Struct neuronika::nn::Conv1d

pub struct Conv1d<Pad: PaddingMode> {
    pub padding: usize,
    pub padding_mode: Pad,
    pub stride: usize,
    pub dilation: usize,
    pub weight: Learnable<Ix3>,
    pub bias: Learnable<Ix1>,
}

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

See also GroupedConv1d.

Fields

padding: usize

padding_mode: Pad

stride: usize

dilation: usize

weight: Learnable<Ix3>

bias: Learnable<Ix1>

Implementations

impl<Pad: PaddingMode> Conv1d<Pad>

pub fn new(
    in_channels: usize,
    out_channels: usize,
    kernel_size: usize,
    padding: usize,
    padding_mode: Pad,
    stride: usize,
    dilation: usize
) -> Self

Creates a new Conv1d.

Arguments

• in_channels - number of planes in the input signal.

• out_channels - number of planes in the output signal.

• kernel_size - size of the kernel, a number for this one-dimensional case.

• padding - padding to be applied to the input, a number for this one-dimensional case.

• padding_mode - padding mode, it can be: Zero, Constant, Reflective or Replicative.

• stride - stride of the convolution, a number for this one-dimensional case.

• dilation - controls the spacing between the kernel points, a number for this one-dimensional case.

The weight and the bias of the layer are initialized from U(-k, k) where k = (1. /(in_channels * kernel_size) as f32).sqrt().

pub fn forward<I, T, U>(
    &self,
    input: I
) -> VarDiff<impl Data<Dim = Ix3> + Forward, impl Gradient<Dim = Ix3> + Overwrite + Backward> where
    I: Convolve<I, Learnable<Ix3>, Pad>,
    I::Output: Into<VarDiff<T, U>>,
    T: Data<Dim = Ix3>,
    U: Gradient<Dim = Ix3> + Overwrite, 

Computes a 1-dimensional convolution (cross correlation).

Arguments

input - signal to convolve.

The input must be of shape (N, Cin, L)

• N is the batch size
• Cin is the number of input channels
• L is the length of the input

The kernel must be of shape (Cout, Cin, Lk)

• Cout is the number of output channels
• Cin is the number of input channels
• Lk is the length of the kernel

The resulting output shape will be (N, Cout, Lout)

Trait Implementations

impl<Pad: PaddingMode> Register for Conv1d<Pad>

fn register_params(&self, params: &mut Vec<RawParam>)

Registers the weight and the bias of this Conv1d instance.

fn register_status(&mut self, _: Rc<Cell<bool>>)

Register self’s status to the model’s status state status.