-
Notifications
You must be signed in to change notification settings - Fork 0
/
stack_overflow1.cpp
313 lines (268 loc) · 9.51 KB
/
stack_overflow1.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
//Shuffle doesn't work in this
/*
Edit 21-01-2020: Made plotting dots bigger, added printing of expected vs predicted part
*/
#include <iostream>
#include<vector>
#include <math.h>
#define PI 3.141592653589793238463
#define N
#define MAXX 60 //maximum value of input example
#define epsilon 0.05
#define epoch 500
using namespace std;
extern "C" FILE *popen(const char *command, const char *mode);
///SIGMOID ACTIVATION DEFINITIONS
double sigmoid(double x) { return 1.0f / (1.0f + exp(-x)); }
double dsigmoid(double x) { return x * (1.0f - x); }
///TANH ACTIVATION DEFINITIONS
double tanh(double x) { return (exp(x)-exp(-x))/(exp(x)+exp(-x)) ;}
double dtanh(double x) {return 1.0f - x*x ;}
///LINEAR ACTIVATION DEFINITIONS
double lin(double x) { return x;}
double dlin(double x) { return 1.0f;}
///ELU ACTIVATION DEFINITIONS
double elu(double x) { if(x>0)
return x;
else return epsilon*(exp(x)-1.0);
}
double delu(double x) { if(x>0)
return 1.0f;
else return epsilon*exp(x);
}
///WEIGHT INITIALIZER
double init_weight() { return (2*rand()/RAND_MAX -1); }
static const int numInputs = 3;
static const int numHiddenNodes = 8;
static const int numOutputs = 1;
static const int numTrainingSets = 40;
const double lr = 0.05f;
double hiddenLayer[numHiddenNodes];
double outputLayer[numOutputs];
double hiddenLayerBias[numHiddenNodes]; ///BIASES OF HIDDEN LAYER (c)
double outputLayerBias[numOutputs]; ///BIASES OF OUTPUT LAYER (b)
double hiddenWeights[numInputs][numHiddenNodes]; ///WEIGHTS OF HIDDEN LAYER (W)
double outputWeights[numHiddenNodes][numOutputs]; ///WEIGHTS OF OUTPUT LAYER (V)
double training_inputs[numTrainingSets][numInputs];
double training_outputs[numTrainingSets][numOutputs];
void shuffle(int *array, size_t n)
{
if (n > 1) //If no. of training examples > 1
{
size_t i;
for (i = 0; i < n - 1; i++)
{
size_t j = i + rand() / (RAND_MAX / (n - i) + 1);
int t = array[j];
array[j] = array[i];
array[i] = t;
}
}
}
double predict(double test_sample[])
{
for (int j=0; j<numHiddenNodes; j++)
{
double activation=hiddenLayerBias[j];
for (int k=0; k<numInputs; k++)
{
activation+=test_sample[k]*hiddenWeights[k][j];
}
hiddenLayer[j] = lin(activation);
}
for (int j=0; j<numOutputs; j++)
{
double activation=outputLayerBias[j];
for (int k=0; k<numHiddenNodes; k++)
{
activation+=hiddenLayer[k]*outputWeights[k][j];
}
outputLayer[j] = lin(activation);
}
//std::cout<<outputLayer[0]<<"\n";
return outputLayer[0];
//std::cout << "Input:" << training_inputs[i][0] << " " << training_inputs[i][1] << " Output:" << outputLayer[0] << " Expected Output: " << training_outputs[i][0] << "\n";
}
int main(int argc, const char * argv[])
{
///TRAINING DATA GENERATION
for (int i = 0; i < numTrainingSets; i++)
{
training_inputs[i][0] = i%MAXX;
double sum = i%MAXX;
training_inputs[i][1] = (i*2)%MAXX;
sum+=(i*2)%MAXX;
training_inputs[i][2] = (i*3)%MAXX;
sum+=(i*3)%MAXX;
training_outputs[i][0]= training_inputs[i][0] + training_inputs[i][1]+training_inputs[i][2];
cout<<"In: "<<training_inputs[i][0]<<", "<<training_inputs[i][1]<<" out: "<<training_outputs[i][0]<<endl;
}
///WEIGHT & BIAS INITIALIZATION
for (int i=0; i<numInputs; i++) {
for (int j=0; j<numHiddenNodes; j++) {
hiddenWeights[i][j] = init_weight();
}
}
for (int i=0; i<numHiddenNodes; i++) {
hiddenLayerBias[i] = init_weight();
for (int j=0; j<numOutputs; j++) {
outputWeights[i][j] = init_weight();
}
}
for (int i=0; i<numOutputs; i++) {
//outputLayerBias[i] = init_weight();
outputLayerBias[i] = 0;
}
///TRAINING
//std::cout<<"start train\n";
vector<double> performance, epo; ///STORE MSE, EPOCH
for (int n=0; n < epoch; n++)
{
double MSE = 0;
shuffle(trainingSetOrder,numTrainingSets);
std::cout<<"\nepoch :"<<n;
for (int i=0; i<numTrainingSets; i++)
{
//int i = trainingSetOrder[x];
int x=i;
//std::cout<<"Training Set :"<<x<<"\n";
/// Forward pass
for (int j=0; j<numHiddenNodes; j++)
