def plot_2d_input_weights():
name = 'XeAe'
weights = get_2d_input_weights()
fig = b.figure(fig_num, figsize = (18, 18))
im2 = b.imshow(weights, interpolation = "nearest", vmin = 0, vmax = wmax_ee, cmap = cmap.get_cmap('hot_r'))
b.colorbar(im2)
b.title('weights of connection' + name)
fig.canvas.draw()
return im2, fig
def plot_2d_excitatory_weights():
name = 'AeAe' + str(n_e)
weights = get_2d_excitatory_weights()
fig = b.figure(fig_num, figsize=(10, 10))
im2 = b.imshow(weights, interpolation='nearest', vmin=0, vmax=wmax_ee, cmap=cmap.get_cmap('hot_r'))
b.colorbar(im2)
b.title('weights of connection ' + name)
fig.canvas.draw()
return im2, fig
def plot_patch_weights():
'''
Plot the weights between convolution patches to view during training.
'''
weights = get_patch_weights()
fig, ax = b.subplots(figsize=(8, 8))
im = ax.imshow(weights, interpolation='nearest', vmin=0, vmax=wmax_ee, cmap=cmap.get_cmap('hot_r'))
b.colorbar(im)
b.title('Between-patch connectivity')
fig.canvas.draw()
return im, fig
def plot_performance(fig_num):
num_evaluations = int(num_examples/update_interval)
time_steps = range(0, num_evaluations)
performance = np.zeros(num_evaluations)
fig = b.figure(fig_num, figsize = (5, 5))
fig_num += 1
ax = fig.add_subplot(111)
im2, = ax.plot(time_steps, performance) #my_cmap
b.ylim(ymax = 100)
b.title('Classification performance')
fig.canvas.draw()
return im2, performance, fig_num, fig
correctionDiffIdxs = np.where(diffLastActivity > 0.5)[0]
correctedPopDiff = [1 - diffLastActivity[i] if (i in correctionDiffIdxs) else diffLastActivity[i] for i in xrange(len(diffLastActivity))]
print correctedErrorSum
rcParams['lines.color'] = 'w'
rcParams['text.color'] = 'w'
rcParams['xtick.color'] = 'w'
rcParams['ytick.color'] = 'w'
rcParams['axes.labelcolor'] = 'w'
rcParams['axes.edgecolor'] = 'w'
b.figure()
b.scatter(resultMonitor[start_time:end_time,2], correctedError, c=range(len(error)), cmap=cmap.gray)
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Activity in B')
b.ylabel('Error')
fi = b.figure(figsize = (5.0,4.6))
ax = plt.subplot(1,1,1)
matplotlib.rcParams.update({'font.size': 22})
b.scatter(resultMonitor[start_time:end_time,1]*1600, resultMonitor[start_time:end_time,0]*1600, c='k', cmap=cmap.gray) #range(len(error))
# b.title('Error: ' + str(correctedErrorSum))
# b.xlabel('Desired activity')
# b.ylabel('Population activity')
ax.set_xticks([0,800,1600])
ax.set_xticklabels(['0', '800', '1600'])
ax.set_yticks([0,800,1600])
ax.set_yticklabels(['0', '800', '1600'], va='center')
save_connections()
else:
np.save(data_path + 'activity/resultPopVecs' + str(num_examples), result_monitor)
np.save(data_path + 'activity/inputNumbers' + str(num_examples), input_numbers)
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if rate_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b.subplot(len(rate_monitors), 1, i)
b.plot(rate_monitors[name].times/b.second, rate_monitors[name].rate, '.')
b.title('Rates of population ' + name)
if spike_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b.subplot(len(spike_monitors), 1, i)
b.raster_plot(spike_monitors[name])
b.title('Spikes of population ' + name)
if spike_counters:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_counters):
b.subplot(len(spike_counters), 1, i)
b.plot(spike_counters['Ae'].count[:])
def plotResults(self):
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if self.rateMonitors:
b.figure()
for i, name in enumerate(self.rateMonitors):
b.subplot(len(self.rateMonitors), 1, i)
b.plot(self.rateMonitors[name].times/b.second, self.rateMonitors[name].rate, '.')
