Optimizing a NengoDL model

Optimizing Nengo models via deep learning training methods is one of the important features of NengoDL. This functionality is accessed via the Simulator.train() method. For example:

with nengo.Network() as net:
    <construct the model>

with nengo_dl.Simulator(net, ...) as sim:
    sim.train(<inputs>, <targets>, <optimizer>, n_epochs=10,

When the Simulator is first constructed, all the parameters in the model (e.g., encoders, decoders, connection weights, biases) are initialized based on the functions/distributions specified during model construction (see the Nengo documentation for more detail on how that works). What the Simulator.train() method does is then further optimize those parameters based on some inputs and desired outputs. We’ll go through each of those components in more detail below.


The first argument to the Simulator.train() function is the input data. We can think of a model as computing a function \(y = f(x, \theta)\), where \(f\) is the model, mapping inputs \(x\) to outputs \(y\) with parameters \(\theta\). This argument is specifying the values for \(x\).

In practice what that means is specifying values for the input Nodes in the model. A Node is a Nengo object that inserts values into a Network, usually used to define external inputs. Simulator.train() will override the normal Node values with the training data that is provided. This is specified as a dictionary {<node>: <array>, ...}, where <node> is the input node for which training data is being defined, and <array> is a numpy array containing the training values. This training array should have shape (n_inputs, n_steps, node.size_out), where n_inputs is the number of training examples, n_steps is the number of simulation steps to train across, and node.size_out is the dimensionality of the Node.

When training a NengoDL model there are two Simulator parameters that must be provided. The first is minibatch_size, which defines how many inputs (out of the total n_inputs defined above) will be used for each optimization step. The second is step_blocks, which tells the simulator the value of n_steps above, so that the simulation graph is configured to run the appropriate number of simulation steps.

Here is an example illustrating how to define the input values for two input nodes:

with nengo.Network() as net:
    a = nengo.Node([0])
    b = nengo.Node([1, 2, 3])

n_inputs = 1000
minibatch_size = 20
n_steps = 10

with nengo_dl.Simulator(
        net, step_blocks=n_steps, minibatch_size=minibatch_size) as sim:
    sim.train(inputs={a: np.random.randn(n_inputs, n_steps, 1),
                      b: np.random.randn(n_inputs, n_steps, 3)},

Input values must be provided for at least one Node, but beyond that can be defined for as many Nodes as desired. Any Nodes that don’t have data provided will take on the values specified during model construction. Also note that inputs can only be defined for Nodes with no incoming connections (i.e., Nodes with size_in == 0).


Returning to the network equation \(y = f(x, \theta)\), the goal in optimization is to find a set of parameter values such that given inputs \(x\) the actual network outputs \(y\) are as close as possible to some target values \(t\). This argument is specifying those desired outputs \(t\).

This works very similarly to defining inputs, except instead of assigning input values to Nodes it assigns target values to Probes. The structure of the argument is similar – a dictionary of {<probe>: <array>, ...}, where <array> has shape (n_inputs, n_steps, probe.size_in). Each entry in the target array defines the desired output for the corresponding entry in the input array.

For example:

with nengo.Network() as net:
    ens = nengo.Ensemble(10, 2)
    p = nengo.Probe(ens)

n_inputs = 1000
minibatch_size = 20
n_steps = 10

with nengo_dl.Simulator(
        net, step_blocks=n_steps, minibatch_size=minibatch_size) as sim:
    sim.train(targets={p: np.random.randn(n_inputs, n_steps, 2)},

Note that these examples use random inputs/targets, for the sake of simplicity. In practice we would do something like targets={p: my_func(inputs)}, where my_func is a function specifying what the ideal outputs are for the given inputs.


The optimizer is the algorithm that defines how to update the network parameters during training. Any of the optimization methods implemented in TensorFlow can be used in NengoDL; more information can be found in the TensorFlow documentation.

An instance of the desired TensorFlow optimizer is created (specifying any arguments required by that optimizer), and that instance is then passed to Simulator.train(). For example:

import tensorflow as tf

with nengo_dl.Simulator(net, ...) as sim:
        learning_rate=0.1, momentum=0.9, use_nesterov=True), ...)


