class neupy.layers.AveragePooling[source]

Average pooling layer.

size : tuple with 2 integers

Factor by which to downscale (vertical, horizontal). (2, 2) will halve the image in each dimension.

stride : tuple or int.

Stride size, which is the number of shifts over rows/cols to get the next pool region. If stride is None, it is considered equal to size (no overlap on pooling regions).

padding : {valid, same}

(pad_h, pad_w), pad zeros to extend beyond four borders of the images, pad_h is the size of the top and bottom margins, and pad_w is the size of the left and right margins.

name : str or None

Layer’s name. Can be used as a reference to specific layer. Name Can be specified as:

  • String: Specified name will be used as a direct reference to the layer. For example, name=”fc”
  • Format string: Name pattern could be defined as a format string and specified field will be replaced with an index. For example, name=”fc{}” will be replaced with fc1, fc2 and so on. A bit more complex formatting methods are acceptable, for example, name=”fc-{:<03d}” will be converted to fc-001, fc-002, fc-003 and so on.
  • None: When value specified as None than name will be generated from the class name.

Defaults to None.


2D pooling

>>> from neupy.layers import *
>>> network = join(
...     Input((10, 10, 3)),
...     AveragePooling((2, 2)),
... )
>>> network
(?, 10, 10, 3) -> [... 2 layers ...] -> (?, 5, 5, 3)

1D pooling

>>> from neupy.layers import *
>>> network = join(
...     Input((30, 10)),
...     Reshape((30, 1, 10)),
...     AveragePooling((2, 1)),
...     Reshape((-1, 10))
... )
>>> network
(?, 30, 10) -> [... 4 layers ...] -> (?, 15, 10)
variables : dict

Variable names and their values. Dictionary can be empty in case if variables hasn’t been created yet.


variable(value, name, shape=None, trainable=True) Initializes variable with specified values.
get_output_shape(input_shape) Computes expected output shape from the layer based on the specified input shape.
output(*inputs, **kwargs) Propagates input through the layer. The kwargs variable might contain additional information that propagates through the network.
options = {'name': Option(class_name='BaseLayer', value=Property(name="name")), 'padding': Option(class_name='BasePooling', value=ChoiceProperty(name="padding")), 'size': Option(class_name='BasePooling', value=TypedListProperty(name="size")), 'stride': Option(class_name='BasePooling', value=Spatial2DProperty(name="stride"))}[source]
pooling_type = 'AVG'[source]