Deep learning integration for Nengo
Project description
NengoDL: Deep learning integration for Nengo
NengoDL is a simulator for Nengo models. That means it takes a Nengo network as input, and allows the user to simulate that network using some underlying computational framework (in this case, TensorFlow).
In practice, what that means is that the code for constructing a Nengo model is exactly the same as it would be for the standard Nengo simulator. All that changes is that we use a different Simulator class to execute the model.
For example:
import nengo
import nengo_dl
import numpy as np
with nengo.Network() as net:
inp = nengo.Node(output=np.sin)
ens = nengo.Ensemble(50, 1, neuron_type=nengo.LIF())
nengo.Connection(inp, ens, synapse=0.1)
p = nengo.Probe(ens)
with nengo_dl.Simulator(net) as sim: # this is the only line that changes
sim.run(1.0)
print(sim.data[p])
However, NengoDL is not simply a duplicate of the Nengo simulator. It also adds a number of unique features, such as:
optimizing the parameters of a model through deep learning training methods
faster simulation speed, on both CPU and GPU
inserting networks defined using TensorFlow (such as convolutional neural networks) directly into a Nengo model
More details can be found in the NengoDL documentation.
Installation
Installation instructions can be found here.
Release History
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
Added ability to manually specify which parts of a model are trainable (see the sim.train documentation)
Added some code examples (see the docs/examples directory, or the pre-built examples in the documentation)
Added the SoftLIFRate neuron type for training LIF networks (based on this paper)
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
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