"""
The Simulator class is the access point for the main features of NengoDL,
including `running <.Simulator.run_steps>` and `training <.Simulator.train>`
a model.
"""
import collections
import copy
import logging
import os
import tempfile
import warnings
from nengo import Ensemble, Connection, Probe, Network, Direct, Node
from nengo.builder.connection import BuiltConnection
from nengo.builder.ensemble import BuiltEnsemble
from nengo.ensemble import Neurons
from nengo.exceptions import (
ReadonlyError,
SimulatorClosed,
NengoWarning,
SimulationError,
ValidationError,
)
from nengo.solvers import NoSolver
import numpy as np
import tensorflow as tf
from tensorflow.python.client.timeline import Timeline
from tensorflow.python.ops import gradient_checker
from nengo_dl import utils, config, objectives
from nengo_dl.builder import NengoBuilder, NengoModel
from nengo_dl.compat import tf_compat, Convolution
from nengo_dl.tensor_graph import TensorGraph
logger = logging.getLogger(__name__)
[docs]class Simulator:
"""
Simulate network using the ``nengo_dl`` backend.
Parameters
----------
network : `~nengo.Network` or None
A network object to be built and then simulated. If None,
then a built model must be passed to ``model`` instead
dt : float
Length of a simulator timestep, in seconds
seed : int
Seed for all stochastic operators used in this simulator
model : `~nengo.builder.Model`
Pre-built model object
dtype : ``tf.DType``
Deprecated, use ``nengo_dl.configure_settings(dtype=...)`` instead.
device : None or ``"/cpu:0"`` or ``"/gpu:[0-n]"``
Device on which to execute computations (if None then uses the
default device as determined by TensorFlow)
unroll_simulation : int
Unroll simulation loop by explicitly building the given number of
iterations into the computation graph (improves simulation speed
but increases build time)
minibatch_size : int
The number of simultaneous inputs that will be passed through the
network
tensorboard : str
If not None, save network output in the TensorFlow summary format to
the given directory, which can be loaded into TensorBoard
progress_bar : bool
If True (default), display progress information when building a model
"""
def __init__(
self,
network,
dt=0.001,
seed=None,
model=None,
dtype=None,
device=None,
unroll_simulation=1,
minibatch_size=None,
tensorboard=None,
progress_bar=True,
):
self.closed = None
self.unroll = unroll_simulation
self.minibatch_size = 1 if minibatch_size is None else minibatch_size
self.data = SimulationData(self, minibatch_size is not None)
if seed is None:
if network is not None and network.seed is not None:
seed = network.seed + 1
else:
seed = np.random.randint(np.iinfo(np.int32).max)
self.seed = seed
# TODO: multi-GPU support
if device is None and not utils.tf_gpu_installed:
warnings.warn(
"No GPU support detected. It is recommended that you "
"install tensorflow-gpu (`pip install tensorflow-gpu`)."
)
logger.info("Running on CPU")
else:
logger.info(
"Running on %s",
"CPU/GPU" if device is None else ("CPU" if "cpu" in device else "GPU"),
)
ProgressBar = utils.ProgressBar if progress_bar else utils.NullProgressBar
# build model (uses default nengo builder)
if model is None:
self.model = NengoModel(
dt=float(dt),
label="%s, dt=%f" % (network, dt),
builder=NengoBuilder(),
fail_fast=False,
)
else:
if dt != model.dt:
warnings.warn(
"Model dt (%g) does not match Simulator "
"dt (%g)" % (model.dt, dt),
NengoWarning,
)
self.model = model
if network is not None:
p = ProgressBar("Building network", "Build")
self.model.build(network, progress=p)
if dtype is not None:
warnings.warn(
"dtype parameter is deprecated; use "
"nengo_dl.configure_settings(dtype=...) instead",
DeprecationWarning,
)
else:
dtype = config.get_setting(self.model, "dtype", tf.float32)
# set up tensorflow graph plan
with ProgressBar(
"Optimizing graph", "Optimization", max_value=None
) as progress:
self.tensor_graph = TensorGraph(
self.model,
self.dt,
unroll_simulation,
dtype,
self.minibatch_size,
device,
progress,
)
# set TensorFlow graph seed
with self.tensor_graph.graph.as_default():
tf_compat.set_random_seed(self.seed)
# construct graph
with ProgressBar(
"Constructing graph", "Construction", max_value=None
) as progress:
self.tensor_graph.build(progress)
# output simulation data for viewing via TensorBoard
if tensorboard is not None:
if not os.path.exists(tensorboard):
os.makedirs(tensorboard)
run_number = (
max(
[int(x[4:]) for x in os.listdir(tensorboard) if x.startswith("run")]
or [-1]
)
+ 1
)
self.summary = tf_compat.summary.FileWriter(
os.path.join(tensorboard, "run_%d" % run_number),
graph=self.tensor_graph.graph,
)
else:
self.summary = None
# start session
session_config = tf_compat.ConfigProto(
allow_soft_placement=False, log_device_placement=False
)
# TODO: XLA compiling doesn't seem to provide any benefit at the
# moment, revisit later after tensorflow has developed it further
# config.graph_options.optimizer_options.global_jit_level = (
# tf.OptimizerOptions.ON_1)
# set any config options specified by user
config_settings = config.get_setting(self.model, "session_config", {})
for c, v in config_settings.items():
attrs = c.split(".")
x = session_config
for a in attrs[:-1]:
x = getattr(x, a)
setattr(x, attrs[-1], v)
self.sess = tf_compat.Session(
graph=self.tensor_graph.graph, config=session_config
)
self.reset(seed=seed)
self.closed = False
[docs] def reset(self, seed=None):
"""
Resets the simulator to initial conditions.
Parameters
----------
seed : int
If not None, overwrite the default simulator seed with this value
(note: this becomes the new default simulator seed)
"""
if self.closed is not None and self.closed:
raise SimulatorClosed("Cannot reset closed Simulator.")
self.n_steps = 0
self.time = 0.0
# initialize variables
self.sess.run(
self.tensor_graph.constant_init_op,
feed_dict=self.tensor_graph.signals.constant_phs,
)
self.soft_reset(include_trainable=True, include_probes=True)
# execute post-build processes (we do this here because
# seed can change each call to reset)
if seed is not None:
self.seed = seed
self.rng = np.random.RandomState(self.seed)
with self.tensor_graph.graph.as_default():
tf_compat.set_random_seed(self.seed)
with self.sess.as_default():
self.tensor_graph.build_post(self.sess, self.rng)
[docs] def soft_reset(self, include_trainable=False, include_probes=False):
"""
Resets the internal state of the simulation, but doesn't
rebuild the graph.
