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mindspore.nn.AvgPool1d

class mindspore.nn.AvgPool1d(kernel_size=1, stride=1, pad_mode='valid')

1D average pooling for temporal data.

Applies a 1D average pooling over an input Tensor which can be regarded as a composition of 1D input planes.

Typically the input is of shape $(N_{in},C_{in},L_{in})$, AvgPool1d outputs regional average in the $(L_{in})$-dimension. Given kernel size $ks=l_{ker}$ and stride $s=s_0$, the operation is as follows:

$$\text{output}(N_i,C_j,l)=\frac{1}{l_{ker}}\sum _{n=0}^{l_{ker}-1}\text{input}(N_i,C_j,s_0\times l+n)$$

Note

pad_mode for training only supports “same” and “valid”.

Parameters

• kernel_size (int) – The size of kernel window used to take the average value, Default: 1.

• stride (int) – The distance of kernel moving, an int number that represents the width of movement is strides, Default: 1.

• pad_mode (str) –

  The optional value for pad mode, is “same” or “valid”, not case sensitive. Default: “valid”.

  • same: Adopts the way of completion. The height and width of the output will be the same as the input. The total number of padding will be calculated in horizontal and vertical directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the last extra padding will be done from the bottom and the right side.

  • valid: Adopts the way of discarding. The possible largest height and width of output will be returned without padding. Extra pixels will be discarded.

Inputs:

• x (Tensor) - Tensor of shape $(N,C_{in},L_{in})$.

Outputs:

Tensor of shape $(N,C_{out},L_{out})$.

Raises

• TypeError – If kernel_size or stride is not an int.

• ValueError – If pad_mode is neither ‘same’ nor ‘valid’ with not case sensitive.

• ValueError – If kernel_size or strides is less than 1.

• ValueError – If length of shape of x is not equal to 3.

Supported Platforms:

Ascend GPU CPU

Examples

>>> pool = nn.AvgPool1d(kernel_size=6, stride=1)
>>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32)
>>> output = pool(x)
>>> result = output.shape
>>> print(result)
(1, 3, 1)