Computes the product along segments of a tensor.
tf.raw_ops.SegmentProdV2(
data, segment_ids, num_segments, name=None
)
Read the section on segmentation for an explanation of segments.
Computes a tensor such that
\(output_i = \prod_j data_j\) where the product is over j
such
that segment_ids[j] == i
.
If the product is empty for a given segment ID i
, output[i] = 1
.
The only difference with SegmentProd is the additional input num_segments
.
This helps in evaluating the output shape in compile time.
num_segments
should be consistent with segment_ids.
e.g. Max(segment_ids) - 1 should be equal to num_segments
for a 1-d segment_ids
With inconsistent num_segments, the op still runs. only difference is,
the output takes the size of num_segments irrespective of size of segment_ids and data.
for num_segments less than expected output size, the last elements are ignored
for num_segments more than the expected output size, last elements are assigned 1.
For example:
@tf.function(jit_compile=True)
def test(c):
return tf.raw_ops.SegmentProdV2(data=c, segment_ids=tf.constant([0, 0, 1]), num_segments=2)
c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
test(c).numpy()
array([[4, 6, 6, 4],
[5, 6, 7, 8]], dtype=int32)
Returns | |
---|---|
A Tensor . Has the same type as data .
|