Struct Conv1d 

pub struct Conv1d<D: Dtype, IT, OT, const RANK: usize> {
    pub in_channels: usize,
    pub out_channels: usize,
    pub kernel_l: usize,
    pub stride: usize,
    pub padding: usize,
    pub has_bias: bool,
    /* private fields */
}

1-D convolution layer: output = conv1d(input, weight) [+ b]

Input shape: [N, C_in, L_in ] Output shape: [N, C_out, L_out]

where: L_out = (L_in + 2*padding - kernel_l) / stride + 1

Fields

in_channels: usize

Number of input channels.

out_channels: usize

Number of output channels.

kernel_l: usize

Convolution kernel length.

stride: usize

Stride.

padding: usize

Zero-padding applied to both sides of the input.

has_bias: bool

Whether to add a learned bias.

Implementations

impl<D: Dtype, IT, OT, const RANK: usize> Conv1d<D, IT, OT, RANK>

pub fn new( in_channels: usize, out_channels: usize, kernel_size: usize, stride: usize, padding: usize, has_bias: bool, ) -> Self

Creates a new Conv1d layer.

Trait Implementations

impl<D: Dtype, IT: Tensor<D, RANK> + EagerTensor, OT: Tensor<D, RANK>, const RANK: usize> Layer<IT> for Conv1d<D, IT, OT, RANK>

type Output = OT

This layer’s output type.

fn call(&self, _input: IT) -> Self::Output

Applies this layer to input.

impl<D: Dtype, const RANK: usize> Layer<SymTensor> for Conv1d<D, SymTensor, SymTensor, RANK>

type Output = SymTensor

This layer’s output type.

fn call(&self, input: SymTensor) -> SymTensor

Applies this layer to input.