def test_not_equal_indices_and_seg_ids_num(self):
with self.session(use_gpu=use_gpu, config=default_config):
data = constant_op.constant(list(range(20)), dtype=dtypes.float32)
data = array_ops.reshape(data, (10, 2))
indices = constant_op.constant(list(range(6)), dtype=dtypes.int32)
segment_ids = constant_op.constant(list(range(7)),
dtype=dtypes.int32)
with self.assertRaises((ValueError, errors.InvalidArgumentError)):
target = de_math.sparse_segment_sum(data, indices, segment_ids)
self.evaluate(target)
def forward_compute(self, data, indices, segment_ids, num_segments=None):
result = de_math.sparse_segment_sum(data,
indices,
segment_ids,
num_segments=num_segments)
expected = math_ops.sparse_segment_sum(data,
indices,
segment_ids,
num_segments=num_segments)
return result, expected
def backward_compute(self, data, indices, segment_ids, num_segments=None):
with backprop.GradientTape(persistent=True) as tape:
tape.watch(data)
result = de_math.sparse_segment_sum(data,
indices,
segment_ids,
num_segments=num_segments)
expected = math_ops.sparse_segment_sum(data,
indices,
segment_ids,
num_segments=num_segments)
result = tape.gradient(result, data)
expected = tape.gradient(expected, data)
return result, expected