tf.raw_ops.FractionalAvgPool

Performs fractional average pooling on the input.

tf.raw_ops.FractionalAvgPool(
    value, pooling_ratio, pseudo_random=False, overlapping=False,
    deterministic=False, seed=0, seed2=0, name=None
)

Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region.

Args:

  • value: A Tensor. Must be one of the following types: float32, float64, int32, int64. 4-D with shape [batch, height, width, channels].
  • pooling_ratio: A list of floats that has length >= 4. Pooling ratio for each dimension of value, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively.
  • pseudo_random: An optional bool. Defaults to False. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper Benjamin Graham, Fractional Max-Pooling for difference between pseudorandom and random.
  • 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.

  • deterministic: An optional bool. Defaults to False. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic.

  • seed: An optional int. Defaults to 0. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.

  • seed2: An optional int. Defaults to 0. An second seed to avoid seed collision.

  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (output, row_pooling_sequence, col_pooling_sequence).

  • output: A Tensor. Has the same type as value.
  • row_pooling_sequence: A Tensor of type int64.
  • col_pooling_sequence: A Tensor of type int64.