Hi Nengo community!
I’m attempting to convert a ANN defined in Keras to an SNN using nengo_dl’s converter. Crucially, I would like Nengo to interpret the first dimension of the input (aside from the batch dimension) as timesteps. As such, I’m setting the ‘temporal_model’ parameter to ‘True’ but am running into errors trying to convert even the first layer of my network (a Conv2d layer). Here is a minimal example reproducing the error:
import keras import nengo_dl inp = keras.Input(shape=(100,10,1), name = 'input_layer') conv = keras.layers.Conv2D(filters=20, kernel_size=(3,1), strides = 1, padding = 'valid', use_bias = False, activation="relu", name = 'conv_layer')(inp) model = keras.Model(inputs=inp, outputs=conv, name = "test_model") converter = nengo_dl.Converter(model, temporal_model=True)
The code results in the following error:
ValidationError: kernel_size: Kernel dimensions (2) does not match input dimensions (1).
I’d love some advice on how to remedy this error. The converter runs just fine with temporal_model set to False. Does anyone know if the Nengo DL converter supports Conv2d (or Conv1d) Keras layers, in particular when the convolution is over the time dimension? I wasn’t able to find any examples online. Any help would be appreciated!