Wolfram Language & System 11.0 (2016)|Legacy Documentation

This is documentation for an earlier version of the Wolfram Language.View current documentation (Version 11.2)


represents a trainable 2D deconvolutional net layer having n output channels and uses kernels of size {sz,sz} to compute the deconvolution.

uses kernels of size {h,w}.

includes options for initial kernels and other parameters.

Details and OptionsDetails and Options

  • The following optional parameters can be included:
  • "Biases"Automaticinitial vector of kernel biases
    "PaddingSize"0amount of zero padding to remove from the input
    "Stride"1convolution step size to use
    "Weights"Automaticinitial matrix of kernel weights
  • 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.
  • 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 inputi.
  • 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 numerical tensor
    "Output"a rank-3 numerical tensor
  • 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 tensor of dimensions d1×d2×d3, the output tensor will be of dimensions 1×2×3, where the channel dimension 1=n and the sizes d2 and d3 are transformed according to i=s(di-1)+k-2p, where is the padding size, is the kernel size, and is the stride size for each dimension.

ExamplesExamplesopen allclose all

Basic Examples  (3)Basic Examples  (3)

Create a 2×2 deconvolution layer with five output channels:

Click for copyable input

Create a deconvolution layer with the input dimensions specified:

Click for copyable input

Initialize the layer:

Click for copyable input

Apply the layer to an input tensor to produce an output tensor:

Click for copyable input

Create a randomly initialized deconvolution layer that takes in an image and returns an image using a NetEncoder and NetDecoder:

Click for copyable input

Apply the convolution layer to an image:

Click for copyable input
Introduced in 2016