Builder

The Builder is in charge of mapping (groups of) Nengo operators to the builder objects that know how to translate those operators into a TensorFlow graph.

class nengo_dl.builder.Builder

Manages the operator build classes known to the nengo_dl build process.

classmethod pre_build(ops, signals, rng, op_builds)

Setup step for build classes, in which they compute any of the values that are constant across simulation timesteps.

Parameters:

ops : tuple of Operator

the operator group to build into the model

signals : signals.SignalDict

mapping from Signal to tf.Tensor (updated by operations)

rng : RandomState

random number generator instance

op_builds : dict of {tuple of Operator, :class:~`.op_builders.OpBuilder`}

pre_build will populate this dictionary with the OpBuilder objects (which execute the pre-build step in their __init__)

classmethod build(ops, signals, op_builds)

Build the computations implementing a single simulator timestep.

Parameters:

ops : tuple of Operator

the operator group to build into the model

signals : signals.SignalDict

mapping from Signal to tf.Tensor (updated by operations)

op_builds : dict of {tuple of Operator, :class:~`.op_builders.OpBuilder`}

mapping from operator groups to the pre-built builder objects

classmethod register(nengo_op)

A decorator for adding a class to the build function registry.

Parameters:

nengo_op : Operator

The operator associated with the build function being decorated.

class nengo_dl.builder.OpBuilder(ops, signals)

The constructor should set up any computations that are fixed for this op (i.e., things that do not need to be recomputed each timestep).

Parameters:

ops : list of Operator

the operator group to build into the model

signals : signals.SignalDict

mapping from Signal to tf.Tensor (updated by operations)

Attributes:

pass_rng : bool

set to True if this build class requires the simulation random number generator to be passed to the constructor

build_step(signals)

This function builds whatever computations need to be executed in each simulation timestep.

Parameters:

signals : signals.SignalDict

mapping from Signal to tf.Tensor (updated by operations)

Returns:

list of ``tf.Tensor``, optional

if not None, the returned tensors correspond to outputs with possible side-effects, i.e. computations that need to be executed in the tensorflow graph even if their output doesn’t appear to be used