tf.raw_ops.FractionalMaxPoolGrad

Computes gradient of the FractionalMaxPool function.

tf.raw_ops.FractionalMaxPoolGrad(
    orig_input,
    orig_output,
    out_backprop,
    row_pooling_sequence,
    col_pooling_sequence,
    overlapping=False,
    name=None
)

orig_input A Tensor. Must be one of the following types: float32, float64, int32, int64. Original input for fractional_max_pool
orig_output A Tensor. Must have the same type as orig_input. Original output for fractional_max_pool
out_backprop A Tensor. Must have the same type as orig_input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_max_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 [20, 16] for fractional max pooling.

name A name for the operation (optional).

A Tensor. Has the same type as orig_input.