if res>last_perf:
last_perf = res
#bestM = read_allparamters_dual(context)
# # Necessary otherwise will only train on 1000 images total.
#
# #############
# if n_epochs == 0 and test_every>0: #Test only
# print 'Test only'
# res = run_classify(context, labels_test)
# acc_hist.append([0, res])
# print res
#
M = process_parameters_auto(context)
d=et.mksavedir()
et.globaldata.context = context
et.save()
et.save(context, 'context.pkl')
et.save(sys.argv, 'sysargv.pkl')
et.save(M,'M.pkl')
et.save(spkcnt,'spkcnt.pkl')
et.save(bestM,'bestM.pkl')
et.save(acc_hist, 'acc_hist.pkl')
et.annotate('res',text=str(acc_hist))
textannotate('last_res',text=str(acc_hist))
textannotate('last_dir',text=d)
#
#
#
#
copyfile(fname_train.shared_mem+'_core_0.dat',
fname_test.syn_wgt_table+'_core_0.dat',)
# cfg_test.core_cfgs[0].W = c_nsat_reader_train.read_c_nsat_weights()[0]
# cfg_train.core_cfgs[0].W = cfg_test.core_cfgs[0].W.copy()
# c_nsat_writer_test.write_L0connectivity()
# c_nsat_writer_train.write_L0connectivity()
if test_every>0:
if i % test_every == test_every-1:
nsat.run_c_nsat(fname_test)
test_spikelist = nsat.importAER(c_nsat_reader_test.read_c_nsat_raw_events()[0],
sim_ticks=sim_ticks_test,
id_list=np.arange(sP, sP+Np))
pip .append([i, float(sum(np.argmax(test_spikelist.id_slice(range(sP,sP+Np)).firing_rate(t_sample_test).T,axis=1) == targets_classify[:N_test]))/N_test*100])
print exp_name
print pip
copyfile(fname_train.shared_mem+'_core_0.dat',
fname_train.syn_wgt_table+'_core_0.dat',)
try:
import experimentTools as et
d=et.mksavedir()
et.save(cfg_test, 'cfg_test.pkl')
et.save(cfg_train, 'cfg_train.pkl')
et.save(pip, 'pip.pkl')
et.annotate('res',text=str(pip))
except ImportError:
print('saving disabled due to missing experiment tools')
nsat.run_c_nsat(fname_train)
print(('Run took {0} seconds'.format(time.time() - t0)))
for j in range(setup.ncores):
#train->test
shutil.copy(exp_name + '/_shared_mem_core_{0}.dat'.format(j),
exp_name_test + '/_wgt_table_core_{0}.dat'.format(j))
#train->train
shutil.copy(exp_name + '/_shared_mem_core_{0}.dat'.format(j),
exp_name + '/_wgt_table_core_{0}.dat'.format(j))
if test_every > 0:
if i % test_every == test_every - 1:
nsat.run_c_nsat(fname_test)
acc, slout = test_accuracy(c_nsat_reader_test,
targets=targets_classify[:N_test],
pop=pop_out,
sim_ticks=sim_ticks_test,
duration=t_sample_test)
pip.append([i, acc])
print(exp_name)
print(pip)
try:
import experimentTools as et
d = et.mksavedir(pre='Results_Scripts/')
et.save(pip, 'pip.pkl')
et.annotate('res', text=str(pip))
except ImportError:
print('saving disabled due to missing experiment tools')
id_list=np.arange(sP, sP + Np))
pip.append([
i,
float(
sum(
np.argmax(test_spikelist.id_slice(range(
sP, sP + Np)).firing_rate(t_sample_test).T,
axis=1) == targets_classify[:N_test])) /
N_test * 100
])
print exp_name
print pip
copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_train.syn_wgt_table + '_core_0.dat',
)
try:
import experimentTools as et
d = et.mksavedir()
et.save(cfg_test, 'cfg_test.pkl')
et.save(cfg_train, 'cfg_train.pkl')
et.save(stats_nsat, 'stats_nsat.pkl')
et.save(pip, 'pip.pkl')
et.annotate('res', text=str(pip))
et.save(c_nsat_reader_train.read_c_nsat_weights()[0], 'W.pkl')
except ImportError:
print('saving disabled due to missing experiment tools')
