-
Notifications
You must be signed in to change notification settings - Fork 9
/
MNIST_SRM_STDP.py
executable file
·285 lines (235 loc) · 8.97 KB
/
MNIST_SRM_STDP.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
#!/bin/python
#-----------------------------------------------------------------------------
# File Name : mnist_feedback.py
# Purpose:
#
# Author: Emre Neftci
#
# Creation Date : 25-04-2013
# Last Modified : Tue 01 Oct 2013 07:46:44 PM PDT
#
# Copyright : (c)
# Licence : GPLv2
#-----------------------------------------------------------------------------
from common import *
def main(Whv, b_v, b_c, b_h, Id, dorun = True, monitors=True, display=False, mnist_data = None):
b_init = np.concatenate([b_v, b_c, b_h])
ion()
defaultclock.reinit()
netobjs = []
#------------------------------------------ Neuron Groups
print "Creating equation"
eqs_v = Equations(eqs_str_lif_nrd,
Cm = 1e-12*farad,
I_inj = + i_inj,
g = g_leak,
tau_noise = tau_noise,
tau_rec = tau_rec)
eqs_h = Equations(eqs_str_lif_nr,
Cm = 1e-12*farad,
I_inj = + i_inj,
g = g_leak,
tau_noise = tau_noise,
tau_rec = tau_rec)
print "Creating Population"
neuron_group_rvisible = NeuronGroup(
N_v+N_c,
model=eqs_v,
threshold = SimpleFunThreshold( exp_prob_beta_gamma(defaultclock.dt, beta, g_leak, gamma, t_ref), state = 'v'),
refractory = t_ref)
neuron_group_rhidden = NeuronGroup(N_h,
model=eqs_h,
threshold = SimpleFunThreshold( exp_prob_beta_gamma(defaultclock.dt, beta, g_leak, gamma, t_ref), state = 'v'),
refractory = t_ref)
netobjs += [neuron_group_rvisible, neuron_group_rhidden]
#Bias group
Bv = PoissonGroup(N_v+N_c, rates = bias_input_rate) #Noise injection to h
Bh = PoissonGroup(N_h, rates = bias_input_rate) #Noise injection to h
netobjs+=[Bv,Bh]
#---------------------- Initialize State Variables
neuron_group_rvisible.I_d = 0.
neuron_group_rvisible.I_noise = 0.
neuron_group_rhidden.I_noise = 0.
#Bias units
Sbv = Synapses(Bv, neuron_group_rvisible,
model='''Afre : 1
Afost : 1
g : 1
w : 1''',
pre ='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afre+=deltaAbias
w=w+g*Afost
I_rec_post+= w''',
post='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afost+=deltaAbias
w=w+g*Afre'''
)
Sbv[:,:] = 'i==j'
Sbv.w[:] = np.concatenate([b_v,b_c])/beta/bias_input_rate/tau_rec
Sbh = Synapses(Bh, neuron_group_rhidden,
model='''Afre : 1
Afost : 1
g : 1
w : 1''',
pre ='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afre+=deltaAbias
w=w+g*Afost
I_rec_post+= w''',
post='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afost+=deltaAbias
w=w+g*Afre'''
)
Sbh[:,:] = 'i==j'
Sbh.w[:] = b_h/beta/bias_input_rate/tau_rec
Srs=Synapses(neuron_group_rvisible, neuron_group_rhidden,
model='''Afre : 1
Afost : 1
g : 1
w : 1''',
pre ='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afre+=deltaA
I_rec_post+= w
w=w+g*Afost''',
post='''Afre=Afre*np.exp((lastupdate-t)/tau_learn)
Afost=Afost*np.exp((lastupdate-t)/tau_learn)
Afost+=deltaA
I_rec_pre+= w
w=w+g*Afre'''
)
Srs[:,:] = True
M_rec = Whv/beta
for i in range(M_rec.shape[0]):
Srs.w[i,:] = M_rec[i,:]
netobjs+=[Sbv,Sbh,Srs]
ev = CountingEventClock(period = dcmt*t_ref)
@network_operation(clock = ev)
def g_update(when='after'):
tmod, n = ev.step()
if tmod == 0:
neuron_group_rvisible.I_d= Id[n]
if tmod == 50:
neuron_group_rvisible.I_d = 0.
if int(t_burn_percent)==tmod:
g_up = 1
Srs.g = Sbv.g = Sbh.g = g_up
elif tmod == 49:
Srs.g = Sbv.g = Sbh.g = +0.
elif 50+int(t_burn_percent)==tmod:
g_down = -1
Srs.g = Sbv.g = Sbh.g = g_down
elif 99==tmod:
Srs.g = 0.
