Applies upper_bound(sorted_search_values, values) along each row.
tf.raw_ops.UpperBound(
sorted_inputs, values, out_type=tf.dtypes.int32, name=None
)
Each set of rows with the same index in (sorted_inputs, values) is treated
independently. The resulting row is the equivalent of calling
np.searchsorted(sorted_inputs, values, side='right').
The result is not a global index to the entire
Tensor, but rather just the index in the last dimension.
A 2-D example: sorted_sequence = [[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]] values = [[2, 4, 9], [0, 2, 6]]
result = UpperBound(sorted_sequence, values)
result == [[1, 2, 4], [0, 2, 5]]
Args | |
|---|---|
sorted_inputs
|
A Tensor. 2-D Tensor where each row is ordered.
|
values
|
A Tensor. Must have the same type as sorted_inputs.
2-D Tensor with the same numbers of rows as sorted_search_values. Contains
the values that will be searched for in sorted_search_values.
|
out_type
|
An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
A Tensor of type out_type.
|