Project information

Release History

1.0.0 (May 30, 2018)

Added

  • User can now directly specify the output error gradient, rather than using targets/objective (useful for when you have some external process for computing error that is not easy to implement as an objective function). See the documentation for details.
  • Added NengoDL white paper

Changed

  • Extra requirements for documentation/testing are now stored in setup.py’s extra_requires instead of requirements-*.txt. For example, instead of doing pip install -r requirements-test.txt, instead use pip install nengo-dl[tests] (or pip install -e .[tests] for a developer installation).
  • Improved efficiency of PES implementation

Removed

  • Removed sphinxcontrib-versioning dependency for building documentation

0.6.2 (May 4, 2018)

Added

  • Added sim.get_nengo_params function to more easily extract model parameters for reuse when building different models.
  • Added Simulator(..., progress_bar=False) option to disable the progress information printed to console when the network is building.
  • TensorFlow session config options can now be set using nengo_dl.configure_settings (e.g., nengo_dl.configure_settings(session_config={"gpu_options.allow_growth": True}))
  • The signal sorting/graph simplificaton functions can now be configured through nengo_dl.configure_settings
  • Added extra_feeds parameter to sim.run/train/loss, which can be used to feed Tensor values directly into the TensorFlow session

Changed

  • Improved speed of PES implementation by adding a custom operator.
  • Renamed project from nengo_dl to nengo-dl (to be more consistent with standard conventions). This only affects the display name of the project on PyPI/GitHub, and the documentation now resides at https://www.nengo.ai/nengo-dl/; there are no functional changes to user code.
  • Minor efficiency improvements to graph planner
  • Avoid using tf.constant, to get around TensorFlow’s 2GB limit on graph size when building large models

Fixed

  • Checking nengo_dl version without nengo installed will no longer result in an error.
  • Updated progress bar to work with progressbar2>=3.37.0
  • Updated PES implementation to work with generic synapse types (see https://github.com/nengo/nengo/pull/1095)
  • Fixed installation to work with pip>=10.0
  • Fixed bug when using a TensorNode with a pre_build function and size_in==0

0.6.1 (March 7, 2018)

Added

  • Added TensorFlow implementation for nengo.SpikingRectifiedLinear neuron type.

Changed

  • Optimizer variables (e.g., momentum values) will only be initialized the first time that optimizer is passed to sim.train. Subsequent calls to sim.train will resume with the values from the previous call.
  • Low-level simulation input/output formats have been reworked to make them slightly easier to use (for users who want to bypass sim.run or sim.train and access the TensorFlow session directly).
  • Batch dimension will always be first (if present) when checking model parameters via sim.data.
  • TensorFlow ops created within the Simulator context will now default to the same device as the Simulator.
  • Update minimum Nengo version to 2.7.0

Fixed

  • Better error message if training data has incorrect rank
  • Avoid reinstalling TensorFlow if one of the nightly build packages is already installed
  • Lowpass synapse can now be applied to multidimensional inputs
  • TensorNodes will no longer be built into the default graph when checking their output dimensionality.

Removed

  • Removed utils.cast_dtype function

0.6.0 (December 13, 2017)

Added

  • The SoftLIFRate neuron type now has an amplitude parameter, which scales the output in the same way as the new amplitude parameter in LIF/LIFRate (see Nengo PR #1325).
  • Added progress_bar=False option to sim.run, which will disable the information about the simulation status printed to standard output (#17).
  • Added progress bars for the build/simulation process.
  • Added truncated backpropagation option to sim.train (useful for reducing memory usage during training). See the documentation for details.

Changed

  • Changed the default tensorboard argument in Simulator from False to None
  • Use the new tf.profiler tool to collect profiling data in sim.run_steps and sim.train when profile=True.
  • Minor improvements to efficiency of build process.
  • Minor improvements to simulation efficiency targeting small ops (tf.reshape/identity/constant).
  • Process inputs are now reseeded for each input when batch processing (if seed is not manually set).
  • Users can pass a dict of config options for the profile argument in run_steps/train, which will be passed on to the TensorFlow profiler; see the tf.profiler documentation for the available options.

