# tf.raw_ops.FractionalAvgPool

<br />

Performs fractional average pooling on the input.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.FractionalAvgPool`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/FractionalAvgPool)

<br />

    tf.raw_ops.FractionalAvgPool(
        value,
        pooling_ratio,
        pseudo_random=False,
        overlapping=False,
        deterministic=False,
        seed=0,
        seed2=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.

<br />

<br />

<br />

| ## Args ||
|---|---|
| `value` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 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 [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) 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: <br /> `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 \[41/3, 26/3\] for fractional avg pooling. |
| `deterministic` | An optional `bool`. Defaults to `False`. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic. |
| `seed` | An optional `int`. Defaults to `0`. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. |
| `seed2` | An optional `int`. Defaults to `0`. An second seed to avoid seed collision. |
| `name` | A name for the operation (optional). |

<br />

<br />

| ## Returns ||
|---|---|
| A tuple of `Tensor` objects (output, row_pooling_sequence, col_pooling_sequence). ||
| `output` | A `Tensor`. Has the same type as `value`. |
| `row_pooling_sequence` | A `Tensor` of type `int64`. |
| `col_pooling_sequence` | A `Tensor` of type `int64`. |

<br />