Nengo-dl simulator and Nengo simulator

General Discussion
9

I tried to use
nengo_converter = nengo_dl.Converter(model, allow_fallback=False, swap_activations={tf.nn.relu: nengo.SpikingRectifiedLinear()})
as you suggested, but looks like the neurons are not converted to Spiking

    model = tf.keras.models.load_model(os.path.join(CHECKPOINT_FILE_PATH, MODEL_NAME))

    nengo_converter = nengo_dl.Converter(model, 
                                        allow_fallback=False, 
                                        swap_activations={tf.nn.relu: nengo.SpikingRectifiedLinear()})
    net = nengo_converter.net
    for ensemble in net.ensembles: print(ensemble, ensemble.neuron_type)
    assert nengo_converter.verify()

the ensemble prints:

<Ensemble "conv2d_4.0"> RectifiedLinear()
<Ensemble "conv2d_5.0"> RectifiedLinear()
<Ensemble "conv2d_6.0"> RectifiedLinear()
<Ensemble "conv2d_7.0"> RectifiedLinear()
<Ensemble "conv2d_8.0"> RectifiedLinear()
<Ensemble "conv2d_9.0"> RectifiedLinear()

Then I tried to use swap_activations={nengo.RectifiedLinear(): nengo.SpikingRectifiedLinear()} as suggested from Daniel from a different topic. This time, the neuron converted to spiking however, an error raised from function

assert nengo_converter.verify()

ValueError: Output of Keras model does not match output of converted Nengo network
My questions are

  1. Is it a known issue with swap_activations={tf.nn.relu: nengo.SpikingRectifiedLinear()}?
  2. Is it safe to use swap_activations={nengo.RectifiedLinear(): nengo.SpikingRectifiedLinear()} in nengo_dl.Converter to make sure the model transfered to spiking.
    swap_activations={tf.nn.relu: nengo:SpikingRectifiedLinear()} makes more sense to me since I’m loading a keras model. The assert nengo_converter.verify() can’t work either. Are there any other parameters that I should be aware of to make the spiking conversion pass the verification function? Thank you