1D Transposed Convolution

Applies a 1D transposed convolution (also called deconvolution) over an input signal. Transposed convolutions are used to upsample the spatial dimensions of the input.

Usage

mlx_conv_transpose1d(
  input,
  weight,
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  output_padding = 0L,
  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.

output_padding

Additional size added to output shape. Default: 0

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

An mlx array with the transposed convolution result

Details

Input has shape (batch, length, in_channels) for 'NWC' layout. Weight has shape (out_channels, kernel_size, in_channels).

See also

mlx_conv1d(), mlx_conv_transpose2d(), mlx_conv_transpose3d()

mlx.nn