tf.raw_ops.SegmentMaxV2  |  TensorFlow v2.16.1 (original) (raw)

tf.raw_ops.SegmentMaxV2

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Computes the maximum along segments of a tensor.

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Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.raw_ops.SegmentMaxV2

tf.raw_ops.SegmentMaxV2(
    data, segment_ids, num_segments, name=None
)

Readthe section on segmentationfor an explanation of segments.

Computes a tensor such that \(output_i = \max_j(data_j)\) where max is over j such that segment_ids[j] == i.

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

The only difference with SegmentMax 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 smallest possible value for the specific numeric type.

For example:

@tf.function(jit_compile=True) def test(c): return tf.raw_ops.SegmentMaxV2(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, 3, 3, 4], [5, 6, 7, 8]], dtype=int32)