Removed

  • Removed backports.print_function dependency

Fixed

  • Fixed a bug where input nodes that were only read as a view were not feedable
  • Updated tensorflow-gpu installation check
  • Improved numerical stability of LIFRate gradients (#26)
  • Added more informative error message when data is provided with fewer items than sim.minibatch_size (#30)

0.5.2 (October 11, 2017)

Added

  • TensorNode outputs can now define a post_build function that will be executed after the simulation is initialized (see the TensorNode documentation for details).
  • Added functionality for outputting summary data during the training process that can be viewed in TensorBoard (see the sim.train documentation).
  • Added some examples demonstrating how to use Nengo DL in a more complicated task using semantic pointers to encode/retrieve information
  • Added sim.training_step variable which will track the current training iteration (can be used, e.g., for TensorFlow’s variable learning rate operations).
  • Users can manually create tf.summary ops and pass them to sim.train summaries
  • The Simulator context will now also set the default TensorFlow graph to the one associated with the Simulator (so any TensorFlow ops created within the Simulator context will automatically be added to the correct graph)
  • Users can now specify a different objective for each output probe during training/loss calculation (see the sim.train documentation).

Changed

  • Resetting the simulator now only rebuilds the necessary components in the graph (as opposed to rebuilding the whole graph)
  • The default "mse" loss implementation will now automatically convert np.nan values in the target to zero error
  • If there are multiple target probes given to sim.train/sim.loss the total error will now be summed across probes (instead of averaged)

Fixed

  • sim.data now implements the full collections.Mapping interface
  • Fixed bug where signal order was non-deterministic for Networks containing objects with duplicate names (#9)
  • Fixed bug where non-slot optimizer variables were not initialized (#11)
  • Implemented a modified PES builder in order to avoid slicing encoders on non-decoded PES connections
  • TensorBoard output directory will be automatically created if it doesn’t exist

0.5.1 (August 28, 2017)

Changed

  • sim.data[obj] will now return live parameter values from the simulation, rather than initial values from the build process. That means that it can be used to get the values of object parameters after training, e.g. sim.data[my_conn].weights.
  • Increased minimum Nengo version to 2.5.0.
  • Increased minimum TensorFlow version to 1.3.0.

0.5.0 (July 11, 2017)

Added

  • Added nengo_dl.tensor_layer to help with the construction of layer-style TensorNodes (see the TensorNode documentation)
  • Added an example demonstrating how to train a neural network that can run in spiking neurons
  • Added some distributions for weight initialization to nengo_dl.dists
  • Added sim.train(..., profile=True) option to collect profiling information during training
  • Added new methods to simplify the Nengo operation graph, resulting in faster simulation/training speed
  • The default graph planner can now be modified by setting the planner attribute on the top-level Network config
  • Added TensorFlow implementation for general linear synapses
  • Added backports.tempfile and backports.print_function requirement for Python 2.7 systems

Changed

  • Increased minimum TensorFlow version to 1.2.0
  • Improved error checking for input/target data
  • Improved efficiency of stateful gradient operations, resulting in faster training speed
  • The functionality for nengo_dl.configure_trainable has been subsumed into the more general nengo_dl.configure_settings(trainable=x). This has resulted in some small changes to how trainability is controlled within subnetworks; see the updated documentation for details.
  • Calling Simulator.train/Simulator.loss no longer resets the internal state of the simulation (so they can be safely intermixed with calls to Simulator.run)

Deprecated

  • The old step_blocks/unroll_simulation syntax has been fully deprecated, and will result in errors if used

Fixed

  • Fixed bug related to changing the output of a Node after the model is constructed (#4)
  • Order of variable creation is now deterministic (helps make saving/loading parameters more reliable)
  • Configuring whether or not a model element is trainable does not affect whether or not that element is minibatched
  • Correctly reuse variables created inside a TensorNode when unroll_simulation > 1
  • Correctly handle probes that aren’t connected to any ops
  • Swapped fan_in/fan_out in dists.VarianceScaling to align with the standard definitions
  • Temporary patch to fix memory leak in TensorFlow (see #11273)
  • Fixed bug related to nodes that had matching output functions but different size_out
  • Fixed bug related to probes that do not contain any data yet

0.4.0 (June 8, 2017)

Added

Changed

  • Updated TensorFuncParam to new Nengo Param syntax
  • The interface for Simulator step_blocks/unroll_simulation has been changed. Now unroll_simulation takes an integer as argument which is equivalent to the old step_blocks value, and unroll_simulation=1 is equivalent to the old unroll_simulation=False. For example, Simulator(..., unroll_simulation=True, step_blocks=10) is now equivalent to Simulator(..., unroll_simulation=10).
  • Simulator.train/Simulator.loss no longer require step_blocks (or the new unroll_simulation) to be specified; the number of steps to train across will now be inferred from the input data.

