Importing Trained Weights


A few quick questions:

  1. Is there some clear cut way to import network weights that you trained in another system? I saw this post that seems related:

  2. My understanding is that most or all Nengo ensembles are fully connected. Is there a way to do 1. with different layer types/structures (e.g., 2D convolution)?

  3. Is 1. done any differently for weights of deep networks still trained in a different system?

  4. Is there any convenience code kicking around for doing 1. and/or 2. with systems built in Tensorflow? I saw the work on NengoDL, but I’m less interested in Tensorflow nodes and more interested in simulating spiking versions of networks trained in Tensorflow.


Okay. So, this link from another forum post answers 1. for me:

2, 3, and 4 seem to be deeply tied to NengoDL. I literally found a convolution2D operation discussed and then removed in relation to NengoDL here:

I think I’m going to call this solved and just ask a bit more about NengoDL.


And, here is the link that better explains how to get spiking neurons functioning with a Tensorflow setup:

Don’t know how I missed that.


So, have you answered your own questions now or do you require further assistance? :slight_smile:


I also have the same problem.
In nengo_dl, there is no problem, but the question is how to do it in nengo core?
In nengo Core, if you want to do off-line learning, you use nengo.solver to solve the decoder.

        conn = nengo.Connection(input_neuron,

Here eval_points is your training data, function is you label. then you can solve the connection weights(decoder).
My question is, can we here first load the pre-trained weights, then the nengo solver solve the optimization problem.


For now, yes! Thanks!


Check out this example for an explanation of how to do this in Nengo Core.