Parameters
----------
include_trainable : bool
If True, also reset any training that has been performed on
network parameters (e.g., connection weights)
include_probes : bool
If True, also clear probe data
"""
init_ops = [self.tensor_graph.local_init_op, self.tensor_graph.global_init_op]
if include_trainable:
init_ops.append(self.tensor_graph.trainable_init_op)
self.sess.run(
init_ops,
feed_dict={ph: v for _, ph, v in self.tensor_graph.base_vars.values()},
)
if include_probes:
for p in self.model.probes:
self.model.params[p] = []
self.n_steps = 0
[docs] def step(self, **kwargs):
"""
Run the simulation for one time step.
Parameters
----------
kwargs : dict
See `.run_steps`
Notes
-----
Progress bar is disabled by default when running via this method.
"""
kwargs.setdefault("progress_bar", False)
self.run_steps(1, **kwargs)
[docs] def run(self, time_in_seconds, **kwargs):
"""
Simulate for the given length of time.
Parameters
----------
time_in_seconds : float
Run the simulator for the given number of simulated seconds
kwargs : dict
See `.run_steps`
"""
if time_in_seconds < 0:
raise ValidationError(
"Must be positive (got %g)" % (time_in_seconds,), attr="time_in_seconds"
)
steps = int(np.round(float(time_in_seconds) / self.dt))
if steps == 0:
warnings.warn(
"%g results in running for 0 timesteps. Simulator "
"still at time %g." % (time_in_seconds, self.time)
)
else:
self.run_steps(steps, **kwargs)
[docs] def run_steps(
self,
n_steps,
data=None,
input_feeds=None,
profile=False,
progress_bar=True,
extra_feeds=None,
):
"""
Simulate for the given number of steps.
Parameters
----------
n_steps : int
The number of simulation steps to be executed
data : dict of {`~nengo.Node`: `~numpy.ndarray`}
Override the values of input Nodes with the given data. Arrays
should have shape ``(sim.minibatch_size, n_steps, node.size_out)``.
input_feeds : dict of {`~nengo.Node`: `~numpy.ndarray`}
Deprecated, use ``data`` instead.
profile : bool
If True, collect TensorFlow profiling information while the
simulation is running (this will slow down the simulation).
Can also pass a string specifying a non-default filename for the
saved profile data.
progress_bar : bool
If True, print information about the simulation status to standard
output.
extra_feeds : dict of {``tf.Tensor``: `~numpy.ndarray`}
Can be used to feed a value for arbitrary Tensors in the simulation
(will be passed directly to the TensorFlow session)
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``.
"""
actual_steps = self.unroll * int(np.ceil(n_steps / self.unroll))
if actual_steps != n_steps:
warnings.warn(
"Number of steps (%d) is not an even multiple of "
"`unroll_simulation` (%d). Simulation will run for %d steps, "
"which may have unintended side effects."
% (n_steps, self.unroll, actual_steps),
RuntimeWarning,
)
if input_feeds is not None:
# TODO: remove this in 3.0.0
warnings.warn(
"The `input_feeds` argument has been renamed; please use "
"`data` instead, as `input_feeds` will not be supported in a "
"future version.",
DeprecationWarning,
)
assert data is None
data = input_feeds
if data is None:
data = actual_steps
else:
# note: we only need to check the shape of the first item, because
# check_data (inside run_batch) will ensure that all the items
# have the same shape
batch_size, input_steps = next(iter(data.values())).shape[:2]
if batch_size != self.minibatch_size:
raise ValidationError(
"Input data must have batch size == sim.minibatch_size "
"(%d != %d)" % (batch_size, self.minibatch_size),
"data",
)
if input_steps != actual_steps:
raise ValidationError(
"Number of timesteps in input data (%d) does not "
"match requested number of steps (%d)" % (input_steps, n_steps),
"data",
)
def callback(_, extra_vals):
assert extra_vals == actual_steps
progress = (
utils.ProgressBar("Simulating", "Simulation", max_value=None)
if progress_bar
else utils.NullProgressBar()
)
with progress:
# note: we request steps_run from extra_fetches so that the
# simulation will always run for the given number of steps, even
# if there are no output probes
probe_data = self.run_batch(
data,
{p: None for p in self.model.probes},
extra_feeds=extra_feeds,
extra_fetches=self.tensor_graph.steps_run,
combine=lambda x: x[0],
isolate_state=False,
callback=callback,
profile=profile,
)
# update stored probe data
for probe, val in probe_data.items():
# drop any extra steps (due to uneven unroll_simulation)
val = val[:, :n_steps]
if probe.sample_every is not None:
# downsample probe according to `sample_every`
period = probe.sample_every / self.dt
steps = np.arange(self.n_steps, self.n_steps + n_steps)
val = val[:, (steps + 1) % period < 1]
self.model.params[probe].append(val)
# update n_steps
# note: we update n_steps according to the number of steps that the
# user asked for, not the number of steps that were actually run
# (in the case of uneven unroll_simulation)
self.n_steps += n_steps
self.time = self.n_steps * self.dt
[docs] def train(
self,
data,
optimizer,
n_epochs=1,
objective=None,
shuffle=True,
truncation=None,
summaries=None,
profile=False,
extra_feeds=None,
progress_bar=True,
):
"""
Optimize the trainable parameters of the network using the given
optimization method, minimizing the objective value over the given
inputs and targets.
Parameters
----------
data : dict of {`~nengo.Node` or `~nengo.Probe`: \
`~numpy.ndarray`} or int
Input values for Nodes in the network or target values for Probes;
arrays should have shape ``(batch_size, n_steps,
node.size_out/probe.size_in)``. If no input data is required,
an integer can be given specifying the number of timesteps to
run the simulation.
optimizer : ``tf.train.Optimizer``
TensorFlow optimizer, e.g.
``tf.train.GradientDescentOptimizer(learning_rate=0.1)``
n_epochs : int
Run training for the given number of epochs (complete passes
through ``data``)
objective : dict of {(tuple of) `~nengo.Probe`: callable or ``None``}
The objective to be minimized. The default applies
`.objectives.mse` to all probes in ``data``. This can be
overridden by passing a dictionary mapping Probes to functions
``f(output, target) -> loss`` that consume the actual output and
target output for the given probe(s) and return a ``tf.Tensor``
representing a scalar loss value. The function may also accept a
single argument ``f(output) -> loss`` if targets are not required.
