tf.math.unsorted_segment_prod

Defined in generated file: python/ops/gen_math_ops.py

免费成长做爱直播有哪些Computes the product along segments of a tensor.

Aliases:

  • tf.compat.v1.math.unsorted_segment_prod
  • tf.compat.v1.unsorted_segment_prod
  • tf.compat.v2.math.unsorted_segment_prod
tf.math.unsorted_segment_prod(
    data,
    segment_ids,
    num_segments,
    name=None
)

Read for an explanation of segments.

This operator is similar to the unsorted segment sum operator found (here)免费成长做爱直播有哪些. Instead of computing the sum over segments, it computes the product of all entries belonging to a segment such that:

\(outputi = \prod{j...} data[j...]\) 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.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 4,  6, 6, 4],
#       [5,  6, 7, 8]]

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.

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.
  • 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.

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