def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_states()
states_core0 = states[0][1]
states_core1 = states[1][1]
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 10):
ax = fig.add_subplot(10, 1, i)
ax.plot(states_core0[:500, i, 0], 'b', lw=3)
ax.set_ylim([0, 110])
fig = plt.figure(figsize=(10, 10))
for i in range(1, 10):
ax = fig.add_subplot(10, 1, i)
ax.plot(states_core1[:500, i, 0], 'r', lw=3)
ax.set_ylim([0, 110])
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
out_spikelist = nsat.importAER(nsat.read_from_file(nsat.fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:, 0, i - 1], 'b', lw=2)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# np.save('states', states_core0)
in_spikelist = SL
ifname = fnames.events + '_core_0.dat'
out_spikelist = nsat.importAER(nsat.read_from_file(ifname),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:-1, 15, i - 1], 'b', lw=1.5)
# fig = plt.figure(figsize=(10, 10))
# for i in range(1, 9):
# ax = fig.add_subplot(8, 1, i)
# ax.plot(states_core0[:-1, 16, i-1], 'b', lw=1.5)
# out_spikelist.raster_plot()
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# wt = c_nsat_reader.read_c_nsat_weights_evo(0)[:, 1, 1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(post=[0])
in_spikelist = SL
out_spikelist = nsat.importAER(nsat.read_from_file(nsat.fnames.events+'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
i = 1
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
i = 4
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(wt[0][:, 1, 1], 'r', lw=3)
ax.set_ylabel('$w$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
plt.savefig('/tmp/%s.png' % (os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
plt.figure()
for i in range(1, 5):
plt.subplot(4, 1, i)
plt.plot(states_core0[:, :, i - 1])
plt.figure()
S = np.maximum(states_core0[:, :, 0], 0)
plt.imshow(
S,
interpolation='spline36',
cmap=plt.get_cmap('gray'), #plt.cm.gray,
aspect='auto',
origin='lower')
spks = nsat.importAER(nsat.read_from_file(nsat.fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=list(range(cfg.core_cfgs[0].n_neurons)))
try:
raster = spks.raster_plot()
raster.savefig('/tmp/%s_raster.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
raster.close()
except:
print('test_nf: Failed to generate /tmp/%s_raster.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
# Plot the results
# events = spks.convert()
# mat = np.zeros((sim_ticks, N_NEURONS[0]))
# print(mat.shape)
# print(len(events[0]))
# print(len(events[1]))
# mat[events[0].astype('i'), events[1].astype('i')] = 1
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 5):
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
spk = nsat.importAER(c_nsat_reader.read_events(0), sim_ticks=sim_ticks)
spk.raster_plot()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(ww[:cfg.core_cfgs[0].n_inputs, cfg.core_cfgs[0].n_inputs, 1], 'k.')
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
print("Plotting data")
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
pip = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=list(range(
cfg.core_cfgs[0].n_neurons))).time_slice(
1000, sim_ticks - 1000).mean_rates()
x = np.arange(pip.shape[0])
from scipy.optimize import curve_fit
# define "to-fit" function
def sigmoid(x, a, b, c):
return a / (1 + np.exp(-b * x / a) * (a - c) / a)
# Fit data to function sigmoid
popt, pcov = curve_fit(sigmoid, x, pip)
print(("Sigmoid's parameters: a = {}, b = {}, c = {}".format(
popt[0], popt[1], popt[2])))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, pip, 'k', lw=2)
ax.plot(x, sigmoid(x, popt[0], popt[1], popt[2]), 'r--', lw=2)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
_, states_core0 = states[0][0], states[0][1]
_, states_core1 = states[1][0], states[1][1]
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, states_core0.shape[2] + 1):
ax = fig.add_subplot(2, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3)
plt.savefig('/tmp/%s_0.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
fig = plt.figure(figsize=(10, 10))
for i in range(1, states_core0.shape[2] + 1):
ax = fig.add_subplot(2, 1, i)
ax.plot(states_core1[:-1, 0, i - 1], 'b', lw=3)
plt.savefig('/tmp/%s_1.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=100,
id_list=[0])
out_spikelist.id_slice([0]).raster_plot(kwargs={'color': 'b'})
plt.show()
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
