-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsnn_1st.py
496 lines (456 loc) · 23.3 KB
/
snn_1st.py
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
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: Weinrot
#
# Created: 07/03/2015
# Copyright: (c) Weinrot 2015
# Licence: <your licence>
#-------------------------------------------------------------------------------
import theano
import theano.tensor as T
import time
import numpy as np
import os
from Choose_frame import create_dataset, read_image
import math
from matplotlib import pyplot as plt
import sys
# !!!!!
# dt is changed manually into 1ms
class SNNgroup(object):
def __init__(self,Ne,Ni,n_inp,W_inp=None,W_inner=None):
'''class SNNgroup's self Parameters:
self.A: update matrix
self.S: neuron state varaibles
self.W_inner: inner-connect weights in the group
self.W_inp: input weights
self.spikes: the spikes matrix in the time t
self.SpkC : spike containers
input : '''
self.number = Ne+Ni
self.Ne = Ne
self.Ni = Ni
self.mV=self.ms=1e-3 # units
dt=1*self.ms # timestep
self.dt = dt
taum=20*self.ms # membrane time constant
taue=5*self.ms
taui=10*self.ms
#self.Vt=-1*self.mV # threshold = -50+49
self.Vt = 15*self.mV #threshold = -55+70
#self.Vr=-11*self.mV # reset = -60+49
self.Vr = 0*self.mV # reset = -70+70
self.Vi = -10*self.mV # VI = -80+70
self.dApre = .0001
#self.dApre = .95 #changed into .95
self.dApost = -self.dApre*1.05
self.tauP = 20*self.ms
#self.input = input
self.n_inp = n_inp
self.weight = .001
self.weightIn = 1.
self.wmax = 200*self.weight
zero = np.array([0]).astype(theano.config.floatX)
self.zero = theano.shared(zero,name='zero',borrow=True)
"""
Equations
---------
eqs='''
dv/dt = (ge*70mV-gi*10-(v+70*mV))/(20*ms) : volt
dge/dt = -ge/(5*self.ms) : volt
dgi/dt = -gi/(10*self.ms) : volt
'''
"""
# Update matrix
A = np.array([[np.exp(-dt/taum),0,0],
[taue/(taum-taue)*(np.exp(-dt/taum)-np.exp(-dt/taue)),np.exp(-dt/taue),0],
[-taui/(taum-taui)*(np.exp(-dt/taum)-np.exp(-dt/taui)),0,np.exp(-dt/taui)]
],dtype=theano.config.floatX).T
A = theano.shared(value=A,name='A',borrow=True)
self.A = A
# State varible : [v;ge;gi] (size=3*self.number)
S = np.ones((1,self.number),dtype=theano.config.floatX)*self.Vr
S = np.vstack((S,np.zeros((2,self.number),dtype=theano.config.floatX)))
self.S_init = S
S = theano.shared(value=S,name='S',borrow=True)
self.S = S
if W_inner == None:
# weights of inner connections (size= self.number*self.number)
self.W_inner_ini = np.ones((self.number,self.number),dtype=theano.config.floatX)*self.weight
#self.W_inner_ini[Ne:,:] = self.weightIn
self.W_inner_ini[Ne:,:] = self.weight
wtmp = np.eye(self.number)
ind = wtmp.nonzero()
self.W_inner_ini[ind]=0
W_inner = theano.shared(value=self.W_inner_ini,name='W_inner',borrow=True)
self.W_inner = W_inner
else:
self.W_inner = theano.shared(W_inner,name='W_inner',borrow=True)
# weights of input connections (size=n_inp*self.number
rng = np.random.RandomState(1234)
if W_inp ==None:
#W_inp = np.ones((self.n_inp,self.number)).astype(theano.config.floatX) #needs specification later
#W_inp = np.random.rand(self.n_inp,self.number).astype(theano.config.floatX)*.00001*self.ms #needs specification later
