def test_all_negative_activities(allclose, plt, seed, Simulator, Solver):
class CheckActivitiesSolver(Solver):
def __call__(self, A, Y, rng=np.random):
assert np.all(A < 0)
return super().__call__(A, Y, rng=rng)
val = -0.5
with nengo.Network(seed=seed) as net:
input = nengo.Node(output=val, label="input")
ens = nengo.Ensemble(
30,
1,
neuron_type=nengo.Tanh(),
encoders=Choice([[1]]),
intercepts=Uniform(0, 0.95),
eval_points=Uniform(-1, -0.1),
)
nengo.Connection(input, ens)
in_p = nengo.Probe(input, "output")
ens_p = nengo.Probe(
ens, "decoded_output", synapse=0.05, solver=CheckActivitiesSolver()
)
with Simulator(net) as sim:
sim.run(0.3)
t = sim.trange()
plt.plot(t, sim.data[in_p], label="Input")
plt.plot(t, sim.data[ens_p], label="Neuron approximation, pstc=0.05")
plt.xlim(right=t[-1])
plt.legend(loc=0)
assert allclose(sim.data[in_p], val, atol=0.1, rtol=0.01)
assert allclose(sim.data[ens_p][-10:], val, atol=0.1, rtol=0.01)
def test_random_spiking(Simulator, inference_only, seed):
with nengo.Network() as net:
config.configure_settings(inference_only=inference_only)
inp = nengo.Node([1])
ens0 = nengo.Ensemble(100, 1, neuron_type=nengo.Tanh(), seed=seed)
ens1 = nengo.Ensemble(
100,
1,
neuron_type=nengo.StochasticSpiking(nengo.Tanh()),
seed=seed,
)
ens2 = nengo.Ensemble(
100,
1,
neuron_type=nengo.PoissonSpiking(nengo.Tanh()),
seed=seed,
)
nengo.Connection(inp, ens0, synapse=None)
nengo.Connection(inp, ens1, synapse=None)
nengo.Connection(inp, ens2, synapse=None)
p0 = nengo.Probe(ens0.neurons)
p1 = nengo.Probe(ens1.neurons)
p2 = nengo.Probe(ens2.neurons)
with pytest.warns(None) as recwarns:
with Simulator(net, seed=seed) as sim:
sim.run_steps(10000)
assert not any("native TensorFlow implementation" in str(w.message)
for w in recwarns)
assert np.allclose(sim.data[p0][0],
np.mean(sim.data[p1], axis=0),
atol=1,
rtol=2e-1)
assert np.allclose(sim.data[p0],
np.mean(sim.data[p2], axis=0),
atol=1,
rtol=1e-1)
def test_simneuronsmerger_warning(rng):
nt = nengo.PoissonSpiking(nengo.Tanh())
op1 = SimNeurons(
nt, J=Signal(shape=(1,)), output=Signal(shape=(1,)), state={"rng": rng}
)
op2 = SimNeurons(
nt, J=Signal(shape=(1,)), output=Signal(shape=(1,)), state={"rng": rng}
)
assert SimNeuronsMerger.is_mergeable(op1, op2)
with pytest.warns(UserWarning, match="Extra state has been modified"):
SimNeuronsMerger.merge([op1, op2])