-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy patharithmetic-test.c
1984 lines (1742 loc) · 52.8 KB
/
arithmetic-test.c
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
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#include <stdio.h>
#include <stdlib.h>
#include <string.h> // strlen()
#include <math.h>
#include <gsl/gsl_matrix.h> // GNU scientific library
#include <gsl/gsl_eigen.h> // ...for finding matrix eigen values
#include <gsl/gsl_complex_math.h> // ...for complex abs value
#include <stdbool.h>
#include "BPTT-RNN.h"
#include "feedforward-NN.h"
extern NNET *create_NN(int, int *);
extern void re_randomize(NNET *, int, int *);
extern RNN *create_BPTT_NN(int, int *);
extern void BPTT_re_randomize(RNN *, int, int *);
extern void free_NN(NNET *, int *);
extern void free_BPTT_NN(RNN *, int *);
extern void forward_prop(NNET *, int, double *);
extern void forward_prop_ReLU(NNET *, int, double *);
extern void forward_prop_SP(NNET *, int, double *);
extern void forward_BPTT(RNN *, int, double *, int);
extern void back_prop(NNET *, double *);
extern void back_prop_ReLU(NNET *, double *);
extern void backprop_through_time(RNN *, double *, int);
extern void pause_graphics();
extern void quit_graphics();
extern void start_NN_plot(void);
extern void start_NN2_plot(void);
extern void start_W_plot(void);
extern void start_K_plot(void);
extern void start_output_plot(void);
extern void start_LogErr_plot(void);
extern void restart_LogErr_plot(void);
extern void plot_NN(NNET *net);
extern void plot_NN2(NNET *net);
extern void plot_W(NNET *net);
extern void plot_W_BPTT(RNN *net);
extern void plot_output(NNET *net, void ());
extern void plot_LogErr(double, double);
extern void flush_output();
extern void plot_tester(double, double);
extern void plot_K();
extern int delay_vis(int);
extern void pause_key();
extern void plot_trainer(double);
extern void plot_ideal(void);
extern void beep(void);
extern double sigmoid(double);
extern void start_timer(), end_timer(char *);
extern double K[];
// **************** 2-Digit Primary-school Subtraction Arithmetic test *****************
// The goal is to perform subtraction like a human child would.
// Input: 2-digit numbers A and B, for example "12", "07"
// Output: A - B, eg: "12" - "07" = "05"
// State vector = [ A1, A0, B1, B0, C1, C0, carry-flag, current-digit, result-ready-flag,
// underflow-error-flag ]
// Algorithm:
// If current-digit = 0:
// if A0 >= B0 then C0 = A0 - B0
// else C0 = 10 + (A0 - B0) , carry-flag = 1
// current-digit = 1
// If current-digit = 1:
// if A1 >= B1 then
// C1 = A1 - B1
// else Underflow Error
// if carry-flag = 0:
// result-ready = 1
// else // carry-flag = 1
// if C1 >= 1
// --C1
// else Underflow error
// result-ready = 1
// This defines the transition operator acting on vector space K1 (of dimension 10)
void transition(double K1[], double K2[])
{
double A1 = floor(K1[0] * 10.0) / 10.0;
double A0 = floor(K1[1] * 10.0) / 10.0;
double B1 = floor(K1[2] * 10.0) / 10.0;
double B0 = floor(K1[3] * 10.0) / 10.0;
double carryFlag = K1[4];
double currentDigit = K1[5];
double C1 = K1[6];
double C0 = K1[7];
double resultReady = K1[8];
double underflowError = K1[9];
if (currentDigit < 0.5)
{
if (A0 >= B0) // C seems to support >= for comparison of doubles
{
C0 = A0 - B0;
carryFlag = 0.0;
}
else
{
C0 = 1.0 + (A0 - B0);
carryFlag = 1.0;
}
currentDigit = 1.0;
resultReady = 0.0;
underflowError = 0.0;
C1 = 0.0; // optional
}
else // current digit = 1
{
resultReady = 1.0;
if (A1 >= B1)
{
C1 = A1 - B1;
underflowError = 0.0;
}
else
{
underflowError = 1.0;
C1 = 0.0; // optional
}
if (carryFlag > 0.5)
{
if (C1 > 0.09999)
C1 -= 0.1;
else
underflowError = 1.0;
}
C0 = C0; // necessary
carryFlag = 0.0; // optional
currentDigit = 1.0; // optional
}
K2[0] = A1;
K2[1] = A0;
K2[2] = B1;
K2[3] = B0;
K2[4] = carryFlag;
K2[5] = currentDigit;
K2[6] = C1;
K2[7] = C0;
K2[8] = resultReady;
K2[9] = underflowError;
}
// Test the transition operator (1 time)
// This tests both the arithmetics of the digits as well as the settings of flags.
