tf.nn.fractional_avg_pool
Performs fractional average pooling on the input.
tf.nn.fractional_avg_pool(
value,
pooling_ratio,
pseudo_random=False,
overlapping=False,
seed=0,
name=None
)
Fractional average pooling is similar to Fractional max pooling in the pooling
region generation step. The only difference is that after pooling regions are
generated, a mean operation is performed instead of a max operation in each
pooling region.
Args |
value
|
A Tensor . 4-D with shape [batch, height, width, channels] .
|
pooling_ratio
|
A list of floats that has length >= 4. Pooling ratio for
each dimension of value , currently only supports row and col dimension
and should be >= 1.0. For example, a valid pooling ratio looks like [1.0,
1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't
allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling
ratio on height and width dimensions respectively.
|
pseudo_random
|
An optional bool . Defaults to False . When set to True ,
generates the pooling sequence in a pseudorandom fashion, otherwise, in a
random fashion. Check paper (Graham, 2015) for difference between
pseudorandom and random.
|
overlapping
|
An optional bool . Defaults to False . When set to True ,
it means when pooling, the values at the boundary of adjacent pooling
cells are used by both cells. For example:
index 0 1 2 3 4
value 20 5 16 3 7
If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used
twice. The result would be [20, 16] for fractional avg pooling.
|
seed
|
An optional int . Defaults to 0 . If set to be non-zero, the
random number generator is seeded by the given seed. Otherwise it is
seeded by a random seed.
|
name
|
A name for the operation (optional).
|
A tuple of Tensor
objects (output
, row_pooling_sequence
,
col_pooling_sequence
).
output: Output Tensor
after fractional avg pooling. Has the same type as
value
.
row_pooling_sequence: A Tensor
of type int64
.
col_pooling_sequence: A Tensor
of type int64
.
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Last updated 2023-03-17 UTC.
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