def test_data_feeder_multioutput_regression(self):
X = np.matrix([[1, 2], [3, 4]])
y = np.array([[1, 2], [3, 4]])
df = data_feeder.DataFeeder(X, y, n_classes=0, batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[3, 4], [1, 2]])
self.assertAllClose(feed_dict['output'], [[3, 4], [1, 2]])
def _setup_data_feeder(self, X, y):
"""Create data feeder, to sample inputs from dataset.
If X and y are iterators, use StreamingDataFeeder.
"""
if hasattr(X, 'next'):
assert hasattr(y, 'next')
self._data_feeder = data_feeder.StreamingDataFeeder(
X, y, self.n_classes, self.batch_size)
else:
self._data_feeder = data_feeder.DataFeeder(X, y, self.n_classes,
self.batch_size)
def test_data_feeder_multioutput_classification(self):
random.seed(42)
X = np.matrix([[1, 2], [3, 4]])
y = np.array([[0, 1, 2], [2, 3, 4]])
df = data_feeder.DataFeeder(X, y, n_classes=5, batch_size=2)
feed_dict_fn = df.get_feed_dict_fn(MockPlaceholder(name='input'),
MockPlaceholder(name='output'))
feed_dict = feed_dict_fn()
self.assertAllClose(feed_dict['input'], [[3, 4], [1, 2]])
self.assertAllClose(
feed_dict['output'],
[[[0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]],
[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0]]])