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_max(embedded_sequence_batch)