0.3.1 (May 12, 2017)

Added

  • Added more documentation on Simulator arguments

Changed

  • Improved efficiency of tree_planner, made it the new default planner

Fixed

  • Correctly handle input feeds when n_steps > step_blocks
  • Detect cycles in transitive planner
  • Fix bug in uneven step_blocks rounding
  • Fix bug in Simulator.print_params
  • Fix bug related to merging of learning rule with different dimensionality
  • Use tf.Session instead of tf.InteractiveSession, to avoid strange side effects if the simulator isn’t closed properly

0.3.0 (April 25, 2017)

Added

  • Use logger for debug/builder output
  • Implemented TensorFlow gradients for sparse Variable update Ops, to allow models with those elements to be trained
  • Added tutorial/examples on using Simulator.train
  • Added support for training models when unroll_simulation=False
  • Compatibility changes for Nengo 2.4.0
  • Added a new graph planner algorithm, which can improve simulation speed at the cost of build time

Changed

  • Significant improvements to simulation speed
    • Use sparse Variable updates for signals.scatter/gather
    • Improved graph optimizer memory organization
    • Implemented sparse matrix multiplication op, to allow more aggressive merging of DotInc operators
  • Significant improvements to build speed
    • Added early termination to graph optimization
    • Algorithmic improvements to graph optimization functions
  • Reorganized documentation to more clearly direct new users to relevant material

Fixed

  • Fix bug where passing a built model to the Simulator more than once would result in an error
  • Cache result of calls to tensor_graph.build_loss/build_optimizer, so that we don’t unnecessarily create duplicate elements in the graph on repeated calls
  • Fix support for Variables on GPU when unroll_simulation=False
  • SimPyFunc operators will always be assigned to CPU, even when device="/gpu:0", since there is no GPU kernel
  • Fix bug where Simulator.loss was not being computed correctly for models with internal state
  • Data/targets passed to Simulator.train will be truncated if not evenly divisible by the specified minibatch size
  • Fixed bug where in some cases Nodes with side effects would not be run if their output was not used in the simulation
  • Fixed bug where strided reads that cover a full array would be interpreted as non-strided reads of the full array

0.2.0 (March 13, 2017)

Initial release of TensorFlow-based NengoDL

0.1.0 (June 12, 2016)

Initial release of Lasagne-based NengoDL

Contributing to NengoDL

Issues and pull requests are always welcome! We appreciate help from the community to make NengoDL better.

Filing issues

If you find a bug in NengoDL, or think that a certain feature is missing, please consider filing an issue. Please search the currently open issues first to see if your bug or feature request already exists. If so, feel free to add a comment to the issue so that we know that multiple people are affected.

Making pull requests

If you want to fix a bug or add a feature to NengoDL, we welcome pull requests. We try to maintain 100% test coverage, so any new features should also include unit tests to cover that change. If you fix a bug it’s also a good idea to add a unit test, so that the bug doesn’t get un-fixed in the future!

Contributor agreement

We require that all contributions be covered under our contributor assignment agreement. Please see the agreement for instructions on how to sign.

NengoDL license

Copyright (c) 2015-2018 Applied Brain Research Inc.

NengoDL is made available under a proprietary license that permits using, copying, sharing, and making derivative works from NengoDL and its source code for any non-commercial purpose, as long as the above copyright notice and this permission notice are included in all copies or substantial portions of the software.

If you would like to use NengoDL commercially, licenses can be purchased from Applied Brain Research, Inc. Please contact info@appliedbrainresearch.com for more information.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Licensed code

NengoDL imports several open source libraries:

To build the documentation, NengoDL uses:

To run the unit tests, NengoDL uses:

Citation

If you would like to cite NengoDL in your research, please cite the white paper:

Rasmussen, Daniel (2018). NengoDL: Combining deep learning and neuromorphic
modelling methods. arXiv:1805.11144, 1–22.

@article{
  Rasmussen2018,
  archivePrefix = {arXiv},
  arxivId = {1805.11144},
  author = {Rasmussen, Daniel},
  journal = {arXiv},
  pages = {1--22},
  title = {{NengoDL}: Combining deep learning and neuromorphic modelling
           methods},
  url = {http://arxiv.org/abs/1805.11144},
  volume = {1805.11144},
  year = {2018}
}