Basic operators

class nengo_dl.op_builders.ResetInc(dst, value=0, tag=None)

class nengo_dl.op_builders.ResetBuilder(ops, signals)

Build a group of Reset operators.

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

class nengo_dl.op_builders.CopyBuilder(ops, signals)

Build a group of Copy operators.

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

class nengo_dl.op_builders.ElementwiseIncBuilder(ops, signals)

Build a group of ElementwiseInc operators.

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

class nengo_dl.op_builders.DotIncBuilder(ops, signals)

Build a group of DotInc operators.

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

class nengo_dl.op_builders.SparseDotIncBuilder(ops, signals)

Build a group of DotInc operators.

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

class nengo_dl.op_builders.SimPyFuncBuilder(ops, signals)

Build a group of SimPyFunc operators.

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

class nengo_dl.tensor_node.SimTensorNodeBuilder(ops, signals)

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