self.W_inp_ini = np.ones((self.n_inp,self.number)).astype(theano.config.floatX)*self.weight
self.W_inp_ini[:,self.Ne:] = self.weightIn
W_inp = theano.shared(self.W_inp_ini,name='W_inp',borrow=True)
self.W_inp = W_inp
else:
self.W_inp = theano.shared(W_inp,name='W_inp',borrow=True)
# Spike Container
#spkC = theano.shared(value=np.empty((1,self.number)).astype(theano.config.floatX),name='spkC',borrow=True)
spkC = np.empty((1,self.number)).astype(theano.config.floatX)
self.spkC = spkC
#spikes=np.empty((self.number,1),dtype=theano.config.floatX)
#self.spikes = theano.shared(value=spikes,name='spikes',borrow=True)
# not sure the dtype of sp_history
self.sp_history = np.array([])
#output = np.empty(self.number,dtype=theano.config.floatX)
#self.output = theano.shared(value=output,name='output',borrow=True)
self.V_record = np.empty((1,self.number))
self.ge_record = np.empty((1,self.number))
self.gi_record = np.empty((1,self.number))
#================================================
# Process Function Initial
# input:: 0-1 vector
'''Update Schedule:
1.Update state variables of SNNgroup: dot(A,S)
1.Update state variables of Synapses: dot(exp(-dt/tau),Ssynapse), including W_inp and W_inner
2.Call thresholding function: S[0,:]>Vt
3.Push spikes into SpikeContainer
4.Propagate spikes via Connection(possibly with delays)
5.Update state variables of Synapses (STDP)
6.Call reset function on neurons which has spiked'''
Ne = self.Ne
Ni = self.Ni
m = T.fmatrix(name='m')
#self.Vt = T.as_tensor_variable(self.Vt,'Vt')
# "Update state function:: stat()"
# return np array
# shape(stat()) = shape(self.S)
S_update = T.dot(self.A,self.S)
self.stat = theano.function(
inputs = [],
outputs = [],
updates = {self.S : S_update})
#============================================================
# Update state of Synapses
# Update matrix of Synapse
A_STDP = np.array([[np.exp(-self.dt/self.tauP),0],[0,np.exp(-self.dt/self.tauP)]],dtype=theano.config.floatX)
# Spre_inner :: pre synapse of inner connections
# Spost_inner:: post synapse of inner connections
# Spre_inp :: pre synapse of input conenctions
# Spost_inp :: post synapse of input connections
self.Spre_inner_ini = np.zeros((self.number,self.number),dtype=theano.config.floatX)
Spre_inner = theano.shared(self.Spre_inner_ini,name='Spre_inner',borrow=True)
self.Spre_inner = Spre_inner
self.Spost_inner_ini = np.zeros((self.number,self.number),dtype=theano.config.floatX)
Spost_inner = theano.shared(value=self.Spost_inner_ini,name='Spost_inner',borrow=True)
self.Spost_inner = Spost_inner
self.Spre_inp_ini = np.zeros((self.n_inp,self.number)).astype(theano.config.floatX) #needs specification later
Spre_inp = theano.shared(value=self.Spre_inp_ini,name='Spre_inp',borrow=True)
self.Spre_inp = Spre_inp
self.Spost_inp_ini = np.zeros((self.n_inp,self.number)).astype(theano.config.floatX) #needs specification later
Spost_inp = theano.shared(value=self.Spost_inp_ini,name='Spost_inp',borrow=True)
self.Spost_inp = Spost_inp
U = T.fscalar('U')
UM = T.fmatrix('UM')
#UpreV = theano.shared(A_STDP[0,0],name='UpreV',borrow=True) # Wpre = UpreV*Wpre
#UpostV = theano.shared(A_STDP[1,1],name='UpostV',borrow=True)
self.tmp = np.array(np.exp(-self.dt/self.tauP).astype(theano.config.floatX))
