API reference¶

This section details the modules, classes, and functions available in NengoDL. It is divided into two sections. The first section describes the objects relevant to NengoDL users. More information on these objects can also be found in the User guide. The second section describes objects that only NengoDL developers need to worry about.

Users¶

These objects are the main access points for the user-facing features of NengoDL.

Simulator¶

The Simulator class is the access point for the main features of NengoDL, including running and training a model.

`nengo_dl.Simulator`	Simulate network using the `nengo_dl` backend.
`nengo_dl.Simulator.reset`	Resets the simulator to initial conditions.
`nengo_dl.Simulator.soft_reset`	Deprecated, use `Simulator.reset` instead.
`nengo_dl.Simulator.predict`	Generate output predictions for the input samples.
`nengo_dl.Simulator.predict_on_batch`	Generate output predictions for a single minibatch of input samples.
`nengo_dl.Simulator.compile`	Configure the model for training/evaluation.
`nengo_dl.Simulator.fit`	Trains the model on some dataset.
`nengo_dl.Simulator.evaluate`	Compute the loss and metric values for the network.
`nengo_dl.Simulator.step`	Run the simulation for one time step.
`nengo_dl.Simulator.run`	Run the simulation for the given length of time.
`nengo_dl.Simulator.run_steps`	Run the simulation for the given number of steps.
`nengo_dl.Simulator.train`	Deprecated, use `Simulator.compile` and `Simulator.fit` instead.
`nengo_dl.Simulator.loss`	Deprecated, use `Simulator.compile` and `Simulator.evaluate` instead.
`nengo_dl.Simulator.save_params`	Save network parameters to the given `path`.
`nengo_dl.Simulator.load_params`	Load network parameters from the given `path`.
`nengo_dl.Simulator.freeze_params`	Stores the live parameter values from the simulation back into a Nengo object definition.
`nengo_dl.Simulator.get_nengo_params`	Extract model parameters in a form that can be used to initialize Nengo objects in a different model.
`nengo_dl.Simulator.check_gradients`	Perform gradient checks for the network (used to verify that the analytic gradients are correct).
`nengo_dl.Simulator.trange`	Create a vector of simulation step times matching probed data.
`nengo_dl.Simulator.close`	Close the simulation, freeing resources.
`nengo_dl.Simulator.get_name`	Returns the standardized string name for input Nodes or output Probes.

nengo_dl.simulator.SimulationData

Data structure used to access simulation data from the model.

class nengo_dl.Simulator(network, dt=0.001, seed=None, model=None, device=None, unroll_simulation=1, minibatch_size=None, progress_bar=True)[source]¶

Simulate network using the nengo_dl backend.

Parameters

networknengo.Network

A network object to be built and then simulated.

dtfloat

Length of a simulator timestep, in seconds.

seedint

Seed for all stochastic operators used in this simulator.

modelModel

Pre-built model object (mainly used for debugging).

deviceNone or "/cpu:0" or "/gpu:[0-n]"

This specifies the computational device on which the simulation will run. The default is None, which means that operations will be assigned according to TensorFlow’s internal logic (generally speaking, this means that things will be assigned to the GPU if GPU support is available, otherwise everything will be assigned to the CPU). The device can be set manually by passing the TensorFlow device specification to this parameter. For example, setting device="/cpu:0" will force everything to run on the CPU. This may be worthwhile for small models, where the extra overhead of communicating with the GPU outweighs the actual computations. On systems with multiple GPUs, device="/gpu:0"/"/gpu:1"/etc. will select which one to use.

unroll_simulationint

This controls how many simulation iterations are executed each time through the outer simulation loop. That is, we could run 20 timesteps as

for i in range(20):
    <run 1 step>

for i in range(5):
    <run 1 step>
    <run 1 step>
    <run 1 step>
    <run 1 step>

This is an optimization process known as “loop unrolling”, and unroll_simulation controls how many simulation steps are unrolled. The first example above would correspond to unroll_simulation=1, and the second would be unroll_simulation=4.

Unrolling the simulation will result in faster simulation speed, but increased build time and memory usage.

In general, unrolling the simulation will have no impact on the output of a simulation. The only case in which unrolling may have an impact is if the number of simulation steps is not evenly divisible by unroll_simulation. In that case extra simulation steps will be executed, which could change the internal state of the simulation and will affect any subsequent calls to sim.run. So it is recommended that the number of steps always be evenly divisible by unroll_simulation.

minibatch_sizeint

The number of simultaneous inputs that will be passed through the network. For example, a single call to Simulator.run will process minibatch_size input instances in parallel. Or when calling Simulator.predict/Simulator.fit with a batch of data, that data will be divided up into minibatch_size chunks.

progress_barbool

If True (default), display progress information when building a model. This will also be the default for the progress_bar argument within Simulator.run and Simulator.run_steps.

Attributes

dataSimulationData: Stores simulation data and parameter values (in particular, the recorded output from probes after calling Simulator.run can be accessed through sim.data[my_probe]).
modelnengo.builder.Model: Built Nengo model, containing the data that defines the network to be simulated.
keras_modeltf.keras.Model: Keras Model underlying the simulation (implements the inference/training loops).
tensor_graphtensor_graph.TensorGraph: Keras Layer implementing the Nengo simulation (built into keras_model).

reset(seed=None, include_trainable=True, include_probes=True, include_processes=True)¶

Resets the simulator to initial conditions.

Parameters

seedint: If not None, overwrite the default simulator seed with this value (note: this becomes the new default simulator seed).
include_trainablebool: If True (default), also reset any online or offline training that has been performed on simulator parameters (e.g., connection weights).
include_probesbool: If True (default), also clear probe data.
include_processes: bool: If True (default), also reset all nengo.Process objects in the model.

Notes

Changing the TensorFlow seed only affects ops created from then on; it has no impact on existing ops (either changing their seed or resetting their random state). So calling Simulator.reset will likely have no impact on any TensorFlow randomness (it will still affect numpy randomness, such as in a nengo.Process, as normal).

soft_reset(include_trainable=False, include_probes=False)¶: Deprecated, use Simulator.reset instead.

predict(x=None, n_steps=None, stateful=False, **kwargs)[source]¶

Generate output predictions for the input samples.

Computation is (optionally) done in batches.

This function implements the tf.keras.Model.predict API.

Parameters

x

Data for input Nodes in the model. This argument is optional; if it is not specified, then data will automatically be generated according to the inputs specified in the Node definitions (e.g., by calling the output function associated with that Node).

x can be specified as:

A dictionary of {nengo.Node or str: numpy.ndarray} indicating the input values for the given nodes. Nodes can be referred to by the Node object itself or by a string name, which will be Node.label if one was specified or "node" (duplicate names will have a number appended, corresponding to the order found in nengo.Network.all_nodes).
A list of numpy.ndarray indicating the input values for each input Node, ordered according to the order in which the Nodes were added to the model (this corresponds to the order found in nengo.Network.all_nodes).
A numpy.ndarray indicating the input value for a single input Node.
A generator or tf.data.Dataset that produces one of the above.

All inputs should have shape (batch_size, n_steps, node.size_out).

For example, if the model only has a single input Node, then x can simply be an ndarray of data for that Node.

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.predict(
        x=np.ones((50, 10, 1)))

If the network has multiple inputs, then x can be specified as a dictionary mapping nengo.Node objects to arrays, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0, 0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.predict(
        x={
            a: np.ones((50, 10, 1)),
            b: np.ones((50, 10, 2))
        }
    )

If an input value is not specified for one of the Nodes in the model then data will be filled in automatically according to the Node definition.

For dynamic input types (e.g., tf.data pipelines or generators), NengoDL tries to avoid introspecting/altering the data before the simulation starts, as this may have unintended side-effects. So data must be specified via one of the standard Keras methods (arrays, list of arrays, or string name dictionary; using a dictionary of Node objects is not supported). In addition, data must be explicitly provided for all input nodes (it will not be automatically generated if data is not specified).

In addition, when using dynamic inputs, data must be provided for the special "n_steps" input. This specifies the number of timesteps that the simulation will run for. Technically this is just a single scalar value (e.g., 10). But Keras requires that all input data be batched, so that input value needs to be duplicated into an array with size (batch_size, 1) (where all entries have the same value, e.g. 10).

with nengo.Network() as net:
    a = nengo.Node([0], label="a")
    p = nengo.Probe(a, label="p")

with nengo_dl.Simulator(net) as sim:
    dataset = tf.data.Dataset.from_tensor_slices(
        {"a": tf.ones((50, 10, 1)),
         "n_steps": tf.ones((50, 1), dtype=tf.int32) * 10}
    ).batch(sim.minibatch_size)

    sim.predict(x=dataset)

n_stepsint

The number of simulation steps to be executed. This parameter is optional; if not specified, the number of simulation steps will be inferred from the input data. However, this parameter can be useful if you don’t want to specify input data (you just want to use the inputs defined by the Nengo Nodes), or if your model does not have any input Nodes (so there is no data to be passed in).

statefulbool

This parameter controls whether or not the saved internal stimulation state will be updated after a run completes. If stateful=False then the initial state of future runs will be unaffected by this run. With stateful=True, future runs will begin from the terminal state of this run.

For example,

# begins in state0, terminates in state1
sim.predict(..., stateful=False)
# begins in state0, terminates in state2
sim.predict(..., stateful=True)
# begins in state2, terminates in state3
sim.predict(..., stateful=False)
# begins in state2, terminates in state4
sim.predict(..., stateful=True)

Note that Simulator.reset can be used to reset the state to initial conditions at any point.

kwargs: dict

Will be passed on to tf.keras.Model.predict.

Returns

probe_valuesdict of {nengo.Probe: numpy.ndarray}: Output values from all the Probes in the network.

predict_on_batch(x=None, n_steps=None, stateful=False, **kwargs)[source]¶

Generate output predictions for a single minibatch of input samples.

Batch size is determined by sim.minibatch_size (i.e., inputs must have shape (sim.minibatch_size, n_steps, node.size_in).

This function implements the tf.keras.Model.predict_on_batch API.

Parameters

x

x can be specified as:

A dictionary of {nengo.Node or str: numpy.ndarray} indicating the input values for the given nodes. Nodes can be referred to by the Node object itself or by a string name, which will be Node.label if one was specified or "node" (duplicate names will have a number appended, corresponding to the order found in nengo.Network.all_nodes).
A list of numpy.ndarray indicating the input values for each input Node, ordered according to the order in which the Nodes were added to the model (this corresponds to the order found in nengo.Network.all_nodes).
A numpy.ndarray indicating the input value for a single input Node.

All inputs should have shape (batch_size, n_steps, node.size_out).

For example, if the model only has a single input Node, then x can simply be an ndarray of data for that Node.

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.predict_on_batch(
        x=np.ones((1, 10, 1)))

If the network has multiple inputs, then x can be specified as a dictionary mapping nengo.Node objects to arrays, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0, 0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.predict_on_batch(
        x={
            a: np.ones((1, 10, 1)),
            b: np.ones((1, 10, 2))
        }
    )

If an input value is not specified for one of the Nodes in the model then data will be filled in automatically according to the Node definition.

n_stepsint

statefulbool

For example,

# begins in state0, terminates in state1
sim.predict_on_batch(..., stateful=False)
# begins in state0, terminates in state2
sim.predict_on_batch(..., stateful=True)
# begins in state2, terminates in state3
sim.predict_on_batch(..., stateful=False)
# begins in state2, terminates in state4
sim.predict_on_batch(..., stateful=True)

Note that Simulator.reset can be used to reset the state to initial conditions at any point.

kwargs: dict

Will be passed on to tf.keras.Model.predict_on_batch.

Returns

probe_valuesdict of {nengo.Probe: numpy.ndarray}: Output values from all the Probes in the network.

compile(*args, loss=None, metrics=None, loss_weights=None, **kwargs)¶

Configure the model for training/evaluation.

Parameters

args

Will be passed on to tf.keras.Model.compile.

loss

Loss functions define the error that will be minimized during training.

Losses can be specified as:

A tf.losses.Loss instance.
A string matching the name of one of the loss functions above.
A function that accepts two arguments (y_true, y_pred) and returns a loss value (represented as a tf.Tensor).
A list of some combination of the above, indicating different loss functions for each output Probe (ordered according to the order in which Probes were added to the model, which corresponds to the order found in Simulator.model.probes).
A dictionary mapping Probe instances or names to loss functions.

The total loss minimized during training will be the sum over the loss computed on each Probe (possibly weighted by loss_weights).

For example,

with nengo.Network() as net:
    node0 = nengo.Node([0])
    node1 = nengo.Node([0])
    probe0 = nengo.Probe(node0)
    probe1 = nengo.Probe(node1)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss={probe0: "mse", probe1: tf.losses.mae})

would compile probe0 to use mean squared error and probe1 to use mean absolute error.

metrics

Metrics are additional values (generally different kinds of losses) that will be computed during training for tracking purposes, but do not affect the result of the training.

They can be specified in all the same ways as loss above.

In addition, multiple metrics can be specified for each output Probe when using a list or dict, by providing multiple functions in a list (e.g., metrics={my_probe: ["mae", "mse"]}).

loss_weightslist or dict

Scalar weights that will be applied to the loss value computed for each output probe before summing them to compute the overall training loss. Can be a list (order corresponding to the order in loss) or a dict mapping Probe instances/names to weights.

kwargs

Will be passed on to tf.keras.Model.compile.

fit(x=None, y=None, n_steps=None, stateful=False, **kwargs)[source]¶

Trains the model on some dataset.

Note that if the model contains spiking neurons, during the execution of this function those neurons will be swapped for the equivalent non-spiking implementation (as opposed to, e.g., Simulator.evaluate, which will use the spiking implementation).

Optimizer and loss functions are defined separately in Simulator.compile.

This function implements the tf.keras.Model.fit API.

