tf.raw_ops.SegmentMinV2

Computes the minimum along segments of a tensor.

tf.raw_ops.SegmentMinV2(
    data, segment_ids, num_segments, name=None
)

Read the section on segmentation for an explanation of segments.

Computes a tensor such that \(output_i = \min_j(data_j)\) where min is over 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().

The only difference with SegmentMin 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) should be equal to num_segments - 1 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 the largest possible value for the specific numeric type.

For example:

@tf.function(jit_compile=True)
def test(c):
  return tf.raw_ops.SegmentMinV2(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([[1, 2, 2, 1],
       [5, 6, 7, 8]], dtype=int32)