Source code for nengo_dl.neuron_builders

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)