b.title('rates of population ' + name)
if self.spikeMonitors:
b.figure()
for i, name in enumerate(self.spikeMonitors):
b.subplot(len(self.spikeMonitors), 1, i)
b.raster_plot(self.spikeMonitors[name])
b.title('spikes of population ' + name)
if name=='Ce':
timePoints = np.linspace(0+(self.singleExampleTime+self.restingTime)/(2*b.second)*1000,
self.runtime/b.second*1000-(self.singleExampleTime+self.restingTime)/(2*b.second)*1000,
self.numExamples)
b.plot(timePoints, self.resultMonitor[:,0]*self.nE, 'g')
b.plot(timePoints, self.resultMonitor[:,1]*self.nE, 'r')
if self.stateMonitors:
b.figure()
for i, name in enumerate(self.stateMonitors):
b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['v'][0], label = name + ' v 0')
b.legend()
b.title('membrane voltages of population ' + name)
b.figure()
for i, name in enumerate(self.stateMonitors):
b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['ge'][0], label = name + ' v 0')
b.legend()
b.title('conductances of population ' + name)
plotWeights = [
# 'XeAe',
# 'XeAi',
# 'AeAe',
# 'AeAi',
# 'AiAe',
# 'AiAi',
# 'BeBe',
# 'BeBi',
# 'BiBe',
# 'BiBi',
# 'CeCe',
# 'CeCi',
'CiCe',
# 'CiCi',
# 'HeHe',
# 'HeHi',
# 'HiHe',
# 'HiHi',
'AeHe',
# 'BeHe',
# 'CeHe',
'HeAe',
# 'HeBe',
# 'HeCe',
]
for name in plotWeights:
b.figure()
# my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#f4f4f4', '#000000'])
# my_cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#000000', '#f4f4f4'])
if name[1]=='e':
nSrc = self.nE
else:
nSrc = self.nI
if name[3]=='e':
nTgt = self.nE
else:
nTgt = self.nI
w_post = np.zeros((nSrc, nTgt))
connMatrix = self.connections[name][:]
for i in xrange(nSrc):
w_post[i, connMatrix.rowj[i]] = connMatrix.rowdata[i]
im2 = b.imshow(w_post, interpolation="nearest", vmin = 0, cmap=cm.get_cmap('gist_ncar')) #my_cmap
b.colorbar(im2)
b.title('weights of connection' + name)
if self.plotError:
error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0])
correctionIdxs = np.where(error > 0.5)[0]
correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))]
correctedErrorSum = np.average(correctedError)
b.figure()
b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=range(len(error)), cmap=cm.get_cmap('gray'))
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Desired activity')
b.ylabel('Population activity')
b.figure()
error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0])
correctionIdxs = np.where(error > 0.5)[0]
correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))]
correctedErrorSum = np.average(correctedError)
b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=self.resultMonitor[:,2], cmap=cm.get_cmap('gray'))
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Desired activity')
b.ylabel('Population activity')
b.ioff()
b.show()
rate_monitors[name + 'e'] = b.PopulationRateMonitor(neuron_groups[name + 'e'], bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(neuron_groups[name + 'i'], bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
if record_spikes:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name + 'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name + 'i'])
if record_spikes:
b.figure(fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'], refresh=1000 * b.ms, showlast=1000 * b.ms)
b.title('Excitatory Spikes')
b.subplot(212)
b.raster_plot(spike_monitors['Ai'], refresh=1000 * b.ms, showlast=1000 * b.ms)
b.title('Inhibitory Spikes')
#------------------------------------------------------------------------------
# create input population and connections from input populations
#------------------------------------------------------------------------------
pop_values = [0, 0, 0]
for i,name in enumerate(input_population_names):
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(input_groups[name + 'e'], bin=(single_example_time+resting_time) / b.second)
for name in input_connection_names:
print 'create connections between', name[0], 'and', name[1]