Error using deep learning custom recurrent layer: Illegal attribute 'State'.
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Rebecca Plant on 8 Nov 2021
Commented: Rebecca Plant on 11 Nov 2021
I'm facing an error with a custom intermediate layer using the Deep Learning Toolbox. The layer gets sequence input data, has two states and no learnable parameters. Its task is simply to output the difference between the current input and the input at the previous time step. I tried designing the layer according to the peephole LSTM example (https://de.mathworks.com/help/deeplearning/ug/define-custom-recurrent-deep-learning-layer.html). Unfortunately, when I try to construct the layer using
I get the error message:
Error using forwardDifferenceLayer
Illegal attribute 'State'.
Here's my code for the layer. Can anyone see what I'm doing wrong? Thanks a lot in advance.
classdef forwardDifferenceLayer < nnet.layer.Layer & nnet.layer.Formattable
% (Optional) Layer properties.
% (Optional) Layer state parameters.
function layer = forwardDifferenceLayer(numHiddenUnits, args)
% (Optional) Create a myLayer.
% This function must have the same name as the class.
args.Name = '';
layer.Name = args.Name;
layer.NumHiddenUnits = numHiddenUnits;
function [Z,hiddenState,cellState] = predict(layer,X)
hiddenState = zeros(size(X));
cellState = X;
hiddenState = X - layer.CellState;
cellState = X;
Z = dlarray(single(hiddenState));
function layer = resetState(layer)
% (Optional) Reset layer state.
layer.HiddenState = zeros(layer.NumHiddenUnits,1);
layer.CellState = zeros(layer.NumHiddenUnits,1);
Swatantra Mahato on 11 Nov 2021
The feature to "Define stateful custom layers" used in the example "Define Custom Recurrent Deep Learning Layer" was added in MATLAB R2021b as mentioned in the release notes
Hence 'State' is not recognized as a legal attribute
Hope this helps
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