def test_mark_signals():
with nengo.Network() as net:
ens0 = nengo.Ensemble(10, 1, neuron_type=nengo.LIF())
ens1 = nengo.Ensemble(20, 1, neuron_type=nengo.Direct())
ens2 = nengo.Ensemble(30, 1)
conn0 = nengo.Connection(ens0, ens1)
conn1 = nengo.Connection(ens0, ens1, learning_rule_type=nengo.PES())
conn2 = nengo.Connection(ens0, ens2, learning_rule_type=nengo.Voja())
nengo.Probe(ens2)
model = nengo.builder.Model()
model.build(net)
tg = tensor_graph.TensorGraph(model, None, None, 1, None,
utils.NullProgressBar(), None)
tg.mark_signals()
assert model.sig[ens0]["encoders"].trainable
assert model.sig[ens1]["encoders"].trainable
assert not model.sig[ens2]["encoders"].trainable
assert model.sig[ens0.neurons]["bias"].trainable
assert model.sig[ens2.neurons]["bias"].trainable
assert model.sig[conn0]["weights"].trainable
assert not model.sig[conn1]["weights"].trainable
assert model.sig[conn2]["weights"].trainable
trainables = (
model.sig[ens0]["encoders"],
model.sig[ens1]["encoders"],
model.sig[ens0.neurons]["bias"],
model.sig[ens2.neurons]["bias"],
model.sig[conn0]["weights"],
model.sig[conn2]["weights"],
)
for op in model.operators:
for sig in op.all_signals:
if sig in trainables:
assert sig.trainable
else:
assert not sig.trainable
def test_planner_config(config_planner):
with nengo.Network() as net:
if config_planner is not None:
net.config.configures(nengo.Network)
if config_planner:
net.config[nengo.Network].set_param(
"planner", nengo.params.Parameter(
"planner", graph_optimizer.noop_planner))
model = nengo.builder.Model()
model.build(net)
sig = nengo.builder.signal.Signal([1])
sig2 = nengo.builder.signal.Signal([1])
sig3 = nengo.builder.signal.Signal([1])
model.add_op(nengo.builder.operator.DotInc(sig, sig2, sig3))
model.add_op(nengo.builder.operator.DotInc(sig, sig2, sig3))
tg = tensor_graph.TensorGraph(model, None, None, tf.float32, 1, None,
utils.NullProgressBar())
assert len(tg.plan) == (2 if config_planner else 1)
def test_mark_signals_config():
with nengo.Network() as net:
config.configure_settings(trainable=None)
net.config[nengo.Ensemble].trainable = False
with nengo.Network():
# check that object in subnetwork inherits config from parent
ens0 = nengo.Ensemble(10, 1, label="ens0")
# check that ens.neurons can be set independent of ens
net.config[ens0.neurons].trainable = True
with nengo.Network():
with nengo.Network():
# check that subnetworks can override parent configs
config.configure_settings(trainable=True)
ens1 = nengo.Ensemble(10, 1, label="ens1")
with nengo.Network():
# check that subnetworks inherit the trainable settings
# from parent networks
ens3 = nengo.Ensemble(10, 1, label="ens3")
# check that instances can be set independent of class
ens2 = nengo.Ensemble(10, 1, label="ens2")
net.config[ens2].trainable = True
model = nengo.builder.Model()
model.build(net)
progress = utils.NullProgressBar()
tg = tensor_graph.TensorGraph(model, None, None, 1, None, progress, None)
tg.mark_signals()
assert not model.sig[ens0]["encoders"].trainable
assert model.sig[ens0.neurons]["bias"].trainable
assert model.sig[ens1]["encoders"].trainable
assert model.sig[ens2]["encoders"].trainable
assert model.sig[ens3]["encoders"].trainable
# check that learning rule connections can be manually set to True
with nengo.Network() as net:
config.configure_settings(trainable=None)
a = nengo.Ensemble(10, 1)
b = nengo.Ensemble(10, 1)
conn0 = nengo.Connection(a, b, learning_rule_type=nengo.PES())
net.config[conn0].trainable = True
model = nengo.builder.Model()
model.build(net)
tg = tensor_graph.TensorGraph(model, None, None, 1, None, progress, None)
with pytest.warns(UserWarning):
tg.mark_signals()
assert model.sig[conn0]["weights"].trainable
with nengo.Network() as net:
config.configure_settings(trainable=None)
a = nengo.Node([0])
ens = nengo.Ensemble(10, 1)
nengo.Connection(a, ens, learning_rule_type=nengo.Voja())
net.config[nengo.Ensemble].trainable = True
model = nengo.builder.Model()
model.build(net)
tg = tensor_graph.TensorGraph(model, None, None, 1, None, progress, None)
with pytest.warns(UserWarning):
tg.mark_signals()
assert model.sig[ens]["encoders"].trainable
# check that models with no toplevel work
sig = nengo.builder.signal.Signal([0])
op = nengo.builder.operator.Reset(sig, 1)
model = nengo.builder.Model()
model.add_op(op)
tg = tensor_graph.TensorGraph(model, None, None, 1, None, progress, None)
with pytest.warns(UserWarning):
tg.mark_signals()
assert not sig.trainable