from nengo.builder.learning_rules import SimBCM, SimOja, SimVoja
import numpy as np
import tensorflow as tf
from nengo_dl.builder import Builder, OpBuilder
[docs]@Builder.register(SimBCM)
class SimBCMBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.learning_rules.SimBCM`
operators."""
def __init__(self, ops, signals):
self.pre_data = signals.combine([op.pre_filtered for op in ops])
self.post_data = signals.combine([op.post_filtered for op in ops])
self.theta_data = signals.combine([op.theta for op in ops])
self.learning_rate = tf.constant(
[[op.learning_rate] for op in ops
for _ in range(op.post_filtered.shape[0])],
dtype=signals.dtype)
self.output_data = signals.combine([op.delta for op in ops])
[docs] def build_step(self, signals):
pre = signals.gather(self.pre_data)
post = signals.gather(self.post_data)
theta = signals.gather(self.theta_data)
post = self.learning_rate * signals.dt * post * (post - theta)
pre = tf.expand_dims(pre, 0)
post = tf.expand_dims(post, 1)
signals.scatter(self.output_data, post * pre)
[docs]@Builder.register(SimOja)
class SimOjaBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.learning_rules.SimOja`
operators."""
def __init__(self, ops, signals):
self.pre_data = signals.combine([op.pre_filtered for op in ops])
self.post_data = signals.combine([op.post_filtered for op in ops])
self.weights_data = signals.combine([op.weights for op in ops])
self.output_data = signals.combine([op.delta for op in ops])
self.learning_rate = tf.constant(
[[op.learning_rate] for op in ops
for _ in range(op.post_filtered.shape[0])],
dtype=signals.dtype)
self.beta = tf.constant(
[[[op.beta]] for op in ops for _ in
range(op.post_filtered.shape[0])],
dtype=signals.dtype)
[docs] def build_step(self, signals):
pre = signals.gather(self.pre_data)
post = signals.gather(self.post_data)
weights = signals.gather(self.weights_data)
update = self.learning_rate * signals.dt * post ** 2
update = -self.beta * weights * tf.expand_dims(update, 1)
update += (tf.expand_dims(self.learning_rate, 1) * signals.dt *
update * tf.expand_dims(pre, 0))
signals.scatter(self.output_data, update)
[docs]@Builder.register(SimVoja)
class SimVojaBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.learning_rules.SimVoja`
operators."""
def __init__(self, ops, signals):
self.pre_data = signals.combine([op.pre_decoded for op in ops])
self.post_data = signals.combine([op.post_filtered for op in ops])
self.learning_data = signals.combine(
[op.learning_signal for op in ops
for _ in range(op.post_filtered.shape[0])])
self.encoder_data = signals.combine([op.scaled_encoders for op in ops])
self.output_data = signals.combine([op.delta for op in ops])
self.scale = tf.constant(
np.concatenate([op.scale[:, None, None] for op in ops], axis=0),
dtype=signals.dtype)
self.learning_rate = tf.constant(
[[op.learning_rate] for op in ops
for _ in range(op.post_filtered.shape[0])], dtype=signals.dtype)
[docs] def build_step(self, signals):
pre = signals.gather(self.pre_data)
post = signals.gather(self.post_data)
learning_signal = signals.gather(self.learning_data)
scaled_encoders = signals.gather(self.encoder_data)
alpha = tf.expand_dims(
self.learning_rate * signals.dt * learning_signal, 1)
post = tf.expand_dims(post, 1)
pre = tf.expand_dims(pre, 0)
update = alpha * (self.scale * post * pre - post * scaled_encoders)
signals.scatter(self.output_data, update)