Some common objective functions can be found in
`nengo_dl.objectives`.
Passing ``None`` as the probe value (instead of a callable)
indicates that the error is being computed outside the simulation,
and the value passed for that probe in ``data`` directly specifies
the output error gradient.
If multiple probes are specified as the key, then the corresponding
output/target values will be passed as a list to the objective
function.
The overall loss value being minimized will be the sum across all
the objectives specified.
shuffle : bool
If True, randomize the data into different minibatches each epoch
truncation: int
If not None, use truncated backpropagation when training the
network, with the given truncation length.
summaries : list of `~nengo.Connection` or \
`~nengo.Ensemble` or \
`~nengo.ensemble.Neurons` or \
``"loss"`` or \
``tf.Tensor``
If not None, collect data during the training process using
TensorFlow's ``tf.summary`` format. The summary objects can be a
Connection (in which case data on the corresponding weights will be
collected), Ensemble (encoders), Neurons (biases), or ``"loss"``
(the loss value for ``objective``). The user can also create their
own summaries and pass in the Tensors representing the summary ops.
profile : bool
If True, collect TensorFlow profiling information while the
simulation is running (this will slow down the simulation).
Can also pass a string specifying a non-default filename for the
saved profile data.
extra_feeds : dict of {``tf.Tensor``: `~numpy.ndarray`}
Can be used to feed a value for arbitrary Tensors in the simulation
(will be passed directly to the TensorFlow session)
progress_bar : bool
If True, print information about the simulation status to standard
output.
Notes
-----
Most deep learning methods require the network to be differentiable,
which means that trying to train a network with non-differentiable
elements will result in an error. Examples of common
non-differentiable elements include `~nengo.LIF`,
`~nengo.Direct`, or processes/neurons that don't have a
custom TensorFlow implementation (see
`.process_builders.SimProcessBuilder`/
`.neuron_builders.SimNeuronsBuilder`)
"""
if isinstance(data, int):
batch_size = self.minibatch_size
n_steps = data
else:
batch_size, n_steps = next(iter(data.values())).shape[:2]
# error checking
synapses = [
x.synapse is not None
for x in (
self.model.toplevel.all_connections
+ (
list(p for p in data if isinstance(p, Probe))
if isinstance(data, dict)
else []
)
)
]
if n_steps == 1 and self.model.toplevel is not None and any(synapses):
warnings.warn(
"Training for one timestep, but the network contains "
"synaptic filters (which will introduce at least a "
"one-timestep delay); did you mean to set synapse=None?"
)
if isinstance(optimizer, dict):
raise ValidationError(
"The second argument to `sim.train` should be a "
"tf.train.Optimizer, not a dictionary; it is likely that this "
"code was written for NengoDL 1.x and needs to be updated for "
"NengoDL 2.x; see "
"https://www.nengo.ai/nengo-dl/project.html#release-history",
"optimizer",
)
# fill in default objective
if objective is None:
if isinstance(data, int):
raise ValidationError(
"Must specify an explicit objective if no input data given",
"objective",
)
objective = {p: objectives.mse for p in data if isinstance(p, Probe)}
if not isinstance(objective, dict):
raise ValidationError(
"Must be a dictionary mapping Probes to objective functions",
"objective",
)
# fill in mse function
for p, o in objective.items():
if o == "mse":
# TODO: remove in 3.0.0
warnings.warn(
"Using the string 'mse' for the objective is deprecated, "
"and will no longer be supported in the future; please "
"use the function `nengo_dl.objectives.mse` in the future",
DeprecationWarning,
)
objective[p] = objectives.mse
# build the output function
apply_optimizer = self.tensor_graph.build_optimizer_func(optimizer, objective)
extra_fetches = dict()
# add summaries
if summaries is not None:
if self.summary is None:
warnings.warn(
"Simulator was created with tensorboard=False; "
"ignoring requested summaries"
)
else:
for i, v in enumerate(summaries):
if isinstance(v, str) and v == "loss":
summaries[i] = objective
summary_op, init = self.tensor_graph.build_summaries(summaries)
if init is not None:
# initialize any variables created when building summaries
self.sess.run(init)
extra_fetches["summaries"] = summary_op
progress = (
utils.ProgressBar(
"Training",
max_value=(
n_epochs
* (batch_size // self.minibatch_size)
* (1 if truncation is None else n_steps // truncation)
),
vars=["loss"],
)
if progress_bar
else utils.NullProgressBar()
)
objective_probes = tuple(objective.keys())
def callback(out_vals, extra_vals):
# update progress bar, with loss value
loss = out_vals[objective_probes][1]
# loss will be {} if only direct grads used when calculating
# gradient
kwargs = {} if loss == {} else dict(loss="%.4f" % loss)
progress.step(**kwargs)
# export summaries to tensorboard
if "summaries" in extra_vals:
# note: the first output value is the new value of the
# global training_step
self.summary.add_summary(
extra_vals["summaries"], out_vals[objective_probes][0]
)
# run training
with progress:
self.run_batch(
data,
{objective_probes: apply_optimizer},
n_epochs=n_epochs,
combine=lambda x: None,
extra_feeds=extra_feeds,
extra_fetches=extra_fetches,
truncation=truncation,
profile=profile,
shuffle=shuffle,
training=True,
callback=callback,
)
[docs] def loss(
self,
data,
objective=None,
combine=np.mean,
extra_feeds=None,
progress_bar=True,
training=False,
):
"""
Compute the loss value for the given objective and inputs/targets.
Parameters
----------
data : dict of {`~nengo.Node` or `~nengo.Probe`: \
`~numpy.ndarray`} or int
Input values for Nodes in the network or target values for Probes;
arrays should have shape ``(batch_size, n_steps,
node.size_out/probe.size_in)``. If no input data is required,
an integer can be given specifying the number of timesteps to
run the simulation.
objective : dict of {(tuple of) `~nengo.Probe`: callable}
The objective to compute the loss. The default applies
`.objectives.mse` to all probes in ``data``. This can be
overridden by passing a dictionary mapping Probes to functions
``f(output, target) -> loss`` that consume the actual output and
target output for the given probe(s) and return a ``tf.Tensor``
representing a scalar loss value. The function may also accept a
single argument ``f(output) -> loss`` if targets are not required.
