torch.nn.functional.conv_transpose1d#

torch.nn.functional.conv_transpose1d(input,weight,bias=None,stride=1,padding=0,output_padding=0,groups=1,dilation=1)→Tensor#

Applies a 1D transposed convolution operator over an input signalcomposed of several input planes, sometimes also called “deconvolution”.

This operator supportsTensorFloat32.

SeeConvTranspose1d for details and output shape.

Note

In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by settingtorch.backends.cudnn.deterministic=True. SeeReproducibility for more information.

Parameters

• input – input tensor of shape$(\text{minibatch},\text{in\_channels},iW)$(minibatch,in_channels,iW)

• weight – filters of shape$(\text{in\_channels},\frac{\text{out\_channels}}{\text{groups}},kW)$(in_channels,groupsout_channels​,kW)

• bias – optional bias of shape$(\text{out\_channels})$(out_channels). Default: None

• stride – the stride of the convolving kernel. Can be a single number or atuple(sW,). Default: 1

• padding –dilation*(kernel_size-1)-padding zero-padding will be added to bothsides of each dimension in the input. Can be a single number or a tuple(padW,). Default: 0

• output_padding – additional size added to one side of each dimension in theoutput shape. Can be a single number or a tuple(out_padW). Default: 0

• groups – split input into groups,$\text{in\_channels}$in_channels should be divisible by thenumber of groups. Default: 1

• dilation – the spacing between kernel elements. Can be a single number ora tuple(dW,). Default: 1

Examples:

>>>inputs=torch.randn(20,16,50)>>>weights=torch.randn(16,33,5)>>>F.conv_transpose1d(inputs,weights)