self.SynFresh = theano.shared(self.tmp,name='SynFresh',borrow=True)
self.UpdateSpre_inner = theano.function(inputs=[],outputs=None,updates={self.Spre_inner:T.dot(self.SynFresh,self.Spre_inner)},allow_input_downcast=True)
self.UpdateSpost_inner = theano.function(inputs=[],outputs=None,updates={self.Spost_inner:T.dot(self.SynFresh,self.Spost_inner)},allow_input_downcast=True)
self.UpdateSpre_inp = theano.function(inputs=[],outputs=None,updates={self.Spre_inp:T.dot(self.SynFresh,self.Spre_inp)},allow_input_downcast=True)
self.UpdateSpost_inp = theano.function(inputs=[],outputs=None,updates={self.Spost_inp:T.dot(self.SynFresh,self.Spost_inp)},allow_input_downcast=True)
#------------------------------------------
#tmp = math.exp(-self.dt/self.tauP)
#tmp = T.as_tensor(0.95122945)
#================================================================
#------------------------------------------
# "thresholding function:: spike_fun()"
# type return :: np.ndarray list
# shape return:: shape(spike_fun()) = (self.number,)
self.spike_fun = theano.function(
inputs = [U], #[self.S]
outputs = (T.gt(self.S[0,:],U))) #type outputs: np.ndarray,shape::(nL,)
#'outputs = (self.S[0,:]>Vt).astype(theano.config.floatX)), #type outputs: list'
#'updates={self.spikes:(self.S[0,:]>Vt).astype(theano.config.floatX)}'
#------------------------------------
#------------------------------------
#=================================================================
# "Push spike into Container function:: spCfun(vector)"
# type vector :: np.array([],dtype=theano.config.floatX)!!!
# type return :: np array
# shape return:: shape(spCfun()) = ( shape(self.spkC)[0]+1 , shape(self.spkC)[1] )
#updates={self.spkC:T.stack(self.spkC,sp)})
'''spike_prop = theano.function( #wrong
inputs = [],
outputs =[],
updates = {self.S:np.dot(self.W_inner,self.spikes)+self.S})#wrong'''
#-------------------------------
#--------------------------------
#====================================================================
# Propagate spikes
# inner connection:
# S_inner = f(inputs, outputs, updates)
# Param:: inputs: spike 0-1 vector
# Param:: inputs: spike is from function-> spike_fun
# S_inner(spk)::-> for i in spk[0:Ne].nonzero()[0]:
# S[1,:] = Winner[i,:]+S[1,:] (excitatory conenction)
# for j in spk[Ne,:].nonzero()[0]:
# S[2,:] = Winner[j,:]+S[2,:] (inhibitory connection)
vinner = T.fvector(name='vinner') # vinner = spk :: np.array((1,self.number)
def add_f1(i,p,q):
np = T.inc_subtensor(p[1,:],q[i,:]) #ge
return {p:np}
def add_f2(i,p,q):
np = T.inc_subtensor(p[2,:],q[i,:]) #gi
return {p:np}
#deltaWinner1,updates1 = theano.scan(fn=lambda i: self.W_inner[i,:]*i+self.S[1,:], sequences=vinner[0:Ne])
deltaWinner1,updates1 = theano.scan(fn=add_f1, sequences=vinner[0:Ne].nonzero()[0],non_sequences=[self.S,self.W_inner])
#deltaWinner2,updates2 = theano.scan(fn=lambda i: self.W_inner[i,:]*i+self.S[2,:], sequences=vinner[Ne:])
deltaWinner2,updates2 = theano.scan(fn=add_f2, sequences=vinner[Ne:].nonzero()[0]+self.Ne,non_sequences=[self.S,self.W_inner])
# S = S+W
self.S_inner1 = theano.function(inputs=[vinner],outputs=None,updates=updates1,allow_input_downcast=True)
self.S_inner2 = theano.function(inputs=[vinner],outputs=None,updates=updates2,allow_input_downcast=True)
