Comments (2)
Yeah I think a warning for the special case where you're training over one timestep would make sense (since in that case we can be pretty sure that synapse!=None
is a mistake). For training over any steps >1 I'd think no warning though, since it would be not unreasonable for someone to want synaptic filtering there.
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Makes sense to me.
If you wanted to be fancy you could check the number of steps versus the time constant of the filter. But that could get messy for more complex filters. Probably better to just have a note about that in some notebook on training networks over time (maybe there already is one).
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Related Issues (20)
- AssertionError running custom neuron with TensorFlow 2.3.0 HOT 3
- Empty probes are Python lists instead of ndarrays
- Creating a simulator while keeping pretrained weights HOT 3
- Uninformative error message when using `sim.compile` on a network with no probed outputs
- Support/examples for converting or embedding Keras RNNs HOT 1
- Support scale_firing_rates with Regular/Poisson/Stochastic spiking wrappers
- Warn if converter's scale_firing_rates would skew the nonlinearities
- Support opting in to spikes on the forward pass
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- load_params misbehaves with scale_firing_rates for some architectures HOT 1
- Converter `synapse` not applied to `neurons`-to-`TensorNode` connections HOT 1
- Converter fails with `tf.keras.applications.EfficientNet`
- Mistake in documentation
- Trainable parameters in Nengo LIF neurons HOT 2
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- sim.predict make GPU full memory HOT 7
- BatchNormalization layer produces LOW accuracy
- Importing Nengo_DL in Google Colab HOT 1
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