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mindspore.mint.nn.ZeroPad1d

class mindspore.mint.nn.ZeroPad1d(padding)

Pad the last dimension of input tensor with 0 using padding.

For more information, please refer to mindspore.mint.nn.functional.pad().

Warning

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

Parameters

padding (Union[int, tuple, list]) – Specifies padding size.

Inputs:

• input (Tensor) - shape is $(N,\ast )$, where $\ast$ means, any number of additional dimensions.

Outputs:

Tensor, the tensor after padding.

Raises

• TypeError – If padding is not an integer of a list or tuple of 2 integers.

• TypeError – If input is not Tensor.

• ValueError – If padding contains negative value.

• ValueError – If padding is a tuple or list, and the length does not match the tensor dimension.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> x = np.ones(shape=(1, 2, 3, 4)).astype(np.float32)
>>> x = ms.Tensor(x)
>>> # padding is tuple
>>> padding = (0, 1)
>>> pad1d = ms.mint.nn.ZeroPad1d(padding)
>>> out = pad1d(x)
>>> print(out)
[[[[1.  1.  1.  1.  0.]
   [1.  1.  1.  1.  0.]
   [1.  1.  1.  1.  0.]]
  [[1.  1.  1.  1.  0.]
   [1.  1.  1.  1.  0.]
   [1.  1.  1.  1.  0.]]]]
>>> print(out.shape)
(1, 2, 3, 5)
>>> # padding is int
>>> padding = 1
>>> pad1d = ms.mint.nn.ZeroPad1d(padding)
>>> out = pad1d(x)
>>> print(out)
[[[[0. 1.  1.  1.  1.  0.]
   [0. 1.  1.  1.  1.  0.]
   [0. 1.  1.  1.  1.  0.]]
  [[0. 1.  1.  1.  1.  0.]
   [0. 1.  1.  1.  1.  0.]
   [0. 1.  1.  1.  1.  0.]]]]
>>> print(out.shape)
(1, 2, 3, 6)