adaptive_avg_pool1d¶

paddle.nn.functional. adaptive_avg_pool1d ( x, output_size, name=None ) [源代码] ¶

根据 output_size 对 Tensor x 计算 1D 自适应平均池化。

注解

详细请参考对应的 Class 请参考：AdaptiveAvgPool1D。

参数¶

x (Tensor) - 自适应平均池化的输入，它是形状为 \([N,C,L]\) 的 3-D Tensor，其中 \(N\) 是批大小，\(C\) 是通道数而 \(L\) 是输入特征的长度，其数据类型为 float32 或 float64。

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¶

Tensor，计算 1D 自适应平均池化的结果，数据类型与输入相同。

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])/(lstart - lend)
>>> #
>>> import paddle
>>> import paddle.nn.functional as F

>>> data = paddle.uniform([1, 3, 32])
>>> pool_out = F.adaptive_avg_pool1d(data, output_size=16)
>>> print(pool_out.shape)
[1, 3, 16]

         