Question: Where does the weight transfer happen?

[cowolff]

Quick question regarding this tutorial:
https://sinabs.readthedocs.io/en/v1.2.10/tutorials/weight_transfer_mnist.html
I understand that the model conversion happens in this line:

sinabs_model = from_model(
    ann, input_shape=input_shape, add_spiking_output=True, synops=False, num_timesteps=num_timesteps
)

What I don't understand is, where does the weight conversion from the ANN to the SNN actually happen?
As far as I understand those 3 files from_torch.py, conversion.py and network.py the model is first recreated in the same "shape" as the original model, just with spiking layers and then handed over to the network class in combination with the old model. This is then returned to the user. Still, I don't understand, where the weights from the ANN are actually transferred to the SNN?

[bauerfe]

Hi @cowolff , thank you for bringing up this question.

Your understanding of how the conversion from ANN to SNN works is pretty good. Let me try to summarize what's happening:

First, the original model is copied, including all layers, weights, and all other parameters. After that the activation layers (e.g. ReLU) are replaced by spiking layers.

So the weights are copied to the new model 1:1 - their shape and values stay exactly the same.

This means that you might have to scale weights or firing thresholds to make sure that the spiking activity is in the desired range. You can read more about this here: https://sinabs.readthedocs.io/en/v1.2.1/tutorials/weight_scaling.html

[cowolff]

Thanks for your reply! But I am still wondering where this copying of the weights is happening in the code of the library? :)

[bauerfe]

In this line, from_module calls the replace_module function (defined here, which first makes a deepcopy of the network and then replaces the ReLU layers.

[bauerfe]

I converted this issue to a discussion. Since there seem to be no further questions, I will close it.