Parameters

x

x can be specified as:

A dictionary of {nengo.Node or str: numpy.ndarray} indicating the input values for the given nodes. Nodes can be referred to by the Node object itself or by a string name, which will be Node.label if one was specified or "node" (duplicate names will have a number appended, corresponding to the order found in nengo.Network.all_nodes).
A list of numpy.ndarray indicating the input values for each input Node, ordered according to the order in which the Nodes were added to the model (this corresponds to the order found in nengo.Network.all_nodes).
A numpy.ndarray indicating the input value for a single input Node.
A generator or tf.data.Dataset that produces one of the above.

All inputs should have shape (batch_size, n_steps, node.size_out).

For example, if the model only has a single input Node, then x can simply be an ndarray of data for that Node.

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.fit(
        x=np.ones((50, 10, 1)), y=np.ones((50, 10, 1)))

If the network has multiple inputs, then x can be specified as a dictionary mapping nengo.Node objects to arrays, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0, 0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.fit(
        x={
            a: np.ones((50, 10, 1)),
            b: np.ones((50, 10, 2))
        },
        y=np.ones((50, 10, 1))
    )

If an input value is not specified for one of the Nodes in the model then data will be filled in automatically according to the Node definition.

Also keep in mind that when using a dynamic input for x the y parameter is unused, and instead the generator should return (x, y) pairs.

with nengo.Network() as net:
    a = nengo.Node([0], label="a")
    p = nengo.Probe(a, label="p")

with nengo_dl.Simulator(net) as sim:
    dataset = tf.data.Dataset.from_tensor_slices(
        ({"a": tf.ones((50, 10, 1)),
          "n_steps": tf.ones((50, 1), dtype=tf.int32) * 10},
         {"p": tf.ones((50, 10, 1))})
    ).batch(sim.minibatch_size)

    sim.compile(loss="mse")
    sim.fit(x=dataset)

y

Target values for Probes in the model. These can be specified in the same ways as the input values in x, except using Probes instead of Nodes. All targets should have shape (batch_size, n_steps, probe.size_in).

For example,

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.fit(
        x={a: np.zeros((50, 10, 1))}, y={p: np.zeros((50, 10, 1))})

Note that data is only specified for the probes used in the loss function (specified when calling Simulator.compile). For example, if we have two probes, but only one is used during training (the other is used for data collection during inference), we could set this up like:

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0])
    p_a = nengo.Probe(a)
    p_b = nengo.Probe(b)

with nengo_dl.Simulator(net) as sim:
    # compiled loss function only depends on p_a
    sim.compile(loss={p_a: "mse"})

    # only specify data for p_a
    sim.fit(
        x={a: np.zeros((50, 10, 1))},  y={p_a: np.zeros((50, 10, 1))})

y is not used if x is a generator. Instead, the generator passed to x should yield (x, y) tuples, where y is in one of the formats described above.

n_stepsint

statefulbool

For example,

# begins in state0, terminates in state1
sim.fit(..., stateful=False)
# begins in state0, terminates in state2
sim.fit(..., stateful=True)
# begins in state2, terminates in state3
sim.fit(..., stateful=False)
# begins in state2, terminates in state4
sim.fit(..., stateful=True)

Note that Simulator.reset can be used to reset the state to initial conditions at any point.

kwargs: dict

Will be passed on to tf.keras.Model.fit.

Returns

historytf.keras.callbacks.History: The history has two attributes: history.epoch is the list of epoch numbers, and history.history is a dictionary keyed by metric names (e.g., “loss”) containing a list of values of those metrics from each epoch.

evaluate(x=None, y=None, n_steps=None, stateful=False, **kwargs)[source]¶

Compute the loss and metric values for the network.

Loss functions and other metrics are defined separately in Simulator.compile.

This function implements the tf.keras.Model.evaluate API.

Parameters

x

x can be specified as:

A dictionary of {nengo.Node or str: numpy.ndarray} indicating the input values for the given nodes. Nodes can be referred to by the Node object itself or by a string name, which will be Node.label if one was specified or "node" (duplicate names will have a number appended, corresponding to the order found in nengo.Network.all_nodes).
A list of numpy.ndarray indicating the input values for each input Node, ordered according to the order in which the Nodes were added to the model (this corresponds to the order found in nengo.Network.all_nodes).
A numpy.ndarray indicating the input value for a single input Node.
A generator or tf.data.Dataset that produces one of the above.

All inputs should have shape (batch_size, n_steps, node.size_out).

For example, if the model only has a single input Node, then x can simply be an ndarray of data for that Node.

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.evaluate(
        x=np.ones((50, 10, 1)), y=np.ones((50, 10, 1)))

If the network has multiple inputs, then x can be specified as a dictionary mapping nengo.Node objects to arrays, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0, 0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.evaluate(
        x={
            a: np.ones((50, 10, 1)),
            b: np.ones((50, 10, 2))
        },
        y=np.ones((50, 10, 1))
    )

If an input value is not specified for one of the Nodes in the model then data will be filled in automatically according to the Node definition.

Also keep in mind that when using a dynamic input for x the y parameter is unused, and instead the generator should return (x, y) pairs.

with nengo.Network() as net:
    a = nengo.Node([0], label="a")
    p = nengo.Probe(a, label="p")

with nengo_dl.Simulator(net) as sim:
    dataset = tf.data.Dataset.from_tensor_slices(
        ({"a": tf.ones((50, 10, 1)),
          "n_steps": tf.ones((50, 1), dtype=tf.int32) * 10},
         {"p": tf.ones((50, 10, 1))})
    ).batch(sim.minibatch_size)

    sim.compile(loss="mse")
    sim.evaluate(x=dataset)

y

For example,

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss="mse")
    sim.evaluate(
        x={a: np.zeros((50, 10, 1))}, y={p: np.zeros((50, 10, 1))})

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0])
    p_a = nengo.Probe(a)
    p_b = nengo.Probe(b)

with nengo_dl.Simulator(net) as sim:
    # compiled loss function only depends on p_a
    sim.compile(loss={p_a: "mse"})

    # only specify data for p_a
    sim.evaluate(
        x={a: np.zeros((50, 10, 1))},  y={p_a: np.zeros((50, 10, 1))})

y is not used if x is a generator. Instead, the generator passed to x should yield (x, y) tuples, where y is in one of the formats described above.

n_stepsint

statefulbool

For example,

# begins in state0, terminates in state1
sim.evaluate(..., stateful=False)
# begins in state0, terminates in state2
sim.evaluate(..., stateful=True)
# begins in state2, terminates in state3
sim.evaluate(..., stateful=False)
# begins in state2, terminates in state4
sim.evaluate(..., stateful=True)

Note that Simulator.reset can be used to reset the state to initial conditions at any point.

kwargs: dict

Will be passed on to tf.keras.Model.evaluate.

Returns

outputsdict of {str: numpy.ndarray}: Computed loss/metric values. The overall loss will be in outputs["loss"], and values for each Probe will be in outputs["probe_name_loss"] or outputs["probe_name_metric_name"].

step(**kwargs)[source]¶

Run the simulation for one time step.

Parameters

kwargsdict: See run_steps

Notes

Progress bar is disabled by default when running via this method.

run(time_in_seconds, **kwargs)[source]¶

Run the simulation for the given length of time.

Parameters

time_in_secondsfloat: Run the simulator for the given number of simulated seconds.
kwargsdict: See run_steps

run_steps(n_steps, data=None, progress_bar=None, stateful=True)[source]¶

Run the simulation for the given number of steps.

Parameters

n_stepsint

The number of simulation steps to be executed.

data :

data can be specified as:

A dictionary of {nengo.Node or str: numpy.ndarray} indicating the input values for the given nodes. Nodes can be referred to by the Node object itself or by a string name, which will be Node.label if one was specified or "node" (duplicate names will have a number appended, corresponding to the order found in nengo.Network.all_nodes).
A list of numpy.ndarray indicating the input values for each input Node, ordered according to the order in which the Nodes were added to the model (this corresponds to the order found in nengo.Network.all_nodes).
A numpy.ndarray indicating the input value for a single input Node.

All inputs should have shape (batch_size, n_steps, node.size_out).

For example, if the model only has a single input Node, then data can simply be an ndarray of data for that Node.

with nengo.Network() as net:
    a = nengo.Node([0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.run_steps(
        10, data=np.ones((1, 10, 1)))

If the network has multiple inputs, then data can be specified as a dictionary mapping nengo.Node objects to arrays, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([0, 0])
    p = nengo.Probe(a)

with nengo_dl.Simulator(net) as sim:
    sim.run_steps(
        10,
        data={
            a: np.ones((1, 10, 1)),
            b: np.ones((1, 10, 2))
        }
    )

If an input value is not specified for one of the Nodes in the model then data will be filled in automatically according to the Node definition.

progress_barbool

If True, print information about the simulation status to standard output.

statefulbool

For example,

# begins in state0, terminates in state1
sim.run_steps(..., stateful=False)
# begins in state0, terminates in state2
sim.run_steps(..., stateful=True)
# begins in state2, terminates in state3
sim.run_steps(..., stateful=False)
# begins in state2, terminates in state4
sim.run_steps(..., stateful=True)

Note that Simulator.reset can be used to reset the state to initial conditions at any point.

Notes

If unroll_simulation=x is specified, and n_steps > x, this will repeatedly execute x timesteps until the the number of steps executed is >= n_steps.

train(*args, **kwargs)[source]¶: Deprecated, use Simulator.compile and Simulator.fit instead.

loss(*args, **kwargs)[source]¶: Deprecated, use Simulator.compile and Simulator.evaluate instead.

save_params(path, include_state=False, include_non_trainable=None)¶

Save network parameters to the given path.

Parameters

pathstr: Filepath of parameter output file.
include_statebool: If True (default False) also save the internal simulation state.

Changed in version 3.2.0: Renamed from include_non_trainable to include_state.

Notes

This function is useful for saving/loading entire models; for saving/loading individual objects within a model, see get_nengo_params.

load_params(path, include_state=False, include_non_trainable=None)¶

Load network parameters from the given path.

Parameters

pathstr: Filepath of parameter input file.
include_statebool: If True (default False) also save the internal simulation state.

Changed in version 3.2.0: Renamed from include_non_trainable to include_state.

Notes

This function is useful for saving/loading entire models; for saving/loading individual objects within a model, see get_nengo_params.

freeze_params(objs)[source]¶

Stores the live parameter values from the simulation back into a Nengo object definition.

This can be helpful for reusing a NengoDL model inside a different Simulator. For example:

with nengo.Network() as net:
    ens = nengo.Ensemble(10, 1)

with nengo_dl.Simulator(net) as sim:
    # < run some optimization >
    sim.freeze_params(net)

with nengo.Simulator(net) as sim2:
    # run the network in the default Nengo simulator, with the
    # trained parameters
    sim2.run(1.0)

Parameters

obj(list of) NengoObject: The Nengo object(s) into which parameter values will be stored. Note that these objects must be members of the Network used to initialize the Simulator.

Notes

This modifies the source object in-place, and it may slightly modify the structure of that object. The goal is to have the object produce the same output as it would if run in the NengoDL simulator. It may not be possible to accurately freeze all possible object; if you run into errors in this process, try manually extracting the parameters you need in your model (from sim.data).

get_nengo_params(nengo_objs, as_dict=False)[source]¶

Extract model parameters in a form that can be used to initialize Nengo objects in a different model.

For example:

with nengo.Network() as net:
    a = nengo.Ensemble(10, 1)
    b = nengo.Ensemble(10, 1)
    c = nengo.Connection(a, b)

with nengo_dl.Simulator(net) as sim:
    # < do some optimization >
    params = sim.get_nengo_params([a, b, c])

with nengo.Network() as new_net:
    # < build some other network >

    # now we want to insert two connected ensembles with
    # the same parameters as our previous network:
    d = nengo.Ensemble(10, 1, **params[0])
    e = nengo.Ensemble(10, 1, **params[1])
    f = nengo.Connection(d, e, **params[2])

Note that this function only returns trainable parameters (e.g. connection weights, biases, or encoders), or parameters that directly interact with those parameters (e.g. gains). Other arguments that are independent of the trainable parameters (e.g. Ensemble.neuron_type or Connection.synapse) should be specified manually (since they may change between models).

Parameters

nengo_objs(list of) Ensemble or Connection: A single object or list of objects for which we want to get the parameters.
as_dictbool: If True, return the values as a dictionary keyed by object label, instead of a list (the default). Note that in this case labels must be unique.

Returns

params(list or dict) of dicts: kwarg dicts corresponding to nengo_objs (passing these dicts as kwargs when creating new Nengo objects will result in a new object with the same parameters as the source object). A single kwarg dict if a single object was passed in, or a list (dict if as_dict=True) of kwargs corresponding to multiple input objects.

check_gradients(inputs=None, outputs=None, atol=1e-05, rtol=0.001)¶

Perform gradient checks for the network (used to verify that the analytic gradients are correct).

Raises a simulation error if the difference between analytic and numeric gradient is greater than atol + rtol * numeric_grad (elementwise).

Parameters

inputslist of numpy.ndarray: Input values for all the input Nodes in the model (ordered according to the order in which Nodes were added to the model). If None, will use all zeros.
outputslist of Probe: Compute gradients wrt this output (if None, computes wrt each output probe).
atolfloat: Absolute error tolerance.
rtolfloat: Relative (to numeric grad) error tolerance.

Notes

Calling this method will reset the internal simulation state.

trange(sample_every=None, dt=None)[source]¶

Create a vector of simulation step times matching probed data.

Note that the range does not start at 0 as one might expect, but at the first timestep (i.e., dt).

Parameters

sample_everyfloat (Default: None): The sampling period of the probe to create a range for. If None, a time value for every dt will be produced.

close()[source]¶

Close the simulation, freeing resources.

Notes

The simulation cannot be restarted after it is closed.

get_name(obj)[source]¶

Returns the standardized string name for input Nodes or output Probes.

These are used when referring to inputs/outputs by string in Keras.

