mx.symbol.Deconvolution

Description

Deconvolution: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.

Usage

mx.symbol.Deconvolution(...)

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.
name	string, optional. Name of the resulting symbol.

Value

out The result mx.symbol