void arithmetic_testA_1()
{
double K1[10], K2[10];
double a1, a0, b1, b0;
// generate A,B randomly
a1 = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
K1[0] = a1;
a0 = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
K1[1] = a0;
b1 = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
K1[2] = b1;
b0 = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
K1[3] = b0;
#define digit(d) ((int)(d * 10.0 + 0.001) + '0')
printf("%c%c, %c%c: ", digit(a1), digit(a0), digit(b1), digit(b0));
K1[4] = 0.0;
K1[5] = 0.0;
K1[6] = 0.0;
K1[7] = 0.0;
K1[8] = 0.0;
K1[9] = 0.0;
LOOP:
// call the transition
transition(K1, K2);
// get result
if (K2[8] > 0.5) // result ready?
{
bool correct = true;
// correct answer
int a = floor(a1 * 10) * 10 + a0 * 10;
int b = floor(b1 * 10) * 10 + b0 * 10;
printf("%d - ", a);
printf("%d = ", b);
int c = a - b;
printf("%d\n", c);
double c1 = (double) (c / 10) / 10.0;
double c0 = (double) (c % 10) / 10.0;
if (c < 0) // result is negative
{
if (K2[9] < 0.5) // underflow should be set but is not
correct = false;
}
else
{
if (K2[9] > 0.5) // underflow should be clear but is set
correct = false;
double err1 = fabs(K2[6] - c1);
double err2 = fabs(K2[7] - c0);
if (err1 > 0.001)
correct = false;
if (err2 > 0.001)
correct = false;
}
printf(" answer = %c%c\t", digit(c1), digit(c0));
printf(" genifer = %c%c\n", digit(K2[6]), digit(K2[7]));
if (correct)
printf("\x1b[32m**** Yes!!!!!!! \x1b[39;49m\n");
else
{
printf("\x1b[31mWrong!!!!!! ");
printf("K2[9] = %f \x1b[39;49m\n", K2[9]);
// beep();
}
}
else
{
for (int k = 0; k < 10; ++k)
K1[k] = K2[k];
goto LOOP;
}
}
// Repeat the test N times
extern void arithmetic_testA();
void arithmetic_testA()
{
for (int n = 0; n < 100; ++n)
{
printf("(%d) ", n);
arithmetic_testA_1();
}
}
// At this point we are able to generate training examples for the transition operator.
// Perhaps now get to the main code to see if R can approximate this operator well?
// The learning algorithm would be to learn the transition operator as one single step.
// This should be very simple and back-prop would do.
#define ForwardPropMethod forward_prop_ReLU
#define ErrorThreshold 0.001
// ************************* EXPERIMENT RESULTS ************************
// Topology = {8, 13, 10, 6} (4 layers)
// Success: time = 30:33, back_prop with sigmoid, steepness = 3.0, learning rate = 0.05
// Fail: time > 123:00, back_prop_SP, slope = 1.5, learning rate = 0.05
// Soft-max success: topology = {8, 19, 19, 19, 19, 6}
// Slope = 1.0, learning rate = 0.05, time < 5 mins produced 100% correct results
// (under this setting, zero-weights "death" occurs in the majority of cases,
// convergence occurs is observed only rarely (~ 1 in 5-10 times).
// For topology {8, 33, 33, 33, 6}, seems always fail.
// ReLU success: topology = {8, 19, 19, 19, 19, 6} descension is rapid!!!
