Is it possible to use variable length arrays with SequenceInputLayer in a custom training loop, i.e. as a dlarray?

40 vues (au cours des 30 derniers jours)
I would like to train a LSTM network on sequence lengths of differing lengths. I have followed the example "nnet/SequenceClassificationUsing1DConvolutionsExample", but for my own application I need to implement such a LSTM network as a dlnetwork and use custom training loop. (It will be part of a generative model.)
% Edited example:
[XTrain,YTrain] = japaneseVowelsTrainData;
[XValidation,TValidation] = japaneseVowelsTestData;
numFeatures = size(XTrain{1},1);
inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;
filterSize = 3;
numFilters = 32;
layers = [ ...
softmaxLayer ]; % last layer removed for dlnetwork
% my code: create the dlnetwork
dlnetJap = dlnetwork( layers );
As I understand it from the documentation, dlarrays have to have fixed dimensions. This would not seem to be a problem as the example code stores the variable length arrays in a cell array. e.g.
net = trainNetwork(XTrain,TTrain,layers,options);
However, a dlnetwork does not permit a cell array as input. (I use analyzeNetwork to demonstrate this rather than showing my whole code. I get the same error when I use forward() inside the dfeval.)
analyzeNetwork( dlnetJap, XTrain );
Error using analyzeNetwork (line 56)
Invalid argument at position 2. Example network inputs must be formatted dlarray objects.
I can't dlarray create cell array as a dlarray.
analyzeNetwork( dlnetJap, dlarray( XTrain, 'CB' ) ); % or any other format
Error using dlarray (line 151)
dlarray is supported only for full arrays of data type double, single, or logical, or for full gpuArrays of these data types.
I also tried converting the array within each cell to a dlarray area but to no avail.
XTrainDl = cell( size(XTrain) );
for i = 1:length(XTrainDl)
XTrainDl{i} = dlarray( XTrain{i}, 'CB' );
analyzeNetwork( dlnetJap, XTrainDl );
Error using analyzeNetwork (line 56)
Invalid argument at position 2. Example network inputs must be formatted dlarray objects.
I can't see a way around this problem. I have already created the generative model based on fully connected layers rather than LSTM. I suppose I could use LSTM with a fixed length input, but my time series data differs in length. I would not like to time normalise to a standard length as that distorts the data. Nor can I pad it at one end because it comes from cyclical data. LSTM seems to be ideal for its ability to deal with sequences of variable lengths, but how to get that to work in a custom training loop?

Réponse acceptée

Joss Knight
Joss Knight le 22 Jan 2022
dlnetwork objects do not take your entire dataset as input, they expect to receive a single batch at a time. You need to loop over your dataset and access the data. You can use iterator objects such as minibatchqueue and arrayDatastore to help you achieve this.
The purpose of dlnetwork is to give you much greater control over how to iterate over your dataset and flexibility over how your network is built, but as a result you need to write more of the code yourself.
dlnetworks are not restricted to fixed size inputs; they do sometimes need to be given example inputs in order to initialize but that doesn't mean that once initialized the input size cannot change. In your code, example input data is not needed for dlnetwork or analyzeNetwork, because your sequenceInputLayer already provides that information.
  3 commentaires
Joss Knight
Joss Knight le 24 Jan 2022
Modifié(e) : Joss Knight le 24 Jan 2022
You need to specify 'OutputType', 'same' for the arrayDatastore otherwise it'll wrap your existing cell elements in another cell. Then you need to write a 'MiniBatchFcn' for minibatchqueue because the sequences all have different length so to concatenate them you either need to concat them as cells, or your need to use padsequences to pad them all to the same length. For them to be consumed by a network in a batch they need to be a numeric array, so you need to do that latter.
Then the other point is that minibatchqueue is designed to return data that can be put into a gpuArray and a dlarray, which means numeric data - it can't handle categorical, just like it can't handle cell. So use onehotencode to convert from categorical into numeric data that your network can consume.
I've given some example code here. Of course, you don't have to use minibatchqueue, you can iterate over the data yourself. But many of the same principles will apply.
[XTrain,YTrain] = japaneseVowelsTrainData;
miniBatchSize = 27;
fmt = 'CTB';
dsXTrain = arrayDatastore( XTrain, 'IterationDimension', 1, 'OutputType', 'same' );
dsYTrain = arrayDatastore( YTrain, 'IterationDimension', 1 );
dsTrain = combine( dsXTrain, dsYTrain );
% setup the batches
mbqTrain = minibatchqueue( dsTrain,...
'MiniBatchSize', miniBatchSize, ...
'PartialMiniBatch', 'discard', ...
'MiniBatchFcn', @concatSequenceData, ...
'MiniBatchFormat', {fmt, 'CB'});
function [x, y] = concatSequenceData(x, y)
x = padsequences(x,2);
y = onehotencode(cat(2,y{:}),1);
Mark White
Mark White le 25 Jan 2022
Thank you. That makes sense. I'd overlooked the OutputType setting.
Having looked at the examples, I see that will have to pad the data, but sorting the series by length mitigates my concern.

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