Function avg_pool1d

tensorflow/python/ops/nn_ops.py:3725–3766

Performs the average pooling on the input. Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. Note internally this op reshapes and uses the underlying 2d operation. Args: input: A 3-D `Tensor` of the format specified by `data_format`. ksize: An

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

@tf_export("nn.avg_pool1d")
def avg_pool1d(input, ksize, strides, padding, data_format="NWC", name=None):  # pylint: disable=redefined-builtin
  """Performs the average pooling on the input.

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

  Note internally this op reshapes and uses the underlying 2d operation.

  Args:
    input: A 3-D `Tensor` of the format specified by `data_format`.
    ksize: An int or list of `ints` that has length `1` or `3`. The size of the
      window for each dimension of the input tensor.
    strides: An int or list of `ints` that has length `1` or `3`. 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: An optional string from: "NWC", "NCW". Defaults to "NWC".
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of format specified by `data_format`.
    The max pooled output tensor.
  """
  with ops.name_scope(name, "AvgPool1D", [input]) as name:
    if data_format is None:
      data_format = "NWC"
    channel_index = 1 if data_format.startswith("NC") else 2
    ksize = [1] + _get_sequence(ksize, 1, channel_index, "ksize")
    strides = [1] + _get_sequence(strides, 1, channel_index, "strides")

    expanding_dim = 1 if data_format == "NWC" else 2
    data_format = "NHWC" if data_format == "NWC" else "NCHW"

    input = array_ops.expand_dims_v2(input, expanding_dim)
    result = gen_nn_ops.avg_pool(
        input,
        ksize=ksize,
        strides=strides,
        padding=padding,
        data_format=data_format,
        name=name)
    return array_ops.squeeze(result, expanding_dim)


@tf_export("nn.avg_pool3d")