Error using deep learning custom recurrent layer: Illegal attribute 'State'.
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Hi everyone,
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
forwardDifferenceLayer(100,'Name','forward difference')
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
properties
% (Optional) Layer properties.
NumHiddenUnits
end
properties (State)
% (Optional) Layer state parameters.
HiddenState
CellState
end
methods
function layer = forwardDifferenceLayer(numHiddenUnits, args)
% (Optional) Create a myLayer.
% This function must have the same name as the class.
arguments
numHiddenUnits
args.Name = '';
end
layer.Name = args.Name;
layer.NumHiddenUnits = numHiddenUnits;
end
function [Z,hiddenState,cellState] = predict(layer,X)
if isempty(layer.CellState)
hiddenState = zeros(size(X));
cellState = X;
else
hiddenState = X - layer.CellState;
cellState = X;
end
Z = dlarray(single(hiddenState));
end
function layer = resetState(layer)
% (Optional) Reset layer state.
layer.HiddenState = zeros(layer.NumHiddenUnits,1);
layer.CellState = zeros(layer.NumHiddenUnits,1);
end
end
end
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采纳的回答
Swatantra Mahato
2021-11-11
Hi Rebecca,
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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