mindspore.ops.lp_pool1d
- mindspore.ops.lp_pool1d(x, norm_type, kernel_size, stride=None, ceil_mode=False)[source]
Applying 1D LPPooling operation on an input Tensor can be regarded as forming a 1D input plane.
Typically the input is of shape \((N, C, L_{in})\) or \((C, L_{in})\), the output is of shape \((N, C, L_{out})\) or \((C, L_{out})\).
\[L_{out} = \left\lfloor\frac{L_{in} - \text{kernel_size}}{\text{stride}} + 1\right\rfloor\]The operation is as follows.
\[f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}\]- Parameters
x (Tensor) – Tensor of shape \((N, C, L_{in})\) or \((C, L_{in})\).
norm_type (Union[int, float]) –
Type of normalization, represents p in the formula, can not be 0,
if p = 1, the result obtained is the sum of elements in the pool nucleus(Proportional to average pooling).
if p = \(\infty\), the result is the result of maximum pooling.
kernel_size (int) – The size of kernel window.
stride (int) – The distance of kernel moving, an int number that represents the width of movement is stride. Default:
None
, which indicates the moving step is kernel_size .ceil_mode (bool) – Whether to use ceil or floor to calculate output shape. Default:
False
.
- Returns
output (Tensor) - LPPool1d result, with shape \((N, C, L_{out})\) or \((C, L_{out})\), It has the same data type as x, where
\[L_{out} = \left\lfloor\frac{L_{in} - \text{kernel_size}}{\text{stride}} + 1\right\rfloor\]
- Raises
TypeError – If x is not a Tensor.
TypeError – If kernel_size or stride is not an int.
TypeError – If ceil_mode is not a bool.
TypeError – If norm_type is neither float nor int.
ValueError – If norm_type is equal to 0.
ValueError – If kernel_size or stride is less than 1.
ValueError – If length of shape of x is not equal to 2 or 3.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> from mindspore import ops >>> from mindspore import Tensor >>> import numpy as np >>> x = Tensor(np.arange(2 * 3 * 4).reshape((2, 3, 4)), dtype=ms.float32) >>> out = ops.lp_pool1d(x, norm_type=1, kernel_size=3, stride=1, ceil_mode=False) >>> print(out) [[[ 3. 6.] [15. 18.] [27. 30.]] [[39. 42.] [51. 54.] [63. 66.]]]