import logging
from nengo.builder.neurons import SimNeurons
from nengo.neurons import (RectifiedLinear, SpikingRectifiedLinear, Sigmoid,
LIF, LIFRate)
import numpy as np
import tensorflow as tf
from nengo_dl import utils
from nengo_dl.builder import Builder, OpBuilder
from nengo_dl.neurons import SoftLIFRate
logger = logging.getLogger(__name__)
[docs]@Builder.register(SimNeurons)
class SimNeuronsBuilder(OpBuilder):
"""Builds a group of :class:`~nengo:nengo.builder.neurons.SimNeurons`
operators.
Calls the appropriate sub-build class for the different neuron types.
Attributes
----------
TF_NEURON_IMPL : list of :class:`~nengo:nengo.neurons.NeuronType`
The neuron types that have a custom implementation
"""
TF_NEURON_IMPL = (RectifiedLinear, SpikingRectifiedLinear, Sigmoid,
LIF, LIFRate, SoftLIFRate)
def __init__(self, ops, signals):
super(SimNeuronsBuilder, self).__init__(ops, signals)
logger.debug("J %s", [op.J for op in ops])
neuron_type = type(ops[0].neurons)
# if we have a custom tensorflow implementation for this neuron type,
# then we build that. otherwise we'll just execute the neuron step
# function externally (using `tf.py_func`), so we just need to set up
# the inputs/outputs for that.
if neuron_type in self.TF_NEURON_IMPL:
# note: we do this two-step check (even though it's redundant) to
# make sure that TF_NEURON_IMPL is kept up to date
if neuron_type == RectifiedLinear:
self.built_neurons = RectifiedLinearBuilder(ops, signals)
elif neuron_type == SpikingRectifiedLinear:
self.built_neurons = SpikingRectifiedLinearBuilder(
ops, signals)
elif neuron_type == Sigmoid:
self.built_neurons = SigmoidBuilder(ops, signals)
elif neuron_type == LIFRate:
self.built_neurons = LIFRateBuilder(ops, signals)
elif neuron_type == LIF:
self.built_neurons = LIFBuilder(ops, signals)
elif neuron_type == SoftLIFRate:
self.built_neurons = SoftLIFRateBuilder(ops, signals)
else:
self.built_neurons = GenericNeuronBuilder(ops, signals)
[docs] def build_step(self, signals):
self.built_neurons.build_step(signals)
[docs]class GenericNeuronBuilder(OpBuilder):
"""Builds all neuron types for which there is no custom Tensorflow
implementation.
Notes
-----
These will be executed as native Python functions, requiring execution to
move in and out of TensorFlow. This can significantly slow down the
simulation, so any performance-critical neuron models should consider
adding a custom TensorFlow implementation for their neuron type instead.
"""
def __init__(self, ops, signals):
super(GenericNeuronBuilder, self).__init__(ops, signals)
self.J_data = signals.combine([op.J for op in ops])
self.output_data = signals.combine([op.output for op in ops])
self.state_data = [signals.combine([op.states[i] for op in ops])
for i in range(len(ops[0].states))]
self.prev_result = []
def neuron_step_math(dt, J, *states): # pragma: no cover
output = None
J_offset = 0
state_offset = [0 for _ in states]
for i, op in enumerate(ops):
# slice out the individual state vectors from the overall
# array
op_J = J[J_offset:J_offset + op.J.shape[0]]
J_offset += op.J.shape[0]
op_states = []
for j, s in enumerate(op.states):
op_states += [states[j][state_offset[j]:
state_offset[j] + s.shape[0]]]
state_offset[j] += s.shape[0]
# call step_math function
# note: `op_states` are views into `states`, which will
# be updated in-place
mini_out = []
for j in range(signals.minibatch_size):
# blank output variable
neuron_output = np.zeros(
op.output.shape, self.output_data.dtype)
op.neurons.step_math(dt, op_J[..., j], neuron_output,
*[s[..., j] for s in op_states])
mini_out += [neuron_output]
neuron_output = np.stack(mini_out, axis=-1)
# concatenate outputs
if output is None:
output = neuron_output
else:
output = np.concatenate((output, neuron_output),
axis=0)
return (output,) + states
self.neuron_step_math = neuron_step_math
self.neuron_step_math.__name__ = utils.sanitize_name(
"_".join([repr(op.neurons) for op in ops]))
[docs] def build_step(self, signals):
J = signals.gather(self.J_data)
states = [signals.gather(x) for x in self.state_data]
states_dtype = [x.dtype for x in self.state_data]
# note: we need to make sure that the previous call to this function
# has completed before the next starts, since we don't know that the
# functions are thread safe
with tf.control_dependencies(self.prev_result), tf.device("/cpu:0"):
ret = tf.py_func(
self.neuron_step_math, [signals.dt, J] + states,
[self.output_data.dtype] + states_dtype,
name=self.neuron_step_math.__name__)
neuron_out, state_out = ret[0], ret[1:]
self.prev_result = [neuron_out]
neuron_out.set_shape(
self.output_data.shape + (signals.minibatch_size,))
signals.scatter(self.output_data, neuron_out)
for i, s in enumerate(self.state_data):
state_out[i].set_shape(s.shape + (signals.minibatch_size,))
signals.scatter(s, state_out[i])
[docs]class RectifiedLinearBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.RectifiedLinear`
neuron operators."""
