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
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
If you are new to Nengo, you should start by reading the Nengo documentation.
Knowledge of TensorFlow is not required to use NengoDL. However, if you want to start constructing your own TensorFlow networks, you can can check out the TensorFlow documentation
These are the only classes that a NengoDL user needs to interact with:
You can read the NengoDL developer documentation if you want to know more about how things are working under the hood.