AdaptiveAvgPool1D¶

class paddle.nn. AdaptiveAvgPool1D ( output_size, name=None ) [源代码] ¶

根据 output_size 对一个输入 Tensor 计算 1D 的自适应平均池化。输入和输出都是以 NCL 格式表示的 3-D Tensor，其中 N 是批大小，C 是通道数而 L 是特征的长度。输出的形状是 \([N, C, output\_size]\)。

计算公式为

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

参数¶

output_size (int) - 输出特征的长度，数据类型为 int。

name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。

返回¶

用于计算 1D 自适应平均池化的可调用对象。

代码示例¶

          >>> # 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]

         