-
Notifications
You must be signed in to change notification settings - Fork 9
/
accuracy_pre_trained.py
executable file
·86 lines (73 loc) · 3.01 KB
/
accuracy_pre_trained.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
#!/bin/python
#-----------------------------------------------------------------------------
# File Name : mnist_feedback.py
# Purpose:
#
# Author: Emre Neftci
#
# Creation Date : 25-04-2013
# Last Modified : Fri 27 Jun 2014 03:41:14 PM PDT
#
# Copyright : (c) UCSD, Emre Neftci, Srinjoy Das, Bruno Pedroni, Kenneth Kreutz-Delgado, Gert Cauwenberghs
# Licence : GPLv2
#-----------------------------------------------------------------------------
import meta_parameters
meta_parameters.parameters_script = 'parameters_accuracy'
from common import *
from MNIST_IF_RATE import main
#Data ran on Results//207__25-11-2013/WSCD.pkl
Wh,Wc,b_init = load_NS_v2(N_v, N_h, N_c, dataset = 'data/WSCD.pkl')
W = np.zeros([N_v+N_c,N_h])
W[:(N_v),:] = Wh
W[N_v:(N_v+N_c),:] = Wc.T
b_h = b_init[(N_v+N_c):]
b_v = b_init[:N_v]
b_c = b_init[N_v:(N_v+N_c)]
data = mnist_data = load_MNIST(1,
min_p = 1e-32,
max_p = 0.99,
binary = True,
seed = None)
def create_Id_no_class(data, min_p = 1e-32, max_p = 0.99, seed = None):
iv_seq, iv_l_seq, train_iv, train_iv_l, test_iv, test_iv_l = data
Idp = np.ones([N, N_v+N_c])*min_p
for i in range(N):
Idp[i,N_v:] = 0.
Idp[i,:N_v] = clamped_input_transform(test_iv[i,:], min_p = min_p, max_p = max_p)
Id = (Idp /beta)
return Id, test_iv, test_iv_l
def wrap_run(Id):
out = main(W, b_v, b_c, b_h, Id = np.array([Id]))
Mh, Mv= out['Mh'], out['Mv']
return monitor_to_spikelist(Mv).firing_rate(tbin)[N_v:,:]
if __name__ == '__main__':
Ids, test_data, test_labels = create_Id_no_class(data)
free_en_perf = classification_free_energy(Wh, Wc.T, b_h, b_c, test_data[:N,:N_v], test_labels[:N], n_c_unit)[0]
print 'free energy all test samples {0}'.format(classification_free_energy(Wh, Wc.T, b_h, b_c, data[4], data[5], n_c_unit)[0])
print 'free energy {1} test samples {0}'.format(free_en_perf, N)
import multiprocessing
pool = multiprocessing.Pool(8)
pool_out = pool.map(wrap_run, Ids)
et.globaldata.free_en_perf = free_en_perf
et.globaldata.test_labels = test_labels
et.globaldata.pool_out = pool_out
et.mksavedir()
et.save()
from plot_options import *
matplotlib.rcParams['figure.subplot.right'] = .8
nt = pool_out[0].shape[1]
res_out = [[None for j in range(N)] for i in range(nt)]
for i in range(1,nt):
for j,r in enumerate(pool_out):
rate_up_to_ti = r[:,:i].mean(axis=1)
res_out[i][j] = rate_up_to_ti.reshape(n_classes,N_c/n_classes).mean(axis=1).argmax() == test_labels[j]
res_out = np.array(res_out[1:])
xaxis = np.arange(tbin,t_sim*1000,tbin)/1000
plot(xaxis,res_out.mean(axis=1), alpha=0.5, linewidth=2, color='b')
axhline(.1,color='k',alpha=0.5,linewidth=3,linestyle='--')
axvline(.2,color='k',alpha=0.5,linewidth=3)
ylim(0,1)
xlim(0,t_sim)
axhline(free_en_perf,color='k',alpha=0.5,linewidth=2,linestyle='-')
yticks([0,.1,1])
ylabel('Accuracy')