Comments (2)
Yes we can add a flag to turn that on or off if you'd like. Although I can't really think of any occasions when I would not want my data shuffled.
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There is a flag for this in the new version of nengo_dl
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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
- 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
- Importing Nengo_DL in Google Colab HOT 1
- `nengo_dl` cannot import `keras.engine` HOT 2
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