Source code for nengo_dl.simulator

from __future__ import print_function, division

from collections import Mapping
import datetime
import logging
import os
import time
import warnings

from nengo.builder import Model
from nengo.exceptions import (ReadonlyError, SimulatorClosed, NengoWarning,
                              SimulationError)
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, DATA_DIR
from nengo_dl.tensor_graph import TensorGraph
from nengo_dl.utils import print_and_flush

logger = logging.getLogger(__name__)


[docs]class Simulator(object): """Simulate network using the ``nengo_dl`` backend. Parameters ---------- network : :class:`~nengo: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, optional length of a simulator timestep, in seconds seed : int, optional seed for all stochastic operators used in this simulator model : :class:`~nengo:nengo.builder.Model`, optional pre-built model object dtype : ``tf.DType``, optional floating point precision to use for simulation device : None or ``"/cpu:0"`` or ``"/gpu:[0-n]"``, optional device on which to execute computations (if None then uses the default device as determined by Tensorflow) unroll_simulation : int, optional 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, optional the number of simultaneous inputs that will be passed through the network tensorboard : bool, optional if True, save network output in the Tensorflow summary format, which can be loaded into Tensorboard """ # unsupported unit tests unsupported = [ ("nengo/tests/test_simulator.py:test_warn_on_opensim_del", "nengo_dl raises a different (more visible) warning (see " "tests/test_nengo_tests.py:test_warn_on_opensim_del"), ("nengo/tests/test_simulator.py:test_signal_init_values", "different method required to manually step simulator (see " "tests/test_nengo_tests.py:test_signal_init_values"), ("nengo/tests/test_simulator.py:test_entry_point", "overridden so we can pass custom test simulators (see " "tests/test_nengo_tests.py:test_entry_point"), ("nengo/tests/test_node.py:test_args", "time is passed as np.float32, not a float (see " "tests/test_nengo_tests.py:test_args"), ("nengo/tests/test_node.py:test_unconnected_node", "need to set `unroll_simulation` to ensure node runs the correct " "number of times (see " "tests/test_nengo_tests.py:test_unconnected_node"), ] def __init__(self, network, dt=0.001, seed=None, model=None, dtype=tf.float32, device=None, unroll_simulation=1, minibatch_size=None, tensorboard=False, step_blocks="deprecated"): self.closed = None self.sess = None self.tensorboard = tensorboard self.unroll = unroll_simulation self.minibatch_size = 1 if minibatch_size is None else minibatch_size if step_blocks != "deprecated" or isinstance(unroll_simulation, bool): # TODO: remove this in 0.5 warnings.warn( "`step_blocks` has been deprecated and will be ignored; " "`Simulator(..., unroll_simulation=n)` is now equivalent to " "`Simulator(..., unroll_simulation=True, step_blocks=n).", DeprecationWarning) # TODO: allow the simulator to be called flexibly with/without # minibatching # TODO: multi-GPU support # build model (uses default nengo builder) if model is None: self.model = Model(dt=float(dt), label="%s, dt=%f" % (network, dt)) 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: print_and_flush("Building network", end="") start = time.time() self.model.build(network, progress_bar=False) print("\rBuilding completed in %s " % datetime.timedelta(seconds=int(time.time() - start))) # set up tensorflow graph plan self.tensor_graph = TensorGraph( self.model, self.dt, unroll_simulation, dtype, self.minibatch_size, device) self.data = ProbeDict( self.model.params, {p: (minibatch_size if self.model.sig[p]["in"].minibatched else -1) for p in self.model.probes}) if seed is None: seed = np.random.randint(np.iinfo(np.int32).max) self.reset(seed=seed)
[docs] def reset(self, seed=None): """Resets the simulator to initial conditions. Parameters ---------- seed : int, optional if not None, overwrite the default simulator seed with this value (note: this becomes the new default simulator seed) """ if self.closed: raise SimulatorClosed("Cannot reset closed Simulator.") # close old session if self.sess is not None: self.close() if seed is not None: self.seed = seed self.rng = np.random.RandomState(self.seed) # TODO: why is setting the tensorflow seed necessary to make # gradient descent training deterministic? tf.set_random_seed(self.seed) # (re)build graph print_and_flush("Constructing graph", end="") start = time.time() self.tensor_graph.build(self.rng) print("\rConstruction completed in %s " % datetime.timedelta(seconds=int(time.time() - start))) # output graph description to tensorboard summary if self.tensorboard: directory = "%s/%s" % (DATA_DIR, self.model.toplevel.label) if os.path.isdir(directory): run_number = max( [int(x[4:]) for x in os.listdir(directory) if x.startswith("run")]) + 1 else: run_number = 0 self.summary = tf.summary.FileWriter( "%s/run_%d" % (directory, run_number), graph=self.tensor_graph.graph) # start session # note: we need to allow soft placement when using tf.while_loop, # because tensorflow pins loop variables to the CPU # TODO: switch allow_soft_placement to False once tensorflow # adds the RefExit GPU kernel config = tf.ConfigProto( allow_soft_placement=True, 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) self.sess = tf.Session(graph=self.tensor_graph.graph, config=config) self.closed = False # initialize variables self.soft_reset(include_trainable=True, include_probes=True) self.n_steps = 0 self.time = 0.0 self.final_bases = [ x[0] for x in self.tensor_graph.base_arrays_init.values()]
[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, optional if True, also reset any training that has been performed on network parameters (e.g., connection weights) include_probes : bool, optional 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) 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 :meth:`.run_steps` """ 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 amount of time to run the simulation for kwargs : dict see :meth:`.run_steps` """ steps = int(np.round(float(time_in_seconds) / self.dt)) self.run_steps(steps, **kwargs)
[docs] def run_steps(self, n_steps, input_feeds=None, profile=False): """Simulate for the given number of steps. Parameters ---------- n_steps : int the number of simulation steps to be executed input_feeds : dict of {:class:`~nengo:nengo.Node`: \ :class:`~numpy:numpy.ndarray`} override the values of input Nodes with the given data. arrays should have shape ``(sim.minibatch_size, n_steps, node.size_out)``. profile : bool, optional if True, collect TensorFlow profiling information while the simulation is running (this will slow down the simulation) 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``. """ if self.closed: raise SimulatorClosed("Simulator cannot run because it is closed.") 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: self._check_data(input_feeds, mode="out", check_mini=True, n_steps=n_steps) print_and_flush("Simulation started", end="") start = time.time() if profile: run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() else: run_options = None run_metadata = None # execute the simulation loop try: steps_run, probe_data = self.sess.run( [self.tensor_graph.steps_run, self.tensor_graph.probe_arrays], feed_dict=self._fill_feed(actual_steps, input_feeds, start=self.n_steps), options=run_options, run_metadata=run_metadata) except (tf.errors.InternalError, tf.errors.UnknownError) as e: if e.op.type == "PyFunc": raise SimulationError( "Function '%s' caused an error (see error log above)" % e.op.name) else: raise e # pragma: no cover # update probe data self._update_probe_data(probe_data, self.n_steps, n_steps) # 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) assert steps_run == actual_steps self.n_steps += n_steps self.time = self.n_steps * self.dt if profile: timeline = Timeline(run_metadata.step_stats) with open("%s/nengo_dl_profile.json" % DATA_DIR, "w") as f: f.write(timeline.generate_chrome_trace_format()) print("\rSimulation completed in %s" % datetime.timedelta(seconds=int(time.time() - start)))
[docs] def train(self, inputs, targets, optimizer, n_epochs=1, objective="mse", shuffle=True): """Optimize the trainable parameters of the network using the given optimization method, minimizing the objective value over the given inputs and targets. Parameters ---------- inputs : dict of {:class:`~nengo:nengo.Node`: \ :class:`~numpy:numpy.ndarray`} input values for Nodes in the network; arrays should have shape ``(batch_size, n_steps, node.size_out)`` targets : dict of {:class:`~nengo:nengo.Probe`: \ :class:`~numpy:numpy.ndarray`} desired output value at Probes, corresponding to each value in ``inputs``; arrays should have shape ``(batch_size, n_steps, probe.size_in)`` optimizer : ``tf.train.Optimizer`` Tensorflow optimizer, e.g. ``tf.train.GradientDescentOptimizer(learning_rate=0.1)`` n_epochs : int, optional run training for the given number of epochs (complete passes through ``inputs``) objective : ``"mse"`` or callable, optional the objective to be minimized. passing ``"mse"`` will train with mean squared error. a custom function ``f(output, target) -> loss`` can