def test_transformer():
from fuel.transformers import ScaleAndShift
from fuel.datasets import IndexableDataset
from fuel.streams import DataStream
from fuel.schemes import ShuffledScheme
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'))]))
scheme = ShuffledScheme(examples=dataset.num_examples, batch_size=2)
data_stream = DataStream(dataset=dataset, iteration_scheme=scheme)
scale = 1.0 / features.std()
shift = - scale * features.mean()
standardized_stream = ScaleAndShift(data_stream=data_stream,
scale=scale, shift=shift,
which_sources=('features',))
for batch in standardized_stream.get_epoch_iterator():
print(batch)
def test_scale_and_shift():
stream = DataStream(
IterableDataset({
'features': [1, 2, 3],
'targets': [0, 1, 0]
}))
wrapper = ScaleAndShift(stream, 2, -1, which_sources=('targets', ))
assert list(wrapper.get_epoch_iterator()) == [(1, -1), (2, 1), (3, -1)]
class TestScaleAndShift(object):
def setUp(self):
dataset = IterableDataset(
OrderedDict([('features', [1, 2, 3]), ('targets', [0, 1, 0])]),
axis_labels={'features': ('batch'), 'targets': ('batch')})
self.stream = DataStream(dataset)
self.wrapper = ScaleAndShift(
self.stream, 2, -1, which_sources=('targets',))
def test_scale_and_shift(self):
assert_equal(list(self.wrapper.get_epoch_iterator()),
[(1, -1), (2, 1), (3, -1)])
def test_axis_labels_are_passed_through(self):
assert_equal(self.wrapper.axis_labels, self.stream.axis_labels)
class TestScaleAndShift(object):
def setUp(self):
dataset = IterableDataset(
OrderedDict([('features', [1, 2, 3]), ('targets', [0, 1, 0])]),
axis_labels={'features': ('batch'), 'targets': ('batch')})
self.stream = DataStream(dataset)
self.wrapper = ScaleAndShift(
self.stream, 2, -1, which_sources=('targets',))
def test_scale_and_shift(self):
assert_equal(list(self.wrapper.get_epoch_iterator()),
[(1, -1), (2, 1), (3, -1)])
def test_axis_labels_are_passed_through(self):
assert_equal(self.wrapper.axis_labels, self.stream.axis_labels)
split_dict = {
'train': {
'images': (0, train_feature.shape[0]),
'targets': (0, train_target.shape[0])
}
}
f.attrs['split'] = H5PYDataset.create_split_array(split_dict)
f.flush()
f.close()
train_set = H5PYDataset('../../data/dataset.hdf5', which_sets=('train',))
#data_stream = DataStream(dataset=train_set, iteration_scheme=scheme)
#state = train_set.open()
scheme = ShuffledScheme(examples=train_set.num_examples, batch_size=4)
data_stream = DataStream(dataset=train_set, iteration_scheme=scheme)
for data in data_stream.get_epoch_iterator():
print(data[0], data[1])
standardized_stream = ScaleAndShift(data_stream=data_stream,
scale=255,shift=0,
which_sources=('features',))
for data in standardized_stream.get_epoch_iterator():
print(data[0], data[1])
#train_set.close(state)
rate = 16000
for i, sample in enumerate(raw_audio[:n_samples]):
pyplot.plot(sample)
pyplot.savefig(save_dir +"original_%i.png" % i)
pyplot.close()
wavfile.write(save_dir + "original_{}.wav".format(i),
rate, sample)
data_stream = ScaleAndShift(data_stream, scale = 1/data_std,
shift = -data_mean/data_std)
data_stream = Mapping(data_stream, _downsample_and_upsample,
add_sources=('upsampled',))
epoch_iterator = data_stream.get_epoch_iterator()
raw_audio_std, upsampled_audio = next(epoch_iterator)
for i in xrange(n_iter-1):
x_tr,y_tr = next(epoch_iterator)
raw_audio_std = numpy.hstack([raw_audio_std, x_tr])
upsampled_audio = numpy.hstack([upsampled_audio, y_tr])
for i,(original_, upsampled_) in enumerate(
zip(raw_audio_std, upsampled_audio)[:n_samples]):
f, (ax1, ax2) = pyplot.subplots(2, sharex=True, sharey=True)
ax1.plot(original_)
ax2.plot(upsampled_)
f.subplots_adjust(hspace=0)
main_loop = load(os.path.join(exp_path, exp_file))
rate = 16000
for i, sample in enumerate(raw_audio[:n_samples]):
pyplot.plot(sample)
pyplot.savefig(save_dir + "original_%i.png" % i)
pyplot.close()
wavfile.write(save_dir + "original_{}.wav".format(i), rate, sample)
data_stream = ScaleAndShift(data_stream, scale=1 / data_std, shift=-data_mean / data_std)
data_stream = Mapping(data_stream, _downsample_and_upsample, add_sources=("upsampled",))
epoch_iterator = data_stream.get_epoch_iterator()
raw_audio_std, upsampled_audio = next(epoch_iterator)
for i in xrange(n_iter - 1):
x_tr, y_tr = next(epoch_iterator)
raw_audio_std = numpy.hstack([raw_audio_std, x_tr])
upsampled_audio = numpy.hstack([upsampled_audio, y_tr])
for i, (original_, upsampled_) in enumerate(zip(raw_audio_std, upsampled_audio)[:n_samples]):
f, (ax1, ax2) = pyplot.subplots(2, sharex=True, sharey=True)
ax1.plot(original_)
ax2.plot(upsampled_)
f.subplots_adjust(hspace=0)
f.savefig(save_dir + "comparison_upsample_%i.png" % i)
def test_scale_and_shift():
stream = DataStream(
IterableDataset(
OrderedDict([('features', [1, 2, 3]), ('targets', [0, 1, 0])])))
wrapper = ScaleAndShift(stream, 2, -1, which_sources=('targets',))
assert list(wrapper.get_epoch_iterator()) == [(1, -1), (2, 1), (3, -1)]
generator.weights_init = IsotropicGaussian(0.01)
generator.biases_init = Constant(0.)
generator.initialize()
#ipdb.set_trace()
cost_matrix = generator.cost_matrix(x, x_mask)
cost = cost_matrix.sum()/x_mask.sum()
cost.name = "sequence_log_likelihood"
##############
# Test with first batch
##############
x_tr, x_mask_tr = next(data_stream.get_epoch_iterator())
f1 = function([x, x_mask], cost)
#print f1(x_tr, x_mask_tr)
#ipdb.set_trace()
################
# Optimization Algorithm
################
cg = ComputationGraph(cost)
model = Model(cost)
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(10.0), Adam(lr)]),
