tonic.datasets.hsd

Module Contents

Classes

HSD	Heidelberg Spiking Dataset <https://arxiv.org/abs/1910.07407> contains the Spiking
SHD	Spiking Heidelberg Digits
SSC	Spiking Speech Commands

class tonic.datasets.hsd.HSD(save_to: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)

Bases: tonic.dataset.Dataset

Heidelberg Spiking Dataset <https://arxiv.org/abs/1910.07407> contains the Spiking Heidelberg Digits (SHD) and the Spiking Speech Commands dataset (SSC).

Parameters:

• save_to (str) –

• transform (Optional[Callable]) –

• target_transform (Optional[Callable]) –

• transforms (Optional[Callable]) –

base_url = 'https://zenkelab.org/datasets/'

sensor_size = (700, 1, 1)

dtype

ordering

__getitem__(index)

__len__()

class tonic.datasets.hsd.SHD(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None)

Bases: HSD

Spiking Heidelberg Digits

@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).

property speaker

test_zip = 'shd_test.h5.zip'

train_zip = 'shd_train.h5.zip'

test_md5 = '1503a5064faa34311c398fb0a1ed0a6f'

train_md5 = 'f3252aeb598ac776c1b526422d90eecb'

folder_name = ''

class tonic.datasets.hsd.SSC(save_to: str, split: str = 'train', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None)

Bases: HSD

Spiking Speech Commands

@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.

• split (string) – One of ‘train’, ‘test’ or ‘valid’.

• 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).

test_zip = 'ssc_test.h5.zip'

train_zip = 'ssc_train.h5.zip'

valid_zip = 'ssc_valid.h5.zip'

test_md5 = 'a35ff1e9cffdd02a20eb850c17c37748'

train_md5 = 'd102be95e7144fcc0553d1f45ba94170'

valid_md5 = 'b4eee3516a4a90dd0c71a6ac23a8ae43'

folder_name = ''