Function pool3d

tensorflow/python/keras/backend.py:5171–5220

3D Pooling. Arguments: x: Tensor or variable. pool_size: tuple of 3 integers. strides: tuple of 3 integers. padding: string, `"same"` or `"valid"`. data_format: string, `"channels_last"` or `"channels_first"`. pool_mode: string, `"max"` or `"avg"`. Returns:

(x,
           pool_size,
           strides=(1, 1, 1),
           padding='valid',
           data_format=None,
           pool_mode='max')

@keras_export('keras.backend.pool3d')
def pool3d(x,
           pool_size,
           strides=(1, 1, 1),
           padding='valid',
           data_format=None,
           pool_mode='max'):
  """3D Pooling.

  Arguments:
      x: Tensor or variable.
      pool_size: tuple of 3 integers.
      strides: tuple of 3 integers.
      padding: string, `"same"` or `"valid"`.
      data_format: string, `"channels_last"` or `"channels_first"`.
      pool_mode: string, `"max"` or `"avg"`.

  Returns:
      A tensor, result of 3D pooling.

  Raises:
      ValueError: if `data_format` is neither `"channels_last"` or
      `"channels_first"`.
      ValueError: if `pool_mode` is neither `"max"` or `"avg"`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format: ' + str(data_format))

  x, tf_data_format = _preprocess_conv3d_input(x, data_format)
  padding = _preprocess_padding(padding)
  if tf_data_format == 'NDHWC':
    strides = (1,) + strides + (1,)
    pool_size = (1,) + pool_size + (1,)
  else:
    strides = (1, 1) + strides
    pool_size = (1, 1) + pool_size

  if pool_mode == 'max':
    x = nn.max_pool3d(
        x, pool_size, strides, padding=padding, data_format=tf_data_format)
  elif pool_mode == 'avg':
    x = nn.avg_pool3d(
        x, pool_size, strides, padding=padding, data_format=tf_data_format)
  else:
    raise ValueError('Invalid pooling mode: ' + str(pool_mode))

  if data_format == 'channels_first' and tf_data_format == 'NDHWC':
    x = array_ops.transpose(x, (0, 4, 1, 2, 3))
  return x


def local_conv(inputs,