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')
acc_hist.append([i, res])
print res
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)
#
#
#
def update_output_weights(output_weights):
output_mtx = fio.mtx_file_to_matrix('inputs/{}/train/fwmat_ho.mtx'.format(
context['directory']))
output_mtx = output_mtx[200:, :].flatten()
output_weights.append(output_mtx)
return output_weights
if __name__ == '__main__':
try:
last_perf = (-1.0, -1.0)
new_test_data = args.gen_data
save = True
et.mksavedir()
et.globaldata.context = context
start_epoch = 0
acc_hist = []
snr_hist = []
weight_stats = {}
output_weights = []
spkcnt = [None for i in range(context['n_epochs'])]
if args.resume is not '':
if args.resume[-1] is not '/':
args.resume += '/'
print 'Loading previous run from {}'.format(args.resume)
et.globaldata.directory = args.resume
M = et.load('M.pkl')