// time 2.45, learning rate = 0.01, leakage = 0.05 or 0.1.
// rectifier(v) = if (v < 0) L*v else v.
// *********************************************************************
/* How to calculate "online" linear regression:
init(): meanX = 0, meanY = 0, varX = 0, covXY = 0, n = 0
update(x,y):
n += 1
dx = x - meanX
dy = y - meanY
varX += (((n-1)/n)*dx*dx - varX)/n
covXY += (((n-1)/n)*dx*dy - covXY)/n
meanX += dx/n
meanY += dy/n
getA(): return covXY/varX
getB(): return meanY - getA()*meanX
*/
void arithmetic_testB()
{
// int neuronsPerLayer[] = {8, 13, 10, 6}; // first = input layer, last = output layer
// int neuronsPerLayer[] = {8, 13, 10, 13, 10, 6};
// int neuronsPerLayer[] = {8, 19, 19, 19, 19, 6};
int neuronsPerLayer[] = {8, 13, 10, 6};
int dimK = 8;
int numLayers = sizeof(neuronsPerLayer) / sizeof(int);
NNET *Net = create_NN(numLayers, neuronsPerLayer);
LAYER lastLayer = Net->layers[numLayers - 1];
double errors[dimK];
#define M 50 // how many errors to record for averaging
double errors1[M], errors2[M]; // two arrays for recording errors
int userKey = 0;
double sum_err1 = 0.0, sum_err2 = 0.0; // sums of errors
int tail = 0; // index for cyclic arrays (last-in, first-out)
for (int i = 0; i < M; ++i) // clear errors to 0.0
errors1[i] = errors2[i] = 0.0;
// start_NN_plot();
start_W_plot();
start_LogErr_plot();
// start_K_plot();
// start_output_plot();
// plot_ideal();
start_timer();
printf("[Q] quit\n\n");
char status[1000], *s;
for (int i = 1; true; ++i)
{
s = status + sprintf(status, "[%05d] ", i);
// Create random K vector (4 + 2 + 2 elements)
for (int k = 0; k < 4; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
for (int k = 4; k < 6; ++k)
K[k] = (rand() / (double) RAND_MAX) > 0.5 ? 1.0 : 0.0;
for (int k = 6; k < 8; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
// printf("*** K = <%lf, %lf>\n", K[0], K[1]);
ForwardPropMethod(Net, dimK, K); // dim K = 8 (dimension of input-layer vector)
// Desired value = K_star
double K_star[10];
transition(K, K_star);
// Difference between actual outcome and desired value:
double training_err = 0.0;
for (int k = 4; k < 10; ++k)
{
double error = K_star[k] - lastLayer.neurons[k - 4].output;
errors[k - 4] = error; // record this for back-prop
training_err += fabs(error); // record sum of errors
}
s += sprintf(s, "|e|=%lf, ", training_err);
// Update error arrays cyclically
// (This is easier to understand by referring to the next block of code)
sum_err2 -= errors2[tail];
sum_err2 += errors1[tail];
sum_err1 -= errors1[tail];
sum_err1 += training_err;
// printf("sum1, sum2 = %lf %lf\n", sum_err1, sum_err2);
double mean_err = (i < M) ? (sum_err1 / i) : (sum_err1 / M);
s += sprintf(s, "mean |e|=%lf, ", mean_err);
if (mean_err < ErrorThreshold)
break;
if (mean_err > 1e10 || mean_err < 0.0 || (i > 5000 && isnan(mean_err)))
{
printf("%s\n", status);
printf("Error overflow.\n");
beep();
pause_key();
userKey = 3;
}
// record new error in cyclic arrays
errors2[tail] = errors1[tail];
errors1[tail] = training_err;
++tail;
if (tail == M) // loop back in cycle
tail = 0;
back_prop(Net, errors); // train the network!
