Function pool

tensorflow/python/ops/nn_ops.py:1182–1352

Performs an N-D pooling operation. In the case that `data_format` does not start with "NC", computes for 0 <= b < batch_size, 0 <= x[i] < output_spatial_shape[i], 0 <= c < num_channels: ``` output[b, x[0], ..., x[N-1], c] = REDUCE_{z[0], ..., z[N-1]} input[b

(
    input,  # pylint: disable=redefined-builtin
    window_shape,
    pooling_type,
    padding,
    dilation_rate=None,
    strides=None,
    name=None,
    data_format=None,
    dilations=None)

@tf_export(v1=["nn.pool"])
def pool(
    input,  # pylint: disable=redefined-builtin
    window_shape,
    pooling_type,
    padding,
    dilation_rate=None,
    strides=None,
    name=None,
    data_format=None,
    dilations=None):
  """Performs an N-D pooling operation.

  In the case that `data_format` does not start with "NC", computes for
      0 <= b < batch_size,
      0 <= x[i] < output_spatial_shape[i],
      0 <= c < num_channels:

  ```
    output[b, x[0], ..., x[N-1], c] =
      REDUCE_{z[0], ..., z[N-1]}
        input[b,
              x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0],
              ...
              x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1],
              c],
  ```

  where the reduction function REDUCE depends on the value of `pooling_type`,
  and pad_before is defined based on the value of `padding` as described in
  the "returns" section of `tf.nn.convolution` for details.
  The reduction never includes out-of-bounds positions.

  In the case that `data_format` starts with `"NC"`, the `input` and output are
  simply transposed as follows:

  ```
    pool(input, data_format, **kwargs) =
      tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
                        **kwargs),
                   [0, N+1] + range(1, N+1))
  ```

  Args:
    input: Tensor of rank N+2, of shape
      `[batch_size] + input_spatial_shape + [num_channels]` if data_format does
      not start with "NC" (default), or
      `[batch_size, num_channels] + input_spatial_shape` if data_format starts
      with "NC".  Pooling happens over the spatial dimensions only.
    window_shape: Sequence of N ints >= 1.
    pooling_type: Specifies pooling operation, must be "AVG" or "MAX".
    padding: The padding algorithm, must be "SAME" or "VALID".
      See the "returns" section of `tf.nn.convolution` for details.
    dilation_rate: Optional.  Dilation rate.  List of N ints >= 1.
      Defaults to [1]*N.  If any value of dilation_rate is > 1, then all values
      of strides must be 1.
    strides: Optional.  Sequence of N ints >= 1.  Defaults to [1]*N.
      If any value of strides is > 1, then all values of dilation_rate must be
      1.