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modified-learning-association-example.py
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modified-learning-association-example.py
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import matplotlib.pyplot as plt
import numpy as np
import nengo
from nengo.dists import Uniform
from nengo.utils.matplotlib import rasterplot
from nengo.processes import PresentInput
num_items = 6
num_periods = 2
d_key = 2
d_value = 3
rng = np.random.RandomState(seed=7)
keysA = nengo.dists.UniformHypersphere(surface=True).sample(2, d_key, rng=rng)
keys = nengo.dists.UniformHypersphere(surface=True).sample(num_items, d_key, rng=rng)
values = nengo.dists.UniformHypersphere(surface=False).sample(
num_items, d_value, rng=rng
)
keys[0][0] = keysA[0][0]
keys[0][1] = keysA[0][1]
keys[1][0] = keysA[0][0]
keys[1][1] = keysA[0][1]
keys[2][0] = keysA[0][0]
keys[2][1] = keysA[0][1]
keys[3][0] = keysA[1][0]
keys[3][1] = keysA[1][1]
keys[4][0] = keysA[1][0]
keys[4][1] = keysA[1][1]
keys[5][0] = keysA[1][0]
keys[5][1] = keysA[1][1]
print(keys)
print(np.shape(values))
values[0][0] = 1.0
values[0][1] = 0.0
values[0][2] = 0.0
#print(values)
values[1][0] = 0.0
values[1][1] = 1.0
values[1][2] = 0.0
#print(values)
values[2][0] = 0.0
values[2][1] = 0.0
values[2][2] = 1.0
#print(values)
values[3][0] = 0.0
values[3][1] = 1.0
values[3][2] = 0.0
#print(values)
values[4][0] = 1.0
values[4][1] = 0.0
values[4][2] = 0.0
values[5][0] = 0.0
values[5][1] = 0.0
values[5][2] = 1.0
#print(values)
intercept = (np.dot(keysA, keysA.T) - np.eye(num_periods)).flatten().max()
print(f"Intercept: {intercept}")
intercept = 0.0
def cycle_array(x, period, dt=0.001):
"""Cycles through the elements"""
i_every = int(round(period / dt))
if i_every != period / dt:
raise ValueError(f"dt ({dt}) does not divide period ({period})")
def f(t):
i = int(round((t - dt) / dt)) # t starts at dt
return x[int(i / i_every) % len(x)]
return f
# Model constants
n_neurons = 200
dt = 0.001
period = 0.3
T = period * num_items * 2
# Model network
model = nengo.Network()
with model:
# Create the inputs/outputs
stim_keys = nengo.Node(output=cycle_array(keys, period, dt))
stim_values = nengo.Node(output=cycle_array(values, period, dt))
learning = nengo.Node(output=lambda t: -int(t >= T / 2))
recall = nengo.Node(size_in=d_value)
# Create the memory
memory = nengo.Ensemble(n_neurons, d_key, intercepts=[intercept] * n_neurons)
# Learn the encoders/keys
voja = nengo.Voja(learning_rate=5e-2, post_synapse=None)
conn_in = nengo.Connection(stim_keys, memory, synapse=None, learning_rule_type=voja)
nengo.Connection(learning, conn_in.learning_rule, synapse=None)
# Learn the decoders/values, initialized to a null function
conn_out = nengo.Connection(
memory,
recall,
learning_rule_type=nengo.PES(1e-3),
function=lambda x: np.zeros(d_value),
)
# Create the error population
error = nengo.Ensemble(n_neurons, d_value)
nengo.Connection(
learning, error.neurons, transform=[[10.0]] * n_neurons, synapse=None
)
# Calculate the error and use it to drive the PES rule
nengo.Connection(stim_values, error, transform=-1, synapse=None)
nengo.Connection(recall, error, synapse=None)
nengo.Connection(error, conn_out.learning_rule)
# Setup probes
p_keys = nengo.Probe(stim_keys, synapse=None)
p_values = nengo.Probe(stim_values, synapse=None)
p_learning = nengo.Probe(learning, synapse=None)
p_error = nengo.Probe(error, synapse=0.005)
p_recall = nengo.Probe(recall, synapse=None)
p_encoders = nengo.Probe(conn_in.learning_rule, "scaled_encoders")
with nengo.Simulator(model, dt=dt) as sim:
sim.run(T)
t = sim.trange()
plt.figure()
plt.title("Keys")
plt.plot(t, sim.data[p_keys])
plt.ylim(-1, 1)
plt.show()
plt.figure()
plt.title("Values")
plt.plot(t, sim.data[p_values])
plt.ylim(-1, 1)
plt.show()
plt.figure()
plt.title("Learning")
plt.plot(t, sim.data[p_learning])
plt.ylim(-1.2, 0.2)
plt.show()
train = t <= T / 2
test = ~train
plt.figure()
plt.title("Value Error During Training")
plt.plot(t[train], sim.data[p_error][train])
plt.show()
plt.figure()
plt.title("Value Error During Recall")
plt.plot(t[test], sim.data[p_recall][test])# - sim.data[p_values][test])
plt.show()
scale = (sim.data[memory].gain / memory.radius)[:, np.newaxis]
def plot_2d(text, xy):
plt.figure()
plt.title(text)
plt.scatter(xy[:, 0], xy[:, 1], label="Encoders")
plt.scatter(keys[:, 0], keys[:, 1], c="red", s=150, alpha=0.6, label="Keys")
plt.xlim(-1.5, 1.5)
plt.ylim(-1.5, 2)
plt.legend()
plt.gca().set_aspect("equal")
plot_2d("Before", sim.data[p_encoders][0].copy() / scale)
plt.show()
plot_2d("After", sim.data[p_encoders][-1].copy() / scale)
plt.show()