Some common objective functions can be found in
`nengo_dl.objectives`.
If multiple probes are specified as the key, then the corresponding
output/target values will be passed as a list to the objective
function.
The overall value returned will be the sum across all
the objectives specified.
combine : callable
Function used to combine objective values from each minibatch.
extra_feeds : dict of {``tf.Tensor``: `~numpy.ndarray`}
Can be used to feed a value for arbitrary Tensors in the simulation
(will be passed directly to the TensorFlow session)
progress_bar : bool
If True, print information about the simulation status to standard
output.
training : bool
If True, run the network in training mode (where, e.g., spiking
neuron models are swapped for the equivalent differentiable
approximation).
Returns
-------
loss : float
Sum of computed error values for each function in ``objective``.
"""
batch_size = (
self.minibatch_size
if isinstance(data, int)
else next(iter(data.values())).shape[0]
)
# fill in default objective
if objective is None:
if isinstance(data, int):
raise ValidationError(
"Must specify an explicit objective if no input data given",
"objective",
)
objective = {p: objectives.mse for p in data if isinstance(p, Probe)}
if not isinstance(objective, dict):
raise ValidationError(
"Must be a dictionary mapping Probes to objective functions",
"objective",
)
# fill in mse function
for p, o in objective.items():
if o == "mse":
# TODO: remove in 3.0.0
warnings.warn(
"Using the string 'mse' for the objective is deprecated, "
"and will no longer be supported in the future; please "
"use the function `nengo_dl.objectives.mse` in the future",
DeprecationWarning,
)
objective[p] = objectives.mse
progress = (
utils.ProgressBar(
"Calculating loss",
"Calculation",
max_value=batch_size // self.minibatch_size,
)
if progress_bar
else utils.NullProgressBar()
)
with progress:
loss = self.run_batch(
data,
objective,
extra_feeds=extra_feeds,
callback=lambda *_: progress.step(),
combine=combine,
training=training,
)
# sum across objectives
loss = np.sum(list(loss.values()))
return loss
[docs] def run_batch(
self,
data,
outputs,
extra_feeds=None,
extra_fetches=None,
n_epochs=1,
truncation=None,
shuffle=False,
profile=False,
training=False,
callback=None,
combine=np.stack,
isolate_state=True,
):
"""
Run the simulation on a batch of input data, computing the given
output functions.
Parameters
----------
data : dict of {`~nengo.Node` or `~nengo.Probe`: \
`~numpy.ndarray`} or int
Input values for Nodes in the network or target values for Probes;
arrays should have shape ``(batch_size, n_steps,
node.size_out/probe.size_in)``. If no input data is required,
an integer can be given specifying the number of timesteps to
run the simulation.
outputs : dict of {(tuple of) `~nengo.Probe`: callable or None}
Functions to apply to probe outputs. Functions can accept one
positional argument (the output from that probe on one minibatch)
or two (also passed the corresponding target value from ``data``).
If a tuple of Probes are given as the key then the first
argument will be a list of probe outputs, and the second
argument will be the corresponding list of target values. The
function can return a ``tf.Tensor``, or tuple of Tensors,
which will be evaluated on each minibatch of data. If ``None``
is given then the return value will be the output value from that
probe.
extra_feeds : dict of {``tf.Tensor``: `~numpy.ndarray`}
Can be used to feed a value for arbitrary Tensors in the simulation
(will be passed directly to the TensorFlow session)
extra_fetches : (list/tuple/dict of) ``tf.Tensor``
Can be used to fetch arbitrary (structures of) Tensor values from
the simulation (will be fetched directly from the TensorFlow
session).
n_epochs : int
Repeat ``data`` for ``n_epochs`` iterations.
truncation : int
If not None, run the simulation ``truncation`` timesteps at a time.
Outputs from each truncation block will be passed sequentially to
``combine``, in the same way as minibatch blocks. Note
that the simulation state is preserved between truncation blocks,
so the sequence forms one continuous run within each minibatch.
shuffle : bool
If True, randomize the data into different minibatches each epoch.
profile : bool
If True, collect TensorFlow profiling information while the
simulation is running (this will slow down the simulation).
Can also pass a string specifying a non-default filename for the
saved profile data.
training : bool
If True, run the network in training mode, otherwise run it in
inference mode (this can affect things like the neuron model
used).
callback : callable
A function that will be called after each minibatch is evaluated.
The function is passed two arguments; the first is a dictionary
corresponding to ``outputs`` with the output values from each
function, and the second is the value of ``extra_feeds``.
combine : callable
The function that will be used to combine the outputs from each
minibatch/truncation block. The values from each output function
on each minibatch will be formed into a list and passed to
``combine`` in order to compute the final return values from
this function. Note that if the output function returns multiple
values, then ``combine`` will be applied separately to each of
those outputs across the minibatches.
isolate_state : bool
If True (default), isolate the simulation state for this run
from the rest of the simulation (so the execution of this run
is not affected by previous runs and will not affect future runs).
If False, then this run begins from the terminal state of the
last run, each minibatch will continue in sequence from the state
of the previous, and future runs will resume from the terminal
state of the last minibatch of this run.
Returns
-------
output_vals : dict of {(tuple of) `~nengo.Probe`: \
(tuple of) `~numpy.ndarray`}
The result of computing ``outputs`` on simulation probe values,
given ``data``. This pseudocode may help to understand how the
return values are constructed given the various parameters of this
function:
.. code-block:: python
output_vals = {}
for probe, func in outputs.items():
probe_vals = []
for i in range(n_epochs):
for minibatch in data:
network_output = run_network(minibatch)
probe_vals.append(func(network_output[probe]))
output_vals[probe] = combine(output_values)
Note that this is not how the values are computed in practice,
as it would be quite inefficient. This pseudocode also omits
some of the finer details (e.g. truncation and state isolation).
Notes
-----
In general, users should call one of the wrappers for this function
(e.g., `.run_steps`, `.train`, or `.loss`),
according to their use case. However, this function can be called
directly to run the simulation in a customized way.
