Function avg_pool_v2

tensorflow/python/ops/nn_ops.py:3580–3638

Performs the avg pooling on the input. Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. 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

(input, ksize, strides, padding, data_format=None, name=None)

@tf_export("nn.avg_pool", v1=["nn.avg_pool_v2"])
def avg_pool_v2(input, ksize, strides, padding, data_format=None, name=None):  # pylint: disable=redefined-builtin
  """Performs the avg pooling on the input.

  Each entry in `output` is the mean of the corresponding size `ksize`
  window in `value`.

  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.
    ksize: An int or list of `ints` that has length `1`, `N` or `N+2`. The size
      of the window for each dimension of the input tensor.
    strides: An int or list of `ints` that has length `1`, `N` or `N+2`. The
      stride of the sliding window for each dimension of the input tensor.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
      the "returns" section of `tf.nn.convolution` for details.
    data_format: A string. Specifies the channel dimension. For N=1 it can be
      either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default)
      or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
    name: Optional name for the operation.

  Returns:
    A `Tensor` of format specified by `data_format`.
    The average pooled output tensor.
  """
  if input.shape is not None:
    n = len(input.shape) - 2
  elif data_format is not None:
    n = len(data_format) - 2
  else:
    raise ValueError(
        "The input must have a rank or a data format must be given.")
  if not 1 <= n <= 3:
    raise ValueError(
        "Input tensor must be of rank 3, 4 or 5 but was {}.".format(n + 2))

  if data_format is None:
    channel_index = n + 1
  else:
    channel_index = 1 if data_format.startswith("NC") else n + 1

  ksize = _get_sequence(ksize, n, channel_index, "ksize")
  strides = _get_sequence(strides, n, channel_index, "strides")

  avg_pooling_ops = {
      1: avg_pool1d,
      2: gen_nn_ops.avg_pool,
      3: gen_nn_ops.avg_pool3d
  }

  op = avg_pooling_ops[n]
  return op(
      input,
      ksize=ksize,
      strides=strides,
      padding=padding,
      data_format=data_format,