Parameters

objnengo.Node or nengo.Probe: Input Node or output Probe

Returns

namestr: Name of the given object

property n_steps¶: The current simulation timestep.

property time¶: The current simulation time.

property seed¶: The simulation random seed.

class nengo_dl.simulator.SimulationData(sim, minibatched)[source]¶

Data structure used to access simulation data from the model.

The main use case for this is to access Probe data; for example, probe_data = sim.data[my_probe]. However, it is also used to access the parameters of objects in the model; for example, after the model has been optimized via Simulator.fit, the updated encoder values for an ensemble can be accessed via trained_encoders = sim.data[my_ens].encoders.

Parameters

simSimulator: The simulator from which data will be drawn
minibatchedbool: If False, discard the minibatch dimension on probe data

Notes

SimulationData shouldn’t be created/accessed directly by the user, but rather via sim.data (which is an instance of SimulationData).

__getitem__(obj)[source]¶

Return the data associated with obj.

Parameters

objProbe or Ensemble or Connection: Object whose simulation data is being accessed

Returns

datandarray or BuiltEnsemble or BuiltConnection: Array containing probed data if obj is a Probe, otherwise the corresponding parameter object

get_params(*obj_attrs)[source]¶

Returns the current parameter values for the given objects.

Parameters

obj_attrslist of (NengoObject, str): The Nengo object and attribute of that object for which we want to know the parameter values (each object-attribute pair specified as a tuple argument to the function).

Returns

paramslist of ndarray: Current values of the requested parameters

Notes

Parameter values should be accessed through sim.data[my_obj] (which will call this function if necessary), rather than directly through this function.

TensorNodes¶

TensorNodes allow parts of a model to be defined using TensorFlow and smoothly integrated with the rest of a Nengo model.

See the documentation for more details.

`nengo_dl.TensorNode`	Inserts TensorFlow code into a Nengo model.
`nengo_dl.Layer`	A wrapper for constructing TensorNodes.

class nengo_dl.TensorNode(tensor_func, shape_in=Default, shape_out=Default, pass_time=Default, label=Default)[source]¶

Inserts TensorFlow code into a Nengo model.

Parameters

tensor_funccallable: A function that maps node inputs to outputs
shape_intuple of int: Shape of TensorNode input signal (not including batch dimension).
shape_outtuple of int: Shape of TensorNode output signal (not including batch dimension). If None, value will be inferred by calling tensor_func.
pass_timebool: If True, pass current simulation time to TensorNode function (in addition to the standard input).
labelstr (Default: None): A name for the node, used for debugging and visualization

property output¶: Ensures that nothing tries to evaluate the output attribute (indicating that something is trying to simulate this as a regular nengo.Node rather than a TensorNode).

property size_in¶: Number of input elements (flattened).

property size_out¶: Number of output elements (flattened).

class nengo_dl.Layer(layer_func)[source]¶

A wrapper for constructing TensorNodes.

This is designed to mimic and integrate with the tf.keras.layers.Layer API, e.g.

with nengo.Network():
    a = nengo.Ensemble(10, 1)
    b = nengo_dl.Layer(tf.keras.layers.Dense(units=10))(a)
    c = nengo_dl.Layer(lambda x: x + 1)(b)
    d = nengo_dl.Layer(nengo.LIF())(c)

Parameters

layer_funccallable or tf.keras.Layer or NeuronType: A function or Keras Layer that takes the value from an input (represented as a tf.Tensor) and maps it to some output value, or a Nengo neuron type (which will be instantiated in a Nengo Ensemble and applied to the input).

__call__(input, transform=None, shape_in=None, synapse=None, return_conn=False, **layer_args)[source]¶

Apply the TensorNode layer to the given input object.

Parameters

inputNengoObject: Object providing input to the layer.
transformndarray: Transform matrix to apply on connection from input to this layer.
shape_intuple of int: If not None, reshape the input to the given shape.
synapsefloat or Synapse: Synapse to apply on connection from input to this layer.
return_connbool: If True, also return the connection linking this layer to input.
layer_argsdict: These arguments will be passed to TensorNode if layer_func is a callable or Keras Layer, or Ensemble if layer_func is a NeuronType.

Returns

objTensorNode or Neurons: A TensorNode that implements the given layer function (if layer_func was a callable/Keras layer), or a Neuron object with the given neuron type, connected to input.
connConnection: If return_conn is True, also returns the connection object linking input and obj.

Notes

The input connection created for the new TensorNode will be marked as non-trainable by default.

__str__()[source]¶: Return str(self).

Converter¶

nengo_dl.Converter can be used to automatically convert a Keras model to a native Nengo Network. This can be useful if, for example, you want to run a model in different Nengo Simulator backends (which will only support the core Nengo objects).

See the documentation for more details.

nengo_dl.Converter

Converts a Keras model to a Nengo network composed of native Nengo objects.

class nengo_dl.Converter(model, allow_fallback=True, inference_only=False, max_to_avg_pool=False, split_shared_weights=False, swap_activations=None, scale_firing_rates=None, synapse=None, temporal_model=False)[source]¶

Converts a Keras model to a Nengo network composed of native Nengo objects.

Parameters

modeltf.keras.Model: Keras model to be converted
allow_fallbackbool: If True, allow layers that cannot be converted to native Nengo objects to be added as a TensorNode instead. Note that if this occurs, the converted Nengo network will only be runnable in the NengoDL simulator.
inference_onlybool: Allow layers to be converted in such a way that inference behaviour will match the source model but training behaviour will not. If inference_only=False then some layers cannot be converted to native Nengo objects (but you can still use allow_fallback=True to use a TensorNode instead).
max_to_avg_poolbool: If True, convert max pooling layers to average pooling layers. Note that this will change the behaviour of the network, so parameters will probably need to be re-trained in NengoDL. If max_to_avg_pool=False then max pooling layers cannot be converted to native Nengo objects (but you can still use allow_fallback=True to use a TensorNode instead).
split_shared_weightsbool: In Keras, applying the same Layer object to different input layers will result in multiple instances of the given layer that share the same weights. This is not supported in Nengo. If split_shared_weights=True then those shared weights will be split into independent sets of weights. They will all be initialized to the same value, so the initial behaviour of the model will be unchanged, but if any further training is performed on the network then the weights in each of those instances may diverge.
swap_activationsdict: A dictionary mapping from TensorFlow activation functions or Nengo neuron types to TensorFlow activation functions or Nengo neuron types. This can be used to change all the activation types in a model to some other type. This is in addition to the default activation map (see LayerConverter). It can be keyed based on either TensorFlow or Nengo activation types, and will be applied both before and after the default activation map, in order to support whatever swap type is most useful for a given model. In particular, swap_activations can be useful for swapping rate neuron types to spiking neuron types, through e.g. {tf.nn.relu: nengo.SpikingRectifiedLinear()} or {nengo.RectifiedLinear(): nengo.SpikingRectifiedLinear()}. Or it can be used to swap activation types that don’t have a native Nengo implementation, e.g. {tf.keras.activatons.elu: tf.keras.activations.relu}.
scale_firing_rates: float or dict: Scales the inputs of neurons by x, and the outputs by 1/x. The idea is that this parameter can be used to increase the firing rates of spiking neurons (by scaling the input), without affecting the overall output (because the output spikes are being scaled back down). Note that this is only strictly true for neuron types with linear activation functions (e.g. ReLU). Nonlinear neuron types (e.g. LIF) will be skewed by this linear scaling on the input/output. scale_firing_rates can be specified as a float, which will be applied to all layers in the model, or as a dictionary mapping Keras model layers to a scale factor, allowing different scale factors to be applied to different layers.
synapsefloat or nengo.synapses.Synapse: Synaptic filter to be applied on the output of all neurons. This can be useful to smooth out the noise introduced by spiking neurons. Note, however, that increasing the synaptic filtering will make the network less responsive to rapid changes in the input, and you may need to present each input value for more timesteps in order to allow the network output to settle.
temporal_modelbool: Set to True if the Keras model contains a temporal dimension (e.g. the inputs and outputs of each layer have shape (batch_size, n_steps, ...)). Note that all layers must be temporal, the Converter cannot handle models with a mix of temporal and non-temporal layers.

Attributes

modeltf.keras.Model: The input Keras model (if input was a Sequential model then this will be the equivalent Functional model).
netnengo.Network: The converted Nengo network.
inputsConverter.KerasTensorDict: Maps from Keras model inputs to input Nodes in the converted Nengo network. For example, my_node = Converter(my_model).inputs[my_model.input].
outputsConverter.KerasTensorDict: Maps from Keras model outputs to output Probes in the converted Nengo network. For example, my_probe = Converter(my_model).outputs[my_model.output].
layersConverter.KerasTensorDict: Maps from Keras model layers to the converted Nengo object. For example, my_neurons = Converter(my_model).layers[my_layer].

verify(training=False, inputs=None, atol=1e-08, rtol=1e-05)[source]¶

Verify that output of converted Nengo network matches the original Keras model.

Parameters

trainingbool: If True, check that optimizing the converted Nengo network produces the same results as optimizing the original Keras model.
inputslist of numpy.ndarray: Testing values for model inputs (if not specified, array of ones will be used).
atolfloat: Absolute tolerance for difference between Nengo and Keras outputs.
rtolfloat: Relative (to Nengo) tolerance for difference between nengo and Keras outputs.

Returns

successbool: True if output of Nengo network matches output of Keras model.

Raises

ValueError: If output of Nengo network does not match output of Keras model.

get_converter(layer)[source]¶

Get instantiated LayerConverter for the given Layer instance.

Note that this caches the results, so calling the function multiple times with the same Layer instance will return the same LayerConverter instance.

Parameters

layertf.keras.layers.Layer: The Keras Layer being converted.

Returns

converterLayerConverter: LayerConverter class for converting layer to Nengo objects.

classmethod register(keras_layer)[source]¶

A decorator for adding a class to the converter registry.

Parameters

keras_layertf.keras.layers.Layer: The Layer associated with the conversion function being registered.

class KerasTensorDict[source]¶: A dictionary-like object that has extra logic to handle Layer/Tensor keys.

class TrackedDict(dict)[source]¶

A dictionary-like object that keeps track of which keys have been accessed.

unused_keys()[source]¶: Returns any keys in the dictionary that have never been read.

Configuration system¶

The configuration system is used to change NengoDL’s default behaviour in various ways.

See the documentation for more details.

`nengo_dl.configure_settings`	Pass settings to `nengo_dl` by setting them as parameters on the top-level Network config.
`nengo_dl.config.get_setting`	Returns config settings (created by `configure_settings`).

nengo_dl.configure_settings(**kwargs)[source]¶

Pass settings to nengo_dl by setting them as parameters on the top-level Network config.

The settings are passed as keyword arguments to configure_settings; e.g., to set trainable use configure_settings(trainable=True).

Parameters

trainablebool or None: Adds a parameter to Nengo Ensembles/Connections that controls whether or not they will be optimized by Simulator.fit. Passing None will use the default nengo_dl trainable settings, or True/False will override the default for all objects. In either case trainability can be further configured on a per-object basis (e.g. net.config[my_ensemble].trainable = True. See the documentation for more details.
plannergraph planning algorithm: Pass one of the graph planners to change the default planner.
sortersignal sorting algorithm: Pass one of the sort algorithms to change the default sorter.
simplifications: list of graph simplification functions: Pass a list of graph simplification functions to change the default simplifications applied. The default list of simplifications can be found in nengo_dl.graph_optimizer.default_simplifications.
inference_onlybool: Set to True if the network will only be run in inference mode (i.e., no calls to Simulator.fit). This may result in a small increase in the inference speed.
lif_smoothingfloat: If specified, use the smoothed SoftLIFRate neuron model, with the given smoothing parameter (sigma), to compute the gradient for LIF neurons (as opposed to using LIFRate).
dtypetf.DType: Set the floating point precision for simulation values.
keep_historybool: Adds a parameter to Nengo Probes that controls whether or not they will keep the history from all simulation timesteps or only the last simulation step. This can be further configured on a per-probe basis (e.g., net.config[my_probe].keep_history = False).
statefulbool: If True (default), the Simulator will be built to support stateful execution (where internal simulation state is preserved between simulator functions such as Simulator.predict). Otherwise all operations will be stateless. Note that this can also be configured individually through the stateful parameter on individual functions.
use_loopbool: If True (default), use a symbolic while loop to run the simulation. Otherwise, simulation iterations are explicitly built into the model, avoiding the while loop. This can improve performance, but the simulation can only run for exactly Simulator.unroll_simulation iterations.
learning_phasebool: The learning phase is used for models that have different behaviour in inference versus training mode (for example, spiking neurons swap their behaviour during training). Normally the learning phase is set automatically depending on what function is being called (e.g. sim.predict will run in inference mode and sim.fit will run in training mode). However, sometimes it can be useful to override this behaviour (for example if we want to check what the output of the model looks like during training, we might want to run sim.predict in training mode). Set learning_phase=True to always run the model in training mode (or False to always run in inference mode). Set learning_phase=None to use the default behaviour.

New in version 3.3.0.

nengo_dl.config.get_setting(model, setting, default=None, obj=None)[source]¶

Returns config settings (created by configure_settings).

Parameters

modelModel or Network: Built model or Network containing all the config settings.
settingstr: Name of the config option to return.
default: The default value to return if config option not set.
objNengoObject: The object on which config setting is stored (defaults to the top-level network).

Returns

config_val: Value of setting if it has been specified, else default.

Neuron types¶

Additions to the neuron types included with Nengo.

`nengo_dl.SoftLIFRate`	LIF neuron with smoothing around the firing threshold.
`nengo_dl.LeakyReLU`	Rectified linear neuron with nonzero slope for values < 0.
`nengo_dl.SpikingLeakyReLU`	Spiking version of `LeakyReLU`.

class nengo_dl.SoftLIFRate(sigma=1.0, **lif_args)[source]¶

LIF neuron with smoothing around the firing threshold.

