Release 3.0.0

Compatible with Nengo 3.0.0

Compatible with TensorFlow 2.0.0

There are a lot of breaking changes in NengoDL 3.0. See the migration guide for all the details.

Added

• Keras Layer classes can now be used with nengo_dl.Layer/tensor_layer.
• TensorGraph can now be used as a Keras Layer.
• Added Simulator.predict/evaluate/fit functions, which implement the Keras Model API.
• Added a warning that changing the TensorFlow seed (e.g. on Simulator.reset) will not affect any existing TensorFlow operations (this was always true in TensorFlow, the warning is just to help avoid confusion).
• Added TensorGraph.build_inputs, which will return a set of Keras Input layers that can be used as input to the TensorGraph layer itself.
• Added nengo_dl.callbacks.TensorBoard. This is identical to tf.keras.callbacks.TensorBoard, except it will also perform profiling during inference (rather than only during training).
• Added stateful option to Simulator.run which can be set to False to avoid updating the saved simulation state at the end of a run.
• Added nengo_dl.configure_settings(stateful=False) option to avoid building the parts of the model responsible for preserving state between executions (this will override any stateful=True arguments in individual functions).
• Added nengo_dl.configure_settings(use_loop=False) option to avoid building the simulation inside a symbolic TensorFlow loop. This may improve simulation speed, but the simulation can only run for exactly unroll_simulation timesteps.
• NengoDL now requires jinja2 (used to template some of the docstrings).
• Added an inputs argument to Simulator.check_gradients, which can be used to control the initial value of input Nodes during the gradient calculations.
• Added nengo_dl.Converter for automatically converting Keras models to native Nengo networks. See the documentation for more details.
• Added Legendre Memory Unit RNN example.

Changed

• Minimum TensorFlow version is now 2.0.0.
• Simulator.save/load_params now uses a single include_non_trainable=True/False (equivalent to the previous include_local). Trainable parameters will always be saved, so the include_global argument is removed.
• Standardized all signals/operations in a simulation to be batch-first.
• The dtype option is now specified as a string (e.g. "float32" rather than tf.float32).
• If the requested number of simulation steps is not evenly divisible by Simulator.unroll_simulation then probe values and sim.time/n_steps will be updated based on the number of steps actually run (rather than the requested number of steps). Note that these extra steps were also run previously, but their results were hidden from the user.
• Renamed TensorGraph.input_ph to TensorGraph.node_inputs.
• Simulator.time/n_steps are now read-only.
• Simulator.n_steps/time are now managed as part of the op graph, rather than manually in the Simulator.
• Renamed nengo_dl.objectives to nengo_dl.losses (to align with tf.losses).
• nengo_dl.objectives.Regularize now takes two arguments (y_true and y_pred) in order to be compatible with the tf.losses.Loss API (y_true is ignored).
• The remove_constant_copies simplification step is now disabled by default. In certain situations this could be an unsafe manipulation (specifically, when using Simulator.save/load_params it could change which parameters are saved). It can be manu
  ally re-enabled through the simplifications configuration option.
• Simulator.check_gradients now only accepts an optional list of Probes (no longer accepts arbitrary Tensors).
• Eager execution is no longer disabled on import (it is still disabled within the Simulator context, for performance reasons; see tensorflow/tensorflow#33052).
• nengo_dl.tensor_layer(x, func, ...) now passes any extra kwargs to the nengo_dl.TensorNode constructor (rather than to func). If you need to pass information to func consider using partial functions (e.g. tensor_layer(functools.partial(x, func, arg=5), ...) or a callable class (e.g., tensor_layer(x, MyFunc(arg=5), ...)). When using Keras Laye
  rs with nengo_dl.tensor_layer, a fully instantiated Layer object should be passed rather than a Layer class (e.g., use tensor_layer(x, tf.keras.layers.Dense(units=10), ...) instead of tensor_layer(x, tf.keras.layers.Dense, units=10)).
• benchmarks.run_profile now uses the TensorBoard format when profiling, see the documentation for instructions on how to view this information (the information is the same, it is just accessed through TensorBoard rather than requiring that it be loaded directly in a Chrome brow
  ser).
• nengo_dl.TensorNode now takes shape_in and shape_out arguments (which specify a possibly multidimensional shape), rather than the scalar size_in and size_out.
• TensorNode functions no longer use the pre_build/post_build functionality. If you need to implement more complex behaviour in a TensorNode, use a custom Keras Layer subclass instead. For example, TensorNodes Layers can create new parameter Variables inside the Layer build method.
• TensorNode now has an optional pass_time parameter which can be set to False to disable passing the current simulation time to the TensorNode function.
• Added nengo_dl.Layer. Similar to the old nengo_dl.tensor_layer, this is a wrapper for constructing TensorNodes, but it mimics the new tf.keras.layers.Layer API rather than the old tf.layers.
• TensorFlow's "control flow v2" is disabled on import, for performance reasons; see tensorflow/tensorflow#33052.
• Renamed nengo_dl.objectives.mse to nengo_dl.losses.nan_mse (to emphasize the special logic it provides for nan targets).
• Connections created by nengo_dl.Layer/tensor_layer will be marked as non-trainable by default.
• Updated all documentation and examples for the new syntax (in particular, see the updated Coming from TensorFlow tutorial and TensorFlow/Keras integration example, and the new Tips and tricks page).
• The training/inference build logic (e.g., swapping spiking neurons with rate implementations) can be overridden by setting the global Keras learning phase (tf.keras.backend.set_learning_phase) before the Simulator is constructed.
• Increased minimum Nengo core version to 3.0.0.
• Reduced size of TensorFlow constants created by Reset ops.
• DotInc operators with different signal sizes will no longer be merged (these merged operators had to use a less efficient sparse matrix multiplication, and in general this cost outweighed the benefit of merging).
• Trainability can now be configured in the config of subnetworks. This replaces the ability to mark Networks as (non)trainable. See the updated documentation for details.
• Training/evaluation target data can now have a different number of timesteps than input data (as long as it aligns with the number of timesteps expected by the loss function).
• Whether or not to display progress bars in Simulator.run and Simulator.run_steps now defaults to the value of Simulator(..., progress_bar=x).

