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MNIST_IF_STDP_SEQ.py
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MNIST_IF_STDP_SEQ.py
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#!/bin/python
#-----------------------------------------------------------------------------
# File Name : mnist_feedback.py
# Purpose:
#
# Author: Emre Neftci
#
# Creation Date : 25-04-2013
# Last Modified : Mon 25 Nov 2013 11:52:42 AM PST
#
# 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):
defaultclock.reinit()
b_init = np.concatenate([b_v, b_c, b_h])
netobjs = []
Wh = Whv[:(N_v),:]
b1 = (sigm(b_v)).mean() #0.47174785699167904
b2 = (sigm(b_h)).mean() #0.55992711057515843
Th = Bu * wb
A = -np.log((-1+1./(t_ref*Bu))*gamma*t_ref)
wmean = np.mean(Wh)#-0.023632872766664939
wh = (A - wmean*b1 - b_h.mean())/Th
wv = (A - wmean*b2 - b_v.mean())/Th
N_helper = 25
i_inj_v_helper = Th/beta_fi
i_inj_h_helper = Th/beta_fi
#------------------------------------------ Neuron Groups
print "Creating equation"
eqs_v = Equations(eqs_str_lif_wnrd,
Cm = 1e-12*farad,
I_inj = i_inj,
g = g_leak,
sigma = wnsigma,
tau_rec = tau_rec)
eqs_h = Equations(eqs_str_lif_wnr,
Cm = 1e-12*farad,
I_inj = i_inj,
g = g_leak,
sigma = wnsigma,
tau_rec = tau_rec)
eqs_v_helper = Equations(eqs_str_lif_wnr,
Cm = 1e-12*farad,
I_inj = i_inj_v_helper,
g = 0.,
sigma = 0.,
tau_rec = tau_rec)
eqs_h_helper = Equations(eqs_str_lif_wnr,
Cm = 1e-12*farad,
I_inj = i_inj_h_helper,
g = 0.,
sigma = 0.,
tau_rec = tau_rec)
print "Creating Population"
neuron_group_rvisible = NeuronGroup(\
N_v+N_c,
model = eqs_v,
threshold = 'v>theta*volt',
refractory = t_ref,
reset = 0*volt
)
neuron_group_rhidden = NeuronGroup(\
N_h,
model = eqs_h,
threshold = 'v>theta*volt',
refractory = t_ref,
reset = 0*volt
)
neuron_group_rvisible_helper = NeuronGroup(\
N_helper,
model = eqs_v_helper,
threshold = 'v>theta*volt',
reset = 0*volt
)
neuron_group_rhidden_helper = NeuronGroup(\
N_helper,
model = eqs_h_helper,
threshold = 'v>theta*volt',
reset = 0*volt
)
neuron_group_rvisible_helper.v = np.random.uniform(0,1,size=N_helper)
neuron_group_rhidden_helper.v = np.random.uniform(0,1,size=N_helper)
netobjs += [neuron_group_rvisible, neuron_group_rhidden, neuron_group_rvisible_helper, neuron_group_rhidden_helper]
@network_operation(clock=defaultclock)
def update_mpot_helper(when='after'):
neuron_group_rvisible_helper.v[neuron_group_rvisible_helper.v<=0.0*volt]=0.0*volt
neuron_group_rhidden_helper.v[neuron_group_rhidden_helper.v<=0.0*volt]=0.0*volt
netobjs += [update_mpot_helper]
#--------------------------- Custom Network Operations
#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.
#---------------------- Connections and Synapses
#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,:]
S_helper_v = Connection(neuron_group_rvisible_helper, neuron_group_rvisible, 'I_rec')
S_helper_v.connect_random(neuron_group_rvisible_helper, neuron_group_rvisible, p=0.5, weight = -wv/N_helper/.5/beta/tau_rec)
S_helperi_v = Synapses(neuron_group_rvisible, neuron_group_rvisible_helper, pre = "I_rec_post+= -wb/.5/N_v/beta_fi/tau_rec")
S_helperi_v.connect_random(neuron_group_rvisible, neuron_group_rvisible_helper,sparseness=0.5)
#
S_helper_h = Connection(neuron_group_rhidden_helper, neuron_group_rhidden, 'I_rec')
S_helper_h.connect_random(neuron_group_rhidden_helper, neuron_group_rhidden, p=0.5, weight = -wh/N_helper/.5/beta/tau_rec)
S_helperi_h = Connection(neuron_group_rhidden, neuron_group_rhidden_helper, 'I_rec')
S_helperi_h.connect_random(neuron_group_rhidden, neuron_group_rhidden_helper, p=0.5, weight = -wb/.5/N_h/beta_fi/tau_rec)
netobjs+= [Sbv,Sbh,Srs,S_helperi_v,S_helperi_h,S_helper_v,S_helper_h]
ev = CountingEventClock(period = dcmt*t_ref)
@network_operation(clock = ev)
def g_update(when='after'):
tmod, n = ev.step()
if tmod < 50:
neuron_group_rvisible.I_d = Id[n]
else:
neuron_group_rvisible.I_d = Id[n]
if tmod<=int(t_burn_percent):
Srs.g = 0.
Sbh.g = Sbv.g = 0.
elif int(t_burn_percent)<=tmod<49:
g_up = 1.
Srs.g = Sbv.g = Sbh.g = learning_rate_decay(n,n0=4000)*g_up
elif 49<=tmod < 50+int(t_burn_percent):
Srs.g = Sbv.g = Sbh.g = +0.
elif 50+int(t_burn_percent) <= tmod <99:
g_down = -1.
Srs.g = Sbv.g = Sbh.g = learning_rate_decay(n,n0=4000)*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[:N_v])
Mc=SpikeMonitor(neuron_group_rvisible[N_v:])
Mvmem=StateMonitor(neuron_group_rvisible[N_v:], varname='v', record=True)
netobjs += [Mh, Mv,Mc,Mvmem]
#MId = StateMonitor(neuron_group_rvisible, varname='I_d', record=True)
#MIt = StateMonitor(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 = True, display=True, mnist_data=mnist_data)
locals().update(loc)