docstring fixed

Author: Gyx-One

tensorlayer/layers/pooling.py

@@ -31,6 +31,7 @@ class PoolLayer(Layer):
     The :class:`PoolLayer` class is a Pooling layer.
     You can choose ``tf.nn.max_pool`` and ``tf.nn.avg_pool`` for 2D input or
     ``tf.nn.max_pool3d`` and ``tf.nn.avg_pool3d`` for 3D input.
+
     Parameters
     ----------
     filter_size : tuple of int
@@ -46,12 +47,15 @@ class PoolLayer(Layer):
         See `TensorFlow pooling APIs <https://tensorflow.google.cn/versions/r2.0/api_docs/python/tf/nn/>`__
     name : None or str
         A unique layer name.
+
     Examples
     ---------
     With TensorLayer
+
     >>> net = tl.layers.Input([None, 50, 50, 32], name='input')
     >>> net = tl.layers.PoolLayer()(net)
     >>> output shape : [None, 25, 25, 32]
+
     """
 
     def __init__(
@@ -93,6 +97,7 @@ def forward(self, inputs):
 
 class MaxPool1d(Layer):
     """Max pooling for 1D signal.
+
     Parameters
     ----------
     filter_size : int
@@ -105,12 +110,15 @@ class MaxPool1d(Layer):
         One of channels_last (default, [batch, length, 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, 32], name='input')
     >>> net = tl.layers.MaxPool1d(filter_size=3, strides=2, padding='SAME', name='maxpool1d')(net)
     >>> output shape : [None, 25, 32]
+
     """
 
     def __init__(
@@ -174,6 +182,7 @@ def forward(self, inputs):
 
 class MeanPool1d(Layer):
     """Mean pooling for 1D signal.
+
     Parameters
     ------------
     filter_size : int
@@ -186,12 +195,15 @@ class MeanPool1d(Layer):
         One of channels_last (default, [batch, length, 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, 32], name='input')
     >>> net = tl.layers.MeanPool1d(filter_size=3, strides=2, padding='SAME')(net)
     >>> output shape : [None, 25, 32]
+
     """
 
     def __init__(
@@ -256,6 +268,7 @@ def forward(self, inputs):
 
 class MaxPool2d(Layer):
     """Max pooling for 2D image.
+
     Parameters
     -----------
     filter_size : tuple of int
@@ -268,12 +281,15 @@ class MaxPool2d(Layer):
         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__(
@@ -327,6 +343,7 @@ def forward(self, inputs):
 
 class MeanPool2d(Layer):
     """Mean pooling for 2D image [batch, height, width, channel].
+
     Parameters
     -----------
     filter_size : tuple of int
@@ -339,12 +356,15 @@ class MeanPool2d(Layer):
         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.MeanPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME')(net)
     >>> output shape : [None, 25, 25, 32]
+
     """
 
     def __init__(
@@ -398,6 +418,7 @@ def forward(self, inputs):
 
 class MaxPool3d(Layer):
     """Max pooling for 3D volume.
+
     Parameters
     ------------
     filter_size : tuple of int
@@ -410,16 +431,20 @@ class MaxPool3d(Layer):
         One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
     name : None or str
         A unique layer name.
+
     Returns
     -------
     :class:`tf.Tensor`
         A max pooling 3-D layer with a output rank as 5.
+
     Examples
     ---------
     With TensorLayer
+
     >>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input')
     >>> net = tl.layers.MaxPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME')(net)
     >>> output shape : [None, 25, 25, 25, 32]
+
     """
 
     def __init__(
@@ -475,6 +500,7 @@ def forward(self, inputs):
 
 class MeanPool3d(Layer):
     """Mean pooling for 3D volume.
+
     Parameters
     ------------
     filter_size : tuple of int
@@ -487,16 +513,20 @@ class MeanPool3d(Layer):
         One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
     name : None or str
         A unique layer name.
+
     Returns
     -------
     :class:`tf.Tensor`
         A mean pooling 3-D layer with a output rank as 5.
+
     Examples
     ---------
     With TensorLayer
+
     >>> net = tl.layers.Input([None, 50, 50, 50, 32], name='input')
     >>> net = tl.layers.MeanPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME')(net)
     >>> output shape : [None, 25, 25, 25, 32]
+
     """
 
     def __init__(
@@ -552,18 +582,22 @@ def forward(self, inputs):
 
 class GlobalMaxPool1d(Layer):
     """The :class:`GlobalMaxPool1d` class is a 1D Global Max Pooling layer.
+
     Parameters
     ------------
     data_format : str
         One of channels_last (default, [batch, length, 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, 30], name='input')
     >>> net = tl.layers.GlobalMaxPool1d()(net)
     >>> output shape : [None, 30]
+
     """
 
     def __init__(
@@ -604,18 +638,22 @@ def forward(self, inputs):
 
 class GlobalMeanPool1d(Layer):
     """The :class:`GlobalMeanPool1d` class is a 1D Global Mean Pooling layer.
+
     Parameters
     ------------
     data_format : str
         One of channels_last (default, [batch, length, 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, 30], name='input')
     >>> net = tl.layers.GlobalMeanPool1d()(net)
     >>> output shape : [None, 30]
+
     """
 
     def __init__(
@@ -655,18 +693,22 @@ def forward(self, inputs):
 
 class GlobalMaxPool2d(Layer):
     """The :class:`GlobalMaxPool2d` class is a 2D Global Max 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.GlobalMaxPool2d()(net)
     >>> output shape : [None, 30]
+
     """
 
     def __init__(
@@ -706,18 +748,22 @@ def forward(self, inputs):
 
 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__(
@@ -758,18 +804,22 @@ def forward(self, inputs):
 
 class GlobalMaxPool3d(Layer):
     """The :class:`GlobalMaxPool3d` class is a 3D Global Max Pooling layer.
+
     Parameters
     ------------
     data_format : str
         One of channels_last (default, [batch, depth, 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, 100, 30], name='input')
     >>> net = tl.layers.GlobalMaxPool3d()(net)
     >>> output shape : [None, 30]
+
     """
 
     def __init__(
@@ -810,18 +860,22 @@ def forward(self, inputs):
 
 class GlobalMeanPool3d(Layer):
     """The :class:`GlobalMeanPool3d` class is a 3D Global Mean Pooling layer.
+
     Parameters
     ------------
     data_format : str
         One of channels_last (default, [batch, depth, 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, 100, 30], name='input')
     >>> net = tl.layers.GlobalMeanPool3d()(net)
     >>> output shape : [None, 30]
+
     """
 
     def __init__(
@@ -861,19 +915,23 @@ def forward(self, inputs):
 
 class CornerPool2d(Layer):
     """Corner pooling for 2D image [batch, height, width, channel], see `here <https://arxiv.org/abs/1808.01244>`__.
+
     Parameters
     ----------
     mode : str
         TopLeft for the top left corner,
         Bottomright for the bottom right corner.
     name : None or str
         A unique layer name.
+
     Examples
     ---------
     With TensorLayer
+
     >>> net = tl.layers.Input([None, 32, 32, 8], name='input')
     >>> net = tl.layers.CornerPool2d(mode='TopLeft',name='cornerpool2d')(net)
     >>> output shape : [None, 32, 32, 8]
+
     """
 
     def __init__(