Iteration schemes¶

class
fuel.schemes.
BatchScheme
(examples, batch_size)[source]¶ Bases:
fuel.schemes.IterationScheme
Iteration schemes that return slices or indices for batches.
For datasets where the number of examples is known and easily accessible (as is the case for most datasets which are small enough to be kept in memory, like MNIST) we can provide slices or lists of labels to the dataset.
Parameters:  examples (int or list) – Defines which examples from the dataset are iterated. If list, its items are the indices of examples. If an integer, it will use that many examples from the beginning of the dataset, i.e. it is interpreted as range(examples)
 batch_size (int) – The request iterator will return slices or list of indices in batches of size batch_size until the end of examples is reached. Note that this means that the last batch size returned could be smaller than batch_size. If you want to ensure all batches are of equal size, then ensure len(examples) or examples is a multiple of batch_size.

requests_examples
= False¶

class
fuel.schemes.
BatchSizeScheme
[source]¶ Bases:
fuel.schemes.IterationScheme
Iteration scheme that returns batch sizes.
For infinite datasets it doesn’t make sense to provide indices to examples, but the number of samples per batch can still be given. Hence BatchSizeScheme is the base class for iteration schemes that only provide the number of examples that should be in a batch.

requests_examples
= False¶


class
fuel.schemes.
ConcatenatedScheme
(schemes)[source]¶ Bases:
fuel.schemes.IterationScheme
Build an iterator by concatenating several schemes’ iterators.
Useful for iterating through different subsets of data in a specific order.
Parameters: schemes (list) – A list of IterationSchemes
, whose request iterators are to be concatenated in the order given.Notes
All schemes being concatenated must produce the same type of requests (batches or examples).

requests_examples
¶


class
fuel.schemes.
ConstantScheme
(batch_size, num_examples=None, times=None)[source]¶ Bases:
fuel.schemes.BatchSizeScheme
Constant batch size iterator.
This subset iterator simply returns the same constant batch size for a given number of times (or else infinitely).
Parameters:  batch_size (int) – The size of the batch to return.
 num_examples (int, optional) – If given, the request iterator will return batch_size until the
sum reaches num_examples. Note that this means that the last
batch size returned could be smaller than batch_size. If you want
to ensure all batches are of equal size, then pass times equal to
num_examples / batchsize
instead.  times (int, optional) – The number of times to return batch_size.

class
fuel.schemes.
IndexScheme
(examples)[source]¶ Bases:
fuel.schemes.IterationScheme
Iteration schemes that return single indices.
This is for datasets that support indexing (like
BatchScheme
) but where we want to return single examples instead of batches.
requests_examples
= True¶


class
fuel.schemes.
IterationScheme
[source]¶ Bases:
object
An iteration scheme.
Iteration schemes provide a datasetagnostic iteration scheme, such as sequential batches, shuffled batches, etc. for datasets that choose to support them.

requests_examples
¶ Whether requests produced by this scheme correspond to single examples (as opposed to batches).
Type: bool
Notes
Iteration schemes implement the
get_request_iterator()
method, which returns an iterator type (e.g. a generator or a class which implements the iterator protocol).Stochastic iteration schemes should generally not be shared between different data streams, because it would make experiments harder to reproduce.


class
fuel.schemes.
SequentialExampleScheme
(examples)[source]¶ Bases:
fuel.schemes.IndexScheme
Sequential examples iterator.
Returns examples in order.

class
fuel.schemes.
SequentialScheme
(examples, batch_size)[source]¶ Bases:
fuel.schemes.BatchScheme
Sequential batches iterator.
Iterate over all the examples in a dataset of fixed size sequentially in batches of a given size.
Notes
The batch size isn’t enforced, so the last batch could be smaller.

class
fuel.schemes.
ShuffledExampleScheme
(*args, **kwargs)[source]¶ Bases:
fuel.schemes.IndexScheme
Shuffled examples iterator.
Returns examples in random order.

class
fuel.schemes.
ShuffledScheme
(*args, **kwargs)[source]¶ Bases:
fuel.schemes.BatchScheme
Shuffled batches iterator.
Iterate over all the examples in a dataset of fixed size in shuffled batches.
Parameters: sorted_indices (bool, optional) – If True, enforce that indices within a batch are ordered. Defaults to False. Notes
The batch size isn’t enforced, so the last batch could be smaller.
Shuffling the batches requires creating a shuffled list of indices in memory. This can be memoryintensive for very large numbers of examples (i.e. in the order of tens of millions).

fuel.schemes.
cross_validation
(scheme_class, num_examples, num_folds, strict=True, **kwargs)[source]¶ Return pairs of schemes to be used for crossvalidation.
Parameters:  scheme_class (subclass of
IndexScheme
orBatchScheme
) – The type of the returned schemes. The constructor is called with an iterator and **kwargs as arguments.  num_examples (int) – The number of examples in the datastream.
 num_folds (int) – The number of folds to return.
 strict (bool, optional) – If True, enforce that num_examples is divisible by num_folds and so, that all validation sets have the same size. If False, the size of the validation set is returned along the iteration schemes. Defaults to True.
Yields: fold (tuple) – The generator returns num_folds tuples. The first two elements of the tuple are the training and validation iteration schemes. If strict is set to False, the tuple has a third element corresponding to the size of the validation set.
 scheme_class (subclass of