mindspore.mint.nn.AdaptiveAvgPool1d

class mindspore.mint.nn.AdaptiveAvgPool1d(output_size)[source]

Apply a 1-D adaptive average pooling over an input signal composed of several input planes.

The output is of size \(L_{out}\), for any input size. The number of output features is equal to the number of input planes.

Warning

This is an experimental API that is subject to change or deletion.

Parameters: output_size (int) – The target output size \(L_{out}\).

Inputs:

input (Tensor) - The input tensor with shape \((N, C, L_{in})\) or \((C, L_{in})\).

Outputs:

Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> input = mindspore.tensor([[[2, 1, 2], [2, 3, 5]]], mindspore.float16)
>>> net = mindspore.mint.nn.AdaptiveAvgPool1d(3)
>>> output = net(input)
>>> print(output)
[[[2. 1. 2.]
  [2. 3. 5.]]]