Python nn.avg_pool方法代码示例

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import avg_pool [as 别名]
def __init__(self, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(AveragePooling1D, self).__init__(
        nn.avg_pool,
        pool_size=pool_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        name=name,
        **kwargs) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import avg_pool [as 别名]
def pool2d(x,
           pool_size,
           strides=(1, 1),
           padding='valid',
           data_format=None,
           pool_mode='max'):
  """2D Pooling.

  Arguments:
      x: Tensor or variable.
      pool_size: tuple of 2 integers.
      strides: tuple of 2 integers.
      padding: one of `"valid"`, `"same"`.
      data_format: one of `"channels_first"`, `"channels_last"`.
      pool_mode: one of `"max"`, `"avg"`.

  Returns:
      A tensor, result of 2D 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))

  padding = _preprocess_padding(padding)
  strides = (1,) + strides + (1,)
  pool_size = (1,) + pool_size + (1,)

  x = _preprocess_conv2d_input(x, data_format)

  if pool_mode == 'max':
    x = nn.max_pool(x, pool_size, strides, padding=padding)
  elif pool_mode == 'avg':
    x = nn.avg_pool(x, pool_size, strides, padding=padding)
  else:
    raise ValueError('Invalid pooling mode:', pool_mode)

  return _postprocess_conv2d_output(x, data_format) 

# 需要导入模块: from tensorflow.python.ops import nn [as 别名]
# 或者: from tensorflow.python.ops.nn import avg_pool [as 别名]
def avg_pool2d(inputs,
               kernel_size,
               stride=2,
               padding='VALID',
               data_format=DATA_FORMAT_NHWC,
               outputs_collections=None,
               scope=None):
  """Adds a 2D average pooling op.

  It is assumed that the pooling is done per image but not in batch or channels.

  Args:
    inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if
      `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if
      `data_format` is `NCHW`.
    kernel_size: A list of length 2: [kernel_height, kernel_width] of the
      pooling kernel over which the op is computed. Can be an int if both
      values are the same.
    stride: A list of length 2: [stride_height, stride_width].
      Can be an int if both strides are the same. Note that presently
      both strides must have the same value.
    padding: The padding method, either 'VALID' or 'SAME'.
    data_format: A string. `NHWC` (default) and `NCHW` are supported.
    outputs_collections: The collections to which the outputs are added.
    scope: Optional scope for name_scope.

  Returns:
    A `Tensor` representing the results of the pooling operation.

  Raises:
    ValueError: if `data_format` is neither `NHWC` nor `NCHW`.
  """
  if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC):
    raise ValueError('data_format has to be either NCHW or NHWC.')
  with ops.name_scope(scope, 'AvgPool2D', [inputs]) as sc:
    inputs = ops.convert_to_tensor(inputs)
    kernel_h, kernel_w = utils.two_element_tuple(kernel_size)
    stride_h, stride_w = utils.two_element_tuple(stride)
    if data_format == DATA_FORMAT_NHWC:
      ksize = [1, kernel_h, kernel_w, 1]
      strides = [1, stride_h, stride_w, 1]
    else:
      ksize = [1, 1, kernel_h, kernel_w]
      strides = [1, 1, stride_h, stride_w]
    outputs = nn.avg_pool(inputs,
                          ksize=ksize,
                          strides=strides,
                          padding=padding,
                          data_format=data_format)
    return utils.collect_named_outputs(outputs_collections, sc, outputs)