Comments (2)
drasmuss
commented on July 20, 2024
I've spent quite a lot of time looking into this (e.g., see #19). The feed_dict approach always ended up being faster.
That being said, supporting tf.data input pipelines is still on my TODO list, because it would be useful to be able to incorporate more complex input pipelines into NengoDL. But that's more of a feature benefit, rather than a performance benefit (and we'd have to find a way to do it that doesn't negatively impact the existing performance).
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drasmuss
commented on July 20, 2024
This was added in #95, you can now use tf.data input piplines with any of the predict/fit/evaluate methods.
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Related Issues (20)
- AttributeError: module 'tensorflow.python.keras.utils.tf_utils' has no attribute 'smart_cond' HOT 1
- Error autogenerating process inputs when data batch size does not match Simulator.minibatch_size
- 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
- Nengo version of ModelCheckpoint callback
- Use no-input nodes by default in converter
- 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
- Which neuromorphic hardware does NengoDL simulate ?
- sim.predict make GPU full memory HOT 7
- BatchNormalization layer produces LOW accuracy
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