// Testing set
if ((i % 5000) == 0)
{
#define NumTrials 20
double test_err = 0.0;
for (int j = 0; j < NumTrials; ++j)
{
// Create random K vector (4 + 2 + 2 elements)
for (int k = 0; k < 4; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
for (int k = 4; k < 6; ++k)
K[k] = (rand() / (double) RAND_MAX) > 0.5 ? 1.0 : 0.0;
for (int k = 6; k < 8; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
// plot_tester(K[0], K[1]);
ForwardPropMethod(Net, dimK, K); // input vector dimension = 8
// Desired value = K_star
double K_star[10];
transition(K, K_star);
double single_err = 0.0;
for (int k = 4; k < 10; ++k)
{
double error = K_star[k] - lastLayer.neurons[k - 4].output;
single_err += fabs(error); // record sum of errors
}
test_err += single_err;
}
test_err /= (float) NumTrials;
s += sprintf(s, "random test e=%1.06lf, ", test_err);
double ratio = (sum_err2 - sum_err1) / sum_err1;
if (ratio > 0)
s += sprintf(s, "|e| ratio=%f", ratio);
else
s += sprintf(s, "|e| ratio=\x1b[31m%f\x1b[39;49m", ratio);
// if (isnan(ratio))
// break;
// if (ratio - 0.5f < 0.0000001) // ratio == 0.5 means stationary
// if (test_err < 0.01)
if (test_err < ErrorThreshold)
break;
}
if ((i % 500) == 0) // display status periodically
{
printf("%s\r", status);
// plot_output(Net);
// flush_output();
plot_W(Net);
plot_LogErr(mean_err, ErrorThreshold);
// plot_NN(Net);
// plot_trainer(0); // required to clear the window
// plot_K();
userKey = delay_vis(0);
}
if (userKey == 1)
break;
else if (userKey == 3) // Re-start with new random weights
{
re_randomize(Net, numLayers, neuronsPerLayer);
userKey = 0;
sum_err1 = 0.0; sum_err2 = 0.0;
tail = 0;
for (int j = 0; j < M; ++j) // clear errors to 0.0
errors1[j] = errors2[j] = 0.0;
i = 1;
restart_LogErr_plot();
start_timer();
printf("\n****** Network re-randomized.\n");
userKey = 0;
beep();
// pause_key();
}
else if (userKey == 2)
// Test learned operator
{
printf("\n");
int ans_correct = 0, ans_negative = 0, ans_wrong = 0, ans_non_term = 0;
#define P 100
for (int i = 0; i < P; ++i)
{
printf("(%d) ", i);
extern int arithmetic_testC_1(NNET *, LAYER);
switch (arithmetic_testC_1(Net, lastLayer))
{
case 1:
++ans_correct;
break;
case 2:
++ans_negative;
break;
case 3:
++ans_wrong;
break;
case 4:
++ans_non_term;
break;
default:
printf("Answer error!\n");
break;
}
}
printf("\n=======================\n");
printf("Answers correct = %d (%.1f%%)\n", ans_correct, ans_correct * 100 / (float) P);
printf("Answers negative = %d (%.1f%%)\n", ans_negative, ans_negative * 100 / (float) P);
printf("Answers wrong = %d (%.1f%%)\n", ans_wrong, ans_wrong * 100 / (float) P);
printf("Answers non-term = %d (%.1f%%)\n", ans_non_term, ans_non_term * 100 / (float) P);
printf("\n");
userKey = 0;
pause_key();
}
}
printf("Terminated....\n");
printf("%s\n", status);
end_timer(NULL);
if (userKey == 0) // terminated successfully? (not 'quit' key)
beep();
// plot_output(Net);
// flush_output();
plot_W(Net);
/****
printf("\n\nTest with: 73 - 37 = 36.\n");
K[0] = 0.7; // A1
K[1] = 0.3; // A0
K[2] = 0.3; // B1
K[3] = 0.7; // B0
K[4] = 0.0; // carry
K[5] = 0.0; // current digit
K[6] = 0.0; // C1
K[7] = 0.0; // C0
K[8] = 0.0; // ready
K[9] = 0.0; // overflow
forward_prop(Net, 8, K);
printf("carry [1.0] = %f\n", lastLayer.neurons[0].output);
printf("current-digit [1.0] = %f\n", lastLayer.neurons[1].output);