"""
n_steps = data if isinstance(data, int) else next(iter(data.values())).shape[1]
# error checking
if self.closed:
raise SimulatorClosed("Simulator cannot run because it is closed.")
if not isinstance(data, int):
self._check_data(data)
if n_steps % self.unroll != 0:
raise ValidationError(
"The number of timesteps in batch data must be evenly "
"divisible by unroll_simulation",
"data",
)
if truncation is not None and truncation % self.unroll != 0:
raise ValidationError(
"Truncation length must be evenly divisible by unroll_simulation",
"truncation",
)
if training and self.tensor_graph.inference_only:
raise ValidationError(
"Network was created with inference_only=True, cannot "
"be run in training mode",
"inference_only",
)
if extra_fetches is None:
extra_fetches = []
# apply functions (if any) to output probes
output_ops, init_ops = self.tensor_graph.build_outputs(outputs)
# initialize any new variables
if init_ops is not None:
self.sess.run(init_ops)
# save the internal state of the simulator
if isolate_state:
tmpdir = tempfile.TemporaryDirectory()
self.save_params(
os.path.join(tmpdir.name, "tmp"),
include_local=True,
include_global=False,
)
# set up profiling
if profile:
run_options = tf_compat.RunOptions(
trace_level=tf_compat.RunOptions.FULL_TRACE
)
run_metadata = tf_compat.RunMetadata()
else:
run_options = None
run_metadata = None
# compute outputs on batch
output_vals = collections.defaultdict(list)
for _ in range(n_epochs):
for offset, mini_data in utils.minibatch_generator(
data,
self.minibatch_size,
truncation=truncation,
shuffle=shuffle,
rng=self.rng,
):
if offset == 0 and isolate_state:
self.soft_reset()
# fill in feed_dict values
if isinstance(mini_data, int):
steps = mini_data
mini_data = None
else:
steps = next(iter(mini_data.values())).shape[1]
feed = self._fill_feed(
steps,
data=mini_data,
training=training,
start=offset + (0 if isolate_state else self.n_steps),
)
if extra_feeds is not None:
feed.update(extra_feeds)
# run the simulation
try:
out_vals, extra_vals = self.sess.run(
(output_ops, extra_fetches),
feed_dict=feed,
options=run_options,
run_metadata=run_metadata,
)
except (tf.errors.InternalError, tf.errors.UnknownError) as e:
if e.op is not None and e.op.type == "PyFunc":
raise SimulationError(
"Function '%s' caused an error (see error log "
"above)" % e.op.name
)
else:
raise e # pragma: no cover (unknown errors)
if callback is not None:
callback(out_vals, extra_vals)
for k, v in out_vals.items():
output_vals[k].append(v)
# restore internal state of simulator
if isolate_state:
self.load_params(
os.path.join(tmpdir.name, "tmp"),
include_local=True,
include_global=False,
)
tmpdir.cleanup()
# combine outputs from each minibatch
for probe, vals in output_vals.items():
# if the output function returns multiple items, keep those
# arrays separate
if isinstance(vals[0], (list, tuple)):
output_vals[probe] = tuple(combine(v) for v in zip(*vals))
else:
output_vals[probe] = combine(vals)
# convert back from defaultdict
output_vals = dict(output_vals)
# output profile data to file
self._profile_output(profile, run_metadata)
return output_vals
[docs] def save_params(self, path, include_global=True, include_local=False):
"""
Save network parameters to the given ``path``.
Parameters
----------
path : str
Filepath of parameter output file
include_global : bool
If True (default True), save global/trainable network variables
include_local : bool
If True (default False), save local (non-trainable) network
variables
Notes
-----
This function is useful for saving/loading entire models; for
saving/loading individual objects within a model, see
`.get_nengo_params`.
"""
if self.closed:
raise SimulatorClosed("Simulation has been closed, cannot save parameters")
with self.tensor_graph.graph.as_default():
vars = []
if include_global:
vars.extend(tf_compat.global_variables())
if include_local:
vars.extend(tf_compat.local_variables())
with tf.device("/cpu:0"):
path = tf_compat.train.Saver(vars).save(self.sess, path)
logger.info("Model parameters saved to %s", path)
[docs] def load_params(self, path, include_global=True, include_local=False):
"""
Load network parameters from the given ``path``.
Parameters
----------
path : str
Filepath of parameter input file
include_global : bool
If True (default True), load global (trainable) network variables
include_local : bool
If True (default False), load local (non-trainable) network
variables
Notes
-----
This function is useful for saving/loading entire models; for
saving/loading individual objects within a model, see
`.get_nengo_params`.
"""
if self.closed:
raise SimulatorClosed("Simulation has been closed, cannot load parameters")
with self.tensor_graph.graph.as_default():
vars = []
if include_global:
vars.extend(tf_compat.global_variables())
if include_local:
vars.extend(tf_compat.local_variables())
with tf.device("/cpu:0"):
tf_compat.train.Saver(vars).restore(self.sess, path)
logger.info("Model parameters loaded from %s", path)
[docs] def freeze_params(self, objs):
"""
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:
.. code-block:: python
with nengo.Network() as net:
< build network >
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``).
"""
if self.closed:
raise SimulatorClosed(
"Simulation has been closed, cannot freeze parameters"
)
if not isinstance(objs, (list, tuple)):
objs = [objs]
for obj in objs:
if obj not in [self.model.toplevel] + self.model.toplevel.all_objects:
raise ValueError(
"%s is not a member of the Network used to "
"initialize the Simulator"
)
if not isinstance(obj, (Network, Ensemble, Connection)):
raise TypeError(
"Objects of type %s do not have parameters to store" % type(obj)
)
if isinstance(obj, Network):
todo = obj.all_ensembles + obj.all_connections
else:
todo = [obj]
for o, params in zip(todo, self.get_nengo_params(todo)):
for k, v in params.items():
setattr(o, k, v)
[docs] def get_nengo_params(self, nengo_objs, as_dict=False):
"""
Extract model parameters in a form that can be used to initialize
Nengo objects in a different model.
For example:
.. code-block:: python
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])
Parameters
----------
nengo_objs : (list of) `~nengo.Ensemble` or `~nengo.Connection`
A single object or list of objects for which we want to get the
parameters.
as_dict : bool
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.
"""
if isinstance(nengo_objs, (list, tuple)):
scalar = False
else:
scalar = True
nengo_objs = [nengo_objs]
# convert neurons to the parent ensemble
nengo_objs = [
obj.ensemble if isinstance(obj, Neurons) else obj for obj in nengo_objs
]
# find all the data we need to fetch
fetches = []
for obj in nengo_objs:
if isinstance(obj, Connection):
fetches.append((obj, "weights"))
elif isinstance(obj, Ensemble):
if isinstance(obj.neuron_type, Direct):
# we cannot transfer direct ensemble parameters, because
# the nengo builder ignores the encoders specified for
# a direct ensemble
raise ValueError(
"get_nengo_params will not work correctly for "
"Direct neuron ensembles. Try manually translating "
"your network using `sim.data` instead."