be passed that consumes the actual output and target output for a probe in ``targets`` and returns a ``tf.Tensor`` representing the scalar loss value for that Probe (loss will be averaged across Probes). shuffle : bool, optional if True, randomize the data into different minibatches each epoch Notes ----- - 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 :class:`~nengo:nengo.LIF`, :class:`~nengo:nengo.Direct`, or processes/neurons that don't have a custom TensorFlow implementation (see :class:`.processes.SimProcessBuilder`/ :class:`.neurons.SimNeuronsBuilder`) - Most TensorFlow optimizers do not have GPU support for networks with sparse reads, which are a common element in Nengo models. If your network contains sparse reads then training will have to be executed on the CPU (by creating the simulator via ``nengo_dl.Simulator(..., device="/cpu:0")``), or is limited to optimizers with GPU support (currently this is only ``tf.train.GradientDescentOptimizer``). Follow `this issue <https://github.com/tensorflow/tensorflow/issues/2314>`_ for updates on Tensorflow GPU support. """ n_steps = next(iter(inputs.values())).shape[1] # error checking if self.closed: raise SimulatorClosed("Simulator cannot be trained because it is " "closed.") self._check_data(inputs, mode="out", n_steps=n_steps) self._check_data(targets, mode="in", n_steps=n_steps) # check for non-differentiable elements in graph # utils.find_non_differentiable( # [self.tensor_graph.invariant_ph[n] for n in inputs], # [self.tensor_graph.probe_arrays[self.model.probes.index(p)] # for p in targets]) # build optimizer op opt_op, opt_slots_init = self.tensor_graph.build_optimizer( optimizer, tuple(targets.keys()), objective) # initialize any variables that were created by the optimizer self.sess.run(opt_slots_init) progress = utils.ProgressBar(n_epochs, "Training") for n in range(n_epochs): for inp, tar in utils.minibatch_generator( inputs, targets, self.minibatch_size, rng=self.rng, shuffle=shuffle): # TODO: set up queue to feed in data more efficiently self.soft_reset() try: self.sess.run([opt_op], feed_dict=self._fill_feed( n_steps, inp, tar)) except tf.errors.InvalidArgumentError: raise SimulationError( "TensorFlow does not yet support this optimizer on " "the GPU; try `Simulator(..., device='/cpu:0')`") progress.step() self.soft_reset()
[docs] def loss(self, inputs, targets, objective): """Compute the loss value for the given objective and inputs/targets. Parameters ---------- inputs : dict of {:class:`~nengo:nengo.Node`: \ :class:`~numpy:numpy.ndarray`} input values for Nodes in the network; arrays should have shape ``(batch_size, n_steps, node.size_out)`` targets : dict of {:class:`~nengo:nengo.Probe`: \ :class:`~numpy:numpy.ndarray`} desired output value at Probes, corresponding to each value in ``inputs``; arrays should have shape ``(batch_size, n_steps, probe.size_in)`` objective : ``"mse"`` or callable the objective used to compute loss. passing ``"mse"`` will use mean squared error. a custom function ``f(output, target) -> loss`` can be passed that consumes the actual output and target output for a probe in ``targets`` and returns a ``tf.Tensor`` representing the scalar loss value for that Probe (loss will be averaged across Probes) Notes ----- Calling this function will reset all values in the network, so it should not be intermixed with calls to :meth:`.Simulator.run`. """ n_steps = next(iter(inputs.values())).shape[1] # error checking if self.closed: raise SimulatorClosed("Loss cannot be computed after simulator is " "closed.") self._check_data(inputs, mode="out", n_steps=n_steps) self._check_data(targets, mode="in", n_steps=n_steps) # get loss op loss = self.tensor_graph.build_loss(objective, tuple(targets.keys())) # compute loss on data loss_val = 0 for i, (inp, tar) in enumerate(utils.minibatch_generator( inputs, targets, self.minibatch_size, rng=self.rng)): self.soft_reset() loss_val += self.sess.run( loss, feed_dict=self._fill_feed(n_steps, inp, tar)) self.soft_reset() loss_val /= i + 1 return loss_val