Fixed

• Fixed bug due to non-determinism of Process state ordering in Python 3.5.
• Nested Keras layers passed to TensorNode will be rebuilt correctly if necessary.

Deprecated

• nengo_dl.tensor_layer has been deprecated. Use nengo_dl.Layer instead; tensor_layer(x, func, **kwargs) is equivalent to Layer(func)(x, **kwargs).

Removed

• Removed the session_config configuration option. Use the updated TensorFlow config system instead.
• Removed the deprecated nengo_dl.Simulator(..., dtype=...) argument. Use nengo_dl.configure_settings(dtype=...) instead.
• Removed the deprecated Simulator.run(..., input_feeds=...) argument. Use Simulator.run(..., data=...) instead.
• Removed the Simulator.sess attribute (Sessions are no longer used in TensorFlow 2.0). The underlying Keras model (Simulator.keras_model) should be used as the entrypoint into the engine underlying a Simulator instead.
• Removed the Simulator.loss function (use Simulator.compile and Simulator.evaluate to compute loss values instead).
• Removed the Simulator.train function (use Simulator.compile and Simulator.fit to optimize a network instead).
• Removed the nengo_dl.objectives.Regularize(weight=x, ...) argument. Use the Simulator.compile(loss_weights=...) functionality instead.
• Removed the Simulator.run(..., extra_feeds=...) argument. TensorFlow 2.0 no longer uses the Session/feed execution model.
• Removed Simulator.run_batch. This functionality is now managed by the underlying Simulator.keras_model.
• Removed TensorGraph.training_step. The training step is now managed by Keras.
• Removed TensorGraph.build_outputs and TensorGraph.build_optimizer_func. Building loss functions/optimizers is now managed by Keras.
• Removed `neng...

Release 2.2.2

Compatible with Nengo 2.8.0 - 3.0.0

Compatible with TensorFlow 1.4.0 - 2.0.0

Fixed

• Compatibility with Nengo 3.0 release

Release 2.2.1

Compatible with Nengo 2.8.0

Compatible with TensorFlow 1.4.0 - 2.0.0

Changed

• Update testing framework to use new nengo pytest ecosystem (pytest-rng, pytest-allclose, and pytest-nengo)
• Disable TensorFlow 2.0 behaviour (e.g. control flow v2) by default. This will be re-enabled when full TensorFlow 2.0 support is added.

Fixed

• Fixed tensorflow-gpu installation check in pep517-style isolated build environments.

Release 2.2.0

Compatible with Nengo 2.8.0

Compatible with TensorFlow 1.4.0 - 2.0.0

Added

• Added a new example demonstrating how to integrate a Keras model with NengoDL (thanks to new contributor @NickleDave).
• Added support for TensorFlow 2.0 (pre-release).
• Added support for sparse transforms (see nengo/nengo#1532).
• Added support for stateful Processes (see nengo/nengo#1387).

Changed

• The default session will now be set to the NengoDL session before calling TensorNodes' post_build function.
• Renamed the pytest unroll_simulation argument to unroll-simulation.
• Switched to nengo-bones templating system for TravisCI config/scripts.
• NengoDL will disable eager execution on import (and will probably not work properly if it is manually re-enabled).
• Increased minimum numpy version to 1.14.5 (required by TensorFlow 1.14).
• Minimum Nengo version is now 2.8.0.
• Update LinearFilter synapse implementation to match recent changes in Nengo core (see nengo/nengo#1535).

