-
Notifications
You must be signed in to change notification settings - Fork 8
Expand file tree
/
Copy pathNALSM_CONSTRUCTOR_MNIST.py
More file actions
1192 lines (998 loc) · 72.2 KB
/
NALSM_CONSTRUCTOR_MNIST.py
File metadata and controls
1192 lines (998 loc) · 72.2 KB
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
import numpy as np
import time
# updated
import NALSM_GEN_SUPPORT as sup
import NALSM_SIM_SUPPORT as runSupport
import NALSM_OUTPUT_LAYER as out_lin_csfr
import NALSM_STDP as plasticity
import NALSM_ASTRO as astrocyte
import NALSM_LIF_v1 as neuron
class simulation_constructor:
def __init__(self
, network_name
, network_path
, source_path
, save_path
, W_RES_EXC_MIN_MAX_IN
, W_RES_INH_MIN_MAX_IN
, W_RES_INP_MIN_MAX_IN
, W_OUT_RES_MIN_MAX_IN
, SAMPLE_INPUT_DURATION_MS_IN
, BATCH_SIZE_IN
, record_samples_in_batch_l_IN
, STDP_POTENTIATION_LR_IN
, RES_LR_DECAY_IN
, ASTRO_BIAS_OFFSET_PERCENTAGE_IN
, ASTRO_W_SCALING_IN
, ASTRO_TAU_IN
, INPUT_CURRENT_IN
, READOUT_LR_IN = 0.1
, RES_INI_COST_W_SCLR_IN = -1
, SPIKE_STORE = True
, INITIALIZE_ASTRO = True
, INITIALIZE_STDP = True
, INITIALIZE_OUTPUT_LAYER=True
, INITIALIZE_RES_OPS=True
):
self.source_path = source_path
self.save_path = save_path
# _____############################################################################################______
# ####################### INITIALIZE STRUCTURES ############################################## START
# _____############################################################################################______
lifs = neuron.CUBA_LIF_network(number_of_networks_IN=BATCH_SIZE_IN)
run_sup = runSupport.run_support(number_of_networks_IN=BATCH_SIZE_IN)
if INITIALIZE_ASTRO == True:
astrs = astrocyte.ASTRO_LR_CONTROLLER(astro_LR_decay_factor_IN=RES_LR_DECAY_IN
, number_of_networks_IN=BATCH_SIZE_IN
)
if INITIALIZE_STDP == True:
stdp = plasticity.stdp(stdp_LR_fixed_potentiation=STDP_POTENTIATION_LR_IN
, res_LR_decay_factor=RES_LR_DECAY_IN
, number_of_networks_IN=BATCH_SIZE_IN
, w_res_exc_min_max=W_RES_EXC_MIN_MAX_IN
, w_res_inh_min_max=W_RES_INH_MIN_MAX_IN
, w_res_inp_min_max=W_RES_INP_MIN_MAX_IN
, w_out_res_min_max=W_OUT_RES_MIN_MAX_IN
)
if INITIALIZE_OUTPUT_LAYER==True:
olc = out_lin_csfr.output_linear_layer(output_layer_size_IN=10)
# ASSUMES MAXIMAL WEIGHT INITIALIZATION
if RES_INI_COST_W_SCLR_IN == -1:
print('INITIALIZATION NETWORKS WITH MAXIMAL WEIGHTS')
names_TF, data_TF, num_of_neurons, orig_names, orig_data = lifs.open_network_stuctures_w_custom_W_set(
net_name=network_name,
netPath=network_path,
set_w_ee_ei_ie_ii_inp_IN=[W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1],W_RES_EXC_MIN_MAX_IN[1]])
else:
print('INITIALIZATION NETWORKS WITH SCALR ABS W: '+str(RES_INI_COST_W_SCLR_IN))
names_TF, data_TF, num_of_neurons, orig_names, orig_data = lifs.open_network_stuctures_w_custom_W_set(
net_name=network_name,
netPath=network_path,
set_w_ee_ei_ie_ii_inp_IN=[RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN,
RES_INI_COST_W_SCLR_IN, RES_INI_COST_W_SCLR_IN])
self.TAU_U_DENSE = data_TF[names_TF.index('TAU_U_DENSE')]
self.W_DENSE = data_TF[names_TF.index('W_DENSE')]
self.SS_T_STATE = data_TF[names_TF.index('SS_T_STATE')]
self.TAU_V = data_TF[names_TF.index('TAU_V')]
self.V_THRSH = data_TF[names_TF.index('V_THRSH')]
self.B = data_TF[names_TF.index('B')]
self.V = data_TF[names_TF.index('V')]
self.S = data_TF[names_TF.index('S')]
self.T_RFR = data_TF[names_TF.index('T_RFR')]
self.T_RFR_STATE = data_TF[names_TF.index('T_RFR_STATE')]
self.I_PH = data_TF[names_TF.index('I_PH')]
self.INPUT_POISSON_VALS = data_TF[names_TF.index('INPUT_POISSON_VALS')]
self.ss_t_state_reset_scalar = data_TF[names_TF.index('ss_t_state_reset_scalar')]
neuron_ranges_dict = dict(orig_data[orig_names.index('neuron_ranges')])
self.res_range = neuron_ranges_dict['res_range']
self.res_exc_range = neuron_ranges_dict['res_exc_range']
self.res_inh_range = neuron_ranges_dict['res_inh_range']
self.inp_range = neuron_ranges_dict['inp_range']
self.out_range = neuron_ranges_dict['out_range']
self.w_mask_np = orig_data[orig_names.index('W_mask')]
self.num_total_neurons = np.shape(self.w_mask_np)[0]
# num_output_neurons = out_range[1] - out_range[0]
self.num_res_neurons = self.res_range[1] - self.res_range[0]
self.num_inp_neurons = self.inp_range[1] - self.inp_range[0]
self.num_total_syns = len(np.where(self.w_mask_np == 1.0)[0])
print('num of neurons in network: ' + str(self.num_total_neurons))
print('num of res neurons in network: ' + str(self.num_res_neurons))
print('num of input neurons in network: ' + str(self.num_inp_neurons))
print('num of syns in network: ' + str(self.num_total_syns))
