oryx.experimental.nn.MaxPooling

Max pooling layer, computes the maximum within the window.

Inherits From: Pooling, Layer, Module, Pytree

View aliases

Main aliases

oryx.experimental.nn.pooling.MaxPooling

oryx.experimental.nn.MaxPooling(
    layer_params, name=None
)

Attributes
info	Returns the info for this Layer.
params	Returns the parameters of this Layer.
state	Returns the state of this Layer.

Methods

call

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call(
    *args, **kwargs
)

Calls the Layer's call_and_update and returns the first result.

call_and_update

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call_and_update(
    *args, rng=None, **kwargs
)

Uses the layer_cau primitive to call `self._call_and_update.

flatten

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flatten()

Converts the Layer to a tuple suitable for PyTree.

initialize

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@classmethod
initialize(
    rng, in_spec, window_shape, strides=None, padding='VALID'
)

Initializes Pooling layers.

Args
rng	Random key.
in_spec	Spec, specifying the input shape and dtype.
window_shape	Int Tuple, specifying the Pooling window shape.
strides	Optional tuple with pooling strides. If None, it will use stride 1 for each dimension in window_shape.
padding	Either the string "SAME" or "VALID" indicating the type of padding algorithm to use. "SAME" would preserve the same input size, while "VALID" would reduce the input size.

Returns
Tuple with the output shape and the LayerParams.

new

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@classmethod
new(
    layer_params, name=None
)

Creates Layer given a LayerParams namedtuple.

Args
layer_params	LayerParams namedtuple that defines the Layer.
name	a string name for the Layer.

Returns
A Layer object.

replace

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replace(
    params=None, state=None, info=None
)

Returns a copy of the layer with replaced properties.

spec

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@classmethod
spec(
    in_spec, window_shape, strides=None, padding='VALID'
)

unflatten

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@classmethod
unflatten(
    data, xs
)

Reconstruct the Layer from a flattened version.

update

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update(
    *args, **kwargs
)

Calls the Layer's call_and_update and returns the second result.

variables

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variables()

Returns the variables dictionary for this Layer.

__call__

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__call__(
    *args, **kwargs
) -> Any

Emulates a regular function call.

A Module's dunder call will ensure state is updated after the function call by calling assign on the updated state before returning the output of the function.

Args
*args	The arguments to the module.
**kwargs	The keyword arguments to the module.

Returns
The output of the module.