This is a rate version of the LIF neuron whose tuning curve has a continuous first derivative, due to the smoothing around the firing threshold. It can be used as a substitute for LIF neurons in deep networks during training, and then replaced with LIF neurons when running the network [1].

Parameters

sigmafloat: Amount of smoothing around the firing threshold. Larger values mean more smoothing.
tau_rcfloat: Membrane RC time constant, in seconds. Affects how quickly the membrane voltage decays to zero in the absence of input (larger = slower decay).
tau_reffloat: Absolute refractory period, in seconds. This is how long the membrane voltage is held at zero after a spike.
amplitudefloat: Scaling factor on the neuron output. Corresponds to the relative amplitude of the output spikes of the neuron.

Notes

Adapted from https://github.com/nengo/nengo-extras/blob/master/nengo_extras/neurons.py

References

1: Eric Hunsberger and Chris Eliasmith (2015): Spiking deep networks with LIF neurons. https://arxiv.org/abs/1510.08829.

rates(x, gain, bias)[source]¶: Estimates steady-state firing rate given gain and bias.

step(dt, J, output)[source]¶: Compute rates in Hz for input current (incl. bias)

step_math(dt, J, output)[source]¶: Backwards compatibility alias for self.step.

class nengo_dl.LeakyReLU(negative_slope=0.3, amplitude=1, **kwargs)[source]¶

Rectified linear neuron with nonzero slope for values < 0.

Parameters

negative_slopefloat: Scaling factor applied to values less than zero.
amplitudefloat: Scaling factor on the neuron output. Note that this will combine multiplicatively with negative_slope for values < 0.

step(dt, J, output)[source]¶: Implement the leaky relu nonlinearity.

step_math(dt, J, output)[source]¶: Backwards compatibility alias for self.step.

class nengo_dl.SpikingLeakyReLU(negative_slope=0.3, amplitude=1, **kwargs)[source]¶

Spiking version of LeakyReLU.

Note that this may output “negative spikes” (i.e. a spike with a sign of -1).

Parameters

negative_slopefloat: Scaling factor applied to values less than zero.
amplitudefloat: Scaling factor on the neuron output. Note that this will combine multiplicatively with negative_slope for values < 0.

rates(x, gain, bias)[source]¶: Use LeakyReLU to determine rates.

step(dt, J, output, voltage)[source]¶: Implement the spiking leaky relu nonlinearity.

step_math(dt, J, output, voltage)[source]¶: Backwards compatibility alias for self.step.

Distributions¶

Additions to the distributions included with Nengo. These distributions are usually used to initialize weight matrices, e.g. nengo.Connection(a.neurons, b.neurons, transform=nengo_dl.dists.Glorot()).

`nengo_dl.dists.TruncatedNormal`	Normal distribution where any values more than some distance from the mean are resampled.
`nengo_dl.dists.VarianceScaling`	Variance scaling distribution for weight initialization (analogous to `tf.initializers.VarianceScaling`).
`nengo_dl.dists.Glorot`	Weight initialization method from [1] (also known as Xavier initialization).
`nengo_dl.dists.He`	Weight initialization method from [1].

class nengo_dl.dists.TruncatedNormal(mean=0, stddev=1, limit=None)[source]¶

Normal distribution where any values more than some distance from the mean are resampled.

Parameters

meanfloat: Mean of the normal distribution.
stddevfloat: Standard deviation of the normal distribution.
limitfloat: Resample any values more than this distance from the mean. If None, then limit will be set to 2 standard deviations.

sample(n, d=None, rng=None)[source]¶

Samples the distribution.

Parameters

nint: Number samples to take.
dint or None: The number of dimensions to return. If this is an int, the return value will be of shape (n, d). If None, the return value will be of shape (n,).
rngRandomState: Random number generator state (if None, will use the default numpy random number generator).

Returns

samples(n,) or (n, d) array_like: Samples as a 1d or 2d array depending on d. The second dimension enumerates the dimensions of the process.

class nengo_dl.dists.VarianceScaling(scale=1, mode='fan_avg', distribution='uniform')[source]¶

Variance scaling distribution for weight initialization (analogous to tf.initializers.VarianceScaling).

Parameters

scalefloat: Overall scale on values.
mode“fan_in” or “fan_out” or “fan_avg”: Whether to scale based on input or output dimensionality, or average of the two.
distribution: “uniform” or “normal”: Whether to use a uniform or truncated normal distribution for weights.

sample(n, d=None, rng=None)[source]¶

Samples the distribution.

Parameters

nint: Number samples to take.
dint or None: The number of dimensions to return. If this is an int, the return value will be of shape (n, d). If None, the return value will be of shape (n,).
rngRandomState: Random number generator state (if None, will use the default numpy random number generator).

Returns

samples(n,) or (n, d) array_like: Samples as a 1d or 2d array depending on d. The second dimension enumerates the dimensions of the process.

class nengo_dl.dists.Glorot(scale=1, distribution='uniform')[source]¶

Weight initialization method from [1] (also known as Xavier initialization).

Parameters

scalefloat: Scale on weight distribution. For rectified linear units this should be sqrt(2), otherwise usually 1.
distribution: “uniform” or “normal”: Whether to use a uniform or normal distribution for weights

References

1(1,2): Xavier Glorot and Yoshua Bengio (2010): Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics. http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf.

class nengo_dl.dists.He(scale=1, distribution='normal')[source]¶

Weight initialization method from [1].

Parameters

scalefloat: Scale on weight distribution. For rectified linear units this should be sqrt(2), otherwise usually 1.
distribution: “uniform” or “normal”: Whether to use a uniform or normal distribution for weights

References

1(1,2): Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. (2015): Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. https://arxiv.org/abs/1502.01852.

Loss functions¶

Some common loss functions (for use with the loss argument in Simulator.compile).

`nengo_dl.losses.nan_mse`	Compute Mean Squared Error between given outputs and targets.
`nengo_dl.losses.Regularize`	An objective function to apply regularization penalties.

nengo_dl.losses.nan_mse(y_true, y_pred)[source]¶

Compute Mean Squared Error between given outputs and targets.

If any values in y_true are nan, that will be treated as zero error for those elements.

Parameters

y_truetf.Tensor: Target values for a Probe in a network.
y_predtf.Tensor: Output values from a Probe in a network.

Returns

msetf.Tensor: Tensor representing the mean squared error.

class nengo_dl.losses.Regularize(order=2, axis=None)[source]¶

An objective function to apply regularization penalties.

This is designed to be applied to a probed signal, e.g.

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node(size_in=1)
    c = nengo.Connection(a, b, transform=1)
    p = nengo.Probe(c, "weights")
    ...

    # this part is optional, but we may often want to only keep the data from
    # the most recent timestep when probing in this way, to save memory
    nengo_dl.configure_settings(keep_history=True)
    net.config[p].keep_history = False

with nengo_dl.Simulator(net) as sim:
    sim.compile(loss={p: nengo_dl.losses.Regularize()})

Parameters

orderint or str: Order of the regularization norm (e.g. 1 for L1 norm, 2 for L2 norm). See https://www.tensorflow.org/api_docs/python/tf/norm for a full description of the possible values for this parameter.
axisint or None: The axis of the probed signal along which to compute norm. If None (the default), the signal is flattened and the norm is computed across the resulting vector. Note that these are only the axes with respect to the output on a single timestep (i.e. batch/time dimensions are not included).

Notes

The mean will be computed across all the non-axis dimensions after computing the norm (including batch/time) in order to compute the overall objective value.

call(y_true, y_pred)[source]¶

Invoke the loss function.

Parameters

y_truetf.Tensor: Ignored
y_predtf.Tensor: The value to be regularized

Returns

outputtf.Tensor: Scalar regularization loss value.

Callbacks¶

Objects to be used with the Keras callback functionality.

See https://www.tensorflow.org/guide/keras/custom_callback for more information on how to use Keras callbacks.

The short answer is that these can be passed to, e.g., Simulator.fit like

sim.fit(..., callbacks=[nengo_dl.callbacks.NengoSummaries(...)]

`nengo_dl.callbacks.NengoSummaries`	Logs the values of Nengo object parameters, to be displayed in TensorBoard.
`nengo_dl.callbacks.TensorBoard`	A version of the Keras TensorBoard callback that also profiles inference.
`nengo_dl.callbacks.IsolateState`	Isolate the internal state of the simulation from any other stateful operations.

class nengo_dl.callbacks.NengoSummaries(log_dir, sim, objects)[source]¶

Logs the values of Nengo object parameters, to be displayed in TensorBoard.

See https://www.tensorflow.org/tensorboard/get_started for general instructions on using TensorBoard.

Parameters

log_dirstr: Directory where log file will be written.
simSimulator: Simulator object which will be used to look up parameter values.
objectslist of nengo.Ensemble or nengo.ensemble.Neurons or nengo.Connection: The object whose parameter values we want to record (passing an Ensemble will log its encoders, Neurons will log biases, and Connection will log connection weights/decoders).

on_epoch_end(epoch, logs=None)[source]¶: Log parameter values at the end of each epoch.

on_train_end(logs=None)[source]¶: Close summary writer at end of training.

class nengo_dl.callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=False, update_freq='epoch', profile_batch=2, embeddings_freq=0, embeddings_metadata=None, **kwargs)[source]¶

A version of the Keras TensorBoard callback that also profiles inference.

on_predict_batch_end(batch, logs=None)[source]¶: Redirect to training function.

on_predict_begin(logs=None)[source]¶: Redirect to training function.

on_predict_end(logs=None)[source]¶: Redirect to training function.

class nengo_dl.callbacks.IsolateState(sim)[source]¶

Isolate the internal state of the simulation from any other stateful operations.

This will cause every batch to begin from the same initial state (the state of the simulation whenever this callback is created). And when this operation completes, the simulation state will be returned to that initial state.

Parameters

simSimulator: The Simulator containing the state we want to control.

reset()[source]¶: Resets the simulation state to the saved state.

on_train_batch_end(batch, logs=None)[source]¶: Reset state at the end of each batch.

on_predict_batch_end(batch, logs=None)[source]¶: Reset state at the end of each batch.

on_test_batch_end(batch, logs=None)[source]¶: Reset state at the end of each batch.

Developers¶

These objects are only relevant to people interested in modifying the implementation of NengoDL (e.g., adding a new neuron type).

Builder¶

The builder manages the mapping between (groups of) Nengo operators and the builder objects that know how to translate those operators into a TensorFlow graph.

`nengo_dl.builder.Builder`	Manages the operator build classes known to the `nengo_dl` build process.
`nengo_dl.builder.BuildConfig`	Stores configuration parameters that may be relevant to parts of the build process.
`nengo_dl.builder.OpBuilder`	Base class for build classes, which implement the logic for building a group of Nengo Operators into TensorFlow.
`nengo_dl.builder.NengoBuilder`	Copy of the default Nengo builder.
`nengo_dl.builder.NengoModel`	Copy of the default Nengo model.

class nengo_dl.builder.Builder(plan)[source]¶

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

Parameters

planlist of tuple of Operator: The groups of operators that will be built

build_pre(signals, config, progress=None)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: Configuration parameters for the build process
progressutils.ProgressBar: Progress bar for ops in plan

build_step(signals, progress=None)[source]¶

Build the computations implementing a single simulator timestep.

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
progressutils.ProgressBar: Progress bar for ops in plan

Returns

side_effectslist of tf.Tensor: 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(signals, progress=None)[source]¶

Calls post build functions for all ops in plan.

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
progressutils.ProgressBar: Progress bar for ops in plan

name_scope(ops)[source]¶: Returns a new TensorFlow name scope for the given ops.

classmethod register(nengo_op)[source]¶

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

Parameters

nengo_opOperator: The operator associated with the build function being decorated.

class nengo_dl.builder.BuildConfig[source]¶

Stores configuration parameters that may be relevant to parts of the build process.

Parameters

inference_onlybool: If True the network should be constructed in “inference only” mode (omitting any support for training operations).
lif_smoothingfloat: Smoothing parameter for LIF gradient approximation.
cpu_onlybool: True if TensorFlow is only running on the CPU (because that was specified by the user or because GPU support is not available).
rngRandomState: Seeded random number generator.
trainingtf.Tensor (bool): True if building in training mode, False for inference mode.

Create new instance of BuildConfig(inference_only, lif_smoothing, cpu_only, rng, training)

class nengo_dl.builder.OpBuilder(ops)[source]¶

Base class for build classes, which implement the logic for building a group of Nengo Operators into TensorFlow.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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(signals)[source]¶

This function will be called after the graph has been built and each time the Simulator is reset.

Note that this function may be called multiple times per session, so it should do any required operations in-place.

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.builder.NengoBuilder[source]¶

Copy of the default Nengo builder.

This class is here so that we can register new build functions for NengoDL without affecting the default Nengo build process.

classmethod build(model, obj, *args, **kwargs)[source]¶

Build obj into model.

This method looks up the appropriate build function for obj and calls it with the model and other arguments provided.

In addition to the parameters listed below, further positional and keyword arguments will be passed unchanged into the build function.

Parameters

modelModel: The Model instance in which to store build artifacts.
objobject: The object to build into the model.

class nengo_dl.builder.NengoModel(*args, fail_fast=True, **kwargs)[source]¶

Copy of the default Nengo model.

This allows us to override certain model behaviours.

Parameters

fail_fastbool: If True, try to call op.make_step when ops are added to the model. Note that NengoDL doesn’t actually use make_step, so errors in that function are not necessarily errors in NengoDL (which is why we want to disable that check). But it might still be useful when debugging new op/build functions, which is why we leave the option.

add_op(op)[source]¶

Add an operator to the model.

Parameters

opOperator: Operator being added to the model.

Notes

This is a copy of the parent nengo.builder.Model.add_op, with the addition of the if self.fail_fast condition.