ini_names, ini_data = run_sup.simulation_initializer(inp_range_IN=self.inp_range
, res_exc_range_IN=self.res_exc_range
, res_inh_range_IN=self.res_inh_range
, res_range_IN=self.res_range
, out_range_IN=self.out_range
, w_mask_sparse_single_net_np_IN=self.w_mask_np
, record_nets_l_IN=record_samples_in_batch_l_IN
, num_neurons_in_network_IN=self.num_total_neurons
, input_sample_duration_IN=SAMPLE_INPUT_DURATION_MS_IN
)
scatter_idx_w_dense_to_s_out_main_tf = ini_data[ini_names.index('scatter_idx_w_dense_to_s_out_main_tf')]
scatter_S_to_S_SUBSET_inputNeurons_tf = ini_data[ini_names.index('scatter_S_to_S_SUBSET_inputNeurons_tf')]
gather_idx_Sin_to_Wdense_tf = ini_data[ini_names.index('gather_idx_Sin_to_Wdense_tf')]
# save_spikes_gath_ind_tf = ini_data[ini_names.index('save_spikes_gath_ind_tf')]
# save_spikes_scatter_inds_l_of_tfs = ini_data[ini_names.index('save_spikes_scatter_inds_l_of_tfs')]
gather_idx_Sout_to_Wdense_tf = ini_data[ini_names.index('gather_idx_Sout_to_Wdense_tf')]
S_exc_and_inp_mask_np = ini_data[ini_names.index('S_exc_and_inp_mask_np')]
S_inh_mask_np = ini_data[ini_names.index('S_inh_mask_np')]
w_dense_exc_to_res_mask_np = ini_data[ini_names.index('w_dense_exc_to_res_mask_np')]
w_dense_inh_to_res_mask_np = ini_data[ini_names.index('w_dense_inh_to_res_mask_np')]
w_dense_inp_to_res_mask_np = ini_data[ini_names.index('w_dense_inp_to_res_mask_np')]
w_dense_res_to_out_mask_np = ini_data[ini_names.index('w_dense_res_to_out_mask_np')]
net_masks_names, net_masks_data = lifs.initialize_network_masks(num_total_neurons_IN=self.num_total_neurons
, num_synapses_in_network_IN=self.num_total_syns
, S_mask_exc_inp_np_IN=S_exc_and_inp_mask_np
, S_mask_inh_np_IN=S_inh_mask_np
,
W_dense_mask_exc_to_res_np_IN=w_dense_exc_to_res_mask_np
,
W_dense_mask_inh_to_res_np_IN=w_dense_inh_to_res_mask_np
,
W_dense_mask_inp_to_res_np_IN=w_dense_inp_to_res_mask_np
,
W_dense_mask_res_to_out_np_IN=w_dense_res_to_out_mask_np
)
S_MASK_EXC_INP = net_masks_data[net_masks_names.index('S_MASK_EXC_INP')]
S_MASK_INH = net_masks_data[net_masks_names.index('S_MASK_INH')]
W_DENSE_MASK_EXC_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_EXC_TO_RES')]
W_DENSE_MASK_INH_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_INH_TO_RES')]
W_DENSE_MASK_INP_TO_RES = net_masks_data[net_masks_names.index('W_DENSE_MASK_INP_TO_RES')]
W_DENSE_MASK_RES_TO_OUT = net_masks_data[net_masks_names.index('W_DENSE_MASK_RES_TO_OUT')]
if SPIKE_STORE == True:
spike_save_names, spike_save_data = lifs.initialize_spike_store(s_IN=self.S
, input_duration_IN=SAMPLE_INPUT_DURATION_MS_IN
, record_nets_l_IN=record_samples_in_batch_l_IN
,
num_neurons_in_single_network_IN=self.num_total_neurons
)
self.spike_save_ops = spike_save_data[spike_save_names.index('spike_save_ops')]
self.zero_out_S_STORE_op = spike_save_data[spike_save_names.index('zero_out_S_STORE_op')]
self.condense_spike_store_to_Fn_Ft_op = spike_save_data[spike_save_names.index('condense_spike_store_to_Fn_Ft_op')]
w_store_names, w_store_data = lifs.initialize_W_DENSE_store(W_DENSE_IN=self.W_DENSE
, num_synapses_in_network_IN=self.num_total_syns
)
self.W_DENSE_STORE = w_store_data[w_store_names.index('W_DENSE_STORE')]
self.save_W_op = w_store_data[w_store_names.index('save_W_op')]
self.reset_W_from_saved_state_op = w_store_data[w_store_names.index('reset_W_from_saved_state_op')]
if INITIALIZE_ASTRO == True:
astro_names, astro_data = astrs.astro_initializer(num_total_neurons_IN=self.num_total_neurons
, inp_range_IN=self.inp_range
, res_range_IN=self.res_range
, a_bias_offset_percentage_IN=ASTRO_BIAS_OFFSET_PERCENTAGE_IN
, w_scaling_IN=ASTRO_W_SCALING_IN
, a_initial_val_IN=STDP_POTENTIATION_LR_IN
, a_tau_IN=ASTRO_TAU_IN
)
self.ASTRO_STDP_LR = astro_data[astro_names.index('ASTRO_STDP_LR')]
self.ASTRO_W = astro_data[astro_names.index('ASTRO_W')]
self.ASTRO_BIAS = astro_data[astro_names.index('ASTRO_BIAS')]
self.ASTRO_STDP_LR_STORE = astro_data[astro_names.index('ASTRO_STDP_LR_STORE')]
self.ASTRO_BIAS_STORE = astro_data[astro_names.index('ASTRO_BIAS_STORE')]
self.ASTRO_W_STORE = astro_data[astro_names.index('ASTRO_W_STORE')]
self.ASTRO_W_MASK = astro_data[astro_names.index('ASTRO_W_MASK')]
self.ASTRO_TAU = astro_data[astro_names.index('ASTRO_TAU')]
if INITIALIZE_STDP == True:
stdp_names, stdp_data = stdp.initialize_learning_parameters_v0(num_total_neurons_IN=self.num_total_neurons)
self.STDP_TRACE = stdp_data[stdp_names.index('STDP_TRACE')]
self.STDP_POTENTIATION_LR = stdp_data[stdp_names.index('STDP_POTENTIATION_LR')]
self.STDP_POTENTIATION_LR_STORE = stdp_data[stdp_names.index('STDP_POTENTIATION_LR_STORE')]
if INITIALIZE_OUTPUT_LAYER == True:
