# tf.compat.v1.nn.fractional_avg_pool

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L6250-L6313) |

Performs fractional average pooling on the input. (deprecated)

    tf.compat.v1.nn.fractional_avg_pool(
        value,
        pooling_ratio,
        pseudo_random=False,
        overlapping=False,
        deterministic=False,
        seed=0,
        seed2=0,
        name=None
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: `seed2` and `deterministic` args are deprecated. Use fractional_avg_pool_v2.

This is a deprecated version of `fractional_avg_pool`.

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.

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| ## Args ||
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| `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. |
| `deterministic` | An optional `bool`. Deprecated; use `fractional_avg_pool_v2` instead. |
| `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. |
| `seed2` | An optional `int`. Deprecated; use `fractional_avg_pool_v2` instead. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
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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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| ## References ||
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| Fractional Max-Pooling: [Graham, 2015](https://arxiv.org/abs/1412.6071) ([pdf](https://arxiv.org/pdf/1412.6071.pdf)) ||

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