def _fill_feed(self, n_steps, inputs, targets=None, start=0): """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 input_feeds : dict of {:class:`~nengo:nengo.Node`: \ :class:`~numpy:numpy.ndarray`} override the values of input Nodes with the given data. arrays should have shape ``(sim.minibatch_size, n_steps, node.size_out)``. targets : dict of {:class:`~nengo:nengo.Probe`: \ :class:`~numpy:numpy.ndarray`}, optional values for target placeholders (only necessary if loss is being computed, e.g. when training the network) start : int, optional initial value of simulator timestep Returns ------- dict of {``tf.Tensor``: :class:`~numpy:numpy.ndarray`} feed values for placeholder tensors in the network """ # fill in loop variables feed_dict = { self.tensor_graph.step_var: start, self.tensor_graph.stop_var: start + n_steps } # fill in values for base variables from previous run # TODO: remove this if we're sure we're not going back to the tensor # approach feed_dict.update( {k: v for k, v in zip( self.tensor_graph.base_vars, self.final_bases) if k.op.type == "Placeholder"}) # fill in input values tmp = self._generate_inputs(inputs, n_steps) feed_dict.update(tmp) # fill in target values if targets is not None: feed_dict.update( {self.tensor_graph.target_phs[p]: np.moveaxis(t, 0, -1) for p, t in targets.items()}) return feed_dict def _generate_inputs(self, input_feeds, n_steps): """Generate inputs for the network (the output values of each Node with no incoming connections). Parameters ---------- input_feeds : dict of {:class:`~nengo:nengo.Node`: \ :class:`~numpy: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 """ if input_feeds is None: input_feeds = {} feed_vals = {} for n in self.tensor_graph.invariant_inputs: # if the output signal is not in sig map, that means no operators # use the output of this node. similarly, if node.size_out is 0, # the node isn't producing any output values. using_output = ( self.model.sig[n]["out"] in self.tensor_graph.sig_map and n.size_out > 0) if using_output: if n in input_feeds: # move minibatch dimension to the end feed_val = np.moveaxis(input_feeds[n], 0, -1) elif isinstance(n.output, np.ndarray): feed_val = np.tile(n.output[None, :, None], (n_steps, 1, self.minibatch_size)) else: func = self.tensor_graph.invariant_funcs[n] feed_val = [] for i in range(self.n_steps + 1, self.n_steps + n_steps + 1): # note: need to copy the output of func, as func # may mutate its outputs in-place on subsequent calls feed_val += [np.array(func(i * self.dt))] feed_val = np.stack(feed_val, axis=0) feed_val = np.tile(feed_val[..., None], (1, 1, self.minibatch_size)) feed_vals[self.tensor_graph.invariant_ph[n]] = feed_val elif (not isinstance(n.output, np.ndarray) and n.output in self.tensor_graph.invariant_funcs.values()): # note: we still call the function even if the output # is not being used, because it may have side-effects func = self.tensor_graph.invariant_funcs[n] for i in range(self.n_steps + 1, self.n_steps + n_steps + 1): func(i * self.dt) return feed_vals def _update_probe_data(self, probe_data, start, n_steps): """Updates the stored probe data (since the last reset) with the data from the latest run. Downsamples the probe data returned from tensorflow (from every simulation timestep) according to probe `sample_every` and the number of steps run. Parameters ---------- probe_data : list of `np.ndarray` probe data from every timestep start : int the simulation timestep at which probe data starts n_steps : int the number of timesteps over which we want to collect data """ # remove any extra timesteps (due to `unroll_simulation` mismatch) probe_data = [p[:n_steps] for p in probe_data] for i, p in enumerate(self.model.probes): if p.sample_every is not None: # downsample probe according to `sample_every` period = p.sample_every / self.dt steps = np.arange(start, start + n_steps) probe_data[i] = probe_data[i][(steps + 1) % period < 1] # update stored probe data self.model.params[p] += [probe_data[i]]
[docs] def save_params(self, path): """Save trainable network parameters to the given ``path``. Parameters ---------- path : str filepath of parameter output file """ if self.closed: raise SimulationError("Simulation has been closed, cannot save " "parameters") with self.tensor_graph.graph.as_default(): path = tf.train.Saver().save(self.sess, path) logger.info("Model parameters saved to %s", path)
[docs] def load_params(self, path): """Load trainable network parameters from the given ``path``. Parameters ---------- path : str filepath of parameter input file """ if self.closed: raise SimulationError("Simulation has been closed, cannot load " "parameters") with self.tensor_graph.graph.as_default(): tf.train.Saver().restore(self.sess, path)
[docs] def print_params(self, msg=None): """Print current values of trainable network parameters. Parameters ---------- msg : str, optional title for print output, useful to differentiate multiple print calls """ if self.closed: raise SimulationError("Simulation has been closed, cannot print " "parameters") param_sigs = {k: v for k, v in self.tensor_graph.sig_map.items() if k.trainable} keys = list(self.tensor_graph.signals.bases.keys()) params = {v.key: self.tensor_graph.base_vars[keys.index(v.key)] for v in param_sigs.values()} param_vals = self.sess.run(params) print("%s:" % "Parameters" if msg is None else msg) for sig, tens in param_sigs.items(): print("-" * 10) print(sig) print(param_vals[tens.key][tens.indices])
