def make(learn_type='hPES', nperd=20, learn_rate=5e-5,
supervision_ratio=0.5, oja=False):
net = nef.Network('Learn Digits') # creates a network in Nengo
input = net.add(Input('input')) # create the input node
pre = net.make('pre', IND * nperd, IND, radius=4.5)
post = net.make('post', OUTD * nperd, OUTD, radius=2.5)
# Create error population
# Established learning connection between input and output populations
if learn_type == 'PES':
pes.make(net, preName='pre', postName='post',
errName='error', N_err=OUTD * nperd, rate=learn_rate, oja=oja)
elif learn_type == 'hPES':
hpes.make(net, preName='pre', postName='post',
errName='error', N_err=OUTD * nperd, rate=learn_rate,
supervisionRatio=supervision_ratio)
# Connect parts of network
net.connect(input.getOrigin('label'), 'error')
net.connect(input.getOrigin('input'), pre)
net.connect('post', 'error', weight=-1)
# Create a gate for turning learning on and off
gating.make(net,name='Gate', gated='error', neurons=40, pstc=0.01)
# Have the 'learning switch position' file drive the gate
net.connect(input.getOrigin('learnswitch'), 'Gate')
return net
def make(self):
import nef
import nef.templates.learned_termination as pes
import nef.templates.hpes_termination as hpes
import nef.templates.gate as gating
if self.net is not None:
return self.net
random.seed(self.seed)
net = nef.Network('Learn Network', seed=random.randrange(0x7fffffff))
net.make('pre', self.nperd * self.in_d, self.in_d)
net.make('post', self.nperd * self.out_d, self.out_d)
net.make_fourier_input('input', dimensions=self.in_d,
base=0.25, high=40)
net.connect('input', 'pre')
if self.learn_type == 'PES':
pes.make(net, preName='pre', postName='post', errName='error',
N_err=self.nperd * self.out_d, rate=self.learn_rate,
oja=self.oja)
elif self.learn_type == 'hPES':
hpes.make(net, preName='pre', postName='post', errName='error',
N_err=self.nperd * self.out_d, rate=self.learn_rate,
supervisionRatio=self.supervision_ratio)
elif self.learn_type == 'control':
net.connect('pre', 'post', func=self.func, origin_name='pre_00')
net.make('error', 1, self.out_d, mode='direct') # Unused
net.connect('pre', 'error', func=self.func)
net.connect('post', 'error', weight=-1)
start = 'test' if self.testtype == 'full' else 'train'
net.make_input('switch', LearnBuilder.get_learning_times(
self.train, self.test, start))
gating.make(net, name='Gate', gated='error', neurons=50, pstc=0.01)
net.connect('switch', 'Gate')
# Calculate actual error
net.make('actual', 1, self.in_d, mode='direct')
net.connect('input', 'actual')
net.make('actual error', 1, self.out_d, mode='direct')
net.connect('actual', 'actual error', func=self.func)
net.connect('post', 'actual error', weight=-1)
self.net = net
return self.net
