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# torch.nn.functional.conv1d

torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor

  Applies a 1D convolution over an input signal composed of several input planes.

  This operator supports TensorFloat32.

  See `Conv1d` 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.

  Note

  This operator supports complex data types i.e. `complex32, complex64, complex128`.

  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 (out_channels,in_channels groups,k W)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)(out_channels,groups in_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 one-element tuple (sW,). Default: 1

    - padding –

      implicit paddings on both sides of the input. Can be a string {‘valid’, ‘same’}, single number or a one-element tuple (padW,). Default: 0 `padding='valid'` is the same as no padding. `padding='same'` pads the input so the output has the same shape as the input. However, this mode doesn’t support any stride values other than 1.

      Warning

      For `padding='same'`, if the `weight` is even-length and `dilation` is odd in any dimension, a full `pad()` operation may be needed internally. Lowering performance.

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

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

  Examples:

      >>> inputs = torch.randn(33, 16, 30)
      >>> filters = torch.randn(20, 16, 5)
      >>> F.conv1d(inputs, filters)

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