def erbp_convnet_2L(data_train, data_classify, targets_classify, nepochs=10):
N_FEAT1 = 16
N_FEAT2 = 32
stride = 2
ksize = 5
exp_name = '/tmp/mnist_convnet_2L'
exp_name_test = '/tmp/mnist_convnet_2L_test/'
inputsize = 28
Nchannel = 1
Nxy = inputsize*inputsize
Nv = Nxy*Nchannel
Nl = 10
Nconv1 = Nv//stride//stride*N_FEAT1//Nchannel
Nconv2 = Nconv1//stride//stride*N_FEAT2//N_FEAT1
Nh = 100
t_sample_test = 3000
t_sample_train = 1500
N_train = 5000
N_test = 1000
test_every = 1
inp_fact = 25
sim_ticks = N_train*t_sample_train
sim_ticks_test = N_test*t_sample_test
np.random.seed(100)
wpg = 96
wgp = 37
erbp_ptype_ = erbp_ptype.copy()
erbp_ptype_.rr_num_bits = 12
erbp_ptype_.hiac = [-7, OFF, OFF]
erf_ntype_ = erf_ntype.copy()
erf_ntype_.plasticity_type = [erbp_ptype_, nonplastic_ptype]
erf_ntype_.Wgain[0] = 2
net_graph = LogicalGraphSetup()
pop_data = net_graph.create_population(Population(name='pop_data',
n=Nv,
core=-1,
is_external=True))
pop_lab = net_graph.create_population(Population(name='pop_lab',
n=Nl,
core=-1,
is_external=True))
pop_conv1 = net_graph.create_population(Population(name='pop_conv1',
n=Nconv1,
core=0,
neuron_cfg=erf_ntype_))
pop_conv2 = net_graph.create_population(Population(name='pop_conv2',
n=Nconv2,
core=1,
neuron_cfg=erf_ntype_))
pop_hid = net_graph.create_population(Population(name='pop_hid',
n=Nh,
core=1,
neuron_cfg=erf_ntype_))
pop_out = net_graph.create_population(Population(name='pop_out',
n=Nl,
core=1,
neuron_cfg=output_ntype))
net_graph.create_connection(pop_data, pop_conv1, 0,
connect_conv2dbank(inputsize,
Nchannel,
N_FEAT1,
stride,
ksize))
net_graph.create_connection(pop_conv1, pop_conv2, 0,
connect_conv2dbank(inputsize//stride,
N_FEAT1,
N_FEAT2,
stride,
ksize))
net_graph.create_connection(pop_conv2, pop_hid, 0,
connect_random_uniform(low=-16, high=16))
net_graph.create_connection(pop_hid, pop_out, 0,
connect_random_uniform(low=-4, high=4))
pop_err_pos = net_graph.create_population(Population(name='pop_err_pos',
n=Nl,
core=0,
neuron_cfg=error_ntype))
pop_err_neg = net_graph.create_population(Population(name='pop_err_neg',
n=Nl,
core=0,
neuron_cfg=error_ntype))
net_graph.create_connection(pop_out, pop_err_pos, 0, connect_one2one(-wpg))
net_graph.create_connection(pop_out, pop_err_neg, 0, connect_one2one(wpg))
net_graph.create_connection(pop_lab, pop_err_pos, 0, connect_one2one(wpg))
net_graph.create_connection(pop_lab, pop_err_neg, 0, connect_one2one(-wpg))
[p, w] = connect_shuffle(2000)(pop_err_pos, pop_conv1)
net_graph.create_connection(pop_err_pos, pop_conv1, 1, [p, +w])
net_graph.create_connection(pop_err_neg, pop_conv1, 1, [p, -w])
[p, w] = connect_shuffle(2000)(pop_err_pos, pop_conv2)
net_graph.create_connection(pop_err_pos, pop_conv2, 1, [p, +w])
net_graph.create_connection(pop_err_neg, pop_conv2, 1, [p, -w])
[p, w] = connect_shuffle(3000)(pop_err_pos, pop_hid)
net_graph.create_connection(pop_err_pos, pop_hid, 1, [p, +w])