Operator builders¶

These objects are used to convert Nengo operators into TensorFlow graph elements.

Basic operators¶

Build classes for basic Nengo operators.

`nengo_dl.op_builders.ResetInc`	A version of `Reset` that increments the target value rather than overwriting.
`nengo_dl.op_builders.ElementwiseSet`	A version of `ElementwiseInc` that overwrites the target rather than incrementing.
`nengo_dl.op_builders.DotSet`	A version of `DotInc` that overwrites the target rather than incrementing.
`nengo_dl.op_builders.SparseDotSet`	A version of `SparseDotInc` that overwrites the target rather than incrementing.
`nengo_dl.op_builders.ResetBuilder`	Build a group of `Reset` operators.
`nengo_dl.op_builders.CopyBuilder`	Build a group of `Copy` operators.
`nengo_dl.op_builders.ElementwiseIncBuilder`	Build a group of `ElementwiseInc` operators.
`nengo_dl.op_builders.sparse_matmul`	Matrix multiplication between sparse matrix A and dense matrix X
`nengo_dl.op_builders.DotIncBuilder`	Build a group of `DotInc` operators.
`nengo_dl.op_builders.SimPyFuncBuilder`	Build a group of `SimPyFunc` operators.
`nengo_dl.op_builders.SparseDotIncBuilder`	Build a group of `SparseDotInc` operators.
`nengo_dl.op_builders.TimeUpdateBuilder`	Build a group of `TimeUpdate` operators.
`nengo_dl.op_builders.SimProbeBuilder`	Build a group of `SimProbe` operators.

class nengo_dl.op_builders.ResetInc(*args, **kwargs)[source]¶

A version of Reset that increments the target value rather than overwriting.

property dst¶: dst is stored in incs rather than sets.

class nengo_dl.op_builders.ElementwiseSet(*args, **kwargs)[source]¶

A version of ElementwiseInc that overwrites the target rather than incrementing.

property Y¶: Y is stored in sets rather than incs.

class nengo_dl.op_builders.DotSet(*args, **kwargs)[source]¶

A version of DotInc that overwrites the target rather than incrementing.

property Y¶: Y is stored in sets rather than incs.

class nengo_dl.op_builders.SparseDotSet(*args, **kwargs)[source]¶

A version of SparseDotInc that overwrites the target rather than incrementing.

property Y¶: Y is stored in sets rather than incs.

class nengo_dl.op_builders.ResetBuilder(ops)[source]¶

Build a group of Reset operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.CopyBuilder(ops)[source]¶

Build a group of Copy operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.ElementwiseIncBuilder(ops)[source]¶

Build a group of ElementwiseInc operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

nengo_dl.op_builders.sparse_matmul(A_indices, A_data, A_shape, X, transpose_x=False)[source]¶

Matrix multiplication between sparse matrix A and dense matrix X

Parameters

A_indicestf.Tensor: (N, 2) array of [row,col] non-zero entries
A_datatf.Tensor: (N,) array of data in the nonzero entries specified in A_indices
A_shapetuple of int: Shape of full A matrix
Xtf.Tensor: Dense matrix being multiplied by A
transpose_xbool: Transpose X before multiply

Returns

dottf.Tensor: Result of matrix multiplication between A and X

class nengo_dl.op_builders.DotIncBuilder(ops)[source]¶

Build a group of DotInc operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.SimPyFuncBuilder(ops)[source]¶

Build a group of SimPyFunc operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.SparseDotIncBuilder(ops)[source]¶

Build a group of SparseDotInc operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.TimeUpdateBuilder(ops)[source]¶

Build a group of TimeUpdate operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.op_builders.SimProbeBuilder(ops)[source]¶

Build a group of SimProbe operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

Neuron types¶

Build classes for Nengo neuron operators.

`nengo_dl.neuron_builders.GenericNeuronBuilder`	Builds all neuron types for which there is no custom TensorFlow implementation.
`nengo_dl.neuron_builders.TFNeuronBuilder`	Base class for `NeuronType` builders with a TF implementation.
`nengo_dl.neuron_builders.RectifiedLinearBuilder`	Build a group of `RectifiedLinear` neuron operators.
`nengo_dl.neuron_builders.SpikingRectifiedLinearBuilder`	Build a group of `SpikingRectifiedLinear` neuron operators.
`nengo_dl.neuron_builders.SigmoidBuilder`	Build a group of `Sigmoid` neuron operators.
`nengo_dl.neuron_builders.TanhBuilder`	Build a group of `Tanh` neuron operators.
`nengo_dl.neuron_builders.LIFRateBuilder`	Build a group of `LIFRate` neuron operators.
`nengo_dl.neuron_builders.SoftLIFRateBuilder`	Build a group of `SoftLIFRate` neuron operators.
`nengo_dl.neuron_builders.LIFBuilder`	Build a group of `LIF` neuron operators.
`nengo_dl.neuron_builders.RegularSpikingBuilder`	Build a group of `RegularSpiking` neuron operators.
`nengo_dl.neuron_builders.StochasticSpikingBuilder`	Build a group of `StochasticSpiking` neuron operators.
`nengo_dl.neuron_builders.PoissonSpikingBuilder`	Build a group of `PoissonSpiking` neuron operators.
`nengo_dl.neuron_builders.SimNeuronsBuilder`	Builds a group of `SimNeurons` operators.

class nengo_dl.neuron_builders.GenericNeuronBuilder(ops)[source]¶

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.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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.neuron_builders.TFNeuronBuilder(ops)[source]¶

Base class for NeuronType builders with a TF implementation.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt, **state)[source]¶: Implements the logic for a single inference step.

training_step(J, dt, **state)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

build_step(signals, **step_kwargs)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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.neuron_builders.RectifiedLinearBuilder(ops)[source]¶

Build a group of RectifiedLinear neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt)[source]¶: Implements the logic for a single inference step.

class nengo_dl.neuron_builders.SpikingRectifiedLinearBuilder(ops)[source]¶

Build a group of SpikingRectifiedLinear neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

step(J, dt, voltage)[source]¶: Implements the logic for a single inference step.

training_step(J, dt, **state)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

class nengo_dl.neuron_builders.SigmoidBuilder(ops)[source]¶

Build a group of Sigmoid neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt)[source]¶: Implements the logic for a single inference step.

class nengo_dl.neuron_builders.TanhBuilder(ops)[source]¶

Build a group of Tanh neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt)[source]¶: Implements the logic for a single inference step.

class nengo_dl.neuron_builders.LIFRateBuilder(ops)[source]¶

Build a group of LIFRate neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt)[source]¶: Implements the logic for a single inference step.

class nengo_dl.neuron_builders.SoftLIFRateBuilder(ops)[source]¶

Build a group of SoftLIFRate neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt)[source]¶: Implements the logic for a single inference step.

class nengo_dl.neuron_builders.LIFBuilder(ops)[source]¶

Build a group of LIF neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

step(J, dt, voltage, refractory_time)[source]¶: Implements the logic for a single inference step.

training_step(J, dt, **state)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

class nengo_dl.neuron_builders.RegularSpikingBuilder(ops)[source]¶

Build a group of RegularSpiking neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

step(J, dt, voltage)[source]¶: Implements the logic for a single inference step.

training_step(J, dt, **state)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

class nengo_dl.neuron_builders.StochasticSpikingBuilder(ops)[source]¶

Build a group of StochasticSpiking neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

step(J, dt)[source]¶: Implements the logic for a single inference step.

training_step(J, dt)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

class nengo_dl.neuron_builders.PoissonSpikingBuilder(ops)[source]¶

Build a group of PoissonSpiking neuron operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

step(J, dt)[source]¶: Implements the logic for a single inference step.

training_step(J, dt)[source]¶

Implements the logic for a single training step.

Note: subclasses only need to implement this if spiking=True. It is used to specify an alternate (differentiable) implementation of the neuron model to be used during training.

class nengo_dl.neuron_builders.SimNeuronsBuilder(ops)[source]¶

Builds a group of SimNeurons operators.

Calls the appropriate sub-build class for the different neuron types.

Attributes

TF_NEURON_IMPLdict of {NeuronType, builder.OpBuilder}: Mapping from neuron types to custom build classes (neurons without a custom builder will use the generic builder).

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

Learning rules¶

Build classes for Nengo learning rule operators.

`nengo_dl.learning_rule_builders.SimBCMBuilder`	Build a group of `SimBCM` operators.
`nengo_dl.learning_rule_builders.SimOjaBuilder`	Build a group of `SimOja` operators.
`nengo_dl.learning_rule_builders.SimVojaBuilder`	Build a group of `SimVoja` operators.
`nengo_dl.learning_rule_builders.build_pes`	Builds a `nengo.PES` object into a Nengo model.
`nengo_dl.learning_rule_builders.SimPESBuilder`	Build a group of `SimPES` operators.

class nengo_dl.learning_rule_builders.SimBCMBuilder(ops)[source]¶

Build a group of SimBCM operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.learning_rule_builders.SimOjaBuilder(ops)[source]¶

Build a group of SimOja operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

class nengo_dl.learning_rule_builders.SimVojaBuilder(ops)[source]¶

Build a group of SimVoja operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

nengo_dl.learning_rule_builders.build_pes(model, pes, rule)[source]¶

Builds a nengo.PES object into a Nengo model.

Overrides the standard Nengo PES builder in order to avoid slicing on axes > 0 (not currently supported in NengoDL).

Parameters

modelModel: The model to build into.
pesPES: Learning rule type to build.
ruleLearningRule: The learning rule object corresponding to the neuron type.

Notes

Does not modify model.params[] and can therefore be called more than once with the same nengo.PES instance.

class nengo_dl.learning_rule_builders.SimPESBuilder(ops)[source]¶

Build a group of SimPES operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

Processes¶

Build classes for Nengo process operators.

`nengo_dl.process_builders.GenericProcessBuilder`	Builds all process types for which there is no custom TensorFlow implementation.
`nengo_dl.process_builders.LowpassBuilder`	Build a group of `Lowpass` synapse operators.
`nengo_dl.process_builders.LinearFilterBuilder`	Build a group of `LinearFilter` synapse operators.
`nengo_dl.process_builders.SimProcessBuilder`	Builds a group of `SimProcess` operators.

class nengo_dl.process_builders.GenericProcessBuilder(ops)[source]¶

Builds all process 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 processes should consider adding a custom TensorFlow implementation for their type instead.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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(signals)[source]¶

This function will be called after the graph has been built and each time the Simulator is reset.

Note that this function may be called multiple times per session, so it should do any required operations in-place.

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

class nengo_dl.process_builders.LowpassBuilder(ops)[source]¶

Build a group of Lowpass synapse operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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.process_builders.LinearFilterBuilder(ops)[source]¶

Build a group of LinearFilter synapse operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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.process_builders.SimProcessBuilder(ops)[source]¶

Builds a group of SimProcess operators.

Calls the appropriate sub-build class for the different process types.

Attributes

TF_PROCESS_IMPLdict of {Process: builder.OpBuilder}: Mapping from process types to custom build classes (processes without a custom builder will use the generic builder).

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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(signals)[source]¶

This function will be called after the graph has been built and each time the Simulator is reset.

Note that this function may be called multiple times per session, so it should do any required operations in-place.

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

Transforms¶

Build classes for Nengo transform operators.

`nengo_dl.transform_builders.ConvSet`	A version of `ConvInc` that overwrites the target rather than incrementing.
`nengo_dl.transform_builders.ConvIncBuilder`	Build a group of `nengo.builder.transforms.ConvInc` operators.

class nengo_dl.transform_builders.ConvSet(*args, **kwargs)[source]¶

A version of ConvInc that overwrites the target rather than incrementing.

property Y¶: Y is stored in sets rather than incs.

class nengo_dl.transform_builders.ConvIncBuilder(ops)[source]¶

Build a group of nengo.builder.transforms.ConvInc operators.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

static mergeable(x, y)[source]¶

Compute the mergeability of two operators of this builder’s type.

Parameters

xnengo.builder.Operator: The operator being tested
ynengo.builder.Operator: The operator being merged into (this is representative of a group of operators that have already been merged)

Returns

mergeablebool: True if x and y can be merged into a single built op, else False.

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).

`nengo_dl.tensor_node.SimTensorNode`	Operator for TensorNodes (constructed by `build_tensor_node`).
`nengo_dl.tensor_node.build_tensor_node`	This is the Nengo build function, so that Nengo knows what to do with TensorNodes.
`nengo_dl.tensor_node.SimTensorNodeBuilder`	Builds a `SimTensorNode` operator into a NengoDL model.

class nengo_dl.tensor_node.SimTensorNode(func, time, input, output, shape_in, tag=None)[source]¶

Operator for TensorNodes (constructed by build_tensor_node).

Parameters

funccallable: The TensorNode function (tensor_func).
timeSignal or None: Signal representing the current simulation time (or None if pass_time is False).
inputSignal or None: Input Signal for the TensorNode (or None if no inputs).
outputSignal: Output Signal for the TensorNode.
shape_intuple of int or None: Shape of input to TensorNode (if None, will leave the shape of input signal unchanged).
tagstr: A label associated with the operator, for debugging

Notes

sets [output]
incs []
reads [time] (if pass_time=True) + [input] (if input is not None)
updates []

nengo_dl.tensor_node.build_tensor_node(model, node)[source]¶: This is the Nengo build function, so that Nengo knows what to do with TensorNodes.

class nengo_dl.tensor_node.SimTensorNodeBuilder(ops)[source]¶

Builds a SimTensorNode operator into a NengoDL model.

Initialize internal OpBuilder implementation.

Note: this should not be building any model operations, this is purely for internal setup of the OpBuilder itself.