)
fetches.extend([(obj, "scaled_encoders"), (obj, "bias")])
else:
raise ValueError(
"Can only get Nengo parameters for Ensembles or Connections"
)
# get parameter values from simulation
data = self.data.get_params(*fetches)
# store parameter values in a form that can be loaded in nengo
params = []
idx = 0
for obj in nengo_objs:
if isinstance(obj, Connection):
weights = data[idx]
idx += 1
if isinstance(obj.pre_obj, Ensemble):
params.append(
{
"solver": NoSolver(weights.T, weights=False),
"function": lambda x, weights=weights: np.zeros(
weights.shape[0]
),
"transform": 1,
}
)
elif isinstance(obj.transform, Convolution):
transform = copy.copy(obj.transform)
# manually bypass the read-only check (we are sure that
# nothing else has a handle to the new transform at this
# point, so this won't cause any problems)
Convolution.init.data[transform] = weights
params.append({"transform": transform})
else:
if all(x == 1 for x in weights.shape):
weights = np.squeeze(weights)
params.append({"transform": weights})
else:
# note: we don't want to change the original gain (even though
# it is rolled into the encoder values), because connections
# direct to `ens.neurons` will still use the gains (and those
# gains are not updated during training, only the encoders)
gain = self.model.params[obj].gain
# the encoders we get from the simulation are the actual
# weights we want in the simulation. but during the build
# process, gains and radius will be applied to the encoders.
# so we need to undo that scaling here, so that the build
# process will result in the correct values.
encoders = data[idx] * obj.radius / gain[:, None]
params.append(
{
"encoders": encoders,
"normalize_encoders": False,
"gain": gain,
"bias": data[idx + 1],
"max_rates": Ensemble.max_rates.default,
"intercepts": Ensemble.intercepts.default,
}
)
idx += 2
# return params in appropriate format
if scalar:
return params[0]
if as_dict:
param_dict = {}
for obj, p in zip(nengo_objs, params):
if obj.label in param_dict:
raise ValueError(
"Duplicate label ('%s') detected; cannot return "
"parameters with as_dict=True" % obj.label
)
else:
param_dict[obj.label] = p
params = param_dict
return params
[docs] def check_gradients(self, outputs=None, atol=1e-5, rtol=1e-3):
"""
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
----------
outputs : ``tf.Tensor`` or list of ``tf.Tensor`` or \
list of `~nengo.Probe`
Compute gradients wrt this output (if None, computes wrt each
output probe)
atol : float
Absolute error tolerance
rtol : float
Relative (to numeric grad) error tolerance
Notes
-----
Calling this function will reset all values in the network, so it
should not be intermixed with calls to `.Simulator.run`.
"""
if self.tensor_graph.inference_only:
raise ValidationError(
"Network was created with inference_only=True, cannot "
"compute gradients",
"inference_only",
)
delta = 1e-3
n_steps = self.unroll * 2
data = {
n: np.zeros((self.minibatch_size, n_steps, n.size_out))
for n in self.tensor_graph.invariant_inputs
}
data.update(
{
p: np.zeros((self.minibatch_size, n_steps, p.size_in))
for p in self.tensor_graph.target_phs
}
)
feed = self._fill_feed(n_steps, data=data, training=True)
if outputs is None:
# note: the x + 0 is necessary because `gradient_checker`
# doesn't work properly if the output variable is a tensorarray
outputs = [x + 0 for x in self.tensor_graph.probe_arrays.values()]
elif isinstance(outputs, tf.Tensor):
outputs = [outputs]
else:
outputs = [self.tensor_graph.probe_arrays[p] + 0 for p in outputs]
# check gradient wrt inp
for node, inp in self.tensor_graph.input_ph.items():
inp_shape = inp.get_shape().as_list()
inp_shape = [n_steps if x is None else x for x in inp_shape]
inp_tens = self.tensor_graph.input_ph[node]
feed[inp_tens] = np.ascontiguousarray(feed[inp_tens])
inp_val = np.ravel(feed[inp_tens])
for out in outputs:
out_shape = out.get_shape().as_list()
out_shape = [n_steps if x is None else x for x in out_shape]
# we need to compute the numeric jacobian manually, to
# correctly handle variables (tensorflow doesn't expect
# state ops in `compute_gradient`, because it doesn't define
# gradients for them)
numeric = np.zeros(
(
np.prod(inp_shape, dtype=np.int32),
np.prod(out_shape, dtype=np.int32),
)
)
for i in range(numeric.shape[0]):
self.soft_reset()
inp_val[i] = delta
plus = self.sess.run(out, feed_dict=feed)
self.soft_reset()
inp_val[i] = -delta
minus = self.sess.run(out, feed_dict=feed)
numeric[i] = np.ravel((plus - minus) / (2 * delta))
inp_val[i] = 0
self.soft_reset()
with tf_compat.variable_scope(tf_compat.get_variable_scope()) as scope:
dx, dy = gradient_checker._compute_dx_and_dy(inp, out, out_shape)
self.sess.run(
tf_compat.variables_initializer(
scope.get_collection("gradient_vars")
)
)
with self.sess.as_default():
analytic = gradient_checker._compute_theoretical_jacobian(
inp,
inp_shape,
np.zeros(inp_shape),
dy,
out_shape,
dx,
extra_feed_dict=feed,
)
if np.any(np.isnan(analytic)) or np.any(np.isnan(numeric)):
raise SimulationError("NaNs detected in gradient")
fail = abs(analytic - numeric) >= atol + rtol * abs(numeric)
if np.any(fail):
raise SimulationError(
"Gradient check failed for input %s and output %s\n"
"numeric values:\n%s\n"
"analytic values:\n%s\n"
% (node, out, numeric[fail], analytic[fail])
)
self.soft_reset()
logger.info("Gradient check passed")
[docs] def trange(self, sample_every=None, dt=None):
"""
Create a vector of 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_every : float (Default: None)
The sampling period of the probe to create a range for.
If None, a time value for every ``dt`` will be produced.
