SHD#
- class tonic.datasets.SHD(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None)[source]#
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@article{cramer2020heidelberg, title={The heidelberg spiking data sets for the systematic evaluation of spiking neural networks}, author={Cramer, Benjamin and Stradmann, Yannik and Schemmel, Johannes and Zenke, Friedemann}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2020}, publisher={IEEE} }
- Parameters:
save_to (string) – Location to save files to on disk. Will put files in an ‘hsd’ subfolder.
train (bool) – If True, uses training subset, otherwise testing subset.
transform (callable, optional) – A callable of transforms to apply to the data.
target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.
- Returns:
A dataset object that can be indexed or iterated over. One sample returns a tuple of (events, targets).