# W3cubDocs

/TensorFlow 2.4

Computes gradient of the FractionalAvgPool function.

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.

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:

`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).
Returns
A `Tensor`. Has the same type as `out_backprop`.