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# 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 signal composed of several input planes, sometimes also called “deconvolution”.

  This operator supports TensorFloat32.

  See `ConvTranspose1d` 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 setting `torch.backends.cudnn.deterministic = True`. See Reproducibility for more information.

  Parameters:

    - input – input tensor of shape (minibatch,in_channels,i W)(\text{minibatch} , \text{in\_channels} , iW)(minibatch,in_channels,iW)

    - weight – filters of shape (in_channels,out_channels groups,k W)(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kW)(in_channels,groups out_channels​,kW)

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

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

    - padding – `dilation * (kernel_size - 1) - padding` zero-padding will be added to both sides 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 the output shape. Can be a single number or a tuple `(out_padW)`. Default: 0

    - groups – split input into groups, in_channels\text{in\_channels}in_channels should be divisible by the number of groups. Default: 1

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

  Examples:

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

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