TensorNodes

To build TensorNode objects we need to define a new Nengo operator (tensor_node.SimTensorNode), a build function that adds that operator into a Nengo graph (tensor_node.build_tensor_node()), and a NengoDL build class that maps that new Nengo operator to TensorFlow operations (tensor_node.SimTensorNodeBuilder).

class nengo_dl.tensor_node.SimTensorNode(func, time, input, output, tag=None)

Operator for TensorNodes (constructed by build_tensor_node()).

Parameters:

func : callable

The TensorNode function (tensor_func)

time : Signal

Signal representing the current simulation time

input : Signal or None

Input Signal for the TensorNode (or None if size_in==0)

output : Signal

Output Signal for the TensorNode

tag : str, optional

A label associated with the operator, for debugging

Notes

1. sets [output]
2. incs []
3. reads [time] if input is None else [time, input]
4. updates []

nengo_dl.tensor_node.build_tensor_node(model, node)

This is the Nengo build function, so that Nengo knows what to do with TensorNodes.

class nengo_dl.tensor_node.SimTensorNodeBuilder(ops, signals, config)

Builds a SimTensorNode operator into a NengoDL model.

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

build_post(ops, signals, sess, rng)

This function will be called after the graph has been built and session/variables initialized.

This should be used to build any random aspects of the operator.

Note that this function may be called multiple times per session, so it should modify the graph in-place.

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)

sess : tf.Session

The initialized simulation session

rng : RandomState

Seeded random number generator