tf.math.unsorted_segment_prod

Computes the product along segments of a tensor.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.unsorted_segment_prod, tf.compat.v1.unsorted_segment_prod

tf.math.unsorted_segment_prod(
    data, segment_ids, num_segments, name=None
)

Read the section on segmentation for an explanation of segments.

This operator is similar to tf.math.unsorted_segment_sum, Instead of computing the sum over segments, it computes the product of all entries belonging to a segment such that:

\(output_i = \prod_{j...} data[j...]\) where the product is over tuples j... such that segment_ids[j...] == i.

For example:

c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.math.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2).numpy()
array([[4, 6, 6, 4],
       [5, 6, 7, 8]], dtype=int32)

If there is no entry for a given segment ID i, it outputs 1.

If the given segment ID i is negative, then the corresponding value is dropped, and will not be included in the result. Caution: On CPU, values in segment_ids are always validated to be less than num_segments, and an error is thrown for out-of-bound indices. On GPU, this does not throw an error for out-of-bound indices. On Gpu, out-of-bound indices result in safe but unspecified behavior, which may include ignoring out-of-bound indices or outputting a tensor with a 0 stored in the first dimension of its shape if num_segments is 0.

Args
data	A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
segment_ids	A Tensor. Must be one of the following types: int32, int64. A tensor whose shape is a prefix of data.shape. The values must be less than num_segments.
num_segments	A Tensor. Must be one of the following types: int32, int64.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type as data.