def plotHeatmap(dataPath, gaussianDistances, ax1=None):
b.rcParams['font.size'] = 20
if ax1==None:
b.figure(figsize=(8,15))
else:
b.sca(ax1)
nE = 1600
averagingWindowSize = 32
spikeCount = np.zeros((len(gaussianDistances), nE))
for i,dist in enumerate(gaussianDistances[:]):
path = dataPath + '/dist'+str(dist)+'/' +'activity/'
spikeCountTemp = np.load(path + 'spikeCountPerExample.npy')
spikeCount[i,:] = spikeCountTemp[25,:,0]#np.loadtxt(path + 'spikeCountAe.txt')
# spikeCount[i,:] = np.roll(spikeCount[i,:], int(0.25*len(spikeCount[i,:])))
spikeCount[i,:] = movingaverage(spikeCount[i,:], averagingWindowSize)
spikeCount[i,:] /= np.max(spikeCount[i,:])
b.imshow(spikeCount[:,:], aspect='auto', extent=[0,1,2,0])
b.colorbar()
b.xlabel('Neuron number (resorted)')
b.xlabel('Neuron number (resorted)')
if ax1==None:
b.savefig(dataPath + '/multipleAnglesHeatmap.png', dpi=300, bbox_inches='tight')
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_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_input(rates):
'''
Plot the current input example during the training procedure.
'''
fig = b.figure(fig_num, figsize = (5, 5))
im = b.imshow(rates.reshape((28, 28)), interpolation = 'nearest', vmin=0, vmax=64, cmap=cmap.get_cmap('gray'))
b.colorbar(im)
b.title('Current input example')
fig.canvas.draw()
return im, fig
def plot_2d_input_weights():
'''
Plot the weights from input to excitatory layer to view during training.
'''
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_input():
fig = b.figure(fig_num, figsize=(5, 5))
im3 = b.imshow(rates.reshape((28, 28)),
interpolation='nearest',
vmin=0,
vmax=64,
cmap=cmap.get_cmap('gray'))
b.colorbar(im3)
b.title('Current input example')
fig.canvas.draw()
return im3, fig
def plot_2d_input_weights():
'''
Plot the weights from input to excitatory layer to view during training.
'''
weights = get_2d_input_weights()
fig = b.figure(fig_num, figsize=(18, 18))
im = b.imshow(weights, interpolation='nearest', vmin=0, vmax=wmax_ee, cmap=cmap.get_cmap('hot_r'))
b.colorbar(im)
b.title('Reshaped weights from input -> excitatory layer')
fig.canvas.draw()
return im, 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_patch_weights():
'''
Plot the weights between convolution patches to view during training.
'''
weights = get_patch_weights()
fig = b.figure(fig_num, figsize=(8, 8))
im = b.imshow(weights, interpolation='nearest', vmin=0, vmax=wmax_ee, cmap=cmap.get_cmap('hot_r'))
for idx in xrange(n_e, n_e * conv_features, n_e):
b.axvline(idx, ls='--', lw=1)
b.axhline(idx, ls='--', lw=1)
b.colorbar(im)
b.title('Between-patch connectivity')
fig.canvas.draw()
return im, fig
def plot_2d_input_weights():
'''
Plot the weights from input to excitatory layer to view during training.
'''
weights = get_2d_input_weights()
fig = b.figure(fig_num, figsize=(18, 18))
im = b.imshow(weights, interpolation='nearest', vmin=0, vmax=wmax_ee, cmap=cmap.get_cmap('hot_r'))
for idx in xrange(conv_size * n_e_sqrt, conv_size * conv_features_sqrt * n_e_sqrt, conv_size * n_e_sqrt):
b.axvline(idx, ls='--', lw=1)
b.axhline(idx, ls='--', lw=1)
b.colorbar(im)
b.title('Reshaped input -> convolution weights')
b.xticks(xrange(0, conv_size * conv_features_sqrt * n_e_sqrt, conv_size * n_e_sqrt))
b.yticks(xrange(0, conv_size * conv_features_sqrt * n_e_sqrt, conv_size * n_e_sqrt))
fig.canvas.draw()
return im, fig
def plot_2d_conv_weights():
'''
Plot the weights from input to excitatory layer to view during training.
'''
weights = get_2d_conv_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(
'2D weights (input -> convolutional, convolutional -> fully-connected)'
)
fig.canvas.draw()
return im2, fig
def plot_2d_input_weights():
'''
Plot the weights from input to excitatory layer to view during training.
'''
weights, ordering = get_2d_input_weights()
fig, ax = b.subplots(figsize=(18, 18))
im = ax.imshow(weights,
interpolation='nearest',
vmin=0,
vmax=wmax_ee,
cmap=cmap.get_cmap('hot_r'))
b.colorbar(im, fraction=0.016)
b.title('Reshaped weights from input to convolutional layer', fontsize=18)
b.xticks(xrange(conv_size, conv_size * (conv_features + 1), conv_size),
xrange(1, conv_features + 1))
b.yticks(xrange(conv_size, conv_size * (n_e + 1), conv_size),
xrange(1, n_e + 1))
b.xlabel('Sorted in order of similarity', fontsize=14)
b.ylabel('Location in input (from top left to bottom right)', fontsize=14)
fig.canvas.draw()
return fig, ax, im, ordering
fig_num += 1
if name[1]=='e':
n_src = n_input
else:
n_src = n_i
if name[3]=='e':
n_tgt = n_e
else:
n_tgt = n_i
w_post = np.zeros((n_src, n_tgt))
connMatrix = connections[name][:]
for i in xrange(n_src):
w_post[i, connMatrix.rowj[i]] = connMatrix.rowdata[i]
im2 = b.imshow(w_post, interpolation="nearest", vmin = 0, cmap=cmap.get_cmap('gist_ncar')) #my_cmap
b.colorbar(im2)
b.title('weights of connection' + name)
plot_2d_input_weights()
# error = np.abs(result_monitor[:,1] - result_monitor[:,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)
#
# figure()
# scatter(result_monitor[:,1], result_monitor[:,0], c=range(len(error)), cmap=cm.gray)
# title('Error: ' + str(correctedErrorSum))
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()