Parameters

opslist of Operator: The operator group to build into the model

build_pre(signals, config)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)
configBuildConfig: General repository for config information builders might want (conglomerated into this object so that we can add/remove config data without having to change the function signature all the time).

build_step(signals)[source]¶

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

Parameters

signalssignals.SignalDict: Mapping from Signal to tf.Tensor (updated by operations)

Returns

side_effectslist of tf.Tensor: 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

Graph construction¶

Manages the data and build processes associated with implementing a Nengo simulation in TensorFlow.

nengo_dl.tensor_graph.TensorGraph

Implement the Nengo simulation as a Keras Layer.

class nengo_dl.tensor_graph.TensorGraph(model, dt, unroll_simulation, minibatch_size, device, progress, seed)[source]¶

Implement the Nengo simulation as a Keras Layer.

Parameters

modelModel: Pre-built Nengo model describing the network to be simulated.
dtfloat: Length of a simulator timestep, in seconds.
unroll_simulationint: Unroll simulation loop by explicitly building unroll_simulation iterations into the computation graph.
minibatch_sizeint: The number of simultaneous inputs that will be passed through the network.
deviceNone or "/cpu:0" or "/gpu:[0-n]": Device on which to execute computations (if None then uses the default device as determined by TensorFlow).
progressutils.ProgressBar: Progress bar for optimization stage.
seedint: Seed for random number generation.

build_inputs()[source]¶

Generates a set of Input layers that can be used as inputs to a TensorGraph layer.

Returns

n_stepstf.keras.layers.Input: Input layer for specifying the number of simulation timesteps.
inputsdict of {nengo.Node: tf.keras.layers.Input}: Input layers for each of the Nodes in the network.

build(input_shape=None)[source]¶

Create any Variables used in the model.

Parameters

input_shapelist of tuple of int: Shapes of all the inputs to this layer.

call(inputs, training=None, progress=None, stateful=False)[source]¶

Constructs the graph elements to simulate the model.

Parameters

inputslist of tf.Tensor: Input layers/tensors for the network (must match the structure defined in build_inputs).
trainingbool: Whether the network is being run in training or inference mode. If None, uses the symbolic Keras learning phase variable.
progressutils.ProgressBar: Progress bar for construction stage.
statefulbool: Whether or not to build the model to support preserving the internal state between executions.

Returns

probe_arrayslist of tf.Tensor: Tensors representing the output of all the Probes in the network (order corresponding to self.model.probes, which is the order the Probes were instantiated).

build_post()[source]¶: Executes post-build processes for operators (after the graph has been constructed and whenever Simulator is reset).

get_tensor(sig)[source]¶

Returns a Tensor corresponding to the given Signal.

Parameters

sigSignal: A signal in the Nengo model.

Returns

tensortf.Tensor: Tensor containing the value of the given Signal.

mark_signals()[source]¶

Mark all the signals in self.model according to whether they represent trainable parameters of the model (parameters that can be optimized by deep learning methods).

Trainable parameters include connection weights, ensemble encoders, and neuron biases. Unless one of those signals is targeted by a Nengo learning rule (otherwise the learning rule update conflicts with the deep learning optimization).

Users can manually specify whether signals are trainable or not using the config system (e.g., net.config[nengo.Ensemble].trainable = False).

The trainable attribute will be set to one of three values:

True: Signal is trainable
False: Signal could be trainable, but has been set to non-trainable (e.g., because the user manually configured that object not to be trainable).
None: Signal is never trainable (e.g., simulator state)

create_signals(sigs)[source]¶

Groups signal data together into larger arrays, and represent each individual signal as a slice into that array.

Parameters

sigslist of Signal: Base signals arranged into the order in which they should reside in memory (e.g., output from graph_optimizer.order_signals)

Signals¶

Represents and manages the internal simulation signals.

`nengo_dl.signals.TensorSignal`	Represents a tensor as an indexed view into a base array.
`nengo_dl.signals.SignalDict`	Handles the mapping from `Signal` to `tf.Tensor`.

class nengo_dl.signals.TensorSignal(slices, key, dtype, shape, minibatch_size, label='TensorSignal')[source]¶

Represents a tensor as an indexed view into a base array.

Parameters

slicestuple of tuple of int: Start/stop indices of slices along the first axis of the base array, corresponding to the data for this signal.
keyobject: Key mapping to the base array that contains the data for this signal.
dtypestr: dtype of the values represented by this signal.
shapetuple of int: View shape of this signal (may differ from shape of base array).
minibatch_sizeint: If not None then this signal contains a minibatch dimension with the given size.
labelstr: Name for this signal, used to make debugging easier.

reset()[source]¶: Reset cached Tensors.

property slices¶: The slices containing the data for this signal in the base array.

property ndim¶: The rank of this signal.

__getitem__(indices)[source]¶

Create a new TensorSignal representing a subset (slice or advanced indexing) of the indices of this TensorSignal.

Parameters

indicesslice or list of int: The desired subset of the indices in this TensorSignal

Returns

sigsignals.TensorSignal: A new TensorSignal representing the subset of this TensorSignal

reshape(shape)[source]¶

Create a new TensorSignal representing a reshaped view of the same data in this TensorSignal (size of data must remain unchanged).

Parameters

shapetuple of int: New shape for the signal (one dimension can be -1 to indicate an inferred dimension size, as in numpy)

Returns

sigsignals.TensorSignal: New TensorSignal representing the same data as this signal but with the given shape

property tf_shape¶: A tf.Tensor representing the shape of this signal.

property tf_indices¶: A tf.Tensor representing the indices of this signal.

property tf_indices_nd¶: A tf.Tensor representing the indices of this signal for use with e.g. scatter_nd.

property tf_slice¶

A tuple of tf.Tensors representing the (start, stop, stride) slice within the base array containing the data for this signal.

This can be used as a more efficient representation of TensorSignal.tf_indices.

property full_shape¶: Shape of the signal including the minibatch dimension.

property minibatched¶: Whether or not this TensorSignal contains a minibatch dimension.

class nengo_dl.signals.SignalDict(dtype, minibatch_size)[source]¶

Handles the mapping from Signal to tf.Tensor.

Takes care of gather/scatter logic to read/write signals within the base arrays.

Parameters

dtypestr: Floating point precision used in signals (e.g. “float32”)
minibatch_sizeint: Number of items in each minibatch

reset()[source]¶

Reset build-specific data structures.

These are data structures that are filled out during the TensorGraph build process (and therefore need to be re-initialized if we build the model again), as opposed to data that is constant for a given Nengo model.

scatter(dst, val, mode='update')[source]¶

Updates the base data corresponding to dst.

Parameters

dstTensorSignal: Signal indicating the data to be modified in base array
valtf.Tensor: Update data (same shape as dst, i.e. a dense array <= the size of the base array)
mode“update” or “inc”: Overwrite/add the data at dst with val

gather(src, force_copy=False)[source]¶

Fetches the data corresponding to src from the base array.

Parameters

srcTensorSignal: Signal indicating the data to be read from base array
force_copybool: If True, always perform a gather, not a slice (this forces a copy). Note that setting force_copy=False does not guarantee that a copy won’t be performed.

Returns

gatheredtf.Tensor: Tensor object corresponding to a dense subset of data from the base array

combine(sigs, label='Combine')[source]¶

Combines several TensorSignals into one by concatenating along the first axis.

Parameters

sigslist of TensorSignal or Signal: Signals to be combined
labelstr: Name for combined signal (to help with debugging)

Returns

sigTensorSignal: New TensorSignal representing the concatenation of the data in sigs

get_tensor_signal(slices, key, dtype, shape, minibatched, signal=None, label='TensorSignal')[source]¶

Creates a new TensorSignal with the given properties.

This should be used rather than instantiating a new TensorSignal directly, as it handles some extra book-keeping.

Parameters

slicestuple of tuple of int: Start/stop indices of slices along the first axis of the base array, corresponding to the data for this signal.
keyobject: Key mapping to the base array that contains the data for this signal
dtypestr: dtype of the values represented by this signal
shapetuple of int: View shape of this signal (may differ from shape of base array)
minibatchedbool: Whether or not this signal contains a minibatch dimension
signalSignal: If not None, associate the new TensorSignal with the given Signal in the sig_map
labelstr: Name for this signal, used to make debugging easier

Returns

sigTensorSignal: A new TensorSignal with the given properties

op_constant(ops, op_sizes, attr, dtype, shape=1, - 1)[source]¶

Creates a tensor representing the constant parameters of an op group.

Parameters

opslist of object: The operators for some merged group of ops
op_sizeslist of int: The number of constant elements in each op
attrstr: The attribute of the op that describes the constant parameter
dtypestr: Numeric type of the parameter
shapetuple of int: Shape for returned constant (this will be ignored in the scalar case). The default adds an empty dimension for broadcasting along the batch axis.

Returns

constanttf.Tensor: Tensor containing the values of attr for the given ops. This will be a scalar if all the ops have the same parameter value, or an array giving the parameter value for each element in each op.

Graph optimization¶

These functions are used to restructure the Nengo operator graph so that it can be simulated more efficiently when converted into a TensorFlow graph.

`nengo_dl.graph_optimizer.mergeable`	Check if the given op can be merged with the candidate group.
`nengo_dl.graph_optimizer.greedy_planner`	Create merged execution plan through greedy selection.
`nengo_dl.graph_optimizer.tree_planner`	Create merged execution plan through exhaustive tree search.
`nengo_dl.graph_optimizer.transitive_planner`	Create merged execution plan through transitive closure construction.
`nengo_dl.graph_optimizer.transitive_closure_recurse`	Computes the transitive closure for the given graph, restricted to the operators with the given builder type.
`nengo_dl.graph_optimizer.noop_planner`	Orders operators into a valid execution order, but does not perform any merging.
`nengo_dl.graph_optimizer.order_signals`	Orders signals and operators to try to structure reads/writes in contiguous blocks.
`nengo_dl.graph_optimizer.hamming_sort`	Reorder signals using heuristics to try to place signals that are accessed by the same operators into adjacent positions (giving priority to larger blocks).
`nengo_dl.graph_optimizer.sort_ops_by_signals`	Rearrange operators to match the order of signals.
`nengo_dl.graph_optimizer.sort_signals_by_ops`	Attempts to rearrange `sigs` so that it is in the same order as operator signals, without changing the overall block order.
`nengo_dl.graph_optimizer.noop_order_signals`	A version of `graph_optimizer.order_signals` that doesn’t do any reordering, for debugging.
`nengo_dl.graph_optimizer.remove_unmodified_resets`	Remove any Reset operators that are targeting a signal that is never modified.
`nengo_dl.graph_optimizer.remove_zero_incs`	Remove any operators where we know the input (and therefore output) is zero.
`nengo_dl.graph_optimizer.remove_reset_incs`	Replace `y=Reset(0) + x` with `y=x`.
`nengo_dl.graph_optimizer.remove_constant_copies`	Change Copies with constant input to Resets.
`nengo_dl.graph_optimizer.remove_identity_muls`	Change y=x*1 ops to y=x Copy ops.
`nengo_dl.graph_optimizer.signal_io_dicts`	Organizes operators into dictionaries according to the signals they set/inc/read/update.
`nengo_dl.graph_optimizer.display_signal_blocks`	Creates a visual depiction of the signals blocks read by each operator group.

nengo_dl.graph_optimizer.mergeable(op, chosen_ops)[source]¶

Check if the given op can be merged with the candidate group.

Parameters

opOperator: The operator to be merged
chosen_opslist of Operator: The operator group to be merged in to

Returns

mergeablebool: True if op can be merged into chosen_ops, else False

nengo_dl.graph_optimizer.greedy_planner(operators)[source]¶

Create merged execution plan through greedy selection.

Always selects the largest available group of operators as the next to be executed.

Parameters

operatorslist of Operator: All the nengo operators in a model (unordered)

Returns

planlist of tuple of Operator: Operators combined into mergeable groups and in execution order

Notes

Originally based on nengo_ocl greedy planner

nengo_dl.graph_optimizer.tree_planner(op_list, max_depth=3)[source]¶

Create merged execution plan through exhaustive tree search.

The max_depth parameter scales the planner between full tree search and greedy search. max_depth==1 is equivalent to greedy_planner, and max_depth==len(op_list) is full tree search (guaranteed to find the optimal plan, but likely very slow).

Parameters

op_listlist of Operator: All the nengo operators in a model (unordered)
max_depthint: The planner will search this many steps ahead before selecting which group to schedule next

Returns

planlist of tuple of Operator: Operators combined into mergeable groups and in execution order

nengo_dl.graph_optimizer.transitive_planner(op_list)[source]¶

Create merged execution plan through transitive closure construction.

This is something like a middle ground between greedy_planner and tree_planner; it can improve simulation time over the greedy planner, but comes with potentially significant build time increases.

Parameters

op_listlist of Operator: All the nengo operators in a model (unordered)

Returns

planlist of tuple of Operator: Operators combined into mergeable groups and in execution order

nengo_dl.graph_optimizer.transitive_closure_recurse(dg, ops, trans, builder_type, builder_types, cache)[source]¶

Computes the transitive closure for the given graph, restricted to the operators with the given builder type.

Parameters

dgdict of {int: set of int}: Dependency graph where dg[a] = {b, c} indicates that operators b and c are dependent on a
opslist of int: The operators for which we want to compute the transitive closure
transdict of {int: set of int}: The transitive closure for the graph (will be filled in-place)
builder_typetype: One of the nengo_dl build classes (e.g., CopyBuilder), specifying the type of operators to include in the transitive closure
builder_typeslist of type: The build class for each operator
cachedict of {frozenset of int: set of int}: Stores base sets which trans will reference (to reduce memory usage, since many elements in trans will have the same value)

Notes

This function uses ints to refer to operators, where the int indicates the index of the operator in the overall op list (this is done to save memory). See transitive_planner.

nengo_dl.graph_optimizer.noop_planner(operators)[source]¶

Orders operators into a valid execution order, but does not perform any merging.

