ZeroPad1d#

classtorch.nn.modules.padding.ZeroPad1d(padding)[source]#

Pads the input tensor boundaries with zero.

ForN-dimensional padding, usetorch.nn.functional.pad().

Parameters

padding (int,tuple) – the size of the padding. If isint, uses the samepadding in both boundaries. If a 2-tuple, uses($\text{padding\_left}$padding_left,$\text{padding\_right}$padding_right)

Shape:

• Input:$(C,W_{in})$(C,Win​) or$(N,C,W_{in})$(N,C,Win​).

• Output:$(C,W_{out})$(C,Wout​) or$(N,C,W_{out})$(N,C,Wout​), where

  $W_{out}=W_{in}+\text{padding\_left}+\text{padding\_right}$Wout​=Win​+padding_left+padding_right

Examples:

>>>m=nn.ZeroPad1d(2)>>>input=torch.randn(1,2,4)>>>inputtensor([[[-1.0491, -0.7152, -0.0749,  0.8530],         [-1.3287,  1.8966,  0.1466, -0.2771]]])>>>m(input)tensor([[[ 0.0000,  0.0000, -1.0491, -0.7152, -0.0749,  0.8530,  0.0000,           0.0000],         [ 0.0000,  0.0000, -1.3287,  1.8966,  0.1466, -0.2771,  0.0000,           0.0000]]])>>>m=nn.ZeroPad1d(2)>>>input=torch.randn(1,2,3)>>>inputtensor([[[ 1.6616,  1.4523, -1.1255],         [-3.6372,  0.1182, -1.8652]]])>>>m(input)tensor([[[ 0.0000,  0.0000,  1.6616,  1.4523, -1.1255,  0.0000,  0.0000],         [ 0.0000,  0.0000, -3.6372,  0.1182, -1.8652,  0.0000,  0.0000]]])>>># using different paddings for different sides>>>m=nn.ZeroPad1d((3,1))>>>m(input)tensor([[[ 0.0000,  0.0000,  0.0000,  1.6616,  1.4523, -1.1255,  0.0000],         [ 0.0000,  0.0000,  0.0000, -3.6372,  0.1182, -1.8652,  0.0000]]])

extra_repr()[source]#

Return the extra representation of the module.

Return type

str