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(). Defaults to the current mlx_default_device() unless noted otherwise (helpers that act on an existing array typically reuse that array's device or stream).

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