printf("C1 [0.0] = %f\n", lastLayer.neurons[2].output);
printf("C0 [0.6] = %f\n", lastLayer.neurons[3].output);
printf("ready [0.0] = %f\n", lastLayer.neurons[4].output);
printf("overflow [0.0] = %f\n", lastLayer.neurons[5].output);
// copy output back to K; second iteration
for (int k = 0; k < 6; ++k)
K[k + 4] = lastLayer.neurons[k].output;
forward_prop(Net, 8, K);
printf("\ncarry [0.0] = %f\n", lastLayer.neurons[0].output);
printf("current-digit [1.0] = %f\n", lastLayer.neurons[1].output);
printf("C1 [0.3] = %f\n", lastLayer.neurons[2].output);
printf("C0 [0.6] = %f\n", lastLayer.neurons[3].output);
printf("ready [1.0] = %f\n", lastLayer.neurons[4].output);
printf("overflow [0.0] = %f\n", lastLayer.neurons[5].output);
****/
if (userKey == 0)
pause_graphics();
else
quit_graphics();
extern void saveNet(NNET *, int, int *, char *, char *);
printf("Saving network data....\n");
saveNet(Net, numLayers, neuronsPerLayer, "", "");
free_NN(Net, neuronsPerLayer);
}
void saveNet(NNET *net, int numLayers, int *neuronsPerLayer, char *comments, char *defaultName)
{
FILE *fp;
if (strlen(defaultName) > 0)
{
fp = fopen(defaultName, "w");
}
else
{
char fileName[1024];
printf("Enter file name [default = %s] :", defaultName);
int c;
while ( (c = getchar()) != EOF && c != '\n' )
;
fgets(fileName, sizeof(fileName), stdin);
fileName[strlen(fileName) - 1] = '\0';
if (strlen(fileName) == 0)
return;
fp = fopen(fileName, "w");
}
fprintf(fp, "%s\n", comments);
#define EndOfComments "**************\n"
fprintf(fp, EndOfComments);
fprintf(fp, "%d\n", numLayers);
for (int l = 0; l < numLayers; ++l)
fprintf(fp, "%d ", neuronsPerLayer[l]);
fprintf(fp, "\n");
for (int l = 1; l < numLayers; ++l) // for each layer
for (int n = 0; n < neuronsPerLayer[l]; ++n) // for each neuron
{
for (int i = 0; i <= neuronsPerLayer[l - 1]; ++i) // for each weight
fprintf(fp, "%f ", (float) net->layers[l].neurons[n].weights[i]);
fprintf(fp, "\n");
}
fclose(fp);
printf("File saved.");
}
NNET *loadNet(int *pNumLayers, int *pNeuronsOfLayer[], char *defaultName)
{
printf("Existing network files:\n");
system("ls *.net");
char fileName[1024];
printf("\nEnter file name, default = [%s] :", defaultName);
int c;
while ( (c = getchar()) != EOF && c != '\n' )
;
fgets(fileName, sizeof(fileName), stdin);
fileName[strlen(fileName) - 1] = '\0';
if (strlen(fileName) == 0)
strcpy(fileName, defaultName);
FILE *fp = fopen(fileName, "r");
// skip comments
char s[2048];
do
fscanf(fp, "%s\n", s);
while (!strcmp(s, EndOfComments));
fscanf(fp, "%d\n", pNumLayers);
*pNeuronsOfLayer = (int *) malloc(*pNumLayers * sizeof(int));
for (int l = 0; l < *pNumLayers; ++l)
fscanf(fp, "%d ", &((*pNeuronsOfLayer)[l]));
fscanf(fp, "\n");
NNET *net = create_NN(*pNumLayers, *pNeuronsOfLayer);
for (int l = 1; l < *pNumLayers; ++l) // for each layer
for (int n = 0; n < (*pNeuronsOfLayer)[l]; ++n) // for each neuron
{
for (int i = 0; i <= (*pNeuronsOfLayer)[l - 1]; ++i) // for each weight
{
float x;
fscanf(fp, "%f ", &x);
// printf("%f ", x);
net->layers[l].neurons[n].weights[i] = (double) x;
}
fscanf(fp, "\n");
}
fclose(fp);
return net;
}
void arithmetic_testC() // verify results for testB
{
NNET *Net;
int numLayers;
int *neuronsPerLayer;
Net = loadNet(&numLayers, &neuronsPerLayer, "operator.net");
LAYER lastLayer = Net->layers[numLayers - 1];
