jaxsnn.base.dataset

Modules

`jaxsnn.base.dataset.circle`
`jaxsnn.base.dataset.constant`
`jaxsnn.base.dataset.dataloader`
`jaxsnn.base.dataset.linear`
`jaxsnn.base.dataset.shd`	SHD Dataset class
`jaxsnn.base.dataset.yinyang`

Functions

jaxsnn.base.dataset.circle_dataset(rng: jax.Array, size: int, mirror: bool = True, bias_spike: Optional[float, None] = 0.0) → Tuple[jax.Array, jax.Array]

jaxsnn.base.dataset.constant_dataset(t_max: float, size: int) → Tuple[jax.Array, jax.Array]

jaxsnn.base.dataset.data_loader(dataset: Tuple[Any, Any], batch_size: int, num_batches: Optional[int, None] = None, rng: Optional[jax.Array, None] = None)

jaxsnn.base.dataset.linear_dataset(rng: jax.Array, size: int, mirror: bool, bias_spike: Optional[float, None]) → Tuple[jax.Array, jax.Array]

jaxsnn.base.dataset.shd_dataset(train: bool, download: bool, t_max: float, root: str, time_scale: float = 1.0, channel_start: int = 0, channel_modulo: int = 1, num_samples: Optional[int, None] = None, dtype: numpy.dtype = <class 'numpy.float32'>) → Tuple[jax.Array, jax.Array]: SHD dataset

jaxsnn.base.dataset.yinyang_dataset(rng: jax.Array, size: int, mirror: bool, bias_spike: Optional[float, None]) → Tuple[jax.Array, jax.Array]