Computes gradient of the FractionalAvgPool function.
Compat aliases for migration
See Migration guide for more details.
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.
int64. Original input tensor shape for
Tensor. Must be one of the following types:
int64. 4-D with shape
[batch, height, width, channels]. Gradients w.r.t. the output of
int64. row pooling sequence, form pooling region with col_pooling_sequence.
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).
Tensor. Has the same type as