olc_names, olc_data = olc.initialize_layer_structures(num_res_neurons_IN=self.num_res_neurons
, initial_batch_size_IN=BATCH_SIZE_IN
)
self.olc_label_Inp = olc_data[olc_names.index('label_Inp')]
self.olc_target_output_ph = olc_data[olc_names.index('target_output_ph')]
self.olc_W1_ph = olc_data[olc_names.index('W1_ph')]
self.olc_b1_ph = olc_data[olc_names.index('b1_ph')]
self.olc_W1 = olc_data[olc_names.index('W1')]
self.olc_b1 = olc_data[olc_names.index('b1')]
self.olc_BS = olc_data[olc_names.index('BS')]
self.olc_S_AGG_RES = olc_data[olc_names.index('S_AGG_RES')]
self.olc_S_AGG_RES_PH = olc_data[olc_names.index('S_AGG_RES_PH')]
# _____############################################################################################______
# ####################### INITIALIZE STRUCTURES ############################################## END
# _____############################################################################################______
# _____############################################################################################______
# ####################### INITIALIZE TF OPS ############################################## START
# _____############################################################################################______
# NEURON OPS
if INITIALIZE_RES_OPS == True:
self.LIF_update_neuron_states_op = lifs.update_neuron_states_wo_R(v_IN=self.V
, tau_v_IN=self.TAU_V
, tau_u_dense_IN=self.TAU_U_DENSE
, b_IN=self.B
, ss_t_state_IN=self.SS_T_STATE
, W_dense_IN=self.W_DENSE
, I_poisson_IN=self.INPUT_POISSON_VALS
,
scatter_idx_W_dense_to_Neurons_IN=scatter_idx_w_dense_to_s_out_main_tf
, scatter_shp_IN=[BATCH_SIZE_IN, self.num_total_neurons]
,
scatter_S_to_S_SUBSET_inputNeurons_IN=scatter_S_to_S_SUBSET_inputNeurons_tf
, batch_by_num_input_neurons_l_IN=[BATCH_SIZE_IN,
self.num_inp_neurons]
, input_current_IN=INPUT_CURRENT_IN
)
self.LIF_propagate_spike_op = lifs.propagate_spikes(spikes_IN=self.S
, ss_t_state_IN=self.SS_T_STATE
, gather_idx_Sin_to_Wdense_IN=gather_idx_Sin_to_Wdense_tf)
self.LIF_evolveCurr_op = lifs.evolve_input_currents(ss_t_state_IN=self.SS_T_STATE)
self.LIF_compute_spikes_op = lifs.register_spikes(v_IN=self.V, v_trsh_IN=self.V_THRSH, s_IN=self.S, t_rfr_state_IN=self.T_RFR_STATE)
self.LIF_reset_v_op = lifs.reset_spiked_neurons(s_IN=self.S, v_IN=self.V)
self.LIF_update_refractory_state_op = lifs.update_refractory_var(t_rfr_IN=self.T_RFR, t_rfr_state_IN=self.T_RFR_STATE, s_IN=self.S)
self.LIF_update_input_poisson_vals_op = lifs.set_new_input_batch(I_PH_IN=self.I_PH
, INPUT_POISSON_VALS_IN=self.INPUT_POISSON_VALS)
self.LIF_reset_V_to_zero_op = lifs.zero_out_V(V_IN=self.V)
self.LIF_reset_S_to_zero_op = lifs.zero_out_S(S_IN=self.S)
self.LIF_reset_T_RFR_STATE_to_zero_op = lifs.zero_out_T_RFR_STATE(T_RFR_STATE_IN=self.T_RFR_STATE)
self.LIF_reset_SS_T_STATE_op = lifs.reset_ss_t_state_op(SS_T_STATE_IN=self.SS_T_STATE
, ss_t_state_reset_scalar_IN=self.ss_t_state_reset_scalar)
# ASTRO OPS
if INITIALIZE_ASTRO == True:
self.update_astro_op = astrs.update_astro_state(ASTRO_STDP_LR_IN=self.ASTRO_STDP_LR
, ASTRO_BIAS_IN=self.ASTRO_BIAS
, ASTRO_W_IN=self.ASTRO_W
, ASTRO_W_MASK_IN=self.ASTRO_W_MASK
, S_IN=self.S
, ASTRO_TAU_IN=self.ASTRO_TAU)
self.ASTRO_decay_astro_res_lr_op = astrs.decay_astro_stdp_lr(astro_lr_IN=self.ASTRO_STDP_LR)
self.ASTRO_decay_astro_w_op = astrs.decay_astro_w(astro_w_IN=self.ASTRO_W)
self.ASTRO_decay_astro_bias_op = astrs.decay_astro_bias(astro_bias_IN=self.ASTRO_BIAS)
self.ASTRO_reset_astro_res_lr_op = astrs.reset_astro_stdp_lr(astro_lr_IN=self.ASTRO_STDP_LR,
astro_lr_store_IN=self.ASTRO_STDP_LR_STORE)
self.ASTRO_reset_astro_w_op = astrs.reset_astro_w(astro_w_IN=self.ASTRO_W, astro_w_store_IN=self.ASTRO_W_STORE)
self.ASTRO_reset_astro_bias_op = astrs.reset_astro_bias(astro_bias_IN=self.ASTRO_BIAS,
astro_bias_store_IN=self.ASTRO_BIAS_STORE)
# STDP OPS
if INITIALIZE_STDP == True:
self.PLASTICITY_update_stdp_only_op = stdp.STDP_w_astro_depression(
W_dense_IN=self.W_DENSE
, S_IN=self.S
# STDP INPUTS
, trace_IN=self.STDP_TRACE
, W_dense_mask_exc_to_res_IN=W_DENSE_MASK_EXC_TO_RES
, W_dense_mask_inh_to_res_IN=W_DENSE_MASK_INH_TO_RES
, W_dense_mask_inp_to_res_IN=W_DENSE_MASK_INP_TO_RES
, W_dense_mask_res_to_out_IN=W_DENSE_MASK_RES_TO_OUT
, S_mask_exc_and_inp_IN=S_MASK_EXC_INP
, S_mask_inh_IN=S_MASK_INH
, astro_LR_IN=self.ASTRO_STDP_LR
, gather_idx_Sin_to_Wdense_IN=gather_idx_Sin_to_Wdense_tf
, gather_idx_Sout_to_Wdense_IN=gather_idx_Sout_to_Wdense_tf
, stdp_potentiation_LR_IN=self.STDP_POTENTIATION_LR
)
self.PLASTICITY_update_trace_op = stdp.update_trace(trace_IN=self.STDP_TRACE, S_IN=self.S)
