Class MaxPool2d

tensorlayer/layers/pooling.py:269–341

Max pooling for 2D image. Parameters ----------- filter_size : tuple of int (height, width) for filter size. strides : tuple of int (height, width) for strides. padding : str The padding method: 'VALID' or 'SAME'. data_format : str One of chan

class MaxPool2d(Layer):
    """Max pooling for 2D image.

    Parameters
    -----------
    filter_size : tuple of int
        (height, width) for filter size.
    strides : tuple of int
        (height, width) for strides.
    padding : str
        The padding method: 'VALID' or 'SAME'.
    data_format : str
        One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
    name : None or str
        A unique layer name.

    Examples
    ---------
    With TensorLayer

    >>> net = tl.layers.Input([None, 50, 50, 32], name='input')
    >>> net = tl.layers.MaxPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME')(net)
    >>> output shape : [None, 25, 25, 32]

    """

    def __init__(
        self,
        filter_size=(3, 3),
        strides=(2, 2),
        padding='SAME',
        data_format='channels_last',
        name=None  # 'maxpool2d'
    ):
        super().__init__(name)
        self.filter_size = filter_size
        if strides is None:
            strides = filter_size
        self.strides = self._strides = strides
        self.padding = padding
        self.data_format = data_format

        self.build()
        self._built = True

        logging.info(
            "MaxPool2d %s: filter_size: %s strides: %s padding: %s" %
            (self.name, str(filter_size), str(strides), str(padding))
        )

    def __repr__(self):
        s = ('{classname}(filter_size={filter_size}' ', strides={strides}, padding={padding}')
        if self.name is not None:
            s += ', name=\'{name}\''
        s += ')'
        return s.format(classname=self.__class__.__name__, **self.__dict__)

    def build(self, inputs_shape=None):