Fixed

• Fixed TensorFlow seeding so that randomness can be reliably controlled by setting the Simulator seed.
• Improved robustness of tensorflow-gpu installation check (in particular, it will now correctly detect GPU dists installed through conda).
• Fixed inspection of TensorNode.tensor_func arguments for partial functions.
• Simulator seed will now be deterministic for a given top-level Network seed.
• Raise a more informative error if user attempts to pickle a Simulator (this is not possible to do with TensorFlow sessions; see the documentation for other methods of saving/loading a NengoDL model).

Removed

• NengoDL no longer supports Python 3.4 (official support for 3.4 ended in March 2019).

Release 2.1.1

Added

• Added nengo_dl.obj as a shortcut alias for nengo_dl.objectives.
• Added tutorial for Nengo users coming to NengoDL
• Added tutorial for TensorFlow users coming to NengoDL

Changed

• Increased minimum progressbar2 version to 3.39.0.
• We now only provide sdist releases, not bdist_wheel. Due to the way the TensorFlow packages are organized, bdist_wheel forces any existing TensorFlow installations (e.g. tensorflow-gpu or tf-nightly) to be overwritten by tensorflow, which we don't want to do.

Removed

• Removed the nef-init tutorial (replaced by the new from-nengo tutorial).

Release 2.1.0

Added

• Added a built-in objective to assist in applying regularization during training.
• Added keep_history config option, which can be set to False on Probes if only the data from the most recent simulation step is desired (as opposed to the default behaviour of keeping the data from all steps).

Changed

• Moved utils.mse to objectives.mse.
• sim.loss will now apply nengo_dl.objectives.mse to all probes in data if no explicit objective is given (mirroring the default behaviour in sim.train).
• The Spaun benchmark network will now be installed through pip rather than manually cloning and importing the repo.

Fixed

• Fixed objective argument parsing if objective is a callable class or method.
• Fixed bug in sim.train 1-step synapse warning when explicitly specifying n_steps (rather than passing in data).

Deprecated

• Passing "mse" as the objective in sim.train/sim.loss is no longer supported. Use the function nengo_dl.objectives.mse instead.

Release 2.0.0

Breaking API changes

• sim.train and sim.loss now accept a single data argument, which combines the previous inputs and targets arguments. For example,

  sim.train({my_node: x}, {my_probe: y}, ...)
  

  is now equivalent to

  sim.train({my_node: x, my_probe: y}, ...)
  

  The motivation for this change is that not all objective functions require target values. Switching to the more generic data argument simplifies the API and makes it more flexible, allowing users to specify whatever training/loss data is actually required.

• The objective argument in sim.train/sim.loss is now always specified as a dictionary mapping probes to objective functions. Note that this was available but optional previously; it was also possible to pass a single value for the objective function, which would be applied to all probes in targets. The latter is no longer supported. For example,

  sim.train(..., objective="mse")
  

  must now be explicitly specified as

  sim.train(..., objective={my_probe: "mse"})
  

  The motivation for this change is that, especially with the other new features introduced in the 2.0 update, there were a lot of different ways to specify the objective argument. This made it somewhat unclear how exactly this argument worked, and the automatic "broadcasting" was also ambiguous (e.g., should the single objective be applied to each probe individually, or to all of them together?). Making the argument explicit helps clarify the mental model.

Added

• An integer number of steps can now be passed for the sim.loss/sim.train data argument, if no input/target data is required.
• The objective dict in sim.train/sim.loss can now contain tuples of probes as the keys, in which case the objective function will be called with a corresponding tuple of probe/target values as each argument.
• Added the sim.run_batch function. This exposes all the functionality that the sim.run/sim.train/sim.loss functions are based on, allowing advanced users full control over how to run a NengoDL simulation.
• Added option to disable progress bar in sim.train and sim.loss.
• Added training argument to sim.loss to control whether the loss is evaluated in training or inference mode.
• Added support for the new Nengo Transform API (see nengo/nengo#1481).

Changed

• Custom objective functions passed to sim.train/sim.loss can now accept a single argument (my_objective(outputs): ... instead of my_objective(outputs, targets): ...) if no target values are required.
• utils.minibatch_generator now accepts a single data argument rather than inputs and targets (see discussion in "Breaking API changes").
• sim.training_step is now the same as tf.train.get_or_create_global_step().
• Switched documentation to new nengo-sphinx-theme.
• Reorganized documentation into "User guide" and "API reference" sections.
• Improve build speed of models with large constants (#69)
• Moved op-specific merge logic into the OpBuilder classes.

Fixed

• Ensure that training step is always updated before TensorBoard events are added (previously it could update before or after depending on the platform).