in_spikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=[0])
spks = out_spikelist.convert("[times,ids]")
spks = np.vstack([spks[0], spks[1]]).astype('int')
pretty_fig(spks, states_core0, t_stop=t_stop)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
c_nsat_writer_train = nsat.C_NSATWriter(cfg_train, path='/tmp/erbp_mnist_train1/', prefix='')
c_nsat_writer_train.write()
c_nsat_writer_test = nsat.C_NSATWriter(cfg_test, path='/tmp/erbp_mnist_test1/', prefix='')
c_nsat_writer_test.write()
fname_train = c_nsat_writer_train.fname
fname_test = c_nsat_writer_test.fname
c_nsat_reader_train = nsat.C_NSATReader(cfg_train, fname_train)
c_nsat_reader_test = nsat.C_NSATReader(cfg_test, fname_test)
print("############# Running simulation #####################")
pip = []
for i in range(nepochs):
nsat.run_c_nsat(fname_train)
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])
ext_evts_data = nsat.exportAER(SL)
cfg.set_ext_events(ext_evts_data)
# Write the C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_stdp')
c_nsat_writer.write()
# Write Intel FPGA hex parameters files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_stdp')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
#ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# wt = c_nsat_reader.read_c_nsat_weights_evo(2)[0]
wt = c_nsat_reader.read_c_nsat_syn_evo()[0][0]
spk = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events +
'_core_0.dat'),
sim_ticks=sim_ticks)
spk.raster_plot()
print((SL[0].spike_times))
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[core][0], states[core][1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(
post=[130, 150, 120])
wt, pids = wt[0], pids[0]
in_spikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=[0])
plt.matplotlib.rcParams['figure.subplot.bottom'] = .1
plt.matplotlib.rcParams['figure.subplot.left'] = .2
plt.matplotlib.rcParams['figure.subplot.right'] = .98
plt.matplotlib.rcParams['figure.subplot.hspace'] = .1
plt.matplotlib.rcParams['figure.subplot.top'] = 1.0
# Plot the results
fig = plt.figure(figsize=(14, 10))
i = 4
ax1 = fig.add_subplot(5, 1, i)
for t in SL[100].spike_times:
plt.axvline(t, color='g', alpha=.4)
ax1.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax1.set_ylabel('$x_m$')
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 5
ax = fig.add_subplot(5, 1, i, sharex=ax1)
for t in SL[0].spike_times:
plt.axvline(t, color='k', alpha=.4)
ax.plot(wt[:, 19, 1], 'r', lw=3)
# ax.imshow(wt[:, :, 1], aspect='auto', interpolation='nearest')
ax.set_ylabel('$w$')
ax.set_xlabel('Time Step')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
for t in np.ceil(SL[0].spike_times):
plt.axvline(t, color='k', alpha=.4)
ax1 = fig.add_subplot(5, 1, 1, sharex=ax1)
out_spikelist.id_slice([0]).raster_plot(display=ax1, kwargs={'color': 'b'})
out_spikelist.id_slice(list(range(1,
30))).raster_plot(display=ax1,
kwargs={'color': 'k'})
ax1.set_xlabel('')
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.tight_layout()
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
fname_test = c_nsat_writer_test.fname
c_nsat_reader_test = nsat.C_NSATReader(cfg_test, fname_test)
# cfg_train.core_cfgs[0].latex_print_parameters(2)
if __name__ == '__main__':
n_epoch = 50
e = []
print("############## Training ##############")
for i in range(n_epoch):
# Call the C NSAT for learning
print("Epoch #: ", i)
c_nsat_writer_train.fname.stats_nsat = \
'/tmp/test_eCD_stats_nsat_full'+str(i)
nsat.run_c_nsat(c_nsat_writer_train.fname)
if n_epoch > 1:
shutil.copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_train.syn_wgt_table + '_core_0.dat',
)
shutil.copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_test.syn_wgt_table + '_core_0.dat',
)
nsat.run_c_nsat(c_nsat_writer_test.fname)
test_spk = nsat.importAER(
nsat.read_from_file(c_nsat_writer_test.fname.events +
'_core_0.dat'),
sim_ticks=sim_ticks_test)
test_spk = nsat.importAER(c_nsat_reader_test.read_events(0),
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
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# w0 = c_nsat_reader.read_synaptic_weights_history(post=0)[0][:, 0, 1]
w0, pids0 = c_nsat_reader.read_synaptic_weights_history(post=[5])
w0 = w0[0][:, 4, 1]
w1, pids1 = c_nsat_reader.read_synaptic_weights_history(post=[6])
w1 = w1[0][:, 4, 1]
w2, pids2 = c_nsat_reader.read_synaptic_weights_history(post=[7])
w2 = w2[0][:, 4, 1]
np.save('w0', w0)
np.save('w1', w1)
np.save('w2', w2)
