Conv1D module

Usage

nn_conv1d(
  in_channels,
  out_channels,
  kernel_size,
  stride = 1,
  padding = 0,
  dilation = 1,
  groups = 1,
  bias = TRUE,
  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, tuple or str, optional) – Padding added to both sides of the input. Default: 0

dilation

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

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

padding_mode

(string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

Details

$$\text{out}(N_i,C_{{\text{out}}_j})=\text{bias}(C_{{\text{out}}_j})+\sum _{k=0}^{C_{in}-1}\text{weight}(C_{{\text{out}}_j},k)\star \text{input}(N_i,k)$$

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 $\lfloor \frac{out\mathrm{\_}channels}{in\mathrm{\_}channels}\rfloor$.

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.

When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size $(N,C_{in},L_{in})$, a depthwise convolution with a depthwise multiplier K, can be constructed by arguments $(C_{\text{in}}=C_{in},C_{\text{out}}=C_{in}\times K,...,\text{groups}=C_{in})$.

Shape

• Input: $(N,C_{in},L_{in})$

• Output: $(N,C_{out},L_{out})$ where

$$L_{out}=\lfloor \frac{L_{in}+2\times \text{padding}-\text{dilation}\times (\text{kernel\_size}-1)-1}{\text{stride}}+1\rfloor$$

Attributes

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

• 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 $k=\frac{groups}{C_{\text{in}}\ast \text{kernel\_size}}$

Examples

if (torch_is_installed()) {
m <- nn_conv1d(16, 33, 3, stride = 2)
input <- torch_randn(20, 16, 50)
output <- m(input)
}