/****
printf("\n\nTest with: 73 - 37 = 36.\n");
K[0] = 0.7; // A1
K[1] = 0.3; // A0
K[2] = 0.3; // B1
K[3] = 0.7; // B0
K[4] = 0.0; // carry
K[5] = 0.0; // current digit
K[6] = 0.0; // C1
K[7] = 0.0; // C0
K[8] = 0.0; // ready
K[9] = 0.0; // overflow
forward_prop(Net, 8, K);
printf("carry [1.0] = %f\n", lastLayer.neurons[0].output);
printf("current-digit [1.0] = %f\n", lastLayer.neurons[1].output);
printf("C1 [0.0] = %f\n", lastLayer.neurons[2].output);
printf("C0 [0.6] = %f\n", lastLayer.neurons[3].output);
printf("ready [0.0] = %f\n", lastLayer.neurons[4].output);
printf("overflow [0.0] = %f\n", lastLayer.neurons[5].output);
// copy output back to K; second iteration
for (int k = 0; k < 6; ++k)
K[k + 4] = lastLayer.neurons[k].output;
forward_prop(Net, 8, K);
printf("\ncarry [0.0] = %f\n", lastLayer.neurons[0].output);
printf("current-digit [1.0] = %f\n", lastLayer.neurons[1].output);
printf("C1 [0.3] = %f\n", lastLayer.neurons[2].output);
printf("C0 [0.6] = %f\n", lastLayer.neurons[3].output);
printf("ready [1.0] = %f\n", lastLayer.neurons[4].output);
printf("overflow [0.0] = %f\n", lastLayer.neurons[5].output);
****/
int ans_correct = 0, ans_negative = 0, ans_wrong = 0, ans_non_term = 0;
#define P 100
for (int i = 0; i < P; ++i)
{
printf("(%d) ", i);
extern int arithmetic_testC_1(NNET *, LAYER);
switch (arithmetic_testC_1(Net, lastLayer))
{
case 1:
++ans_correct;
break;
case 2:
++ans_negative;
break;
case 3:
++ans_wrong;
break;
case 4:
++ans_non_term;
break;
default:
printf("Answer error!\n");
break;
}
}
printf("\n=======================\n");
printf("Answers correct = %d (%.1f%%)\n", ans_correct, ans_correct * 100 / (float) P);
printf("Answers negative = %d (%.1f%%)\n", ans_negative, ans_negative * 100 / (float) P);
printf("Answers wrong = %d (%.1f%%)\n", ans_wrong, ans_wrong * 100 / (float) P);
printf("Answers non-term = %d (%.1f%%)\n", ans_non_term, ans_non_term * 100 / (float) P);
free_NN(Net, neuronsPerLayer);
free(neuronsPerLayer);
}
int arithmetic_testC_1(NNET *Net, LAYER lastLayer)
{
double K1[10], K2[10];
double a1, a0, b1, b0;
int ans = 0;
// generate A,B randomly
a1 = floor((rand() / (float) RAND_MAX) * 10.0) / 10.0;
K1[0] = a1;
a0 = floor((rand() / (float) RAND_MAX) * 10.0) / 10.0;
K1[1] = a0;
b1 = floor((rand() / (float) RAND_MAX) * 10.0) / 10.0;
K1[2] = b1;
b0 = floor((rand() / (float) RAND_MAX) * 10.0) / 10.0;
K1[3] = b0;
#define digit(d) ((int)(d * 10.0 + 0.001) + '0')
printf("%c%c, %c%c: ", digit(a1), digit(a0), digit(b1), digit(b0));
// correct answer
int a = floor(a1 * 10) * 10 + a0 * 10;
int b = floor(b1 * 10) * 10 + b0 * 10;
printf("%d - ", a);
printf("%d = ", b);
int c = a - b;
printf("%d\n", c);
double c1 = (double) (c / 10) / 10.0;
double c0 = (double) (c % 10) / 10.0;
// double carryFlag = rand() / (double) RAND_MAX;
// K1[4] = carryFlag;
// double currentDigit = rand() / (double) RAND_MAX;
// K1[5] = currentDigit;
K1[4] = 0.0; // carry flag
K1[5] = 0.0; // current digit (0 or 1)
K1[6] = 0.0; // C1
K1[7] = 0.0; // C0
K1[8] = 0.0; // result ready flag
K1[9] = 0.0; // underflow flag
int looped = 0;
LOOP:
// call the transition operator
ForwardPropMethod(Net, 8, K1); // input vector dimension = 8
for (int k = 4; k < 10; ++k) // 4..10 = output vector
K2[k] = lastLayer.neurons[k - 4].output;
// get result
if (K2[8] > 0.5) // result ready?