# w = c_nsat_reader.read_synaptic_weights()
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks)
np.save('spk0', out_spikelist[0].spike_times)
np.save('spk1', out_spikelist[1].spike_times)
np.save('spk2', out_spikelist[2].spike_times)
np.save('spk3', out_spikelist[3].spike_times)
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(5, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(5, 2, 2)
ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(5, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(5, 2, 4)
ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(5, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(5, 2, 6)
ax.plot(states_core0[:-1, 1, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
fig = plt.figure()
ax = fig.add_subplot(4, 1, 1)
for i in range(4):
for i in out_spikelist[0].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='lal')
ax.set_ylabel('Spikes')
ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 2)
ax.step(w0, 'r', lw=2, zorder=10, where='post', label='lala')
for i in out_spikelist[1].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dsds')
# ax.set_ylabel('Spikes')
# ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 3)
ax.step(w1, 'r', lw=2, zorder=10, where='post', label='ta')
for i in out_spikelist[2].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dss')
# ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 4)
ax.step(w2, 'r', lw=2, zorder=10, where='post', label='ar')
for i in out_spikelist[3].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dadsa')
# ax.set_xlim([0, sim_ticks])
ax.set_xlim([0, sim_ticks])
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
wt, _ = c_nsat_reader.read_c_nsat_weights_evo([0, 1])
wt = wt[0]
out_spikelist = nsat.importAER(nsat.read_from_file(fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks)
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 2)
ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 4)
ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(4, 2, 6)
ax.plot(states_core0[:-1, 1, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(4, 2, 7)
ax.plot(wt[:, 1, 1], 'r', lw=3)
ax.set_ylabel('$w$')
ax = fig.add_subplot(4, 2, 8)
ax.plot(wt[:, 0, 1], 'r', lw=3)
ax.set_ylabel('$w$')
fig = plt.figure(figsize=(10, 5))
plt.plot(wt[:, 0, 1], 'r', lw=2, zorder=10)
plt.plot(wt[:, 1, 1], 'y', lw=2, zorder=10)
# for i in out_spikelist[0].spike_times:
# plt.axvline(i, color='k', lw=1, zorder=0)
# for i in out_spikelist[1].spike_times:
# plt.axvline(i, color='b', lw=1, zorder=0)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run():
# Call the C NSAT
global spk0
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
#ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# wt = c_nsat_reader.read_c_nsat_syn_evo()[0][0]
wt, pids = c_nsat_reader.read_synaptic_weights_history()
wt = wt[0]
spk = nsat.importAER(c_nsat_reader.read_events(0), sim_ticks=sim_ticks)
spk.raster_plot()
print((SL[0].spike_times))
print((SL[1].spike_times))
print((spk[0].spike_times))
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.plot(ww[:N_INPUTS[0], N_INPUTS[0], 1], 'k.')
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
ax.set_ylabel('$V_m$')
# ax = fig.add_subplot(4, 2, 2)
# ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
# ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
# ax = fig.add_subplot(4, 2, 4)
# ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
# ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
# ax = fig.add_subplot(4, 2, 6)
# ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$x_m$')
# ax.set_ylabel('$x_m$')
print(wt.shape)
ax = fig.add_subplot(4, 2, 7)
# ax.plot(wt[wt[:, 0, :] == 0, 0], wt[wt[:, 1, :] == 0, 4], 'r', lw=3)
ax.plot(wt[:, 0, 0], wt[:, 1, 0], 'r', lw=3)
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
for i in spk0[1].spike_times:
plt.axvline(i, color='r', lw=1)
ax.set_ylabel('$w$')
ax = fig.add_subplot(4, 2, 8)
# ax.plot(wt[wt[:, 1, :] == 1, 0], wt[wt[:, 1, :] == 1, 4], 'r', lw=3)
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
for i in spk0[1].spike_times:
plt.axvline(i, color='r', lw=1)
ax.set_ylabel('$w$')
# ax = fig.add_subplot(4, 2, 8)
# ax.plot(wt[:, 1, 1], 'r', lw=3)
# ax.set_ylabel('$w$')
# fig = plt.figure(figsize=(10, 5))
# for i in spk[0].spike_times:
# plt.plot(wt[:, 1, 1], 'r', lw=3)
# plt.axvline(i, color='k', lw=1)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))