def __init__(self, ops, signals):
super(RectifiedLinearBuilder, self).__init__(ops, signals)
self.J_data = signals.combine([op.J for op in ops])
self.output_data = signals.combine([op.output for op in ops])
if all(op.neurons.amplitude == 1 for op in ops):
self.amplitude = None
else:
self.amplitude = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"amplitude", signals.dtype)
[docs] def build_step(self, signals):
J = signals.gather(self.J_data)
out = tf.nn.relu(J)
if self.amplitude is not None:
out *= self.amplitude
signals.scatter(self.output_data, out)
[docs]class SpikingRectifiedLinearBuilder(RectifiedLinearBuilder):
"""Build a group of :class:`~nengo:nengo.SpikingRectifiedLinear` neuron
operators."""
def __init__(self, ops, signals):
super(SpikingRectifiedLinearBuilder, self).__init__(ops, signals)
self.voltage_data = signals.combine([op.states[0] for op in ops])
self.alpha = 1 if self.amplitude is None else self.amplitude
self.alpha /= signals.dt
[docs] def build_step(self, signals):
J = signals.gather(self.J_data)
voltage = signals.gather(self.voltage_data)
voltage += tf.nn.relu(J) * signals.dt
n_spikes = tf.floor(voltage)
signals.scatter(self.output_data, self.alpha * n_spikes)
voltage -= n_spikes
signals.scatter(self.voltage_data, voltage)
[docs]class SigmoidBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.Sigmoid` neuron operators."""
def __init__(self, ops, signals):
super(SigmoidBuilder, self).__init__(ops, signals)
self.J_data = signals.combine([op.J for op in ops])
self.output_data = signals.combine([op.output for op in ops])
self.tau_ref = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"tau_ref", signals.dtype)
[docs] def build_step(self, signals):
J = signals.gather(self.J_data)
signals.scatter(self.output_data, tf.nn.sigmoid(J) / self.tau_ref)
[docs]class LIFRateBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.LIFRate` neuron operators."""
def __init__(self, ops, signals):
super(LIFRateBuilder, self).__init__(ops, signals)
self.tau_ref = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"tau_ref", signals.dtype)
self.tau_rc = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"tau_rc", signals.dtype)
self.amplitude = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"amplitude", signals.dtype)
self.J_data = signals.combine([op.J for op in ops])
self.output_data = signals.combine([op.output for op in ops])
self.zeros = tf.zeros(self.J_data.shape + (signals.minibatch_size,),
signals.dtype)
self.zero = tf.constant(0, dtype=signals.dtype)
self.one = tf.constant(1, dtype=signals.dtype)
self.epsilon = tf.constant(1e-15, dtype=signals.dtype)
[docs] def build_step(self, signals):
j = signals.gather(self.J_data)
j -= self.one
# note: we convert all the j to be positive before this calculation
# (even though we'll only use the values that are already positive),
# otherwise we can end up with nans in the gradient
rates = self.amplitude / (
self.tau_ref + self.tau_rc * tf.log1p(tf.reciprocal(
tf.maximum(j, self.epsilon))))
signals.scatter(self.output_data, tf.where(j > self.zero, rates,
self.zeros))
[docs]class LIFBuilder(LIFRateBuilder):
"""Build a group of :class:`~nengo:nengo.LIF` neuron operators."""
def __init__(self, ops, signals):
super(LIFBuilder, self).__init__(ops, signals)
self.min_voltage = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"min_voltage", signals.dtype)
self.amplitude /= signals.dt
self.voltage_data = signals.combine([op.states[0] for op in ops])
self.refractory_data = signals.combine([op.states[1] for op in ops])
[docs] def build_step(self, signals):
J = signals.gather(self.J_data)
voltage = signals.gather(self.voltage_data)
refractory = signals.gather(self.refractory_data)
delta_t = tf.clip_by_value(signals.dt - refractory, self.zero,
signals.dt)
dV = (voltage - J) * tf.expm1(-delta_t / self.tau_rc)
voltage += dV
spiked = voltage > self.one
spikes = tf.cast(spiked, signals.dtype) * self.amplitude
signals.scatter(self.output_data, spikes)
partial_ref = -self.tau_rc * tf.log1p((self.one - voltage) /
(J - self.one))
# FastLIF version (linearly approximate spike time when calculating
# remaining refractory period)
# partial_ref = signals.dt * (voltage - self.one) / dV
refractory = tf.where(spiked, self.tau_ref - partial_ref,
refractory - signals.dt)
signals.mark_gather(self.J_data)
signals.scatter(self.refractory_data, refractory)
voltage = tf.where(spiked, self.zeros,
tf.maximum(voltage, self.min_voltage))
signals.scatter(self.voltage_data, voltage)
[docs]class SoftLIFRateBuilder(LIFRateBuilder):
"""Build a group of :class:`.SoftLIFRate` neuron operators."""
def __init__(self, ops, signals):
super(SoftLIFRateBuilder, self).__init__(ops, signals)
self.sigma = signals.op_constant(
[op.neurons for op in ops], [op.J.shape[0] for op in ops],
"sigma", signals.dtype)
[docs] def build_step(self, signals):
j = signals.gather(self.J_data)
j -= self.one
z = tf.nn.softplus(j / self.sigma) * self.sigma
z += self.epsilon
rates = self.amplitude / (
self.tau_ref + self.tau_rc * tf.log1p(tf.reciprocal(z)))
signals.scatter(self.output_data, rates)