nengo-dl 0.2.0

Deep learning integration for Nengo

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 your 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

0.2.0 (March 13, 2017)

Initial release of TensorFlow-based NengoDL

0.1.0 (June 12, 2016)

Initial release of Lasagne-based NengoDL