net_graph.create_connection(pop_err_neg, pop_hid, 1, [p, -w])
net_graph.create_connection(pop_err_pos, pop_out, 1, connect_one2one(wgp))
net_graph.create_connection(pop_err_neg, pop_out, 1, connect_one2one(-wgp))
setup = net_graph.generate_multicore_setup(NSATSetup)
spk_rec_mon = [[] for i in range(setup.ncores)]
cfg_train = setup.create_configuration_nsat(sim_ticks=sim_ticks,
w_check=False,
spk_rec_mon=spk_rec_mon,
monitor_spikes=False,
gated_learning=[True, True],
plasticity_en=[True, True])
spk_rec_mon = [[] for i in range(setup.ncores)]
spk_rec_mon[pop_out.core] = pop_out.addr
cfg_test = cfg_train.copy()
cfg_test.sim_ticks = sim_ticks_test
cfg_test.plasticity_en[:] = False
cfg_test.spk_rec_mon = spk_rec_mon
cfg_test.monitor_spikes = True
SL_train = create_spike_train(data_train[:N_train],
t_sample_train,
scaling=inp_fact,
with_labels=True)
ext_evts_data_train = nsat.exportAER(SL_train)
SL_test = create_spike_train(data_classify[:N_test],
t_sample_test,
scaling=inp_fact,
with_labels=False)
ext_evts_data_test = nsat.exportAER(SL_test)
cfg_test.set_ext_events(ext_evts_data_test)
cfg_train.set_ext_events(ext_evts_data_train)
c_nsat_writer_train = nsat.C_NSATWriter(cfg_train,
path=exp_name,
prefix='')
c_nsat_writer_train.write()
c_nsat_writer_test = nsat.C_NSATWriter(cfg_test,
path=exp_name_test,
prefix='')
c_nsat_writer_test.write()
fname_train = c_nsat_writer_train.fname
fname_test = c_nsat_writer_test.fname
c_nsat_reader_test = nsat.C_NSATReader(cfg_test, fname_test)
pip, total_time = [], []
t0t, tft = 0, 0
for i in range(nepochs):
t0 = time.time()
nsat.run_c_nsat(fname_train)
tf = time.time()
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:
t0t = time.time()
nsat.run_c_nsat(fname_test)
tft = time.time()
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(acc)
total_time.append(tf - t0 + tft - t0t)
return pip, total_time
def erbp_mlp_2L_multicore(data_train, data_classify, targets_classify,
nepochs=10):
exp_name = '/tmp/mnist_mlp_2L'
exp_name_test = '/tmp/mnist_mlp_2L_test/'
inputsize = 28
Nchannel = 1
Nxy = inputsize*inputsize
Nv = Nxy*Nchannel
Nl = 10
Nh = 100
t_sample_test = 3000
t_sample_train = 1500
N_train = 500
N_test = 100
test_every = 1
inp_fact = 25
sim_ticks = N_train*t_sample_train
sim_ticks_test = N_test*t_sample_test
np.random.seed(100)
wpg = 96
wgp = 37
net_graph = LogicalGraphSetup()
pop_data = net_graph.create_population(Population(name='data',
n=Nv,
core=-1,
is_external=True))
pop_lab = net_graph.create_population(Population(name='lab',
n=Nl,
core=-1,
is_external=True))
pop_hid1 = net_graph.create_population(Population(name='hid1',
n=Nh,
core=0,
neuron_cfg=erf_ntype))
pop_hid2 = net_graph.create_population(Population(name='hid2',
n=Nh,
core=1,
neuron_cfg=erf_ntype))
pop_out = net_graph.create_population(Population(name='out',
n=Nl,
core=0,
neuron_cfg=output_ntype))
pop_err_pos = net_graph.create_population(Population(name='err_pos',