"""
if dt is not None:
if sample_every is not None:
raise ValidationError(
"Cannot specify both `dt` and `sample_every`. "
"Use `sample_every` only.",
attr="dt",
obj=self,
)
warnings.warn("`dt` is deprecated. Use `sample_every` instead.")
sample_every = dt
period = 1 if sample_every is None else sample_every / self.dt
steps = np.arange(1, self.n_steps + 1)
return self.dt * steps[steps % period < 1]
[docs] def close(self):
"""
Close the simulation, freeing resources.
Notes
-----
The simulation cannot be restarted after it is closed. This is not a
technical limitation, just a design decision made for all Nengo
simulators.
"""
if not self.closed:
# note: we use getattr in case it crashes before the object is
# created
if getattr(self, "sess", None) is not None:
self.sess.close()
self.sess = None
if getattr(self, "summary", None) is not None:
self.summary.close()
self.closed = True
def _fill_feed(self, n_steps, data=None, start=0, training=False):
"""
Create a feed dictionary containing values for all the placeholder
inputs in the network, which will be passed to ``tf.Session.run``.
Parameters
----------
n_steps : int
The number of execution steps
data : dict of {`~nengo.Node` or `~nengo.Probe` : `~numpy.ndarray`}
Input values for Nodes and target values for Probes. Arrays
should have shape ``(sim.minibatch_size, n_steps,
node.size_out/probe.size_in)``.
start : int
Initial value of simulator timestep
training : bool
Whether we are running in training or inference mode
Returns
-------
feed_dict : dict of {``tf.Tensor``: `~numpy.ndarray`}
Feed values for placeholder tensors in the network
"""
# fill in constants
feed_dict = {
self.tensor_graph.step_var: start,
self.tensor_graph.stop_var: start + n_steps,
}
if not self.tensor_graph.inference_only:
feed_dict[self.tensor_graph.signals.training] = training
inputs = {}
targets = {}
if data is not None:
for k, v in data.items():
if isinstance(k, Node):
inputs[k] = v
elif isinstance(k, Probe):
targets[k] = v
else:
# this should be caught in check_data
raise NotImplementedError()
# fill in input values
feed_dict.update(self._generate_inputs(inputs, n_steps))
# fill in target values
for p, t in targets.items():
if p not in self.tensor_graph.target_phs:
raise ValidationError(
"%s is not a valid target; this is probably because "
"it is not used in the objective function" % p,
"targets",
)
feed_dict[self.tensor_graph.target_phs[p]] = t
return feed_dict
def _generate_inputs(self, data, n_steps):
"""
Generate inputs for the network (the output values of each Node with
no incoming connections).
Parameters
----------
data : dict of {`~nengo.Node`: `~numpy.ndarray`}
Override the values of input Nodes with the given data. Arrays
should have shape ``(sim.minibatch_size, n_steps, node.size_out)``.
n_steps : int
Number of simulation timesteps for which to generate input data
Returns
-------
feed_vals : dict of {`~nengo.Node`: `~numpy.ndarray}
Simulation values for all the input Nodes in the network.
"""
feed_vals = {}
for n, output in self.tensor_graph.input_funcs.items():
if n in data:
# move minibatch dimension to the end
feed_val = np.moveaxis(data[n], 0, -1)
elif isinstance(output, np.ndarray):
# tile to n_steps/minibatch size
feed_val = np.tile(
output[None, :, None], (n_steps, 1, self.minibatch_size)
)
else:
# call output function to determine value
feed_val = np.zeros(
(n_steps, n.size_out, self.minibatch_size),
dtype=self.tensor_graph.dtype.as_numpy_dtype,
)
for i in range(n_steps):
# note: need to copy the output of func, as func
# may mutate its outputs in-place on subsequent calls.
# this assignment will broadcast the output along the
# minibatch dimension if required.
feed_val[i] = np.transpose(
[func((i + self.n_steps + 1) * self.dt) for func in output]
)
# note: we still call the function (above) even if the output
# is not being used, because it may have side-effects
if n in self.tensor_graph.input_ph:
feed_vals[self.tensor_graph.input_ph[n]] = feed_val
return feed_vals
def _check_data(self, data, n_batch=None, n_steps=None):
"""
Performs error checking on simulation data.
Parameters
----------
data : dict of {`~nengo.Node` or `~nengo.Probe`: `~numpy.ndarray`}
Array of data associated with given objects in model (Nodes or
Probes)
n_batch : int
Number of elements in batch (if None, will just verify that all
data items have same batch size)
n_steps : int
Number of simulation steps (if None, will just verify that all
data items have same number of steps)
"""
for d, x in data.items():
if x.ndim != 3:
raise ValidationError(
"should have rank 3 (batch_size, n_steps, dimensions), "
"found rank %d" % x.ndim,
"data",
)
if isinstance(d, Node):
if d not in self.tensor_graph.invariant_inputs:
raise ValidationError(
"%s is not an input Node (a nengo.Node with "
"size_in==0), or is from a different network." % d,
"data",
)
elif isinstance(d, Probe):
if d not in self.model.probes:
raise ValidationError("%s is from a different network" % d, "data")
else:
raise ValidationError(
"Data objects must be Nodes or Probes, not %s" % d, "data"
)
args = [n_batch, n_steps]
labels = ["batch size", "number of timesteps"]
for i in range(2):
if args[i] is None:
val = next(iter(data.values())).shape[i]
for n, x in data.items():
if x.shape[i] != val:
raise ValidationError(
"Elements have different %s: %s vs %s"
% (labels[i], val, x.shape[0]),
"data",
)
else:
for n, x in data.items():
if x.shape[i] != args[i]:
raise ValidationError(
"Data for %s has %s=%s, which does not match "
"expected size (%s)" % (n, labels[i], x.shape[i], args[i]),
"data",
)
for n, x in data.items():
if x.shape[0] < self.minibatch_size:
raise ValidationError(
"Size of minibatch (%d) for %s data less than Simulation "
"`minibatch_size` (%d)" % (x.shape[0], n, self.minibatch_size),
"data",
)
d = n.size_out if isinstance(n, Node) else n.size_in
if x.shape[2] != d:
raise ValidationError(
"Dimensionality of data (%s) does not match "
"dimensionality of %s (%s)" % (x.shape[2], n, d),
"data",
)
def _profile_output(self, profile, run_metadata):
"""
Outputs profile information to file.