self.PLASTICITY_decay_res_lr_op = stdp.decay_res_lr(res_lr_IN=self.STDP_POTENTIATION_LR)
self.PLASTICITY_reset_res_lr_op = stdp.reset_res_lr(res_lr_IN=self.STDP_POTENTIATION_LR,
res_lr_store_IN=self.STDP_POTENTIATION_LR_STORE)
# output layer ops
if INITIALIZE_OUTPUT_LAYER == True:
olc_ops_names, olc_ops_data = olc.initialize_layer_ops(S_AGG_RES_IN=self.olc_S_AGG_RES
, label_Inp_IN=self.olc_label_Inp
, target_output_ph_IN=self.olc_target_output_ph
, W1_IN=self.olc_W1
, b1_IN=self.olc_b1
, BS_IN=self.olc_BS
, lr_IN=READOUT_LR_IN
)
self.OLC_train_step_op = olc_ops_data[olc_ops_names.index('train_step')]
self.OLC_accuracy_eval_per_batch_op = olc_ops_data[olc_ops_names.index('accuracy_eval_per_batch')]
self.OLC_agg_spikes_op = olc.aggregate_spikes_for_output_layer_op(S_AGG_RES_IN=self.olc_S_AGG_RES
, S_IN=self.S
, res_range_IN=self.res_range
)
self.OLC_zero_out_S_AGG_RES_op = olc.zero_out_S_AGG_RES_op(S_AGG_RES_IN=self.olc_S_AGG_RES)
self.OLC_set_S_AGG_to_previously_saved_batch_op = olc.assign_previously_saved_S_AGG(S_AGG_RES_IN=self.olc_S_AGG_RES,S_AGG_RES_PH_IN=self.olc_S_AGG_RES_PH)
w_ini_names, w_ini_data = olc.initialize_saved_output_layer_weights(W_IN=self.olc_W1
, b_IN=self.olc_b1
, new_W_IN=self.olc_W1_ph
, new_b_IN=self.olc_b1_ph
)
self.OLC_set_new_W_op = w_ini_data[w_ini_names.index('set_new_W_op')]
self.OLC_set_new_b_op = w_ini_data[w_ini_names.index('set_new_b_op')]
# _____############################################################################################______
# ####################### INITIALIZE TF OPS ############################################## END
# _____############################################################################################______
#########################################################################################################
############################## INITIALIZE BATCH NETWORK ############################################# END
#########################################################################################################
################### VAL/TST GENERATE OPS ################################################################# START
def generate_validation_or_test_spike_data(self
, sess_IN
, saver_object_IN
, valid_or_test_data_INPUT_VEC_IN
, valid_or_test_data_LABEL_VEC_IN
, valid_or_test_data_LABEL_SCALAR_IN
, BATCH_SIZE_IN
, SAMPLE_INPUT_DURATION_MS_IN
, NUM_DATA_SAMPLES_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, root_save_name_IN):
'''
GENERATES VALIDATION/TEST SPIKE COUNT DATA FOR NALSM MODEL
:param sess_IN: TF SESSION
:param saver_object_IN: OJECT FOR SAVING DATA
:param valid_or_test_data_INPUT_VEC_IN: INPUT DATA
:param valid_or_test_data_LABEL_VEC_IN: LABEL DATA IN ONE HOT VECTOR FORM
:param valid_or_test_data_LABEL_SCALAR_IN: RAW OUTPUT DATA(0,1,2,3...) SCALARS
:param BATCH_SIZE_IN: SIZE OF BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: TIME OF PRESENTING EACH SAMPLE TO LIQUID
:param NUM_DATA_SAMPLES_IN: TOTAL NUMBER OF TETS OR VALIDATION SAMPLES
:param spike_store_batch_mod_IN: WHICH BATCH TO STORE SPIKE DATA FROM
:param record_samples_in_batch_l_IN: SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param root_save_name_IN: SAVE NAME
:return:
'''
ITERATIONS = int(np.floor(NUM_DATA_SAMPLES_IN/BATCH_SIZE_IN))
print('VALIDATION/TESTING RUNNING FOR '+str(ITERATIONS)+' ITERATIONS WITH BATCH SIZE: '+str(BATCH_SIZE_IN))
spikes_files_l = []
s_agg_files_l = []
for ITER in range(0, ITERATIONS):
if ITER > 0:
print('VAL/TST Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(valid_or_test_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(valid_or_test_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
for bt_s in range(0,len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op, feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
sess_IN.run(self.ASTRO_reset_astro_w_op)
sess_IN.run(self.ASTRO_reset_astro_bias_op)
sess_IN.run(self.PLASTICITY_reset_res_lr_op)
sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
if ITER%spike_store_batch_mod_IN==spike_store_batch_mod_IN-1:
sess_IN.run(self.spike_save_ops[t])
sess_IN.run(self.OLC_agg_spikes_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
sess_IN.run(self.PLASTICITY_update_stdp_only_op)
sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
sess_IN.run(self.PLASTICITY_decay_res_lr_op)
sess_IN.run(self.ASTRO_decay_astro_res_lr_op)
sess_IN.run(self.ASTRO_decay_astro_w_op)
sess_IN.run(self.ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
################### EXTRACT SPIKE DATA ############### START
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
spike_rec_per_batch = sess_IN.run(self.condense_spike_store_to_Fn_Ft_op)
