def make_norm_net(self,
min_input_magnitude=0.7,
max_input_magnitude=2.5,
**args):
ens_args = dict(args)
ens_args['dimensions'] = args.get('dimensions', vocab.sp_dim)
ens_args['radius_scale'] = args.get('radius_scale',
self.get_optimal_sp_radius())
ens_args['n_neurons_norm'] = args.get('n_neurons_norm',
self.n_neurons_ens)
ens_args['n_neurons_norm_sub'] = args.get('n_neurons_norm_sub',
self.n_neurons_ens)
ens_args['n_neurons_prod'] = args.get('n_neurons_prod',
self.n_neurons_cconv)
norm_net = VectorNormalize(min_input_magnitude, max_input_magnitude,
**ens_args)
with norm_net:
disable_ens = self.make_thresh_ens_net()
norm_net.disable = disable_ens.input
for net in norm_net.networks:
if net.label == "Product":
self.make_inhibitable(net, inhib_scale=5.0)
nengo.Connection(disable_ens.output, net.inhibit)
return norm_net
def make_norm_net(self, min_input_magnitude=0.7, max_input_magnitude=2.5,
**args):
ens_args = dict(args)
ens_args['dimensions'] = args.get('dimensions', self.sp_dim)
ens_args['n_neurons_norm'] = args.get('n_neurons_norm',
self.n_neurons_ens)
ens_args['n_neurons_norm_sub'] = args.get('n_neurons_norm_sub',
self.n_neurons_ens)
ens_args['n_neurons_prod'] = args.get('n_neurons_prod',
self.n_neurons_cconv)
norm_net = VectorNormalize(min_input_magnitude, max_input_magnitude,
**ens_args)
with norm_net:
norm_net.disable = nengo.Node(size_in=1)
norm_prod_net = None
for net in norm_net.networks:
if net.label == "Product":
norm_prod_net = net
for ensemble in norm_prod_net.all_ensembles:
nengo.Connection(norm_net.disable, ensemble.neurons,
transform=[[-3]] * ensemble.n_neurons)
return norm_net
def make_norm_net(self, min_input_magnitude=0.7, max_input_magnitude=2.5,
**args):
ens_args = dict(args)
ens_args['dimensions'] = args.get('dimensions', vocab.sp_dim)
ens_args['radius_scale'] = args.get('radius_scale',
self.get_optimal_sp_radius())
ens_args['n_neurons_norm'] = args.get('n_neurons_norm',
self.n_neurons_ens)
ens_args['n_neurons_norm_sub'] = args.get('n_neurons_norm_sub',
self.n_neurons_ens)
ens_args['n_neurons_prod'] = args.get('n_neurons_prod',
self.n_neurons_cconv)
norm_net = VectorNormalize(min_input_magnitude, max_input_magnitude,
**ens_args)
with norm_net:
disable_ens = self.make_thresh_ens_net()
norm_net.disable = disable_ens.input
for net in norm_net.networks:
if net.label == "Product":
self.make_inhibitable(net, inhib_scale=5.0)
nengo.Connection(disable_ens.output, net.inhibit)
return norm_net