{
double activation=hiddenLayerBias[j];
//std::cout<<"Training Set :"<<x<<"\n";
for (int k=0; k<numInputs; k++) {
activation+=training_inputs[x][k]*hiddenWeights[k][j];
}
hiddenLayer[j] = lin(activation);
}
for (int j=0; j<numOutputs; j++) {
double activation=outputLayerBias[j];
for (int k=0; k<numHiddenNodes; k++)
{
activation+=hiddenLayer[k]*outputWeights[k][j];
}
outputLayer[j] = lin(activation);
}
//std::cout << "Input:" << training_inputs[x][0] << " " << " Output:" << outputLayer[0] << " Expected Output: " << training_outputs[x][0] << "\n";
MSE += (1/numOutputs)*pow( training_outputs[x][0] - outputLayer[0], 2);
/// Backprop
/// For V
double deltaOutput[numOutputs];
for (int j=0; j<numOutputs; j++) {
double errorOutput = (training_outputs[i][j]-outputLayer[j]);
deltaOutput[j] = errorOutput*dlin(outputLayer[j]);
}
/// For W
double deltaHidden[numHiddenNodes];
for (int j=0; j<numHiddenNodes; j++) {
double errorHidden = 0.0f;
for(int k=0; k<numOutputs; k++) {
errorHidden+=deltaOutput[k]*outputWeights[j][k];
}
deltaHidden[j] = errorHidden*dlin(hiddenLayer[j]);
}
///Updation
/// For V and b
for (int j=0; j<numOutputs; j++) {
//b
outputLayerBias[j] += deltaOutput[j]*lr;
for (int k=0; k<numHiddenNodes; k++)
{
outputWeights[k][j]+= hiddenLayer[k]*deltaOutput[j]*lr;
}
}
/// For W and c
for (int j=0; j<numHiddenNodes; j++) {
//c
hiddenLayerBias[j] += deltaHidden[j]*lr;
//W
for(int k=0; k<numInputs; k++) {
hiddenWeights[k][j]+=training_inputs[i][k]*deltaHidden[j]*lr;
}
}
}
//Averaging the MSE
MSE /= numTrainingSets;
///Steps to PLOT PERFORMANCE PER EPOCH
performance.push_back(MSE*100);
epo.push_back(n);
}
// Print weights
std::cout << "Final Hidden Weights\n[ ";
for (int j=0; j<numHiddenNodes; j++) {
std::cout << "[ ";
for(int k=0; k<numInputs; k++) {
std::cout << hiddenWeights[k][j] << " ";
}
std::cout << "] ";
}
std::cout << "]\n";
std::cout << "Final Hidden Biases\n[ ";
for (int j=0; j<numHiddenNodes; j++) {
std::cout << hiddenLayerBias[j] << " ";
}
std::cout << "]\n";
std::cout << "Final Output Weights";
for (int j=0; j<numOutputs; j++) {
std::cout << "[ ";
for (int k=0; k<numHiddenNodes; k++) {
std::cout << outputWeights[k][j] << " ";
}
std::cout << "]\n";
}
std::cout << "Final Output Biases\n[ ";
for (int j=0; j<numOutputs; j++) {
std::cout << outputLayerBias[j] << " ";
}
std::cout << "]\n";
///Plot the results
vector<double> x;
vector<double> y1, y2;
double test_input[1000][numInputs];
for (int i = 0; i < 15; i++)
{
x.push_back(i);
test_input[i][0] = (rand()%MAXX);
test_input[i][1] = (rand()%MAXX);
test_input[i][2] = (rand()%MAXX);
y1.push_back(test_input[i][0] + test_input[i][1]+test_input[i][2]);
double pred = predict(test_input[i]);
y2.push_back(pred);
cout<<"\nExpected: "<< (test_input[i][0] + test_input[i][1] + test_input[i][2]) <<" Got: "<< pred;
}
FILE * gp = popen("gnuplot", "w");
fprintf(gp, "set terminal wxt size 600,400 \n");
fprintf(gp, "set grid \n");
fprintf(gp, "set title '%s' \n", "f(x) = Addition of two variables");
fprintf(gp, "set style line 1 lt 3 pt 7 ps 0.5 lc rgb 'green' lw 1 \n");
fprintf(gp, "set style line 2 lt 3 pt 7 ps 0.5 lc rgb 'red' lw 1 \n");
fprintf(gp, "plot '-' w p ls 1, '-' w p ls 2 \n");
///Exact f(x) = addition -> Green Graph
for (int k = 0; k < x.size(); k++) {
fprintf(gp, "%f %f \n", x[k], y1[k]);
}
fprintf(gp, "e\n");
///Neural Network Approximate -> Red Graph
for (int k = 0; k < x.size(); k++) {
fprintf(gp, "%f %f \n", x[k], y2[k]);
}
fprintf(gp, "e\n");
fflush(gp);
///FILE POINTER FOR SECOND PLOT (PERFORMANCE GRAPH)
FILE * gp1 = popen("gnuplot", "w");
fprintf(gp1, "set terminal wxt size 600,400 \n");
fprintf(gp1, "set grid \n");
fprintf(gp1, "set title '%s' \n", "Performance");
fprintf(gp1, "set style line 1 lt 3 pt 7 ps 0.1 lc rgb 'green' lw 1 \n");
fprintf(gp1, "set style line 2 lt 3 pt 7 ps 0.1 lc rgb 'red' lw 1 \n");
fprintf(gp1, "plot '-' w p ls 1 \n");
for (int k = 0; k < epo.size(); k++) {
fprintf(gp1, "%f %f \n", epo[k], performance[k]);
}
fprintf(gp1, "e\n");
fflush(gp1);
system("pause");
//pclose(gp);
return 0;
}