sess_IN.run(self.zero_out_S_STORE_op)
spikes_save_filename = str(root_save_name_IN) + '_SPIKES_BATCH_' + str(ITER) + '.spikes'
saver_object_IN.save_data(signal=1
, names=['ITER','spike_rec_per_batch', 'scalar_labels_in_batch_for_spike_data_l']
, data=[ITER,spike_rec_per_batch, scalar_labels_in_batch_for_spike_data_l]
, save_filename=spikes_save_filename
)
spike_rec_per_batch = 0
scalar_labels_in_batch_for_spike_data_l.clear()
spikes_files_l.append(spikes_save_filename)
################### EXTRACT SPIKE DATA ############### END
################### SAVE SPIKE COUNT DATA ############### START
s_agg_save_filename = str(root_save_name_IN) + '_S_AGG_BATCH_' + str(ITER) + '.datasagg'
saver_object_IN.save_data(signal=1
, names=['ITER','olc_S_AGG_RES', 'target_output_for_linear_layer_labels_in_batch_arr','scalar_labels_in_batch_arr']
, data=[ITER,sess_IN.run(self.olc_S_AGG_RES), np.vstack(target_output_for_linear_layer_labels_in_batch_l),np.asarray(scalar_labels_in_batch_l)]
, save_filename=s_agg_save_filename
)
s_agg_files_l.append(s_agg_save_filename)
target_output_for_linear_layer_labels_in_batch_l.clear()
scalar_labels_in_batch_l.clear()
################### SAVE SPIKE COUNT DATA ############### END
return spikes_files_l,s_agg_files_l
def generate_validation_or_test_spike_data_STATIC(self
, sess_IN
, saver_object_IN
, valid_or_test_data_INPUT_VEC_IN
, valid_or_test_data_LABEL_VEC_IN
, valid_or_test_data_LABEL_SCALAR_IN
, BATCH_SIZE_IN
, SAMPLE_INPUT_DURATION_MS_IN
, NUM_DATA_SAMPLES_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, root_save_name_IN):
'''
GENERATES VALIDATION/TEST SPIKE COUNT DATA FOR LSM MODEL
:param sess_IN: TF SESSION
:param saver_object_IN: OJECT FOR SAVING DATA
:param valid_or_test_data_INPUT_VEC_IN: INPUT DATA
:param valid_or_test_data_LABEL_VEC_IN: LABEL DATA IN ONE HOT VECTOR FORM
:param valid_or_test_data_LABEL_SCALAR_IN: RAW OUTPUT DATA(0,1,2,3...) SCALARS
:param BATCH_SIZE_IN: SIZE OF BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: TIME OF PRESENTING EACH SAMPLE TO LIQUID
:param NUM_DATA_SAMPLES_IN: TOTAL NUMBER OF TETS OR VALIDATION SAMPLES
:param spike_store_batch_mod_IN: WHICH BATCH TO STORE SPIKE DATA FROM
:param record_samples_in_batch_l_IN: SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param root_save_name_IN: SAVE NAME
:return:
'''
ITERATIONS = int(np.floor(NUM_DATA_SAMPLES_IN/BATCH_SIZE_IN))
print('VALIDATION/TESTING RUNNING FOR '+str(ITERATIONS)+' ITERATIONS WITH BATCH SIZE: '+str(BATCH_SIZE_IN))
spikes_files_l = []
s_agg_files_l = []
for ITER in range(0, ITERATIONS):
if ITER > 0:
print('VAL/TST Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(valid_or_test_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(valid_or_test_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
for bt_s in range(0,len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(valid_or_test_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op, feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
# sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
# sess_IN.run(self.ASTRO_reset_astro_w_op)
# sess_IN.run(self.ASTRO_reset_astro_bias_op)
# sess_IN.run(self.PLASTICITY_reset_res_lr_op)
# sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
if ITER%spike_store_batch_mod_IN==spike_store_batch_mod_IN-1:
sess_IN.run(self.spike_save_ops[t])
sess_IN.run(self.OLC_agg_spikes_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
# sess_IN.run(self.PLASTICITY_update_stdp_only_op)
# sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
# sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
# sess_IN.run(self.PLASTICITY_decay_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_res_lr_op)
# sess_IN.run(self.ASTRO_decay_astro_w_op)
# sess_IN.run(self.ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
################### EXTRACT SPIKE DATA ############### START
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN-1:
spike_rec_per_batch = sess_IN.run(self.condense_spike_store_to_Fn_Ft_op)
sess_IN.run(self.zero_out_S_STORE_op)
spikes_save_filename = str(root_save_name_IN) + '_SPIKES_BATCH_' + str(ITER) + '.spikes'
saver_object_IN.save_data(signal=1
, names=['ITER','spike_rec_per_batch', 'scalar_labels_in_batch_for_spike_data_l']
, data=[ITER,spike_rec_per_batch, scalar_labels_in_batch_for_spike_data_l]
, save_filename=spikes_save_filename
)
spike_rec_per_batch = 0
scalar_labels_in_batch_for_spike_data_l.clear()
spikes_files_l.append(spikes_save_filename)
################### EXTRACT SPIKE DATA ############### END
################### SAVE SPIKE COUNT DATA ############### START