#------------------------------------------
#------------------------------------------
# outter connection (input spikes):
# type input: index list
voutter = T.fvector(name='voutter')
#deltaWoutter = theano.scan(fn=lambda j: self.W_inp[j,:]+self.S[1,:],sequences=voutter)
deltaWoutter,updatesout1 = theano.scan(fn=add_f1,sequences=voutter.nonzero()[0],non_sequences=[self.S,self.W_inp])
self.S_inp = theano.function(inputs=[voutter],outputs=None,updates=updatesout1,allow_input_downcast=True)
#------------------------------------
#-------------------------------------
#=====================================================================
# Update Synapses (STDP | STDC)
# Pre:: Apre += self.dApre, w+=Apost
# Post:: Apost+=self.dApost, w+=Apre
#
# USpreInner :: Perform Pre function No.1 in inner connections
# UWInner :: Perform Pre function No.2 in inner connections
# UpreInner :: Function
def add_synap_pre(i,p,po,s,q):
# i :: sequence
# p :: pre | post
# s :: dApre | dApost
# q :: W
index = T.nonzero(q[i,:self.Ne])
np = T.inc_subtensor(p[i,index],s)
## tmp = p[i,:]
## tmp=T.inc_subtensor(tmp[index],s)
## np=T.set_subtensor(p[i,:],tmp)
#np = T.inc_subtensor(p[i,:],s)
nw = T.inc_subtensor(q[i,:],po[i,:])
nw=T.clip(nw,0,self.wmax)
return {p:np,q:nw}
def add_synap_pre_inp(i,p,po,s,q):
# i :: sequence
# p :: pre | post
# s :: dApre | dApost
# q :: W
index = T.nonzero(q[i,:self.Ne])
np = T.inc_subtensor(p[i,index],s)
## tmp = p[i,:]
## tmp=T.inc_subtensor(tmp[index],s)
## np=T.set_subtensor(p[i,:],tmp)
#np = T.inc_subtensor(p[i,:],s)
nw = T.inc_subtensor(q[i,:],po[i,:])
nw=T.clip(nw,0,self.wmax)
return {p:np,q:nw}
def add_synap_post(i,po,p,s,q):
# i:: sequence
# po:: post
# p:: pre
# s:: dA
# q:: W
index = T.nonzero(q[:self.Ne,i])
npo = T.inc_subtensor(po[index,i],s)
nw = T.inc_subtensor(q[:,i],p[:,i])
nw = T.clip(nw,0,self.wmax)
return {po:npo,q:nw}
def add_synap_post_inp(i,po,p,s,q):
# i:: sequence
# po:: post
# p:: pre
# s:: dA
# q:: W
index = T.nonzero(q[:self.Ne,i])
npo = T.inc_subtensor(po[index,i],s)
nw = T.inc_subtensor(q[:,i],p[:,i])
nw = T.clip(nw,0,self.wmax)
return {po:npo,q:nw}
add_dA = T.fscalar('add_dA')
add_p,add_po,add_q = T.fmatrices('add_p','add_po','add_q')
#-------------------------------------------------------------------------
#USinner,updatesUinner = theano.scan(fn=add_synap_pre,sequences=vinner,non_sequences=[self.Spre_inner,self.Spost_inp,self.dApre,self.W_inner])
'USinner,updatesUinner = theano.scan(fn=add_synap_pre,sequences=vinner.nonzero()[0],non_sequences=[add_p,add_po,add_dA,add_q])'
#USinner1,updatesUinner1 = theano.scan(fn=add_synap_pre,sequences=vinner,non_sequences=[self.Spost_inner,self.Spre_inner,self.dApost,self.W_inner])
#-------------------------------------------------------------------------
#UpostInner = theano.function(inputs[vinner],updates={self.Spost_inner:USpostInner})
#UpostInp = theano.function(inputs=[vinner],updates={self.W_inner:UWInnerpost})
'USinner_f = theano.function(inputs=[vinner,add_p,add_po,add_dA,add_q],outputs=None,updates=updatesUinner)'
#USinner_step2 = theano.function(inputs=[vinner,add_p,add_po,add_dA,add_q],outputs=None,updates=updatesUinner)
