1D Convolution

Applies a 1D convolution over the input signal.

Usage

mlx_conv1d(
  input,
  weight,
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  groups = 1L,
  device = mlx_default_device()
)

Arguments

input

Input mlx array. Shape depends on dimensionality (see individual functions).

weight

Weight array. Shape depends on dimensionality (see individual functions).

stride

Stride of the convolution. Can be a scalar or vector (length depends on dimensionality). Default: 1 for 1D, c(1,1) for 2D, c(1,1,1) for 3D.

padding

Amount of zero padding. Can be a scalar or vector (length depends on dimensionality). Default: 0 for 1D, c(0,0) for 2D, c(0,0,0) for 3D.

dilation

Spacing between kernel elements. Can be a scalar or vector (length depends on dimensionality). Default: 1 for 1D, c(1,1) for 2D, c(1,1,1) for 3D.

groups

Number of blocked connections from input to output channels. Default: 1.

device

Execution target: supply "gpu", "cpu", or an mlx_stream created via mlx_new_stream(). Default: mlx_default_device().

Value

Convolved output array

Details

Input has shape (N, L, C_in) where N is batch size, L is sequence length, and C_in is number of input channels. Weight has shape (C_out, kernel_size, C_in).

See also

mlx.core.conv1d