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class paddle.nn. AdaptiveAvgPool1D ( output_size: int, name: Optional[str] = None ) [source]
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A 1D adaptive average pooling over an input signal composed of several input planes, based on output_size. Input and output are in NCL format, where N is batch size, C is the number of channels and L is the length of the feature. The shape of output will be \([N, C, output\_size]\).

The formulation for average adaptive pool1d is

\[ \begin{align}\begin{aligned}lstart &= \lfloor i * L_{in} / L_{out}\rfloor,\\lend &= \lceil(i + 1) * L_{in} / L_{out}\rceil,\\Output(i) &= \frac{\sum Input[lstart:lend]}{lend - lstart}.\end{aligned}\end{align} \]
Parameters

  • output_size (int) – The target output size. Its data type must be int.

  • name (str|None, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

A callable object for computing 1D adaptive average pooling.

Examples

>>> # average adaptive pool1d
>>> # suppose input data in shape of [N, C, L], `output_size` is m or [m],
>>> # output shape is [N, C, m], adaptive pool divide L dimension
>>> # of input data into m grids averagely and performs poolings in each
>>> # grid to get output.
>>> # adaptive max pool performs calculations as follow:
>>> #
>>> #     for i in range(m):
>>> #         lstart = floor(i * L / m)
>>> #         lend = ceil((i + 1) * L / m)
>>> #         output[:, :, i] = sum(input[:, :, lstart: lend])/(lend - lstart)
>>> #
>>> import paddle
>>> import paddle.nn as nn

>>> data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
>>> AdaptiveAvgPool1D = nn.AdaptiveAvgPool1D(output_size=16)
>>> pool_out = AdaptiveAvgPool1D(data)
>>> print(pool_out.shape)
[1, 3, 16]
forward ( input: Tensor ) → Tensor

forward?

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

extra_repr ( ) → str

extra_repr?

Extra representation of this layer, you can have custom implementation of your own layer.