Parameters

operatorslist of Operator: All the nengo operators in a model (unordered)

Returns

planlist of tuple of Operator: Operators in execution order

nengo_dl.graph_optimizer.order_signals(plan, n_passes=10)[source]¶

Orders signals and operators to try to structure reads/writes in contiguous blocks.

Parameters

planlist of tuple of Operator: Operator execution plan (e.g., output from greedy_planner)
n_passesint: Number of repeated passes through the operator reordering stage

Returns

signalslist of Signal: Signals organized into the order in which we want them arranged in memory
planlist of tuple of Operator: Input plan with operators reordered within groups to align with order of signals

nengo_dl.graph_optimizer.hamming_sort(blocks)[source]¶

Reorder signals using heuristics to try to place signals that are accessed by the same operators into adjacent positions (giving priority to larger blocks).

Parameters

blocksdict of {Signal: frozenset of int}: Dictionary indicating which io blocks each signal is a part of

Returns

sort_idxsdict of {Signal: int}: Indices indicating where each signal should be in the sorted list

nengo_dl.graph_optimizer.sort_ops_by_signals(sorted_io, sigs, sig_idxs, new_plan, blocks, op_sigs)[source]¶

Rearrange operators to match the order of signals.

Note: the same operators can be associated with multiple IO blocks if they have multiple inputs, so rearranging the operators according to one of those blocks could mess up the order with respect to the other IO block. We iterate through the IO blocks in increasing size so that the largest blocks win out.

Parameters

sorted_iolist of tuple of (Operator, int): The operators that form each IO block, sorted by increasing size of the block. In the case that a group of operators participate in multiple IO blocks, the integer distinguishes which one of those blocks this block is associated with.
sigslist of Signal: Signals that have been arranged into a given order by other parts of the algorithm
sig_idxsdict of {Signal: int}: Sorted indices of signals
new_plandict of {tuple of Operator: tuple of Operator}: Mapping from original operator group to the sorted operators
blocksdict of {Signal: frozenset of int}: Indicates which IO blocks each signal participates in
op_sigsdict of {Operator: list of Signal}: The signals accessed by each operator

Returns

new_plandict of {tuple of Operator: tuple of Operator}: Mapping from original operator group to the sorted operators
sig_idxsdict of {Signal: int}: Signal indices, possibly updated to match new op order

nengo_dl.graph_optimizer.sort_signals_by_ops(sorted_io, sigs, sig_idxs, new_plan, blocks, op_sigs)[source]¶

Attempts to rearrange sigs so that it is in the same order as operator signals, without changing the overall block order.

Parameters

sorted_iolist of tuple of (Operator, int): The operators that form each io block, sorted by increasing size of the io block. In the case that a group of operators participate in multiple io blocks, the integer distinguishes which one of those blocks this block is associated with.
sigslist of Signal: Signals to be sorted
sig_idxsdict of {Signal: int}: Sorted indices of signals
new_plandict of {tuple of Operator: tuple of Operator}: Mapping from original operator group to the sorted operators
blocksdict of {Signal: frozenset of int}: Indicates which io blocks each signal participates in
op_sigsdict of {Operator: list of Signal}: The signals accessed by each operator

Returns

sig_idxsdict of {Signal: int}: Sorted indices of signals

nengo_dl.graph_optimizer.noop_order_signals(plan, **_)[source]¶: A version of graph_optimizer.order_signals that doesn’t do any reordering, for debugging.

nengo_dl.graph_optimizer.remove_unmodified_resets(operators)[source]¶

Remove any Reset operators that are targeting a signal that is never modified.

If a signal is reset, but never inced/updated after that, we can just set the default signal value to the reset value and remove the reset. Note: this wouldn’t normally happen, but it can happen if we removed some of the incs (e.g. in remove_zero_incs).

Parameters

operatorslist of Operator: Operators in the model

Returns

new_operatorslist of Operator: Modified list of operators

nengo_dl.graph_optimizer.remove_zero_incs(operators)[source]¶

Remove any operators where we know the input (and therefore output) is zero.

If the input to a DotInc/ElementwiseInc/Copy/ConvInc is zero then we know that the output of the op will be zero, so we can just get rid of it.

Parameters

operatorslist of Operator: Operators in the model

Returns

new_operatorslist of Operator: Modified list of operators

nengo_dl.graph_optimizer.remove_reset_incs(operators)[source]¶

Replace y=Reset(0) + x with y=x.

If a signal is Reset and Inc’d, we can change that to a Set that combines the two ops (note: any other incs of that signal can proceed as normal)

Parameters

operatorslist of Operator: operators in the model

Returns

new_operatorslist of Operator: modified list of operators

Notes

In practice, this modification can hurt more than it helps. Inc operators are cheaper to compute the gradient for, and changing Incs to Incs and Sets splits up the Inc merge groups. It tends to provide the most value for models consisting of long linear chains of objects.

nengo_dl.graph_optimizer.remove_constant_copies(operators)[source]¶

Change Copies with constant input to Resets.

If a Copy has no dependencies, or just one Reset() dependency, then we can change it to an op that just directly sets the output signal to the Copy input value.

Parameters

operatorslist of Operator: Operators in the model

Returns

new_operatorslist of Operator: Modified list of operators

nengo_dl.graph_optimizer.remove_identity_muls(operators)[source]¶

Change y=x*1 ops to y=x Copy ops.

If one of the inputs to a DotInc/ElementwiseInc is 1 then we can skip the multiplication and change it to a Copy op.

Parameters

operatorslist of Operator: Operators in the model

Returns

new_operatorslist of Operator: Modified list of operators

nengo_dl.graph_optimizer.signal_io_dicts(operators)[source]¶

Organizes operators into dictionaries according to the signals they set/inc/read/update.

Parameters

operatorslist of Operator: Operators in the model

Returns

setsdict of {Signal: list of Operator}: A dictionary indicating all the Operators that set each signal.
incsdict of {Signal: list of Operator}: A dictionary indicating all the Operators that inc each signal.
readsdict of {Signal: list of Operator}: A dictionary indicating all the Operators that read each signal.
updatesdict of {Signal: list of Operator}: A dictionary indicating all the Operators that update each signal.

nengo_dl.graph_optimizer.display_signal_blocks(operators, all_signals)[source]¶

Creates a visual depiction of the signals blocks read by each operator group.

Parameters

operatorslist of tuple of Operator: Operator execution plan
all_signalslist of Signal: Base signals arranged into some order

Returns

signal_blocksstr: A string where each row corresponds to one operator group, and the non-blank characters in the line indicate that the operator group reads/writes that signal (with a number used to distinguish the different signal blocks within the operator group).

Layer converters¶

Tools for automatically converting a Keras model to a Nengo network.

`nengo_dl.converter.LayerConverter`	Base class for converter classes, which contain the logic for mapping some Keras layer type to Nengo objects.
`nengo_dl.converter.ConvertModel`	Convert `tf.keras.Model` to Nengo objects.
`nengo_dl.converter.ConvertSequential`	Convert `tf.keras.Sequential` to Nengo objects.
`nengo_dl.converter.ConvertFallback`	Convert layers which do not have a native Nengo equivalent into a `TensorNode`.
`nengo_dl.converter.ConvertAvgPool`	Base class for converting average pooling layers to Nengo objects.
`nengo_dl.converter.ConvertActivation`	Convert `tf.keras.layers.Activation` to Nengo objects.
`nengo_dl.converter.ConvertAdd`	Convert `tf.keras.layers.Add` to Nengo objects.
`nengo_dl.converter.ConvertAverage`	Convert `tf.keras.layers.Average` to Nengo objects.
`nengo_dl.converter.ConvertAvgPool1D`	Convert `tf.keras.layers.AvgPool1D` to Nengo objects.
`nengo_dl.converter.ConvertAvgPool2D`	Convert `tf.keras.layers.AvgPool2D` to Nengo objects.
`nengo_dl.converter.ConvertAvgPool3D`	Convert `tf.keras.layers.AvgPool3D` to Nengo objects.
`nengo_dl.converter.ConvertBatchNormalization`	Convert `tf.keras.layers.BatchNormalization` to Nengo objects.
`nengo_dl.converter.ConvertConcatenate`	Convert `tf.keras.layers.Concatenate` to Nengo objects.
`nengo_dl.converter.ConvertConv`	Base class for converting convolutional layers to Nengo objects.
`nengo_dl.converter.ConvertConv1D`	Convert `tf.keras.layers.Conv1D` to Nengo objects.
`nengo_dl.converter.ConvertConv2D`	Convert `tf.keras.layers.Conv2D` to Nengo objects.
`nengo_dl.converter.ConvertConv3D`	Convert `tf.keras.layers.Conv3D` to Nengo objects.
`nengo_dl.converter.ConvertDense`	Convert `tf.keras.layers.Dense` to Nengo objects.
`nengo_dl.converter.ConvertFlatten`	Convert `tf.keras.layers.Flatten` to Nengo objects.
`nengo_dl.converter.ConvertInput`	Convert `tf.keras.layers.InputLayer` to Nengo objects.
`nengo_dl.converter.ConvertReLU`	Convert `tf.keras.layers.ReLU` to Nengo objects.
`nengo_dl.converter.ConvertLeakyReLU`	Convert `tf.keras.layers.LeakyReLU` to Nengo objects.
`nengo_dl.converter.ConvertReshape`	Convert `tf.keras.layers.Reshape` to Nengo objects.
`nengo_dl.converter.ConvertUpSampling`	Base class for converting upsampling layers to Nengo objects.
`nengo_dl.converter.ConvertUpSampling1D`	Convert `tf.keras.layers.UpSampling1D` to Nengo objects.
`nengo_dl.converter.ConvertUpSampling2D`	Convert `tf.keras.layers.UpSampling2D` to Nengo objects.
`nengo_dl.converter.ConvertUpSampling3D`	Convert `tf.keras.layers.UpSampling3D` to Nengo objects.
`nengo_dl.converter.ConvertZeroPadding`	Base class for converting zero-padding layers to Nengo objects.
`nengo_dl.converter.ConvertZeroPadding1D`	Convert `tf.keras.layers.ZeroPadding1D` to Nengo objects.
`nengo_dl.converter.ConvertZeroPadding2D`	Convert `tf.keras.layers.ZeroPadding2D` to Nengo objects.
`nengo_dl.converter.ConvertZeroPadding3D`	Convert `tf.keras.layers.ZeroPadding3D` to Nengo objects.
`nengo_dl.converter.ConvertKerasSpiking`	Base class for converting KerasSpiking layers.
`nengo_dl.converter.ConvertSpikingActivation`	Convert `keras_spiking.SpikingActivation` to Nengo objects.
`nengo_dl.converter.ConvertLowpassAlpha`	Convert `keras_spiking.Lowpass` to Nengo objects.
`nengo_dl.converter.ConvertTimeDistributed`	Convert `tf.keras.layers.TimeDistributed` to Keras objects.

class nengo_dl.converter.LayerConverter(layer, converter)[source]¶

Base class for converter classes, which contain the logic for mapping some Keras layer type to Nengo objects.

Subclasses must implement the LayerConverter.convert method. They may optionally extend LayerConverter.convertible if this layer type requires custom logic for whether or not a layer can be converted to Nengo objects.

Subclasses should override the unsupported_args class parameter if there are certain non-default Layer attributes that are not supported by the converter. This is a list of names for attributes that must have the default value for the layer to be convertible. The default is assumed to be None, or a tuple of ("attribute_name", default_value) can be specified. If there are parameters that are supported in inference mode but not in training mode, they should be added to the unsupported_training_args parameter.

Subclasses should override the has_weights class parameter if the layer type being converted contains internal weights (this affects how the converter will handle duplicate layers).

Parameters

layertf.keras.layers.Layer: The Layer object being converted.
converterConverter: The parent Converter class running the conversion process.

add_nengo_obj(node_id, biases=None, activation=None)[source]¶

Builds a Nengo object for the given Node of this layer.

Parameters

node_idint: The index of the Keras Node currently being built on this layer.
biasesnumpy.ndarray or None: If not None, add trainable biases with the given value.
activationcallable or None: The TensorFlow activation function to be used (None will be interpreted as linear activation).

Returns

objnengo.Node or nengo.ensemble.Neurons or nengo_dl.TensorNode: The Nengo object whose output corresponds to the output of the given Keras Node.

add_connection(node_id, obj, input_idx=0, trainable=False, **kwargs)[source]¶

Adds a Connection from one of the inputs of the Node being built to the Nengo object.

Parameters

node_idint: The index of the Keras Node currently being built on this layer.
objNengoObject: The Nengo object implementing this Node.
input_idxint: Which of the inputs we want to add a Connection for (in the case of layers that have multiple inputs).
trainablebool: Whether or not the weights associated with the created Connection should be trainable.
kwargsdict: Will be passed on to nengo.Connection.

Returns

connnengo.Connection: The constructed Connection object.

get_input_obj(node_id, tensor_idx=0)[source]¶

Returns the Nengo object corresponding to the given input of this layer.

Parameters

node_idint: The index of the Keras Node currently being built on this layer.
tensor_idxint: The index of the input we want to look up (for layers with multiple inputs).

Returns

objnengo.Node or nengo.ensemble.Neurons or nengo_dl.TensorNode: The Nengo object whose output corresponds to the given input of this layer.

input_shape(node_id, full_shape=False)[source]¶

Returns the input shape of the given node.

Parameters

node_idint: The node whose shape we want to look up.
full_shapebool: Whether or not the returned shape should include the batch/time dimensions.

Returns

shape(list of) tuple of int: A single tuple shape if the node has one input, or a list of shapes if the node as multiple inputs.

output_shape(node_id, full_shape=False)[source]¶

Returns the output shape of the given node.

Parameters

node_idint: The node whose shape we want to look up.
full_shapebool: Whether or not the returned shape should include the batch/time dimensions.

Returns

shape(list of) tuple of int: A single tuple shape if the node has one output, or a list of shapes if the node as multiple outputs.

static set_trainable(obj, trainable)[source]¶

Set trainable config attribute of object.

