def test_iterate_scheme():
from fuel.datasets import IndexableDataset
from fuel.schemes import (SequentialScheme, ShuffledScheme,SequentialExampleScheme, ShuffledExampleScheme)
seed = 1234
rng = numpy.random.RandomState(seed)
features = rng.randint(256, size=(8, 2, 2))
targets = rng.randint(4, size=(8, 1))
dataset = IndexableDataset(indexables=OrderedDict([('features', features),
('targets', targets)]),
axis_labels=OrderedDict([('features', ('batch', 'height', 'width')),
('targets', ('batch', 'index'))]))
schemes = [SequentialScheme(examples=8, batch_size=5),
ShuffledScheme(examples=8, batch_size=3),
SequentialExampleScheme(examples=8),
ShuffledExampleScheme(examples=8)]
# for scheme in schemes:
# print(list(scheme.get_request_iterator()))
state = dataset.open()
scheme = ShuffledScheme(examples=dataset.num_examples, batch_size=3)
for request in scheme.get_request_iterator():
data = dataset.get_data(state=state, request=request)
print(data[0].shape, data[1].shape)
dataset.close(state)
def test_indexabel_dataset():
from fuel.datasets import IndexableDataset
seed = 1234
rng = numpy.random.RandomState(seed)
features = rng.randint(256, size=(8, 2, 2))
targets = rng.randint(4, size=(8, 1))
dataset = IndexableDataset(indexables=OrderedDict([('features', features),
('targets', targets)]),
axis_labels=OrderedDict([('features', ('batch', 'height', 'width')),
('targets', ('batch', 'index'))]))
state = dataset.open()
print('State is {}.'.format(state))
print(dataset.get_data(state=state, request=[1, 0]))
dataset.close(state=state)