mindspore.mint.nn.functional.adaptive_avg_pool1d

mindspore.mint.nn.functional.adaptive_avg_pool1d(input, output_size)

Performs 1D adaptive average pooling on a multi-plane input signal. That is, for any input size, the size of the specified output is L. The number of output features is equal to the number of input features.

Warning

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

Parameters

• input (Tensor) – The input of adaptive_avg_pool1d, which is a 2D or 3D tensor.

• output_size (int) – The target output feature size. output_size is an integer.

Returns

Tensor, with the same type as the input.

Shape of the output is input_shape[:len(input_shape) - 1] + [output_size].

Raises

• ValueError – If output_size is not integer.

• TypeError – If input is not a Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input = Tensor([[2,3],[3,4]],dtype=mindspore.float16)
>>> output = mint.nn.functional.adaptive_avg_pool1d(input, 3)
>>> print(output)
[[2.  2.5 3. ]
 [3.  3.5 4. ]]