Class GlobalMeanPool2d

tensorlayer/layers/pooling.py:749–802

The :class:`GlobalMeanPool2d` class is a 2D Global Mean Pooling layer. Parameters ------------ 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 uniq

class GlobalMeanPool2d(Layer):
    """The :class:`GlobalMeanPool2d` class is a 2D Global Mean Pooling layer.

    Parameters
    ------------
    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, 100, 100, 30], name='input')
    >>> net = tl.layers.GlobalMeanPool2d()(net)
    >>> output shape : [None, 30]

    """

    def __init__(
        self,
        data_format='channels_last',
        name=None  # 'globalmeanpool2d'
    ):
        super().__init__(name)

        self.data_format = data_format

        self.build()
        self._built = True

        logging.info("GlobalMeanPool2d %s" % self.name)

    def __repr__(self):
        s = '{classname}('
        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):
        pass

    def forward(self, inputs):
        if self.data_format == 'channels_last':
            outputs = tf.reduce_mean(input_tensor=inputs, axis=[1, 2], name=self.name)
        elif self.data_format == 'channels_first':
            outputs = tf.reduce_mean(input_tensor=inputs, axis=[2, 3], name=self.name)
        else:
            raise ValueError(
                "`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
            )
        return outputs


class GlobalMaxPool3d(Layer):