DeconvolutionLayer

DeconvolutionLayer[n,sz]

represents a trainable deconvolutional net layer having n output channels and using kernels of size sz to compute the deconvolution.

DeconvolutionLayer[n,{s}]

represents a layer performing one-dimensional deconvolutions with kernels of size s.

DeconvolutionLayer[n,{h,w}]

represents a layer performing two-dimensional deconvolutions with kernels of size h×w.

DeconvolutionLayer[n,kernel,opts]

includes options for initial kernels and other parameters.

Details and Options

DeconvolutionLayer[…][input] explicitly computes the output from applying the layer to input.
DeconvolutionLayer[…][{input₁,input₂,…}] explicitly computes outputs for each of the input_i.
The following optional parameters can be included:

"Biases"	Automatic	initial vector of kernel biases
"ChannelGroups"	1	number of channel groups
Interleaving	False	the position of the channel dimension
LearningRateMultipliers	Automatic	learning rate multipliers for kernel weights and/or biases
PaddingSize	0	amount of padding to remove from the output
"Stride"	1	convolution step size to use
"Weights"	Automatic	initial matrix of kernel weights

The settings for PaddingSize and "Stride" can be of the following forms:
n use the value n for all dimensions

{…,n_i,…} use the value n_i for the i dimension
By setting "GroupNumber"g, the m input channels and n output channels are split into g groups of m/g and n/g channels, respectively, where m and n are required to be divisible by g. Separate deconvolutions are performed connecting the i group of input channels with the i group of output channels, and results are concatenated in the output. Each input/output group pair is connected by n/g×m/g deconvolution, so setting "GroupNumber"g effectively reduces the number of distinct deconvolutions from n×m to n×m/g.
With Automatic settings, weights and biases are added automatically when NetInitialize or NetTrain is used.
The setting "Biases"->None specifies that no biases should be used.
With the setting InterleavingFalse, the channel dimension is taken to be the first dimension of the input and output arrays.
With the setting InterleavingTrue, the channel dimension is taken to be the last dimension of the input and output arrays.
If weights and biases have been added, DeconvolutionLayer[…][input] explicitly computes the output from applying the layer.
DeconvolutionLayer[…][{input₁,input₂,…}] explicitly computes outputs for each of the input_i.
When given a NumericArray as input, the output will be a NumericArray.
NetExtract can be used to extract weights and biases from a DeconvolutionLayer object.
DeconvolutionLayer is typically used inside NetChain, NetGraph, etc.
DeconvolutionLayer exposes the following ports for use in NetGraph etc.:
"Input" a rank-3 array

"Output" a rank-3 array
When it cannot be inferred from other layers in a larger net, the option "Input"->{d₁,d₂,d₃} can be used to fix the input dimensions of DeconvolutionLayer.
Given an input array of dimensions d₁×d₂×d₃, the output array will be of dimensions ××, where the channel dimension =n and the sizes d₂ and d₃ are transformed according to =s(d_i-1)+k-2p, where is the padding size, is the kernel size, and is the stride size for each dimension.
Options[DeconvolutionLayer] gives the list of default options to construct the layer. Options[DeconvolutionLayer[…]] gives the list of default options to evaluate the layer on some data.
Information[DeconvolutionLayer[…]] gives a report about the layer.
Information[DeconvolutionLayer[…],prop] gives the value of the property prop of DeconvolutionLayer[…]. Possible properties are the same as for NetGraph.