s_agg_save_filename = str(root_save_name_IN) + '_S_AGG_BATCH_' + str(ITER) + '.datasagg'
saver_object_IN.save_data(signal=1
, names=['ITER','olc_S_AGG_RES', 'target_output_for_linear_layer_labels_in_batch_arr','scalar_labels_in_batch_arr']
, data=[ITER,sess_IN.run(self.olc_S_AGG_RES), np.vstack(target_output_for_linear_layer_labels_in_batch_l),np.asarray(scalar_labels_in_batch_l)]
, save_filename=s_agg_save_filename
)
s_agg_files_l.append(s_agg_save_filename)
target_output_for_linear_layer_labels_in_batch_l.clear()
scalar_labels_in_batch_l.clear()
################### SAVE SPIKE COUNT DATA ############### END
return spikes_files_l,s_agg_files_l
################### VAL/TST GENERATE OPS ################################################################# END
def evaluate_output_layer_on_validation_or_test_s_agg_data(self
, sess_IN
, data_batched_INPUT_VEC_IN
, data_batched_LABEL_VEC_IN
, data_batched_LABEL_SCALAR_IN
):
'''
EVALUATES OUTPUT LAYER ON SINGLE BATCH OF DATA
:param sess_IN: TF SESSION
:param data_batched_INPUT_VEC_IN: INPUT DATA
:param data_batched_LABEL_VEC_IN: OUTPUT DATA ONE HOT FORMAT
:param data_batched_LABEL_SCALAR_IN: SCALAR LABELS
:return:
'''
accuracy_l = []
for batch in range(0,len(data_batched_INPUT_VEC_IN)):
sess_IN.run(self.OLC_set_S_AGG_to_previously_saved_batch_op, feed_dict={self.olc_S_AGG_RES_PH:data_batched_INPUT_VEC_IN[batch]})
accuracy_l.append(sess_IN.run(self.OLC_accuracy_eval_per_batch_op, feed_dict={self.olc_label_Inp:data_batched_LABEL_SCALAR_IN[batch]}))
return accuracy_l
def evaluate_output_layer_on_FULL_validation_or_test_s_agg_data(self
, sess_IN
, saver_object_IN
, list_of_data_files_IN
, num_of_simultaneous_open_files=2000):
'''
EVALUATES PERFORMANCE OF OUTPUT LAYER ON VALIDATION OR TEST DATA
:param sess_IN: TF SESSION
:param saver_object_IN: OBJECT FOR SAVING DATA
:param list_of_data_files_IN: SPECIFIES DATA TO TEST/VALIDATE ON
:param num_of_simultaneous_open_files: NUMBER OF BATCHES TO LOAD AT ONE TIME
:return:
'''
accuracy_list_full = []
data_batched_INPUT_VEC_l = []
data_batched_LABEL_VEC_l = []
data_batched_LABEL_SCALAR_l = []
for fn in range(0,len(list_of_data_files_IN)):
names, data = sup.unpack_file(filename=list_of_data_files_IN[fn],dataPath=self.source_path)
data_batched_INPUT_VEC_l.append(data[names.index('olc_S_AGG_RES')])
data_batched_LABEL_VEC_l.append(data[names.index('target_output_for_linear_layer_labels_in_batch_arr')])
data_batched_LABEL_SCALAR_l.append(data[names.index('scalar_labels_in_batch_arr')])
if len(data_batched_INPUT_VEC_l)==num_of_simultaneous_open_files or (fn==(len(list_of_data_files_IN)-1) and len(data_batched_INPUT_VEC_l)>0):
accuracy_list_full.extend(self.evaluate_output_layer_on_validation_or_test_s_agg_data(sess_IN=sess_IN
, data_batched_INPUT_VEC_IN=data_batched_INPUT_VEC_l
, data_batched_LABEL_VEC_IN=data_batched_LABEL_VEC_l
, data_batched_LABEL_SCALAR_IN=data_batched_LABEL_SCALAR_l
))
data_batched_INPUT_VEC_l.clear()
data_batched_LABEL_VEC_l.clear()
data_batched_LABEL_SCALAR_l.clear()
if len(accuracy_list_full)==len(list_of_data_files_IN):
print('ALL EVALUATION FILES PROCESSED AND ACCOUNTED FOR TOTALING: '+str(len(list_of_data_files_IN)))
else:
print('ERROR: NOT ALL EVALUTATION FILES PROCEED AND ACCOUNTED, ACCOUNTED ONLY: '+str(len(accuracy_list_full))+' out of '+str(len(list_of_data_files_IN)))
average_accuracy = np.average(accuracy_list_full)
return average_accuracy
################### TRAINING OPS ######################################################################## START
def train_on_set_number_of_batches(self
, sess_IN
, saver_object_IN
, training_iteration_range_IN
, training_dataset_size_IN
, list_of_data_files_IN
, BATCH_SIZE_IN
, root_save_name_IN
, train_data_INPUT_VEC_IN
, train_data_LABEL_VEC_IN
, train_data_LABEL_SCALAR_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, SAMPLE_INPUT_DURATION_MS_IN
, REPEATS_ON_ITER_RANGE_IN=1
):
'''
GENERATES SPIKE COUNT DATA FOR EACH SAMPLE AND TRAINS THE OUTPUT LAYER ONCE ON ALL GENERATED BATCHS
THIS FUNCTION USED FOR NALSM MODEL, WITH ASTROCYTE MODULATED STDP
:param sess_IN: TF SESSION
:param saver_object_IN: OBJECT FOR SAVING FILES
:param training_iteration_range_IN: BATCHES TO GENERATE AND TRAIN
:param training_dataset_size_IN: FULL SIZE OF TRAINING SET
:param list_of_data_files_IN: ALREADY GENERATED SPIKE COUNT FILES (S_AGG FILES)
:param BATCH_SIZE_IN: BATCH SIZE
:param root_save_name_IN: CORE SAVE NAME
:param train_data_INPUT_VEC_IN: INPUT DATA
:param train_data_LABEL_VEC_IN: LABELS IN FORMAT FOR TRAINING OUTPUT LAYER(ONE HOT VECTOR)
:param train_data_LABEL_SCALAR_IN: RAW LABELS (0,1,2,3,4..)