USinner_inner_pre,updatesUinner_inner_pre = theano.scan(fn=add_synap_pre,sequences=vinner[:self.Ne].nonzero()[0],non_sequences=[self.Spre_inner,self.Spost_inner,add_dA,self.W_inner])
self.USinner_f_inner_pre = theano.function(inputs=[vinner,add_dA],outputs=None,updates=updatesUinner_inner_pre,allow_input_downcast=True)
USinner_innerpost,updatesUinner_inner_post = theano.scan(fn=add_synap_post,sequences=vinner[:self.Ne].nonzero()[0],non_sequences=[self.Spost_inner,self.Spre_inner,add_dA,self.W_inner])
self.USinner_f_inner_post = theano.function(inputs=[vinner,add_dA],outputs=None,updates=updatesUinner_inner_post,allow_input_downcast=True)
USinner_inp_pre,updatesUSinner_inp_pre =theano.scan(fn=add_synap_pre_inp,sequences=vinner.nonzero()[0],non_sequences=[self.Spre_inp,self.Spost_inp,add_dA,self.W_inp])
self.USinner_f_inp_pre = theano.function(inputs=[vinner,add_dA],outputs=None,updates=updatesUSinner_inp_pre,allow_input_downcast=True)
USinner_inp_post,updatesUSinner_inp_post =theano.scan(fn=add_synap_post_inp,sequences=vinner[:self.Ne].nonzero()[0],non_sequences=[self.Spost_inp,self.Spre_inp,add_dA,self.W_inp])
self.USinner_f_inp_post = theano.function(inputs=[vinner,add_dA],outputs=None,updates=updatesUSinner_inp_post,allow_input_downcast=True)
# Call reset function
def reset_v(index,vr):
nv = T.set_subtensor(self.S[0,index],vr)
return{self.S:nv}
resetV,resetV_update = theano.scan(fn=reset_v,sequences=vinner.nonzero()[0],non_sequences=[U])
self.resetV_f = theano.function(inputs=[vinner,U],outputs=None,updates=resetV_update,allow_input_downcast=True)
setvalue = T.fscalar('setvalue')
iv = T.ivector('iv')
def reset_state(i,value,state):
nstate = T.set_subtensor(state[i,:],value)
return {state:nstate}
reset_S_state,Upreset_S_state = theano.scan(fn=reset_state,sequences=iv,non_sequences=[setvalue,self.S])
self.reset_S_fn = theano.function(inputs=[iv,setvalue],outputs=None,updates=Upreset_S_state)
def reset_syn(self):
self.Spost_inner.set_value(self.Spost_inner_ini)
self.Spost_inp.set_value(self.Spost_inp_ini)
self.Spre_inner.set_value(self.Spre_inner_ini)
self.Spre_inp.set_value(self.Spre_inp_ini)
#self.reset_S_fn([1,2],0.)
self.S.set_value(self.S_init)
def state_update(self,input,itera,fired=None,stdp=False):
Ne = self.Ne
Ni = self.Ni
#self.Spre_record = np.empty()
if itera ==0:
#=====================================================================
#===================================================================
# main process
'''Update Schedule:
1.Update state variables of SNNgroup: dot(A,S)
1.Update state variables of Synapses: dot(exp(-dt/tau),Ssynapse), including W_inp and W_inner
2.Call thresholding function: S[0,:]>Vt
3.Push spikes into SpikeContainer
4.Propagate spikes via Connection(possibly with delays)
5.Update state variables of Synapses (STDP)
6.Call reset function on neurons which has spiked'''
#---------------------------------------------
#----------------------------------------------
#---------------
#self.V_record = np.vstack((self.V_record,self.S.get_value()[0,:]))
self.stat()