Parameters
----------
profile : bool or str
If True or a string (filename), output profile information to file
run_metadata : ``tf.RunMetadata``
TensorFlow RunMetadata proto populated with profiling data
"""
if not profile:
return
trace = Timeline(step_stats=run_metadata.step_stats)
if isinstance(profile, str):
filename = profile
else:
filename = "nengo_dl_profile.json"
with open(filename, "w") as f:
f.write(trace.generate_chrome_trace_format())
@property
def dt(self):
"""The time (in seconds) represented by one simulation timestep."""
return self.model.dt
@dt.setter
def dt(self, _):
raise ReadonlyError(attr="dt", obj=self)
@property
def training_step(self):
"""The number of training iterations that have been executed."""
return self.tensor_graph.training_step
def __enter__(self):
self._graph_context = self.tensor_graph.graph.as_default()
self._device_context = self.tensor_graph.graph.device(self.tensor_graph.device)
self._graph_context.__enter__()
self._device_context.__enter__()
self.sess.__enter__()
return self
def __exit__(self, *args):
self.sess.__exit__(*args)
self._device_context.__exit__(*args)
self._graph_context.__exit__(*args)
self.close()
def __del__(self):
"""
Raise a RuntimeWarning if the Simulator is deallocated while open.
"""
if self.closed is not None and not self.closed:
warnings.warn(
"Simulator with model=%s was deallocated while open. "
"Simulators should be closed manually to ensure resources "
"are properly freed." % self.model,
RuntimeWarning,
)
self.close()
def __getstate__(self):
raise NotImplementedError(
"TensorFlow does not support pickling; see "
"https://www.nengo.ai/nengo-dl/training.html"
"#saving-and-loading-parameters "
"for information on how to save/load a NengoDL model."
)
[docs]class SimulationData(collections.Mapping):
"""
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.train`, the updated
encoder values for an ensemble can be accessed via
``trained_encoders = sim.data[my_ens].encoders``.
Parameters
----------
sim : `.Simulator`
The simulator from which data will be drawn
minibatched : bool
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).
"""
def __init__(self, sim, minibatched):
self.sim = sim
self.minibatched = minibatched
[docs] def __getitem__(self, obj):
"""Return the data associated with ``obj``.
Parameters
----------
obj : `~nengo.Probe` or `~nengo.Ensemble` or `~nengo.Connection`
Object whose simulation data is being accessed
Returns
-------
data : `~numpy.ndarray` or \
`~nengo.builder.ensemble.BuiltEnsemble` or \
`~nengo.builder.connection.BuiltConnection`
Array containing probed data if ``obj`` is a
`~nengo.Probe`, otherwise the corresponding
parameter object
"""
if obj not in self.sim.model.params:
raise ValidationError(
"Object is not in parameters of model %s" % self.sim.model, str(obj)
)
data = self.sim.model.params[obj]
if isinstance(obj, Probe):
if len(data) == 0:
return []
data = np.concatenate(data, axis=1)
if not self.minibatched:
data = data[0]
data.setflags(write=False)
elif isinstance(obj, Ensemble):
if isinstance(obj.neuron_type, Direct):
# direct mode ensemble
gain = bias = None
scaled_encoders = encoders = self.get_params((obj, "scaled_encoders"))[
0
]
else:
# get the live simulation values
scaled_encoders, bias = self.get_params(
(obj, "scaled_encoders"), (obj, "bias")
)
# infer the related values (rolled into scaled_encoders)
gain = (
obj.radius
* np.linalg.norm(scaled_encoders, axis=-1)
/ np.linalg.norm(data.encoders, axis=-1)
)
encoders = obj.radius * scaled_encoders / gain[:, None]
# figure out max_rates/intercepts from neuron model
max_rates, intercepts = obj.neuron_type.max_rates_intercepts(gain, bias)
data = BuiltEnsemble(
data.eval_points,
encoders,
intercepts,
max_rates,
scaled_encoders,
gain,
bias,
)
elif isinstance(obj, Connection):
# get the live simulation values
weights = self.get_params((obj, "weights"))[0]
# impossible to recover transform
transform = None
data = BuiltConnection(
data.eval_points, data.solver_info, weights, transform
)
return data
[docs] def get_params(self, *obj_attrs):
"""
Returns the current parameter values for the given objects.
Parameters
----------
obj_attrs : list 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
-------
params : list of `~numpy.ndarray`
Current values of the requested parameters
Notes
-----
Parameter values should be accessed through ``sim.data``
(which will call this function if necessary), rather than directly
through this function.
"""
if self.sim.closed:
warnings.warn(
"Checking parameters after simulator is closed; "
"cannot fetch live values, so the initial values "
"will be returned."
)
return [
getattr(self.sim.model.params[obj], attr) for obj, attr in obj_attrs
]
params = []
sigs = []
fetches = {}
for obj, attr in obj_attrs:
sig_obj, sig_attr = self._attr_map(obj, attr)
sig = self.sim.model.sig[sig_obj][sig_attr]
sigs.append(sig)
if sig not in self.sim.tensor_graph.signals:
# if sig isn't in sig_map then that means it isn't used
# anywhere in the simulation (and therefore never changes), so
# we can safely return the static build value
params.append(getattr(self.sim.model.params[obj], attr))
else:
# this is a live parameter value we need to fetch from the
# simulation. we queue them up and fetch them all at once to
# be more efficient
placeholder = object()
fetches[placeholder] = self.sim.tensor_graph.get_tensor(sig)
params.append(placeholder)
# get the live parameter values
fetched = self.sim.sess.run(fetches)
# final updating of parameters
for i, sig in enumerate(sigs):
# fill in placeholder values
if type(params[i]) == object:
params[i] = fetched[params[i]]
# handle minibatch dimension
if sig.minibatched:
if not self.minibatched:
params[i] = params[i][..., 0]
else:
params[i] = np.moveaxis(params[i], -1, 0)
return params
def _attr_map(self, obj, attr):
"""
Maps from ``sim.data[obj].attr`` to the equivalent
``model.sig[obj][attr]``.
Parameters
----------
obj : ``NengoObject``
The nengo object for which we want to know the parameters
attr : str
The parameter of ``obj`` to be returned
Returns
-------
obj : ``NengoObject``
The nengo object to key into ``model.sig``
attr : str
The name of the signal corresponding to input attr
"""
if isinstance(obj, Ensemble) and attr == "bias":
return obj.neurons, attr
elif isinstance(obj, Ensemble) and attr == "scaled_encoders":
return obj, "encoders"
return obj, attr
def __len__(self):
return len(self.sim.model.params)
def __iter__(self):
return iter(self.sim.model.params)