:param spike_store_batch_mod_IN: WHEN TO STORE ALL SPIKES FROM LIQUID
:param record_samples_in_batch_l_IN: WHICH SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: DURATION OF SAMPLE INPUT PRESENTED TO LIQUID (250 ms)
:param REPEATS_ON_ITER_RANGE_IN: ALWAYS 1
:return:
'''
max_num_of_save_files = int(training_dataset_size_IN/BATCH_SIZE_IN)
print('max number of training data save files: '+str(max_num_of_save_files))
raw_iters_arr = training_iteration_range_IN[0]+np.arange(training_iteration_range_IN[1]-training_iteration_range_IN[0])
all_iters_arr = np.mod(raw_iters_arr,max_num_of_save_files)
idx = np.where(raw_iters_arr< max_num_of_save_files)[0]
iters_with_no_existing_data_l = raw_iters_arr[idx]
print('-----------------------------------: '+str(raw_iters_arr))
print('------------------------all_iters_l: ' + str(all_iters_arr))
print('-----------idx for no_existing_data: '+str(idx))
print('------iters_with_no_existing_data_l: ' + str(iters_with_no_existing_data_l))
spikes_files_l = []
s_agg_files_l = []
st_time_train_total = time.time()
if len(iters_with_no_existing_data_l)>0:
print('PROCEEDING TO GENERATE NEW S AGG RES DATA')
for ITER in iters_with_no_existing_data_l:
print('SPIKE DATA NEEDS TO BE GENERATED, NOW GENERATING SPIKE DATA FOR BATCH: ' + str(ITER))
# iter_ned is the batch number or the ITER from eval function
if ITER > iters_with_no_existing_data_l[0]:
print('Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(train_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(train_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
for bt_s in range(0, len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op,
feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
sess_IN.run(self.ASTRO_reset_astro_w_op)
sess_IN.run(self.ASTRO_reset_astro_bias_op)
sess_IN.run(self.PLASTICITY_reset_res_lr_op)
sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)
sess_IN.run(self.LIF_reset_v_op)
# SPIKE RECORD OP
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
sess_IN.run(self.spike_save_ops[t])
sess_IN.run(self.OLC_agg_spikes_op)
sess_IN.run(self.LIF_propagate_spike_op)
sess_IN.run(self.LIF_update_neuron_states_op)
sess_IN.run(self.LIF_evolveCurr_op)
sess_IN.run(self.LIF_update_refractory_state_op)
############### MAIN LIF FUNCTIONS ###################### END
############### PLASTICITY FUNCTIONS ###################### START
sess_IN.run(self.PLASTICITY_update_stdp_only_op)
sess_IN.run(self.PLASTICITY_update_trace_op)
############### PLASTICITY FUNCTIONS ###################### END
############### ASTRO FUNCTIONS ########################## START
sess_IN.run(self.update_astro_op)
############### ASTRO FUNCTIONS ############################ END
############### PLASTICITY ASTRO DECAYS ###################### START
sess_IN.run(self.PLASTICITY_decay_res_lr_op)
sess_IN.run(self.ASTRO_decay_astro_res_lr_op)
sess_IN.run(self.ASTRO_decay_astro_w_op)
sess_IN.run(self.ASTRO_decay_astro_bias_op)
############### PLASTICITY ASTRO DECAYS ###################### END
################### EXTRACT SPIKE DATA ############### START
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
spike_rec_per_batch = sess_IN.run(self.condense_spike_store_to_Fn_Ft_op)
sess_IN.run(self.zero_out_S_STORE_op)
saver_object_IN.save_data(signal=1
, names=['ITER', 'spike_rec_per_batch',
'scalar_labels_in_batch_for_spike_data_l']
, data=[ITER, spike_rec_per_batch,
scalar_labels_in_batch_for_spike_data_l]
, save_filename=str(root_save_name_IN) + '_BATCH_' + str(
ITER) + '.spikes'
)
spike_rec_per_batch = 0
scalar_labels_in_batch_for_spike_data_l.clear()
spikes_files_l.append(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.spikes')
################### EXTRACT SPIKE DATA ############### END
################### SAVE SPIKE COUNTS DATA ############### START
if ITER<iters_with_no_existing_data_l[-1]:
saver_object_IN.save_data(signal=1
, names=['ITER', 'olc_S_AGG_RES',
'target_output_for_linear_layer_labels_in_batch_arr',
'scalar_labels_in_batch_arr']
, data=[ITER, sess_IN.run(self.olc_S_AGG_RES),
np.vstack(target_output_for_linear_layer_labels_in_batch_l),
np.asarray(scalar_labels_in_batch_l)]
,
save_filename=str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'
)
else:
sup.save_non_tf_data(names=['ITER', 'olc_S_AGG_RES',
'target_output_for_linear_layer_labels_in_batch_arr',
'scalar_labels_in_batch_arr']
, data=[ITER, sess_IN.run(self.olc_S_AGG_RES),
np.vstack(target_output_for_linear_layer_labels_in_batch_l),
np.asarray(scalar_labels_in_batch_l)]
, filename=str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'
, savePath=self.save_path)
print('LAST S_AGG BATCH OF BLOCK GENERATED AND SAVED TO: '+str(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg'))
s_agg_files_l.append(str(root_save_name_IN) + '_BATCH_' + str(ITER) + '.datasagg')
target_output_for_linear_layer_labels_in_batch_l.clear()
scalar_labels_in_batch_l.clear()
################### SAVE SPIKE COUNTS DATA ############### END
################### OPEN AND TRAIN OUTPUT LAYER ON GENERATED BATCHES ############### START
print('TIME TO GENERATE SPIKE NEW: '+str(time.time()-st_time_train_total))
print('PROCEEDING TO ASSEMBLE DATA FOR TRAINING OUTPUT LAYER ON S AGG DATA')
# extract all data files
st_time_train_output_total = time.time()
tp2_target_output_l = []
tp2_S_AGG_RES_input_l = []
for iter_ed in all_iters_arr:
print('SPIKE DATA WAS ALREADY GENEATED, OPENING FILE WITH DATA USING BATCH NUMBER: ' + str(iter_ed))
tp2_names, tp2_data = sup.unpack_file(
filename=str(root_save_name_IN) + '_BATCH_' + str(iter_ed) + '.datasagg',dataPath=self.source_path)
tp2_target_output_l.append(
tp2_data[tp2_names.index('target_output_for_linear_layer_labels_in_batch_arr')])
tp2_S_AGG_RES_input_l.append(tp2_data[tp2_names.index('olc_S_AGG_RES')])
print('ASSEMBLED ' + str(len(tp2_target_output_l)) + ' DATA BATCHES FROM SAVED FILES, PROCEEDING TO TRAIN...')