{
double err1 = 0.0, err2 = 0.0;
bool correct = true;
if (c < 0) // answer is negative
{
if (K2[9] < 0.5) // underflow is clear but should be set
correct = false;
}
else
{
if (K2[9] >= 0.5) // underflow is set but should be clear
correct = false;
err1 = fabs(K2[6] - c1);
err2 = fabs(K2[7] - c0);
printf(" err1, err2 = %f, %f\n", err1, err2);
if (err1 > 0.099999)
correct = false;
if (err2 > 0.099999)
correct = false;
}
printf(" answer = %c%c ", digit(c1), digit(c0));
printf(" genifer = %c%c\n", digit(K2[6]), digit(K2[7]));
// printf(" C1*,C0* = %f, %f ", c1, c0);
// printf(" C1,C0 = %f, %f\n", K2[6], K2[7]);
if (correct && c > 0)
{
ans = 1;
printf("\x1b[32m***************** Yes!!!! ****************\x1b[39;49m\n");
}
else if (correct)
{
ans = 2;
printf("\x1b[34mNegative YES \x1b[39;49m\n");
}
else
{
ans = 3;
printf("\x1b[31mWrong!!!! ");
if (c < 0)
printf(" underflow = %f \x1b[39;49m\n", K2[9]);
else
printf("\x1b[35m err1, err2 = %f, %f \x1b[39;49m\n", err1, err2);
// beep();
}
}
else
{
for (int k = 4; k < 10; ++k)
K1[k] = K2[k];
if (looped < 1)
{
++looped;
goto LOOP;
}
else
{
ans = 4;
printf("\x1b[31mNon-termination: ");
printf("result ready = %f \x1b[39;49m\n", K2[8]);
// beep();
}
}
return ans;
}
// Now we combine the 2 arithmetic steps into 1 step, as a single operator.
// This operator is supposedly very hard to learn.
#define ForwardPropMethod forward_prop_ReLU
#define ErrorThreshold 0.001
void arithmetic_testD() // same as testB, except learns entire operator in 1 step
{
// first = input layer, last = output layer
int neuronsPerLayer[] = {8, 19, 19, 19, 19, 6};
int dimK = 8;
int numLayers = sizeof(neuronsPerLayer) / sizeof(int);
NNET *Net = create_NN(numLayers, neuronsPerLayer);
LAYER lastLayer = Net->layers[numLayers - 1];
double errors[dimK];
#define M 50 // how many errors to record for averaging
double errors1[M], errors2[M]; // two arrays for recording errors
int userKey = 0;
double sum_err1 = 0.0, sum_err2 = 0.0; // sums of errors
int tail = 0; // index for cyclic arrays (last-in, first-out)
for (int i = 0; i < M; ++i) // clear errors to 0.0
errors1[i] = errors2[i] = 0.0;
// start_NN_plot();
start_W_plot();
start_LogErr_plot();
// start_K_plot();
// start_output_plot();
// plot_ideal();
start_timer();
printf("[Q] quit\n\n");
char status[1000], *s;
for (int i = 1; true; ++i)
{
s = status + sprintf(status, "[%05d] ", i);
// Create random K vector (4 + 2 + 2 elements)
for (int k = 0; k < 4; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
for (int k = 4; k < 6; ++k)
K[k] = (rand() / (double) RAND_MAX) > 0.5 ? 1.0 : 0.0;
for (int k = 6; k < 8; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
// printf("*** K = <%lf, %lf>\n", K[0], K[1]);
ForwardPropMethod(Net, dimK, K); // dim K = 8 (dimension of input-layer vector)
// Desired value = K_star
double K_star[10];
transition(K, K_star);
memcpy(K, K_star, sizeof(K_star)); // order = (target, source) !
