schemes.py
1.65 KB
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import six
import math
import numpy
from picklable_itertools import imap
from picklable_itertools.extras import partition_all
from fuel.schemes import BatchScheme
class SequentialShuffledScheme(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.
"""
def __init__(self, num_examples, batch_size, rng):
self.num_examples = num_examples
self.batch_size = batch_size
self.rng = rng
def get_request_iterator(self):
return SequentialShuffledIterator(self.num_examples, self.batch_size,
self.rng)
class SequentialShuffledIterator(six.Iterator):
def __init__(self, num_examples, batch_size, rng):
self.num_examples = num_examples
self.batch_size = batch_size
self.rng = rng
self.batch_indexes = range(int(math.ceil(num_examples/ float(batch_size))))
self.rng.shuffle(self.batch_indexes)
self.current = 0
self.current_batch = 0
def __iter__(self):
self.rng.shuffle(self.batch_indexes)
return self
def __next__(self):
if self.current >= self.num_examples:
raise StopIteration
current_index = self.batch_indexes[self.current_batch]
slice_ = slice(current_index * self.batch_size,
min(self.num_examples,
(current_index + 1) * self.batch_size))
self.current += self.batch_size
self.current_batch += 1
return slice_