def test_batch_mean(self):
correct = np.array([-2. / 3, 1., 21. / 4])
with clean_session():
array = tf.constant([
[1, -8, 5, 4, 9],
[0, 2, 7, 8, 1],
[2, -8, 6, 4, 9],
], dtype=tf.float32)
mask = tf.constant([
[1, 1, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 1, 1, 1],
], dtype=tf.float32)
bad_mask = tf.constant([
[1, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[1, 0, 1, 1, 1],
], dtype=tf.float32)
bm = reduce_mean(SequenceBatch(array, mask))
assert_almost_equal(bm.eval(), correct, decimal=5)
bm2 = reduce_mean(SequenceBatch(array, bad_mask))
with pytest.raises(InvalidArgumentError):
bm2.eval()
# try allow_empty option
bm3 = reduce_mean(SequenceBatch(array, bad_mask), allow_empty=True)
assert_almost_equal(bm3.eval(), np.array([-2. / 3, 0., 21. / 4]))
def test_multidim(self):
npa = lambda arr: np.array(arr, dtype=np.float32)
correct = npa([
npa([4, 7, 10]) / 2,
npa([8, 14, 20]) / 3,
npa([13, 16, 19]) / 3,
])
with clean_session():
array = tf.constant([[[1., 2., 3.],
[3., 5., 7.],
[0., 0., 0.]],
[[2., 4., 6.],
[3., 5., 7.],
[3., 5., 7.]],
[[9., 9., 9.],
[3., 5., 7.],
[1., 2., 3.]]], dtype=tf.float32)
mask = tf.constant([
[1, 1, 0],
[1, 1, 1],
[1, 1, 1],
], dtype=tf.float32)
bm = reduce_mean(SequenceBatch(array, mask))
assert_almost_equal(bm.eval(), correct, decimal=5)
def test(self):
npa = lambda arr: np.array(arr, dtype=np.float32)
correct = npa([
npa([3, 5, 7]),
npa([3, 5, 7]),
npa([9, 9, 9]),
])
with clean_session():
array = tf.constant(
[[[1., 2., 3.], [3., 5., 7.], [100., 200., 2000.]],
[[2., 4., 6.], [3., 5., 7.], [3., 5., 7.]],
[[9., 9., 9.], [3., 5., 7.], [1., 2., 3.]]],
dtype=tf.float32)
mask = tf.constant([
[1, 1, 0],
[1, 1, 1],
[1, 1, 1],
],
dtype=tf.float32)
bm = reduce_max(SequenceBatch(array, mask))
assert_almost_equal(bm.eval(), correct, decimal=5)
bad_mask = tf.constant([
[0, 0, 0],
[1, 1, 1],
[1, 1, 1],
],
dtype=tf.float32)
bm2 = reduce_mean(SequenceBatch(array, bad_mask))
with pytest.raises(InvalidArgumentError):
bm2.eval()
def embed_sequences(self, embedded_sequence_batch):
return reduce_mean(embedded_sequence_batch, allow_empty=self._allow_empty)
def test_empty(self):
with clean_session():
array = tf.constant(np.empty((0, 10, 20)))
mask = tf.constant(np.empty((0, 10)))
bm = reduce_mean(SequenceBatch(array, mask))
assert bm.eval().shape == (0, 20)