Deprecated

• The sim.run input_feeds argument has been renamed to data (for consistency with other simulator functions).

Removed

• NengoDL no longer supports Python 2 (see https://python3statement.org/ for more information)

Release 1.2.1

Added

• Added a warning if users run one-timestep training with a network containing synaptic filters.

Changed

• Test Simulator parameters are now controlled through pytest arguments, rather than environment variables.
• Disable INFO-level TensorFlow logging (from C side) on import. Added a NengoDL log message indicating the device the simulation will run on, as a more concise replacement.
• Boolean signals are now supported (#61)

Fixed

• Avoid backpropagating NaN gradients from spiking neurons.
• Fixed an error that was thrown when calling get_tensor on a Signal that was first initialized inside the Simulation while loop (#56)
• Allow TensorNodes to run in Nengo GUI.
• Avoid bug in TensorFlow 1.11.0 that prevents certain models from running (see tensorflow/tensorflow#23383). Note that this doesn't prevent this from occurring in user models, as we cannot control the model structure there. If your model hangs indefinitely when you call sim.train, try downgrading to TensorFlow 1.10.0.
• Ensure that sim.training_step is always updated after the optimization step (in certain race conditions it would sometimes update part-way through the optimization step).

Release 1.2.0

Added

• NengoDL will now automatically use a rate-based approximation to compute the gradient for spiking neuron types, if one is known (no more need to manually swap neuron types for training and inference).
• Added nengo_dl.configure_settings(inference_only=True) option, which will build the network in inference-only mode. This will slightly improve the inference speed of the simulation, but the network will not be trainable.
• Added nengo_dl.configure_settings(lif_smoothing=x) option, which will control how much smoothing is applied to the LIF function during gradient calculations (if any).
• Added documentation on the various NengoDL config options.
• Added better validation for TensorNode output when size_out != None (#51)

Changed

• More informative error message if the user tries to pass target values for a probe that isn't used in the objective function.
• Switched to ADD_N gradient accumulation (from TREE); this will increase the memory usage during training, but improve performance.
• Revert to Timeline profiling method. tf.profiler can produce incorrect output, and isn't maintained any more (tensorflow/tensorflow#15214 (comment))
• Reduce memory usage during training by caching temporary variables used when computing ScatterUpdate gradient.
• Increase minimum TensorFlow version to 1.4.0.
• Increased minimum NumPy version to 1.12.1 (required by TensorFlow)
• Sort write signals as well as reads during graph optimization (encourages tighter partitioning, which can improve training/inference speed).
• Moved configure_settings from utils.py to config.py.

Fixed

• Fixed a bug where nengo_dl.dists.VarianceScaling(..., distribution="normal") did not respect the seed if one was given.

Deprecated

• The Simulator(dtype=...) argument has been deprecated; use nengo_dl.configure_settings(dtype=...) instead. Will be removed in 1.3.0.

Release 1.1.0

Added

• The default TensorFlow Session is now set to the underlying Simulator session within the Simulator context.
• Added CLI for benchmarks.py
• Added sim.freeze_params tool, to more easily extract model parameters for reuse in different Simulators.
• Added documentation on saving and loading model parameters.
• Added Spaun example in benchmarks.py

Changed

• Move tensorflow-gpu installation check to Simulator init, and only apply if device=None.
• Switched to pylint for style checks.
• TensorFlow INFO-level log messages are now disabled by default on import
• All previous releases now tracked in documentation
• Updated spiking MNIST example to simplify and improve performance.
• Passing unknown configuration options to nengo_dl.configure_settings will now give a more explicit error message.
• Improved speed of parameter fetching though get_nengo_params
• Raise a warning if user tries to train a network with non-differentiable elements (requires tensorflow>=1.9.0)
• Improved accuracy of SoftLIFRate implementation for small values (#45)
• Simplified how TensorSignals are loaded into the TensorFlow graph

Fixed

• Better handling of Simulator errors not associated with a specific op (fixes #41)
• Fixed node outputs changing after simulator is built (fixes #4)
• Fixed some broken cross references in the documentation
• Fixed several edge cases for get_nengo_params; don't use trained gains for direct neuron connections, error raised if get_nengo_params applied to an Ensemble with Direct neurons
• Compatible with tensorflow==1.9.0 release
• Fixed bug in nengo_dl.configure_settings(session_config=...) when passing a pre-build model to the Simulator instead of a Network
• Fixed TensorFlow version comparisons for 1.10.0

Deprecated

• Simulator.trange argument dt has been deprecated (replaced with sample_every, see nengo/nengo#1384)

Removed

• Removed nengo_dl.DATA_DIR constant
• Removed benchmarks.compare_backends (use whitepaper2018_plots.py:compare_backends instead)
• Removed ghp-import dependency