Comments (4)
If you use sim.save_params
/load_params
then this will work. When using keras_model.save_weights
you're also saving the internal simulation state (which has a minibatch dimension), which is why those parameters don't transfer between models with different minibatch size.
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Good to know. For reference the reason [save|load]_weights
is being used as opposed to [save|load]_params
is because I'm using the ModelCheckpoint
callback with save_weights_only=True
. Setting this to False
triggers some issue in the serialization/deserialization logic when trying to load them back in.
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Going to use this issue to track the idea of implementing some thin wrapper around ModelCheckpoint
that will call sim.save_params
instead of sim.keras_model.save_weights
.
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Note to future self. An easier fix might be to store simulator state as a simple tf.Variable
(not added through layer.add_weights
). Then Keras wouldn't track it, and sim.keras_model.save_weights
would produce the same behaviour as sim.save_params
(only saving the trainable parameters of the model).
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Related Issues (20)
- AssertionError running custom neuron with TensorFlow 2.3.0 HOT 3
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- 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
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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
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