Python tensorflow.python.ops.nn.avg_pool() Examples

The following are 11 code examples of tensorflow.python.ops.nn.avg_pool(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.nn , or try the search function .
Example #1
Source File: pooling.py    From lambda-packs with MIT License 5 votes vote down vote up 
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)
Example #2
Source File: pooling.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __init__(self, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(AveragePooling2D, self).__init__(
        nn.avg_pool,
        pool_size=pool_size, strides=strides,
        padding=padding, data_format=data_format, name=name, **kwargs)
Example #3
Source File: pooling.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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)
Example #4
Source File: pooling.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __init__(self, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(AveragePooling2D, self).__init__(
        nn.avg_pool,
        pool_size=pool_size, strides=strides,
        padding=padding, data_format=data_format, name=name, **kwargs)
Example #5
Source File: pooling.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
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)
Example #6
Source File: pooling.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(AveragePooling2D, self).__init__(
        nn.avg_pool,
        pool_size=pool_size, strides=strides,
        padding=padding, data_format=data_format, name=name, **kwargs)
Example #7
Source File: pooling.py    From keras-lambda with MIT License 5 votes vote down vote up 
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)
Example #8
Source File: pooling.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(AveragePooling2D, self).__init__(
        nn.avg_pool,
        pool_size=pool_size, strides=strides,
        padding=padding, data_format=data_format, name=name, **kwargs)
Example #9
Source File: backend.py    From lambda-packs with MIT License 4 votes vote down vote up 
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)
Example #10
Source File: layers.py    From deep_image_model with Apache License 2.0 4 votes vote down vote up 
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)
Example #11
Source File: backend.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
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)