Computes gradient of the FractionalAvgPool function.

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.

Args | |
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`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:
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 | |
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A `Tensor` . Has the same type as `out_backprop` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/FractionalAvgPoolGrad