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)