Applies a 1D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size \((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be precisely described as:
nn_conv1d(
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
groups = 1,
bias = TRUE,
padding_mode = "zeros"
)(int): Number of channels in the input image
(int): Number of channels produced by the convolution
(int or tuple): Size of the convolving kernel
(int or tuple, optional): Stride of the convolution. Default: 1
(int, tuple or str, optional) – Padding added to both sides of the input. Default: 0
(int or tuple, optional): Spacing between kernel elements. Default: 1
(int, optional): Number of blocked connections from input channels to output channels. Default: 1
(bool, optional): If TRUE, adds a learnable bias to the
output. Default: TRUE
(string, optional): 'zeros', 'reflect',
'replicate' or 'circular'. Default: 'zeros'
Input: \((N, C_{in}, L_{in})\)
Output: \((N, C_{out}, L_{out})\) where
$$ L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation} \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor $$
weight (Tensor): the learnable weights of the module of shape \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\). The values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)
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
\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)
$$ \mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) + \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k) $$
where \(\star\) is the valid cross-correlation operator, \(N\) is a batch size, \(C\) denotes a number of channels, \(L\) is a length of signal sequence.
stride controls the stride for the cross-correlation, a single
number or a one-element tuple.
padding controls the amount of implicit zero-paddings on both sides
for padding number of points.
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 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).
if (torch_is_installed()) {
m <- nn_conv1d(16, 33, 3, stride = 2)
input <- torch_randn(20, 16, 50)
output <- m(input)
}
Run the code above in your browser using DataLab