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# tf.math.unsorted_segment_min

Computes the minimum along segments of a tensor.

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


Read the section on segmentation for an explanation of segments.

This operator is similar to the unsorted segment sum operator found (here). Instead of computing the sum over segments, it computes the minimum such that:

$$output_i = \min_{j...} data_[j...]$$ where min is over tuples j... such that segment_ids[j...] == i.

If the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits<T>::max().

#### For example:

c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2)
# ==> [[ 1,  2, 2, 1],
#       [5,  6, 7, 8]]


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, int64, bfloat16, uint16, 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.