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 |
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 .
|