def Test(net, mnist, start, end, N_hidden, T, v0, u0, I0, ge0, \
neuron_names, synapse_names, state_monitor_names, spike_monitor_names, parameters):
hit, miss = 0, 0
hit_ind, miss_ind = np.zeros(10, dtype=int), np.zeros(10, dtype=int)
print "Testing"
#for number in range(start, end):
number = start - 1
count = 0
while count < end - start:
number += 1
#pudb.set_trace()
label = mnist[1][number]
if label[0] == 0 or label[0] == 1:
count += 1
print "\tlabel = ", label,
print "\tnumber = ", number
net = snn.Run(net, mnist, number, T, v0, u0, I0, ge0, \
neuron_names, synapse_names, state_monitor_names, \
spike_monitor_names, parameters)
S_l, S_i = _netoutput(net, spike_monitor_names, N_hidden)
S_d = init.out(label)
print "\t\tS_l = ", S_l
print "\t\tS_d = ", S_d
print "\t\tS_i = ", S_i
index = init.out_inverse(S_d)
result = Compare(S_l, S_d)
if result == True:
hit_ind[index] += 1
hit += 1
else:
miss_ind[index] += 1
miss += 1
return hit, miss, hit_ind, miss_ind
def ReSuMe(net, mnist, start, end, Pc, N_hidden, T, N_h, N_o, v0, u0, I0, ge0, neuron_names, synapse_names, state_monitor_names, spike_monitor_names, parameters):
trained = False
N = len(mnist[0])
N_hidden_last = len(net[neuron_names[-2]])
N_out = len(net[neuron_names[-1]])
N_h = 1
N_o = 1
correct = 0
while True:
print "========================== R O U N D =========================="
correct = 0
number = start - 1
count = 0
while count < end - start:
dw = np.zeros(len(net[synapse_names[-1]]))
number += 1
label = mnist[1][number]
if label[0] == 0 or label[0] == 1:
count += 1
print "number = ", number
k = 0
for i in range(1):
k += 1
N_h = init.out(mnist[1][number][0])
desired_index = number / 2
lst = range(N_hidden_last)
rnd.shuffle(lst)
net = snn.Run(net, mnist, number, T, v0, u0, I0, ge0, \
neuron_names, synapse_names, state_monitor_names, \
spike_monitor_names, parameters)
S_l, S_i = _netoutput(net, spike_monitor_names, N_hidden)
S_d = init.out(label)
if len(S_l[0]) == S_d[0]:
net.restore()
correct += 1
break
print "\t\tS_l = ", S_l
print "\t\tS_d = ", S_d
print "\t\tS_i = ", S_i
modified = False
w = net[synapse_names[-1]].w[:]
j = 0
if min(S_i) == []:
pudb.set_trace()
t_in_tmp = np.copy(S_i / br.ms)
t_in = t_in_tmp.flatten()
dw = _set_out_spike(net, j, t_in, S_l[j], S_d[j], N_hidden)
if type(dw) == np.ndarray:
print "\t\t\tdw = ", dw
modified = True
w += dw
net.restore()
net[synapse_names[-1]].w[:] = w.clip(0.1)
net.store()
if modified == False:
break
if correct >= 0.8*(end - start):
break
init._save_weights(net, synapse_names, 0, len(synapse_names))
F = open("weights/trained.txt", 'w')
F.write("True")
F.close()
return net