Sbh.g = Sbv.g = 0.
if tmod==50:
#neuron_group_rvisible.I_DATA=0
Srs.Afre=0
Srs.Afost=0
Sbv.Afre=0
Sbv.Afost=0
Sbh.Afre=0
Sbh.Afost=0
netobjs+=[g_update]
w_hist_v = []
w_hist_c = []
b_hist_vc = []
b_hist_h = []
if display:
iv_seq, iv_l_seq, train_iv, train_iv_l, test_iv, test_iv_l = mnist_data
figure()
res_hist_test=[]
res_hist_train=[]
test_data = test_iv
test_labels = test_iv_l
train_data = train_iv[:200]
train_labels = train_iv_l[:200]
plot_every = 10
@network_operation(clock=EventClock(dt=plot_every*dcmt*t_ref))
def plot_performance(when='after'):
n = ev.n
Wt = Srs.w.data.reshape(N_v+N_c,N_h)
w_hist_v.append(Wt[:N_v,:].mean())
w_hist_c.append(Wt[N_v:,:].mean())
b_hist_vc.append(Sbv.w.data.mean())
b_hist_h.append(Sbh.w.data.mean())
W=Srs.w.data.copy().reshape(N_v+N_c, N_h)*beta
Wvh=W[:N_v,:]
Wch=W[N_v:,:]
mBv = Sbv.w.data*beta*tau_rec*bias_input_rate
mBh = Sbh.w.data*beta*tau_rec*bias_input_rate
b_c = mBv[N_v:(N_v+N_c)]
b_v = mBv[:N_v]
b_h = mBh
mB = np.concatenate([mBv,mBh])
accuracy_test = classification_free_energy(Wvh, Wch, b_h, b_c, test_data, test_labels, n_c_unit)[0]
res_hist_test.append(accuracy_test)
accuracy_train = classification_free_energy(Wvh, Wch, b_h, b_c, train_data, train_labels, n_c_unit)[0]
res_hist_train.append(accuracy_train)
clf()
plot(res_hist_train, 'go-', linewidth=2)
plot(res_hist_test, 'ro-', linewidth=2)
axhline(0.1)
axhline(0.85)
axhline(0.9, color='r')
xlim([0,t_sim/(plot_every*dcmt*t_ref)])
ylim([0.0,1])
a=plt.axes([0.7,0.1,0.2,0.2])
a.plot(w_hist_v,'b.-')
a.plot(w_hist_c,'k.-')
a.plot(b_hist_vc,'g.-')
a.plot(b_hist_h,'r.-')
print accuracy_test
draw()
netobjs += [plot_performance]
#--------------------------- Monitors
if monitors:
Mh=SpikeMonitor(neuron_group_rhidden)
Mv=SpikeMonitor(neuron_group_rvisible)
netobjs += [Mh, Mv]
#MId = StateMonitor(neuron_group_rvisible, varname='I_d', record=True)
#MIt = Statneuron_group_rvisibleeMonitor(Sbh,varname='g',record=[0])
net = Network(netobjs)
if dorun:
import time
tic = time.time()
net.run(t_sim)
toc = time.time()-tic
print toc
return locals()
if __name__ == '__main__':
Id = create_Id()
W, b_v, b_c, b_h = create_rbm_parameters()
mnist_data = load_mnist_data()
loc = main(W, b_v, b_c, b_h, Id =create_Id(), monitors = False, display=True, mnist_data=mnist_data)
locals().update(loc)
W=Srs.w.data.copy().reshape(N_v+N_c, N_h)*beta
Wvh=W[:N_v,:]
Wch=W[N_v:,:]
mBv = Sbv.w.data*beta*tau_rec*bias_input_rate
mBh = Sbh.w.data*beta*tau_rec*bias_input_rate
b_c = mBv[N_v:(N_v+N_c)]
b_v = mBv[:N_v]
b_h = mBh
mB = np.concatenate([mBv,mBh])
d = et.mksavedir()
et.save_file(__file__)
et.globaldata.W = W
et.globaldata.mB = mB
try:
et.globaldata.Mv = monitor_to_spikelist(Mv)
et.globaldata.Mh = monitor_to_spikelist(Mh)
except NameError:
print "SpikeMonitors are not defined"
et.globaldata.res_hist_train = res_hist_train
et.globaldata.res_hist_test = res_hist_test
et.globaldata.w_hist_v = w_hist_v
et.globaldata.w_hist_c = w_hist_c
et.globaldata.b_hist_vcn = res_hist_train
et.globaldata.b_hist_h = res_hist_test
et.save({'Wh':Wvh, 'Wc':Wch, 'b_vch': mB}, 'WSCD.pkl')
et.save()
et.savefig('progress.png', format='png')