for rir in range(0,REPEATS_ON_ITER_RANGE_IN):
print('WHOLE_ITER_SUBSET_RANGE TRAINING iteration: '+str(rir))
for train_iter in range(0, len(tp2_target_output_l)):
sess_IN.run(self.OLC_set_S_AGG_to_previously_saved_batch_op,
feed_dict={self.olc_S_AGG_RES_PH: tp2_S_AGG_RES_input_l[train_iter]})
sess_IN.run(self.OLC_train_step_op,
feed_dict={self.olc_target_output_ph: tp2_target_output_l[train_iter]})
tp2_target_output_l.clear()
tp2_S_AGG_RES_input_l.clear()
print('TRAINING OUTPUT LAYER ON BATCH BLOCK COMPLETE, TIME TAKE: '+str(time.time()-st_time_train_output_total))
################### OPEN AND TRAING OUTPUT LAYER ON GENERATED BATCHES ############### END
return spikes_files_l, s_agg_files_l
def train_on_set_number_of_batches_STATIC(self
, sess_IN
, saver_object_IN
, training_iteration_range_IN
, training_dataset_size_IN
, list_of_data_files_IN
, BATCH_SIZE_IN
, root_save_name_IN
, train_data_INPUT_VEC_IN
, train_data_LABEL_VEC_IN
, train_data_LABEL_SCALAR_IN
, spike_store_batch_mod_IN
, record_samples_in_batch_l_IN
, SAMPLE_INPUT_DURATION_MS_IN
, REPEATS_ON_ITER_RANGE_IN=1
):
'''
GENERATES SPIKE COUNT DATA FOR EACH SAMPLE AND TRAINS THE OUTPUT LAYER ONCE ON ALL GENERATED BATCHS
THIS FUNCTION USED FOR LSM MODEL, WITH STATIC/FIXED WEIGHTS
:param sess_IN: TF SESSION
:param saver_object_IN: OBJECT FOR SAVING FILES
:param training_iteration_range_IN: BATCHES TO GENERATE AND TRAIN
:param training_dataset_size_IN: FULL SIZE OF TRAINING SET
:param list_of_data_files_IN: ALREADY GENERATED SPIKE COUNT FILES (S_AGG FILES)
:param BATCH_SIZE_IN: BATCH SIZE
:param root_save_name_IN: CORE SAVE NAME
:param train_data_INPUT_VEC_IN: INPUT DATA
:param train_data_LABEL_VEC_IN: LABELS IN FORMAT FOR TRAINING OUTPUT LAYER(ONE HOT VECTOR)
:param train_data_LABEL_SCALAR_IN: RAW LABELS (0,1,2,3,4..)
:param spike_store_batch_mod_IN: WHEN TO STORE ALL SPIKES FROM LIQUID
:param record_samples_in_batch_l_IN: WHICH SAMPLES TO STORE SPIKE DATA FROM IN BATCH
:param SAMPLE_INPUT_DURATION_MS_IN: DURATION OF SAMPLE INPUT PRESENTED TO LIQUID (250 ms)
:param REPEATS_ON_ITER_RANGE_IN: ALWAYS 1
:return:
'''
max_num_of_save_files = int(training_dataset_size_IN/BATCH_SIZE_IN)
print('max number of training data save files: '+str(max_num_of_save_files))
raw_iters_arr = training_iteration_range_IN[0]+np.arange(training_iteration_range_IN[1]-training_iteration_range_IN[0])
all_iters_arr = np.mod(raw_iters_arr,max_num_of_save_files)
idx = np.where(raw_iters_arr< max_num_of_save_files)[0]
iters_with_no_existing_data_l = raw_iters_arr[idx]
print('-----------------------------------: '+str(raw_iters_arr))
print('------------------------all_iters_l: ' + str(all_iters_arr))
print('-----------idx for no_existing_data: '+str(idx))
print('------iters_with_no_existing_data_l: ' + str(iters_with_no_existing_data_l))
spikes_files_l = []
s_agg_files_l = []
st_time_train_total = time.time()
if len(iters_with_no_existing_data_l)>0:
print('PROCEEDING TO GENERATE NEW S AGG RES DATA')
for ITER in iters_with_no_existing_data_l:
print('SPIKE DATA NEEDS TO BE GENERATED, NOW GENERATING SPIKE DATA FOR BATCH: ' + str(ITER))
# iter_ned is the batch number or the ITER from eval function
# edit the iter time printing....
if ITER > iters_with_no_existing_data_l[0]:
print('Epoch ' + str(ITER - 1) + ' competed in ' + str(time.time() - st_time))
st_time = time.time()
scalar_labels_in_batch_for_spike_data_l = []
batch_samples_agg_temp = []
scalar_labels_in_batch_l = []
target_output_for_linear_layer_labels_in_batch_l = []
for bt in range(0, BATCH_SIZE_IN):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt)
batch_samples_agg_temp.append(train_data_INPUT_VEC_IN[sample_idx])
target_output_for_linear_layer_labels_in_batch_l.append(train_data_LABEL_VEC_IN[sample_idx])
scalar_labels_in_batch_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
if ITER % spike_store_batch_mod_IN == spike_store_batch_mod_IN - 1:
for bt_s in range(0, len(record_samples_in_batch_l_IN)):
sample_idx = ((ITER * BATCH_SIZE_IN) + bt_s)
scalar_labels_in_batch_for_spike_data_l.append(train_data_LABEL_SCALAR_IN[sample_idx])
sess_IN.run(self.LIF_update_input_poisson_vals_op,
feed_dict={self.I_PH: np.vstack(batch_samples_agg_temp)})
sess_IN.run(self.LIF_reset_V_to_zero_op)
sess_IN.run(self.LIF_reset_S_to_zero_op)
sess_IN.run(self.LIF_reset_T_RFR_STATE_to_zero_op)
sess_IN.run(self.LIF_reset_SS_T_STATE_op)
# sess_IN.run(self.ASTRO_reset_astro_res_lr_op)
# sess_IN.run(self.ASTRO_reset_astro_w_op)
# sess_IN.run(self.ASTRO_reset_astro_bias_op)
# sess_IN.run(self.PLASTICITY_reset_res_lr_op)
# sess_IN.run(self.reset_W_from_saved_state_op)
sess_IN.run(self.OLC_zero_out_S_AGG_RES_op)
for t in range(0, SAMPLE_INPUT_DURATION_MS_IN):
############### LIF FUNCTIONS ###################### START
sess_IN.run(self.LIF_compute_spikes_op)