n=Nl,
core=0,
neuron_cfg=error_ntype))
pop_err_neg = net_graph.create_population(Population(name='err_neg',
n=Nl,
core=0,
neuron_cfg=error_ntype))
net_graph.create_connection(pop_data, pop_hid1, 0,
connect_random_uniform(low=-16, high=16))
net_graph.create_connection(pop_hid1, pop_hid2, 0,
connect_random_uniform(low=-16, high=16))
net_graph.create_connection(pop_hid2, pop_out, 0,
connect_random_uniform(low=-4, high=4))
net_graph.create_connection(pop_out, pop_err_pos, 0, connect_one2one(-wpg))
net_graph.create_connection(pop_out, pop_err_neg, 0, connect_one2one(wpg))
net_graph.create_connection(pop_lab, pop_err_pos, 0, connect_one2one(wpg))
net_graph.create_connection(pop_lab, pop_err_neg, 0, connect_one2one(-wpg))
p, w = connect_shuffle(3000)(pop_err_pos, pop_hid1)
net_graph.create_connection(pop_err_pos, pop_hid1, 1, [p, w])
net_graph.create_connection(pop_err_neg, pop_hid1, 1, [p, -w])
p, w = connect_shuffle(3000)(pop_err_pos, pop_hid2)
net_graph.create_connection(pop_err_pos, pop_hid2, 1, [p, w])
net_graph.create_connection(pop_err_neg, pop_hid2, 1, [p, -w])
net_graph.create_connection(pop_err_pos, pop_out, 1, connect_one2one(wgp))
net_graph.create_connection(pop_err_neg, pop_out, 1, connect_one2one(-wgp))
setup = net_graph.generate_multicore_setup(NSATSetup)
spk_rec_mon = [[] for i in range(setup.ncores)]
spk_rec_mon[pop_out.core] = pop_out.addr
cfg_train = setup.create_configuration_nsat(sim_ticks=sim_ticks,
w_check=False,
spk_rec_mon=spk_rec_mon,
monitor_spikes=True,
gated_learning=True,
plasticity_en=True)
spk_rec_mon = [[] for i in range(setup.ncores)]
spk_rec_mon[pop_out.core] = pop_out.addr
cfg_test = cfg_train.copy()
cfg_test.sim_ticks = sim_ticks_test
cfg_test.plasticity_en[:] = False
cfg_test.spk_rec_mon = spk_rec_mon
cfg_test.monitor_spikes = True
SL_train = create_spike_train(data_train[:N_train],
t_sample_train,
scaling=inp_fact,
with_labels=True)
ext_evts_data_train = nsat.exportAER(SL_train)
SL_test = create_spike_train(data_classify[:N_test],
t_sample_test,
scaling=inp_fact,
with_labels=False)
ext_evts_data_test = nsat.exportAER(SL_test)
cfg_test.set_ext_events(ext_evts_data_test)
cfg_train.set_ext_events(ext_evts_data_train)
c_nsat_writer_train = nsat.C_NSATWriter(cfg_train,
path=exp_name,
prefix='')
c_nsat_writer_train.write()
c_nsat_writer_test = nsat.C_NSATWriter(cfg_test,
path=exp_name_test,
prefix='')
c_nsat_writer_test.write()
fname_train = c_nsat_writer_train.fname
fname_test = c_nsat_writer_test.fname
c_nsat_reader_test = nsat.C_NSATReader(cfg_test, fname_test)
pip, tt = [], []
tft, t0t = 0, 0
for i in range(nepochs):
t0 = time.time()
nsat.run_c_nsat(fname_train)
tf = time.time()
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:
t0t = time.time()
nsat.run_c_nsat(fname_test)
tft = time.time()
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(acc)
t_total = (tf - t0) + (tft - t0t)
tt.append(t_total)
return pip, tt