transition(K, K_star); // transition again!
// Difference between actual outcome and desired value:
double training_err = 0.0;
for (int k = 4; k < 10; ++k)
{
double error = K_star[k] - lastLayer.neurons[k - 4].output;
errors[k - 4] = error; // record this for back-prop
training_err += fabs(error); // record sum of errors
}
s += sprintf(s, "|e|=%lf, ", training_err);
// Update error arrays cyclically
// (This is easier to understand by referring to the next block of code)
sum_err2 -= errors2[tail];
sum_err2 += errors1[tail];
sum_err1 -= errors1[tail];
sum_err1 += training_err;
// printf("sum1, sum2 = %lf %lf\n", sum_err1, sum_err2);
double mean_err = (i < M) ? (sum_err1 / i) : (sum_err1 / M);
s += sprintf(s, "mean |e|=%lf, ", mean_err);
if (mean_err < ErrorThreshold)
break;
if (mean_err > 1e10 || mean_err < 0.0 || (i > 5000 && isnan(mean_err)))
{
printf("%s\n", status);
printf("Error overflow.\n");
beep();
pause_key();
userKey = 3;
}
// record new error in cyclic arrays
errors2[tail] = errors1[tail];
errors1[tail] = training_err;
++tail;
if (tail == M) // loop back in cycle
tail = 0;
back_prop(Net, errors); // train the network!
// Testing set
if ((i % 5000) == 0)
{
#define NumTrials 20
double test_err = 0.0;
for (int j = 0; j < NumTrials; ++j)
{
// Create random K vector (4 + 2 + 2 elements)
for (int k = 0; k < 4; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
for (int k = 4; k < 6; ++k)
K[k] = (rand() / (double) RAND_MAX) > 0.5 ? 1.0 : 0.0;
for (int k = 6; k < 8; ++k)
K[k] = floor((rand() / (double) RAND_MAX) * 10.0) / 10.0;
// plot_tester(K[0], K[1]);
ForwardPropMethod(Net, dimK, K); // input vector dimension = 8
// Desired value = K_star
double K_star[10];
transition(K, K_star);
double single_err = 0.0;
for (int k = 4; k < 10; ++k)
{
double error = K_star[k] - lastLayer.neurons[k - 4].output;
single_err += fabs(error); // record sum of errors
}
test_err += single_err;
}
test_err /= (float) NumTrials;
s += sprintf(s, "random test |e|=%1.06lf, ", test_err);
double ratio = (sum_err2 - sum_err1) / sum_err1;
if (ratio > 0)
s += sprintf(s, "|e| ratio=%f", ratio);
else
s += sprintf(s, "|e| ratio=\x1b[31m%f\x1b[39;49m", ratio);
// if (isnan(ratio))
// break;
// if (ratio - 0.5f < 0.0000001) // ratio == 0.5 means stationary
// if (test_err < 0.01)
if (test_err < ErrorThreshold)
break;
}
if ((i % 50) == 0) // display status periodically
{
printf("%s\r", status);
// plot_output(Net);
// flush_output();
plot_W(Net);
plot_LogErr(mean_err, ErrorThreshold);
// plot_NN(Net);
// plot_trainer(0); // required to clear the window
// plot_K();
userKey = delay_vis(0);
}
if (userKey == 1)
break;
else if (userKey == 3) // Re-start with new random weights
{
re_randomize(Net, numLayers, neuronsPerLayer);