Parameters

objNengoObject: The object to be assigned.
trainable: bool: Trainability value of obj.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertModel(layer, converter)[source]¶

Convert tf.keras.Model to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertSequential(seq_model, converter)[source]¶: Convert tf.keras.Sequential to Nengo objects.

class nengo_dl.converter.ConvertFallback(*args, **kwargs)[source]¶

Convert layers which do not have a native Nengo equivalent into a TensorNode.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAvgPool(layer, converter)[source]¶

Base class for converting average pooling layers to Nengo objects.

convert(node_id, dimensions)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

class nengo_dl.converter.ConvertActivation(layer, converter)[source]¶

Convert tf.keras.layers.Activation to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAdd(layer, converter)[source]¶

Convert tf.keras.layers.Add to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAverage(layer, converter)[source]¶

Convert tf.keras.layers.Average to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAvgPool1D(layer, converter)[source]¶

Convert tf.keras.layers.AvgPool1D to Nengo objects.

Also works for tf.keras.layers.GlobalAvgPool1D, and tf.keras.layers.MaxPool1D/GlobalMaxPool1D (if max_to_avg_pool=True).

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAvgPool2D(layer, converter)[source]¶

Convert tf.keras.layers.AvgPool2D to Nengo objects.

Also works for tf.keras.layers.GlobalAvgPool2D, and tf.keras.layers.MaxPool2D/GlobalMaxPool2D (if max_to_avg_pool=True).

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertAvgPool3D(layer, converter)[source]¶

Convert tf.keras.layers.AvgPool3D to Nengo objects.

Also works for tf.keras.layers.GlobalAvgPool3D, and tf.keras.layers.MaxPool3D/GlobalMaxPool3D (if max_to_avg_pool=True).

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertBatchNormalization(layer, converter)[source]¶

Convert tf.keras.layers.BatchNormalization to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

class nengo_dl.converter.ConvertConcatenate(layer, converter)[source]¶

Convert tf.keras.layers.Concatenate to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

class nengo_dl.converter.ConvertConv(layer, converter)[source]¶

Base class for converting convolutional layers to Nengo objects.

convert(node_id, dimensions)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertConv1D(layer, converter)[source]¶

Convert tf.keras.layers.Conv1D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertConv2D(layer, converter)[source]¶

Convert tf.keras.layers.Conv2D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertConv3D(layer, converter)[source]¶

Convert tf.keras.layers.Conv3D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertDense(layer, converter)[source]¶

Convert tf.keras.layers.Dense to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertFlatten(layer, converter)[source]¶

Convert tf.keras.layers.Flatten to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertInput(layer, converter)[source]¶

Convert tf.keras.layers.InputLayer to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertReLU(layer, converter)[source]¶

Convert tf.keras.layers.ReLU to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertLeakyReLU(layer, converter)[source]¶

Convert tf.keras.layers.LeakyReLU to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertReshape(layer, converter)[source]¶

Convert tf.keras.layers.Reshape to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertUpSampling(layer, converter)[source]¶

Base class for converting upsampling layers to Nengo objects.

convert(node_id, dimensions)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertUpSampling1D(layer, converter)[source]¶

Convert tf.keras.layers.UpSampling1D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertUpSampling2D(layer, converter)[source]¶

Convert tf.keras.layers.UpSampling2D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertUpSampling3D(layer, converter)[source]¶

Convert tf.keras.layers.UpSampling3D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertZeroPadding(layer, converter)[source]¶

Base class for converting zero-padding layers to Nengo objects.

convert(node_id, dimensions)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertZeroPadding1D(layer, converter)[source]¶

Convert tf.keras.layers.ZeroPadding1D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertZeroPadding2D(layer, converter)[source]¶

Convert tf.keras.layers.ZeroPadding2D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertZeroPadding3D(layer, converter)[source]¶

Convert tf.keras.layers.ZeroPadding3D to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertKerasSpiking(layer, converter)[source]¶

Base class for converting KerasSpiking layers.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

class nengo_dl.converter.ConvertSpikingActivation(layer, converter)[source]¶

Convert keras_spiking.SpikingActivation to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

class nengo_dl.converter.ConvertLowpassAlpha(layer, converter)[source]¶

Convert keras_spiking.Lowpass to Nengo objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

class nengo_dl.converter.ConvertTimeDistributed(layer, converter)[source]¶

Convert tf.keras.layers.TimeDistributed to Keras objects.

convert(node_id)[source]¶

Convert the given node of this layer to Nengo objects

Parameters

node_idint: The index of the inbound node to be converted.

Returns

outputNengoObject: Nengo object whose output corresponds to the output of the Keras layer node.

classmethod convertible(layer, converter)[source]¶

Check whether the given Keras layer is convertible to native Nengo objects.

Parameters

layertf.keras.layers.Layer: The Keras Layer we want to convert.
converterConverter: The Converter object running the conversion process.

Returns

convertiblebool: True if the layer can be converted to native Nengo objects, else False.

Utilities¶

Utility objects used throughout the code base.

`nengo_dl.utils.sanitize_name`	Remove illegal TensorFlow name characters from string.
`nengo_dl.utils.function_name`	Get the name of the callable object `func`.
`nengo_dl.utils.align_func`	Decorator that ensures the output of `func` is a valid `ndarray` with the given dtype.
`nengo_dl.utils.ProgressBar`	Handles progress bar display for some tracked process.
`nengo_dl.utils.SubProgressBar`	A progress bar representing a sub-task within an overall progress bar.
`nengo_dl.utils.NullProgressBar`	A progress bar that does nothing.

nengo_dl.utils.sanitize_name(name)[source]¶

Remove illegal TensorFlow name characters from string.

Valid TensorFlow name characters are [A-Za-z0-9_.\-/]

Parameters

namestr: Name to be sanitized

Returns

sanitizedstr: Sanitized name

nengo_dl.utils.function_name(func, sanitize=True)[source]¶

Get the name of the callable object func.

Parameters

funccallable: Callable object (e.g., function, callable class)
sanitizebool: If True, remove any illegal TensorFlow name characters from name

Returns

namestr: Name of func (optionally sanitized)

nengo_dl.utils.align_func(output_dtype)[source]¶

Decorator that ensures the output of func is a valid ndarray with the given dtype.

Parameters

output_dtypestr or tf.DType or dtype: Desired dtype of function output(s)

Raises

nengo.exceptions.SimulationError: If the function returns None or a non-finite value.

class nengo_dl.utils.ProgressBar(present='', past=None, max_value=1, **kwargs)[source]¶

Handles progress bar display for some tracked process.

Parameters

presentstr: Description of process in present (e.g., “Simulating”)
paststr: Description of process in past (e.g., “Simulation”)
max_valueint or None: The maximum number of steps in the tracked process (or None if the maximum number of steps is unknown)

Notes

Launches a separate thread to handle the progress bar display updates.

Initializes a progress bar with sane defaults

start(**kwargs)[source]¶: Start tracking process, initialize display.

finish(**kwargs)[source]¶: Stop tracking process, finish display.

step()[source]¶: Advance the progress bar one step.

sub(msg=None, **kwargs)[source]¶

Creates a new progress bar for tracking a sub-process.

Parameters

msgstr: Description of sub-process

property max_steps¶: Alias for max_value to allow this to work with Nengo progress bar interface.

class nengo_dl.utils.SubProgressBar(present='', past=None, max_value=1, **kwargs)[source]¶

A progress bar representing a sub-task within an overall progress bar.

Initializes a progress bar with sane defaults

finish(**kwargs)[source]¶: Finishing a sub-progress bar doesn’t start a new line.

class nengo_dl.utils.NullProgressBar(present='', past=None, max_value=1, **kwargs)[source]¶

A progress bar that does nothing.

Used to replace ProgressBar when we want to disable output.

Initializes a progress bar with sane defaults

sub(*args, **kwargs)[source]¶: Noop for creating a sub-progress bar.

step(**kwargs)[source]¶: Noop for incrementing the progress bar.

Benchmarks¶

Benchmark networks and utilities for evaluating NengoDL’s performance.

`nengo_dl.benchmarks.cconv`	Circular convolution (EnsembleArray) benchmark.
`nengo_dl.benchmarks.integrator`	Single integrator ensemble benchmark.
`nengo_dl.benchmarks.pes`	PES learning rule benchmark.
`nengo_dl.benchmarks.basal_ganglia`	Basal ganglia network benchmark.
`nengo_dl.benchmarks.mnist`	A network designed to stress-test tensor layers (based on mnist net).
`nengo_dl.benchmarks.spaun`	Builds the Spaun network.
`nengo_dl.benchmarks.random_network`	A randomly interconnected network of ensembles.
`nengo_dl.benchmarks.lmu`	A network containing a single Legendre Memory Unit cell and dense readout.
`nengo_dl.benchmarks.run_profile`	Run profiler on a benchmark network.

nengo_dl.benchmarks.cconv(dimensions, neurons_per_d, neuron_type)[source]¶

Circular convolution (EnsembleArray) benchmark.

Parameters

dimensionsint: Number of dimensions for vector values
neurons_per_dint: Number of neurons to use per vector dimension
neuron_typeNeuronType: Simulation neuron type

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.integrator(dimensions, neurons_per_d, neuron_type)[source]¶

Single integrator ensemble benchmark.

Parameters

dimensionsint: Number of dimensions for vector values
neurons_per_dint: Number of neurons to use per vector dimension
neuron_typeNeuronType: Simulation neuron type

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.pes(dimensions, neurons_per_d, neuron_type)[source]¶

PES learning rule benchmark.

Parameters

dimensionsint: Number of dimensions for vector values
neurons_per_dint: Number of neurons to use per vector dimension
neuron_typeNeuronType: Simulation neuron type

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.basal_ganglia(dimensions, neurons_per_d, neuron_type)[source]¶

Basal ganglia network benchmark.

Parameters

dimensionsint: Number of dimensions for vector values
neurons_per_dint: Number of neurons to use per vector dimension
neuron_typeNeuronType: Simulation neuron type

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.mnist(use_tensor_layer=True)[source]¶

A network designed to stress-test tensor layers (based on mnist net).

Parameters

use_tensor_layerbool: If True, use individual tensor_layers to build the network, as opposed to a single TensorNode containing all layers.

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.spaun(dimensions)[source]¶

Builds the Spaun network.

See [1] for more details.

Parameters

dimensionsint: Number of dimensions for vector values

Returns

netnengo.Network: benchmark network

Notes

This network needs to be installed via

pip install git+https://github.com/drasmuss/spaun2.0.git

References

1: Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang, and Daniel Rasmussen (2012). A large-scale model of the functioning brain. Science, 338:1202-1205.

nengo_dl.benchmarks.random_network(dimensions, neurons_per_d, neuron_type, n_ensembles, connections_per_ensemble, seed=0)[source]¶

A randomly interconnected network of ensembles.

Parameters

dimensionsint: Number of dimensions for vector values
neurons_per_dint: Number of neurons to use per vector dimension
neuron_typeNeuronType: Simulation neuron type
n_ensemblesint: Number of ensembles in the network
connections_per_ensembleint: Outgoing connections from each ensemble

Returns

netnengo.Network: benchmark network

nengo_dl.benchmarks.lmu(theta, input_d, native_nengo=False, dtype='float32')[source]¶

A network containing a single Legendre Memory Unit cell and dense readout.

See [1] for more details.

Parameters

thetaint: Time window parameter for LMU.
input_dint: Dimensionality of input signal.
native_nengobool: If True, build the LMU out of Nengo objects. Otherwise, build the LMU directly in TensorFlow, and use a TensorNode to wrap the whole cell.
dtypestr: Float dtype to use for internal parameters of LMU when native_nengo=False (native_nengo=True will use the dtype of the Simulator).

Returns

netnengo.Network: Benchmark network

References

1: Aaron R. Voelker, Ivana Kajić, and Chris Eliasmith. Legendre memory units: continuous-time representation in recurrent neural networks. In Advances in Neural Information Processing Systems. 2019. https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.

nengo_dl.benchmarks.run_profile(net, train=False, n_steps=150, do_profile=True, reps=1, dtype='float32', **kwargs)[source]¶

Run profiler on a benchmark network.

Parameters

netNetwork: The nengo Network to be profiled.
trainbool: If True, profile the Simulator.fit function. Otherwise, profile the Simulator.run function.
n_stepsint: The number of timesteps to run the simulation.
do_profilebool: Whether or not to run profiling
repsint: Repeat the run this many times (only profile data from the last run will be kept).
dtypestr: Simulation dtype (e.g. “float32”)

Returns

exec_timefloat: Time (in seconds) taken to run the benchmark, taking the minimum over reps.

Notes

kwargs will be passed on to Simulator

Interface¶

The benchmark module also includes a command-line interface for building and running the benchmarks:

benchmarks¶

Command-line interface for benchmarks.

benchmarks [OPTIONS] COMMAND1 [ARGS]... [COMMAND2 [ARGS]...]...

build¶

Builds one of the benchmark networks

benchmarks build [OPTIONS]

Options

--benchmark <benchmark>¶: Name of benchmark network

--dimensions <dimensions>¶: Number of dimensions

--neurons_per_d <neurons_per_d>¶: Neurons per dimension

--neuron_type <neuron_type>¶: Nengo neuron model

--kwarg <kwarg>¶: Arbitrary kwarg to pass to benchmark network (key=value)

profile¶

Runs profiling on a network (call after ‘build’)

benchmarks profile [OPTIONS]

Options

--train, --no-train¶: Whether to profile training (as opposed to running) the network

--n_steps <n_steps>¶: Number of steps for which to run the simulation

--batch_size <batch_size>¶: Number of inputs to the model

--device <device>¶: TensorFlow device on which to run the simulation

--unroll <unroll>¶: Number of steps for which to unroll the simulation

--time-only¶: Only count total time, rather than profiling internals