def test_pes_learning_rate(Simulator, plt, seed):
n = 50
dt = 0.0005
T = 1.0
initial = 0.7
desired = -0.9
epsilon = 1e-3 # get to factor epsilon with T seconds
# Get activity vector and initial decoders
with Network(seed=seed) as model:
x = nengo.Ensemble(n,
1,
seed=seed,
neuron_type=nengo.neurons.LIFRate())
y = nengo.Node(size_in=1)
conn = nengo.Connection(x, y, synapse=None)
with Simulator(model, dt=dt) as sim:
a = get_activities(sim.model, x, [initial])
d = sim.data[conn].weights
assert np.any(a > 0)
# Use util function to calculate learning_rate
init_error = float(desired - np.dot(d, a))
learning_rate, gamma = pes_learning_rate(epsilon / abs(init_error), a, T,
dt)
# Build model with no filtering on any connections
with model:
stim = nengo.Node(output=initial)
ystar = nengo.Node(output=desired)
conn.learning_rule_type = nengo.PES(pre_tau=1e-15,
learning_rate=learning_rate)
nengo.Connection(stim, x, synapse=None)
nengo.Connection(ystar, conn.learning_rule, synapse=0, transform=-1)
nengo.Connection(y, conn.learning_rule, synapse=0)
p = nengo.Probe(y, synapse=None)
decoders = nengo.Probe(conn, 'weights', synapse=None)
with Simulator(model, dt=dt) as sim:
sim.run(T)
# Check that the final error is exactly epsilon
assert np.allclose(abs(desired - sim.data[p][-1]), epsilon)
# Check that all of the errors are given exactly by gamma**k
k = np.arange(len(sim.trange()))
error = init_error * gamma**k
assert np.allclose(sim.data[p].flatten(), desired - error)
# Check that all of the decoders are equal to their analytical solution
dk = d.T + init_error * a.T[:, None] * (1 - gamma**k) / np.dot(a, a.T)
assert np.allclose(dk, np.squeeze(sim.data[decoders].T))
plt.figure()
plt.plot(sim.trange(), sim.data[p], lw=5, alpha=0.5)
plt.plot(sim.trange(), desired - error, linestyle='--', lw=5, alpha=0.5)
def test_pes_learning_rate(Simulator, plt, seed):
n = 50
dt = 0.0005
T = 1.0
initial = 0.7
desired = -0.9
epsilon = 1e-3 # get to factor epsilon with T seconds
# Get activity vector and initial decoders
with Network(seed=seed) as model:
x = nengo.Ensemble(
n, 1, seed=seed, neuron_type=nengo.neurons.LIFRate())
y = nengo.Node(size_in=1)
conn = nengo.Connection(x, y, synapse=None)
sim = Simulator(model, dt=dt)
a = get_activities(sim.model, x, [initial])
d = sim.data[conn].weights
assert np.any(a > 0)
# Use util function to calculate learning_rate
init_error = float(desired - np.dot(d, a))
learning_rate, gamma = pes_learning_rate(
epsilon / abs(init_error), a, T, dt)
# Build model with no filtering on any connections
with model:
stim = nengo.Node(output=initial)
ystar = nengo.Node(output=desired)
conn.learning_rule_type = nengo.PES(
pre_tau=1e-15, learning_rate=learning_rate)
nengo.Connection(stim, x, synapse=None)
nengo.Connection(ystar, conn.learning_rule, synapse=0, transform=-1)
nengo.Connection(y, conn.learning_rule, synapse=0)
p = nengo.Probe(y, synapse=None)
decoders = nengo.Probe(conn, 'weights', synapse=None)
sim = Simulator(model, dt=dt)
sim.run(T)
# Check that the final error is exactly epsilon
assert np.allclose(abs(desired - sim.data[p][-1]), epsilon)
# Check that all of the errors are given exactly by gamma**k
k = np.arange(len(sim.trange()))
error = init_error * gamma ** k
assert np.allclose(sim.data[p].flatten(), desired - error)
# Check that all of the decoders are equal to their analytical solution
dk = d.T + init_error * a.T[:, None] * (1 - gamma ** k) / np.dot(a, a.T)
assert np.allclose(dk, np.squeeze(sim.data[decoders].T))
plt.figure()
plt.plot(sim.trange(), sim.data[p], lw=5, alpha=0.5)
plt.plot(sim.trange(), desired - error, linestyle='--', lw=5, alpha=0.5)
def test_pes_learning_rate_fail():
with pytest.raises(ValueError):
pes_learning_rate(0.1, [[1, 2], [3, 4]], 1)
def test_pes_learning_rate_fail():
with pytest.raises(ValueError):
pes_learning_rate(0.1, [[1, 2], [3, 4]], 1)
