Applies a 1D transposed convolution operator over an input image composed of several input planes.

## Usage

```
nn_conv_transpose1d(
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
output_padding = 0,
groups = 1,
bias = TRUE,
dilation = 1,
padding_mode = "zeros"
)
```

## Arguments

- in_channels
(int): Number of channels in the input image

- out_channels
(int): Number of channels produced by the convolution

- kernel_size
(int or tuple): Size of the convolving kernel

- stride
(int or tuple, optional): Stride of the convolution. Default: 1

- padding
(int or tuple, optional):

`dilation * (kernel_size - 1) - padding`

zero-padding will be added to both sides of the input. Default: 0- output_padding
(int or tuple, optional): Additional size added to one side of the output shape. Default: 0

- groups
(int, optional): Number of blocked connections from input channels to output channels. Default: 1

- bias
(bool, optional): If

`True`

, adds a learnable bias to the output. Default:`TRUE`

- dilation
(int or tuple, optional): Spacing between kernel elements. Default: 1

- padding_mode
(string, optional):

`'zeros'`

,`'reflect'`

,`'replicate'`

or`'circular'`

. Default:`'zeros'`

## Details

This module can be seen as the gradient of Conv1d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation).

`stride`

controls the stride for the cross-correlation.`padding`

controls the amount of implicit zero-paddings on both sides for`dilation * (kernel_size - 1) - padding`

number of points. See note below for details.`output_padding`

controls the additional size added to one side of the output shape. See note below for details.`dilation`

controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what`dilation`

does.`groups`

controls the connections between inputs and outputs.`in_channels`

and`out_channels`

must both be divisible by`groups`

. For example,At groups=1, all inputs are convolved to all outputs.

At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

At groups=

`in_channels`

, each input channel is convolved with its own set of filters (of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

## Note

Depending of the size of your kernel, several (of the last)
columns of the input might be lost, because it is a valid `cross-correlation`

*,
and not a full cross-correlation*.
It is up to the user to add proper padding.

The `padding`

argument effectively adds `dilation * (kernel_size - 1) - padding`

amount of zero padding to both sizes of the input. This is set so that
when a `~torch.nn.Conv1d`

and a `~torch.nn.ConvTranspose1d`

are initialized with same parameters, they are inverses of each other in
regard to the input and output shapes. However, when `stride > 1`

,
`~torch.nn.Conv1d`

maps multiple input shapes to the same output
shape. `output_padding`

is provided to resolve this ambiguity by
effectively increasing the calculated output shape on one side. Note
that `output_padding`

is only used to find output shape, but does
not actually add zero-padding to output.

In some circumstances when using the CUDA backend with 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`

.

## Shape

Input: \((N, C_{in}, L_{in})\)

Output: \((N, C_{out}, L_{out})\) where $$ L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation} \times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1 $$

## Attributes

weight (Tensor): the learnable weights of the module of shape \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) \(\mbox{kernel\_size})\). The values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

bias (Tensor): the learnable bias of the module of shape (out_channels). If

`bias`

is`TRUE`

, then the values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

## Examples

```
if (torch_is_installed()) {
m <- nn_conv_transpose1d(32, 16, 2)
input <- torch_randn(10, 32, 2)
output <- m(input)
}
```