Stochastic Training

class kerch.feature.Stochastic(*args, **kwargs)

Bases: Cache

Parameters:

• cache_level (str, optional) – Cache level for saving temporary execution results during the execution. The higher the cache, the more is saved. Defaults to 'normal'. We refer to the Cache Management documentation for further information.

• logging_level (int, optional) – Logging level for this specific instance. If the value is None, the current default kerch global log level will be used. Defaults to None (default kerch logging level). We refer to the Logging in Kerch documentation for further information.

cache_keys(private: bool = False) → Iterable[str]

Returns an iterable containing the different cache keys. We refer to the Cache Management documentation for more information.

Parameters:

private (bool, optional) – Some cache elements are private and are not returned unless set to True. Defaults to False.

property cache_level: str

Cache level for saving temporary execution results during the execution. The higher the cache, the more is saved. Defaults to 'normal' unless set otherwise during instantiation. The different possible values are:

• "none": the cache is non-existent and everything is computed on the go.

• "light": the cache is very light. For example, only the kernel matrix and statistics of the sample points are saved.

• "normal": same as light, but the statistics of the out-of-sample points are also saved.

• "heavy": in addition to the statistics, the final kernel matrices of the out-of-sample points are saved.

• "total": every step of any computation is saved.

We refer to the Cache Management documentation for further information.

property idx: Tensor

Indices used when performing various operations. This is only relevant in the case of stochastic training.

property num_idx: int

Number of selected indices when performing various operations. This is only relevant in the case of stochastic training.

print_cache(private: bool = False) → None

Prints the cache content. We refer to the Cache Management documentation for further information.

Parameters:

private (bool, optional) – Some cache elements are private and are not returned unless set to True. Defaults to False.

reset(recurse=False, reset_persisting=True) → None

Resets the cache to be empty. We refer to the Cache Management documentation for more information.

Parameters:

• recurse (bool, optional) – If True, resets the cache of this module and also of its potential children. otherwise, it only resets the cache for this module. Defaults to True.

• reset_persisting (bool, optional) – Persisting elements are meant to resist to a cache reset (see _save()). The option allows to also reset them if True. Defaults to True.

stochastic(idx=None, prop=None)

Resets which subset of the samples are to be used until the next call of this function. This is relevant in the case of stochastic training.

Parameters:

• idx (int[], optional) – Indices of the sample subset relative to the original sample set., defaults to None

• prop (double, optional) – Instead of giving indices, passing a proportion of the original sample set is also possible. The indices will be uniformly randomly chosen without replacement. The value must be chosen such that \(0 <\) prop_stochastic \(\leq 1\)., defaults to None.

If None is specified for both idx_stochastic and prop_stochastic, all samples are used and the subset equals the original sample set. This is also the default behavior if this function is never called, nor the parameters specified during initialization.

Note

Both idx_stochastic and prop_stochastic cannot be filled together as conflict would arise.

train(mode=True)

Activates the training mode, which disables the gradients computation and disables stochasticity. For the gradients and other things, we refer to the torch.nn.Module documentation. For the stochastic part, when put in evaluation mode (False), all the sample points are used for the computations, regardless of the previously specified indices.