# mx.nd.Deconvolution¶

## Description¶

Computes 1D or 2D transposed convolution (aka fractionally strided convolution) of the input tensor. This operation can be seen as the gradient of Convolution operation with respect to its input. Convolution usually reduces the size of the input. Transposed convolution works the other way, going from a smaller input to a larger output while preserving the connectivity pattern.

## Arguments¶

Argument

Description

data

NDArray-or-Symbol.

Input tensor to the deconvolution operation.

weight

NDArray-or-Symbol.

Weights representing the kernel.

bias

NDArray-or-Symbol.

Bias added to the result after the deconvolution operation.

kernel

Shape(tuple), required.

Deconvolution kernel size: (w,), (h, w) or (d, h, w). This is same as the kernel size used for the corresponding convolution

stride

Shape(tuple), optional, default=[].

The stride used for the corresponding convolution: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.

dilate

Shape(tuple), optional, default=[].

Dilation factor for each dimension of the input: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.

pad

Shape(tuple), optional, default=[].

The amount of implicit zero padding added during convolution for each dimension of the input: (w,), (h, w) or (d, h, w). (kernel-1)/2 is usually a good choice. If target_shape is set, pad will be ignored and a padding that will generate the target shape will be used. Defaults to no padding.

adj

Shape(tuple), optional, default=[].

Adjustment for output shape: (w,), (h, w) or (d, h, w). If target_shape is set, adj will be ignored and computed accordingly.

target.shape

Shape(tuple), optional, default=[].

Shape of the output tensor: (w,), (h, w) or (d, h, w).

num.filter

int (non-negative), required.

Number of output filters.

num.group

int (non-negative), optional, default=1.

Number of groups partition.

workspace

long (non-negative), optional, default=512.

Maximum temporary workspace allowed (MB) in deconvolution.This parameter has two usages. When CUDNN is not used, it determines the effective batch size of the deconvolution kernel. When CUDNN is used, it controls the maximum temporary storage used for tuning the best CUDNN kernel when limited_workspace strategy is used.

no.bias

boolean, optional, default=1.

Whether to disable bias parameter.

cudnn.tune

{None, ‘fastest’, ‘limited_workspace’, ‘off’},optional, default=’None’.

Whether to pick convolution algorithm by running performance test.

cudnn.off

boolean, optional, default=0.

Turn off cudnn for this layer.

layout

{None, ‘NCDHW’, ‘NCHW’, ‘NCW’, ‘NDHWC’, ‘NHWC’},optional, default=’None’.

Set layout for input, output and weight. Empty for default layout, NCW for 1d, NCHW for 2d and NCDHW for 3d.NHWC and NDHWC are only supported on GPU.

## Value¶

out The result mx.ndarray