# tf.raw_ops.FractionalAvgPoolGrad

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Computes gradient of the FractionalAvgPool function.

#### View aliases


**Compat aliases for migration**

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

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

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    tf.raw_ops.FractionalAvgPoolGrad(
        orig_input_tensor_shape,
        out_backprop,
        row_pooling_sequence,
        col_pooling_sequence,
        overlapping=False,
        name=None
    )

Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
out_backprop to those indices that form the same pooling cell. Therefore, we
just need to know the shape of original input tensor, instead of the whole
tensor.

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| ## Args ||
|---|---|
| `orig_input_tensor_shape` | A `Tensor` of type `int64`. Original input tensor shape for `fractional_avg_pool` |
| `out_backprop` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_avg_pool`. |
| `row_pooling_sequence` | A `Tensor` of type `int64`. row pooling sequence, form pooling region with col_pooling_sequence. |
| `col_pooling_sequence` | A `Tensor` of type `int64`. column pooling sequence, form pooling region with row_pooling sequence. |
| `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. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `out_backprop`. ||

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