[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: self.sess.close() self.closed = True self.sess = None # note: we use getattr in case it crashes before the summary # object is created if getattr(self, "summary", None) is not None: self.summary.close()
def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() @property def dt(self): """(float) The time step of the simulator.""" return self.model.dt @dt.setter def dt(self, dummy): raise ReadonlyError(attr='dt', obj=self) 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()
[docs] def trange(self, 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 ---------- dt : float, optional the sampling period of the probe to create a range for; if None, the simulator's ``dt`` will be used. """ dt = self.dt if dt is None else dt n_steps = int(self.n_steps * (self.dt / dt)) return dt * np.arange(1, n_steps + 1)
[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`` compute gradients wrt this output (if None, computes wrt each output probe) atol : float, optional absolute error tolerance rtol : float, optional 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 :meth:`.Simulator.run`. """ delta = 1e-3 n_steps = self.unroll * 2 feed = self._fill_feed( n_steps, {n: np.zeros((self.minibatch_size, n_steps, n.size_out)) for n in self.tensor_graph.invariant_inputs}, {p: np.zeros((self.minibatch_size, n_steps, p.size_in)) for p in self.tensor_graph.target_phs}) 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] elif isinstance(outputs, tf.Tensor): outputs = [outputs] # check gradient wrt inp for node, inp in self.tensor_graph.invariant_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.invariant_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() dx, dy = gradient_checker._compute_dx_and_dy( inp, out, out_shape) 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")
def _check_data(self, data, mode="out", check_mini=False, n_steps=None): """Performs error checking on simulation data. Parameters ---------- data : dict of {``nengo object``: :class:`~numpy:numpy.ndarray`} array of data associated with given object in model mode : "in" or "out", optional whether this data corresponds to the input or output of the nengo object check_mini : bool, optional if True, also validate minibatch_size n_steps : int, optional number of simulation steps (if None, will use sim.unroll) """ for n, x in data.items(): shape = (self.minibatch_size if check_mini else None, self.unroll if n_steps is None else n_steps, n.size_out if mode == "out" else n.size_in) if any(x is not None and x != y for x, y in zip(shape, x.shape)): raise SimulationError( "Shape of data array %s does not match expected shape " "(%s, %s, %s.size_%s) -> %s" % (x.shape, "sim.minibatch_size" if check_mini else "_", "sim.unroll" if n_steps is None else "n_steps", n, mode, shape))
class ProbeDict(Mapping): """Map from :class:`~nengo:nengo.Probe` -> :class:`~numpy:numpy.ndarray`, used to access output of the model after simulation. This is more like a view on the dict that the simulator manipulates. However, for speed reasons, the simulator uses Python lists, and we want to return NumPy arrays. Additionally, this mapping is readonly, which is more appropriate for its purpose. Parameters ---------- raw : dict of {:class:`~nengo:nengo.Probe`: \ list of :class:`~numpy:numpy.ndarray`} the raw probe output from the simulator (a list of arrays containing the output from each ``run_steps`` execution segment) minibatches : dict of {:class:`~nengo:nengo.Probe`: int or None} the minibatch size for each probe in the dictionary (or -1 if the probed signal does not have a minibatch dimension) Notes ----- ProbeDict should never be created/accessed directly by the user, but rather via ``sim.data`` (which is an instance of ProbeDict). """ def __init__(self, raw, minibatches): self.raw = raw self.minibatches = minibatches # TODO: add cache back in? def __getitem__(self, key): rval = self.raw[key] if isinstance(rval, list): # combine data from run_steps iterations rval = np.concatenate(rval, axis=0) if self.minibatches[key] != -1: if self.minibatches[key] is None: # get rid of batch dimension rval = rval[..., 0] else: # move batch dimension to front rval = np.moveaxis(rval, -1, 0) rval.setflags(write=False) return rval def __iter__(self): return iter(self.raw) def __len__(self): return len(self.raw)