p);
}
}
}
// Encode
void dA::get_hidden_values(int *x, double *y) {
for(int i=0; i<n_hidden; i++) {
y[i] = 0;
for(int j=0; j<n_visible; j++) {
y[i] += W[i][j] * x[j];
}
y[i] += hbias[i];
y[i] = sigmoid(y[i]);
}
}
// Decode
void dA::get_reconstructed_input(double *y, double *z) {
for(int i=0; i<n_visible; i++) {
z[i] = 0;
for(int j=0; j<n_hidden; j++) {
z[i] += W[j][i] * y[j];
}
z[i] += vbias[i];
z[i] = sigmoid(z[i]);
}
}
void dA::train(int *x, double lr, double corruption_level) {
int *tilde_x = new int[n_visible];
double *y = new double[n_hidden];
double *z = new double[n_visible];
double *L_vbias = new double[n_visible];
double *L_hbias = new double[n_hidden];
double p = 1 - corruption_level;
get_corrupted_input(x, tilde_x, p);
get_hidden_values(tilde_x, y);
get_reconstructed_input(y, z);
// vbias
for(int i=0; i<n_visible; i++) {
L_vbias[i] = x[i] - z[i];
vbias[i] += lr * L_vbias[i] / N;
}
// hbias
for(int i=0; i<n_hidden; i++) {
L_hbias[i] = 0;
for(int j=0; j<n_visible; j++) {
L_hbias[i] += W[i][j] * L_vbias[j];
}
L_hbias[i] *= y[i] * (1 - y[i]);
hbias[i] += lr * L_hbias[i] / N;
}
// W
for(int i=0; i<n_hidden; i++) {
for(int j=0; j<n_visible; j++) {
W[i][j] += lr * (L_hbias[i] * tilde_x[j] + L_vbias[j] * y[i]) / N;
}
}
delete[] L_hbias;
delete[] L_vbias;
delete[] z;
delete[] y;
delete[] tilde_x;
}
void dA::reconstruct(int *x, double *z) {
double *y = new double[n_hidden];
get_hidden_values(x, y);
get_reconstructed_input(y, z);
delete[] y;
}
// LogisticRegression
LogisticRegression::LogisticRegression(int size, int in, int out) {
N = size;
n_in = in;
n_out = out;
W = new double*[n_out];
for(int i=0; i<n_out; i++) W[i] = new double[n_in];
b = new double[n_out];
for(int i=0; i<n_out; i++) {
for(int j=0; j<n_in; j++) {
W[i][j] = 0;
}
b[i] = 0;
}
}
LogisticRegression::~LogisticRegression() {
for(int i=0; i<n_out; i++) delete[] W[i];
delete[] W;
delete[] b;
}
void LogisticRegression::train(int *x, int *y, double lr) {
double *p_y_given_x = new double[n_out];
double *dy = new double[n_out];
for(int i=0; i<n_out; i++) {
p_y_given_x[i] = 0;
for(int j=0; j<n_in; j++) {
p_y_given_x[i] += W[i][j] * x[j];
}
p_y_given_x[i] += b[i];
}
softmax(p_y_given_x);
for(int i=0; i<n_out; i++) {
dy[i] = y[i] - p_y_given_x[i];
for(int j=0; j<n_in; j++) {
W[i][j] += lr * dy[i] * x[j] / N;
}
b[i] += lr * dy[i] / N;
}
delete[] p_y_given_x;
delete[] dy;
}
void LogisticRegression::softmax(double *x) {
double max = 0.0;
double sum = 0.0;
for(int i=0; i<n_out; i++) if(max < x[i]) max = x[i];
for(int i=0; i<n_out; i++) {
x[i] = exp(x[i] - max);
sum += x[i];
}
for(int i=0; i<n_out; i++) x[i] /= sum;
}
void LogisticRegression::predict(int *x, double *y) {
for(int i=0; i<n_out; i++) {
y[i] = 0;
for(int j=0; j<n_in; j++) {
y[i] += W[i][j] * x[j];
}
y[i] += b[i];
}
softmax(y);
}
void test_sda() {
srand(0);
double pretrain_lr = 0.1;
double corruption_level = 0.3;
int pretraining_epochs = 1000;
double finetune_lr = 0.1;
int finetune_epochs = 500;
int train_N = 10;
int test_N = 4;
int n_ins = 28;
int n_outs = 2;
int hidden_layer_sizes[] = {15, 15};
int n_layers = sizeof(hidden_layer_sizes) / sizeof(hidden_layer_sizes[0]);
// training data
int train_X[10][28] = {
{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1}
};
int train_Y[10][2] = {
{1, 0},
{1, 0},
{1, 0},
{1, 0},
{1, 0},
{0, 1},
{0, 1},
{0, 1},
{0, 1},
{0, 1}
};
// construct SdA
SdA sda(train_N, n_ins, hidden_layer_sizes, n_outs, n_layers);
// pretrain
sda.pretrain(*train_X, pretrain_lr, corruption_level, pretraining_epochs);
// finetune
sda.finetune(*train_X, *train_Y, finetune_lr, finetune_epochs);
// test data
int test_X[4][28] = {
{1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1}
};
double test_Y[4][28];
// test
for(int i=0; i<test_N; i++) {
sda.predict(test_X[i], test_Y[i]);
for(int j=0; j<n_outs; j++) {
printf("%.5f ", test_Y[i][j]);
}
cout << endl;
}
}
int main() {
test_sda();
return 0;
}
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