# Record
## self.V_record = np.vstack((self.V_record,self.S.get_value()[0,:]))
## self.V_record = self.V_record[1:,:]
## self.ge_record = np.vstack((self.ge_record,self.S.get_value()[1,:]))
## self.ge_record = self.ge_record[1:,:]
## self.gi_record=np.vstack((self.gi_record,self.S.get_value()[2,:]))
## self.gi_record = self.gi_record[1:,:]
# update Synapses
if stdp!=False:
self.UpdateSpre_inner()
self.UpdateSpost_inner()
self.UpdateSpre_inp()
self.UpdateSpost_inp()
# Thresholding
spktmp = self.spike_fun(self.Vt) #(1,n), 0-1 vector, int8 np.array
self.out = spktmp
# supervised term
if fired != None:
# fired is a vector
#fired = np.concatenate([fired,spktmp[self.Ne:]])
self.error = len(np.nonzero(spktmp[:Ne] != fired)[0])
spktmp = fired
# spike containing
## self.spkC = np.vstack((self.spkC,spktmp))
## self.spkC = self.spkC[1:,:]
# Propagate Spike
if len(spktmp[0:Ne].nonzero()[0])!=0:
self.S_inner1(spktmp)
if len(spktmp[Ne:].nonzero()[0])!=0:
self.S_inner2(spktmp)
if len(input[:].nonzero()[0])!=0: # input is a 0-1 vector
self.S_inp(input)
# STDP
if stdp!=False:
if len(input[:].nonzero()[0])!=0:
#Pre (inp)
self.USinner_f_inp_pre(input,self.dApre)
if len(spktmp[:self.Ne].nonzero()[0])!=0:
#Pre (inner)
self.USinner_f_inner_pre(spktmp,self.dApre)
#Post (inner)
self.USinner_f_inner_post(spktmp,self.dApost)
#Post (inp)
self.USinner_f_inp_post(spktmp,self.dApost)
# Reset
if len(spktmp[:].nonzero()[0])!=0:
self.resetV_f(spktmp,self.Vr)
#-----------------
#==========================================================================
else:
## if itera ==47:
## print 'iteration is 47'
#self.V_record = np.vstack((self.V_record,self.S.get_value()[0,:]))
# updata state S
self.stat()
# Record
## self.V_record = np.vstack((self.V_record,self.S.get_value()[0,:]))
## self.ge_record = np.vstack((self.ge_record,self.S.get_value()[1,:]))
## self.gi_record=np.vstack((self.gi_record,self.S.get_value()[2,:]))
#update state of Synapses
if stdp!=False:
self.UpdateSpre_inner()
self.UpdateSpost_inner()
self.UpdateSpre_inp()
self.UpdateSpost_inp()
#threshold function
spktmp = self.spike_fun(self.Vt) #(1,n), 0-1 vector, float32 np.array
self.out = spktmp
#supervised term
if fired != None:
#fired = np.concatenate([fired,spktmp[self.Ne:]])
self.error = len(np.nonzero(spktmp[:Ne] != fired[:Ne])[0])
spktmp = fired
## else:
## spktmp = self.spike_fun() #(1,n), 0-1 vector, float32 np.array
# push spike into Container
## self.spkC = np.vstack((self.spkC,spktmp))
# propagate spikes
if len(spktmp[0:self.Ne].nonzero()[0])!=0:
self.S_inner1(spktmp)
if len(spktmp[self.Ne:].nonzero()[0])!=0:
self.S_inner2(spktmp)
if len(input[:].nonzero()[0])!=0: # input is a 0-1 vector
self.S_inp(input)
# Update Synapses(STDP|STDC)
if stdp!=False:
if len(input[:].nonzero()[0])!=0:
self.USinner_f_inp_pre(input,self.dApre)
if len(spktmp[:self.Ne].nonzero()[0])!=0:
self.USinner_f_inner_pre(spktmp,self.dApre)
self.USinner_f_inner_post(spktmp,self.dApost)
self.USinner_f_inp_post(spktmp,self.dApost)
# Reset S
## if itera ==47:
## print 'iteration is 47'
if len(spktmp[:].nonzero()[0])!=0:
self.resetV_f(spktmp,self.Vr)
def run(self):
# run the network
self.state_update()
def load_data(dataDir):
# return img_data
# shape:: (n_img, all_pixel)
imageList = os.listdir(dataDir)
dataset = []
for i in imageList:
tmp = read_image(dataDir+i).flatten()
tmp = tmp/255.*4.
dataset.append(tmp)
return dataset