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)