Returns min/max k values and their indices of the input operand in an approximate manner.
tf.approx_top_k(
input: Annotated[Any, TV_ApproxTopK_T],
k: int,
reduction_dimension: int = -1,
recall_target: float = 0.95,
is_max_k: bool = True,
reduction_input_size_override: int = -1,
aggregate_to_topk: bool = True,
name=None
)
See https://arxiv.org/abs/2206.14286 for the algorithm details.
This op is only optimized on TPU currently.
Args |
input
|
A Tensor . Must be one of the following types: half , bfloat16 , float32 .
Array to search. Must be at least 1-D of the floating type
|
k
|
An int that is >= 0 . Specifies the number of min/max-k.
|
reduction_dimension
|
An optional int . Defaults to -1 .
Integer dimension along which to search. Default: -1.
|
recall_target
|
An optional float . Defaults to 0.95 .
Recall target for the approximation. Range in (0,1]
|
is_max_k
|
An optional bool . Defaults to True .
When true, computes max-k; otherwise computes min-k.
|
reduction_input_size_override
|
An optional int . Defaults to -1 .
When set to a positive value, it overrides the size determined by
input[reduction_dim] for evaluating the recall. This option is useful when
the given input is only a subset of the overall computation in SPMD or
distributed pipelines, where the true input size cannot be deferred by the
input shape.
|
aggregate_to_topk
|
An optional bool . Defaults to True .
When true, aggregates approximate results to top-k. When false, returns the
approximate results. The number of the approximate results is implementation
defined and is greater equals to the specified k .
|
name
|
A name for the operation (optional).
|
Returns |
A tuple of Tensor objects (values, indices).
|
values
|
A Tensor . Has the same type as input .
|
indices
|
A Tensor of type int32 .
|