def test_works_on_all_sources(self):
transformer = FilterSources(
self.stream, sources=("features", "targets"))
assert_equal(transformer.sources, ('features', 'targets'))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.ones((2, 2, 2)), numpy.array([0, 1])),
(numpy.ones((2, 2, 2)), numpy.array([0, 1]))])
def test_works_on_all_sources(self):
transformer = FilterSources(
self.stream, sources=("features", "targets"))
assert_equal(transformer.sources, ('features', 'targets'))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.ones((2, 2, 2)), numpy.array([0, 1])),
(numpy.ones((2, 2, 2)), numpy.array([0, 1]))])
def test_filter_sources():
stream = DataStream(
IndexableDataset(OrderedDict([("features", numpy.ones((4, 2, 2))), ("targets", numpy.array([0, 1, 0, 1]))])),
iteration_scheme=SequentialScheme(4, 2),
)
transformer = FilterSources(stream, sources=("features",))
assert_equal(transformer.sources, ("features",))
assert len(next(transformer.get_epoch_iterator())) == 1
transformer = FilterSources(stream, sources=("features", "targets"))
assert_equal(transformer.sources, ("features", "targets"))
assert len(next(transformer.get_epoch_iterator())) == 2
transformer = FilterSources(stream, sources=("targets", "features"))
assert_equal(transformer.sources, ("features", "targets"))
assert len(next(transformer.get_epoch_iterator())) == 2
assert_raises(ValueError, transformer.get_data, [0, 1])
assert_raises(ValueError, FilterSources, stream, ["error", "targets"])
def test_filter_sources():
stream = DataStream(
IndexableDataset(
OrderedDict([('features', numpy.ones((4, 2, 2))),
('targets', numpy.array([0, 1, 0, 1]))])),
iteration_scheme=SequentialScheme(4, 2))
transformer = FilterSources(stream, sources=("features",))
assert_equal(transformer.sources, ('features',))
assert len(next(transformer.get_epoch_iterator())) == 1
transformer = FilterSources(stream, sources=("features", "targets"))
assert_equal(transformer.sources, ('features', 'targets'))
assert len(next(transformer.get_epoch_iterator())) == 2
transformer = FilterSources(stream, sources=("targets", "features"))
assert_equal(transformer.sources, ('features', 'targets'))
assert len(next(transformer.get_epoch_iterator())) == 2
assert_raises(ValueError, transformer.get_data, [0, 1])
assert_raises(ValueError, FilterSources, stream, ['error', 'targets'])
def _transpose(data):
return tuple(array.swapaxes(0,1) for array in data)
batch_size = 10
dataset = Handwriting(('train',))
data_stream = DataStream.default_stream(
dataset, iteration_scheme=SequentialScheme(
50, batch_size))
data_stream = FilterSources(data_stream,
sources = ('features',))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
epoch = data_stream.get_epoch_iterator()
for batch in epoch:
print batch[0].shape
print "Segmented:"
data_stream = SegmentSequence(data_stream, add_flag = True)
epoch = data_stream.get_epoch_iterator()
for batch in epoch:
print batch[0].shape, batch[2]
#ipdb.set_trace()
def test_works_on_sourcessubset(self):
transformer = FilterSources(self.stream, sources=("features",))
assert_equal(transformer.sources, ('features',))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.ones((2, 2, 2)),), (numpy.ones((2, 2, 2)),)])
def test_works_on_sourcessubset(self):
transformer = FilterSources(self.stream, sources=("features",))
assert_equal(transformer.sources, ('features',))
assert_equal(list(transformer.get_epoch_iterator()),
[(numpy.ones((2, 2, 2)),), (numpy.ones((2, 2, 2)),)])
sources = ('features',))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
data_stream = ForceFloatX(data_stream)
dataset = Handwriting(('valid',))
valid_stream = DataStream.default_stream(
dataset, iteration_scheme=SequentialScheme(
dataset.num_examples, 10*batch_size))
valid_stream = FilterSources(valid_stream,
sources = ('features',))
valid_stream = Padding(valid_stream)
valid_stream = Mapping(valid_stream, _transpose)
valid_stream = ForceFloatX(valid_stream)
x_tr = next(data_stream.get_epoch_iterator())
x = tensor.tensor3('features')
x_mask = tensor.matrix('features_mask')
transition = [GatedRecurrent(dim=hidden_size_recurrent,
name = "gru_{}".format(i) ) for i in range(3)]
transition = RecurrentStack( transition,
name="transition", skip_connections = True)
emitter = BivariateGMMEmitter(k = k)
source_names = [name for name in transition.apply.states if 'states' in name]
readout = Readout(
readout_dim = readout_size,
def _transpose(data):
return tuple(array.swapaxes(0,1) for array in data)
batch_size = 10
dataset = Handwriting(('train',))
data_stream = DataStream.default_stream(
dataset, iteration_scheme=SequentialScheme(
50, batch_size))
data_stream = FilterSources(data_stream,
sources = ('features',))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
epoch = data_stream.get_epoch_iterator()
for batch in epoch:
print batch[0].shape
print "Segmented:"
data_stream = SegmentSequence(data_stream, add_flag = True)
epoch = data_stream.get_epoch_iterator()
for batch in epoch:
print batch[0].shape, batch[2]
#ipdb.set_trace()
data_stream = FilterSources(data_stream, sources=('features', ))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
data_stream = ForceFloatX(data_stream)
dataset = Handwriting(('valid', ))
valid_stream = DataStream.default_stream(dataset,
iteration_scheme=SequentialScheme(
dataset.num_examples,
10 * batch_size))
valid_stream = FilterSources(valid_stream, sources=('features', ))
valid_stream = Padding(valid_stream)
valid_stream = Mapping(valid_stream, _transpose)
valid_stream = ForceFloatX(valid_stream)
x_tr = next(data_stream.get_epoch_iterator())
x = tensor.tensor3('features')
x_mask = tensor.matrix('features_mask')
transition = [
GatedRecurrent(dim=hidden_size_recurrent, name="gru_{}".format(i))
for i in range(3)
]
transition = RecurrentStack(transition,
name="transition",
skip_connections=True)
emitter = BivariateGMMEmitter(k=k)