The goal in optimization is to minimize the error between the network’s actual outputs \(y\) and the targets \(t\). The objective is the function \(e = o(y, t)\) that computes an error value \(e\), given \(y\) and \(t\).

The default objective in NengoDL is the standard mean squared error. This will be used if the user doesn’t specify an objective.

Users can specify a custom objective by creating a function and passing that to the objective argument in Simulator.train(). Note that the objective is defined using TensorFlow operators. It should accept Tensors representing outputs and targets as input (with shape (minibatch_size, n_steps, probe.size_in)) and return a scalar Tensor representing the error. This example manually computes mean squared error, rather than using the default:

import tensorflow as tf

def my_objective(outputs, targets):
    return tf.reduce_mean((targets - outputs) ** 2)

with nengo_dl.Simulator(net, ...) as sim:
    sim.train(objective=my_objective, ...)

If there are multiple output Probes defined in targets, then the error will be computed for each output individually (using the specified objective). Then the error will be averaged across outputs to produce an overall error value.

Note that the Simulator.loss() function can be used to check the loss (error) value for a given objective.

Other parameters

  • n_epochs: run training for this many passes through the input data
  • shuffle: if True (default), randomly assign data to different minibatches each epoch


Here is a complete example showing how to train a network using NengoDL. The function being learned here is not particularly interesting (multiplying by 2), but it shows how all of the above parts can fit together.

import nengo
import nengo_dl
import numpy as np
import tensorflow as tf

with nengo.Network(seed=0) as net:
    # these parameter settings aren't necessary, but they set things up in
    # a more standard machine learning way, for familiarity
    net.config[nengo.Ensemble].neuron_type = nengo.RectifiedLinear()
    net.config[nengo.Ensemble].gain = nengo.dists.Choice([1])
    net.config[nengo.Ensemble].bias = nengo.dists.Uniform(-1, 1)
    net.config[nengo.Connection].synapse = None

    # connect up our input node, and 3 ensembles in series
    a = nengo.Node([0.5])
    b = nengo.Ensemble(30, 1)
    c = nengo.Ensemble(30, 1)
    d = nengo.Ensemble(30, 1)
    nengo.Connection(a, b)
    nengo.Connection(b, c)
    nengo.Connection(c, d)

    # define our outputs with a probe on the last ensemble in the chain
    p = nengo.Probe(d)

n_steps = 5  # the number of simulation steps we want to run our model for
mini_size = 10 # minibatch size

with nengo_dl.Simulator(net, step_blocks=n_steps, minibatch_size=mini_size,
                        device="/cpu:0") as sim:
    # create input/target data. this could be whatever we want, but here
    # we'll train the network to output 2x its input
    input_data = np.random.uniform(-1, 1, size=(10000, n_steps, 1))
    target_data = input_data * 2

    # train the model, passing `input_data` to our input node `a` and
    # `target_data` to our output probe `p`. we can use whatever TensorFlow
    # optimizer we want here.
    sim.train({a: input_data}, {p: target_data},
              tf.train.MomentumOptimizer(1e-2, 0.9), n_epochs=10)

    # run the model to see the results of the training. note that this will
    # use the input values specified in our `nengo.Node` definition
    # above (0.5)

    # so the output should be 1
    assert np.allclose(sim.data[p], 1, atol=1e-2)


    # or if we wanted to see the performance on a test dataset, we could do
    test_data = np.random.uniform(-1, 1, size=(mini_size, n_steps, 1))
    sim.run_steps(n_steps, input_feeds={a: test_data})

    assert np.allclose(test_data * 2, sim.data[p], atol=1e-2)


  • Almost all deep learning methods require the network to be differentiable, which means that trying to train a network with non-differentiable elements will result in an error. Examples of common non-differentiable elements include nengo:nengo.LIF, nengo:nengo.Direct, or processes/neurons that don’t have a custom TensorFlow implementation (see processes.SimProcessBuilder/ neurons.SimNeuronsBuilder)
  • Most TensorFlow optimizers do not have GPU support for networks with sparse reads, which are a common element in Nengo models. If your network contains sparse reads then training will have to be executed on the CPU (by creating the simulator via nengo_dl.Simulator(..., device="/cpu:0")), or is limited to optimizers with GPU support (currently this is only tf.train.GradientDescentOptimizer). Follow this issue for updates on Tensorflow GPU support.