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dlarray/dlgradient Value to differentiate is non-scalar. It must be a traced real dlarray scalar.

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Hello, I am working on auto differentiation. But it came up with a error shown as title.
data = randn (3, 5000, 100);
numChannels=size(data,1);
numObservations=size(data,3);
XTrain = data(:,:,1:floor(0.9*numObservations));
XTest = data(:,:,floor(0.9*numObservations)+1:end);
numHiddenUnits=100;
numLatentChannels=1;
layersE = [
sequenceInputLayer(numChannels,Normalization="zscore")
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(2*numLatentChannels)
samplingLayerSeq
];
Unrecognized function or variable 'samplingLayerSeq'.
layersD = [
sequenceInputLayer(numLatentChannels,Normalization="zscore")
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(numChannels)
];
netE = dlnetwork(layersE);
netD = dlnetwork(layersD);
numEpochs = 150;
miniBatchSize = 20;
learnRate = 1e-2;
dsTrain = arrayDatastore(XTrain,IterationDimension=3);
numOutputs = 1;
mbq = minibatchqueue(dsTrain,numOutputs, ...
MiniBatchSize = miniBatchSize, ...
MiniBatchFcn=@preprocessMiniBatch, ...
MiniBatchFormat="CBT", ...
PartialMiniBatch="discard");
trailingAvgE = [];
trailingAvgSqE = [];
trailingAvgD = [];
trailingAvgSqD = [];
numObservationsTrain = size(XTrain,3);
numIterationsPerEpoch = ceil(numObservationsTrain / miniBatchSize);
numIterations = numEpochs * numIterationsPerEpoch;
monitor = trainingProgressMonitor( ...
Metrics="Loss", ...
Info="Epoch", ...
XLabel="Iteration");
epoch = 0;
iteration = 0;
% Loop over epochs.
while epoch < numEpochs && ~monitor.Stop
epoch = epoch + 1;
% Shuffle data.
shuffle(mbq);
% Loop over mini-batches.
while hasdata(mbq) && ~monitor.Stop
iteration = iteration + 1;
% Read mini-batch of data.
X = next(mbq);
% X = dlarray(X,'CBT');
% Evaluate loss and gradients.
[loss,gradientsE,gradientsD] = dlfeval(@modelLoss,netE,netD,X);
% Update learnable parameters.
[netE,trailingAvgE,trailingAvgSqE] = adamupdate(netE, ...
gradientsE,trailingAvgE,trailingAvgSqE,iteration,learnRate);
[netD, trailingAvgD, trailingAvgSqD] = adamupdate(netD, ...
gradientsD,trailingAvgD,trailingAvgSqD,iteration,learnRate);
end
end
%% model loss
function [loss,gradientsE,gradientsD] = modelLoss(netE,netD,X)
% Forward through encoder.
[Z,mu,logSigmaSq] = forward(netE,X);
% Forward through decoder.
Y = forward(netD,Z);
% Calculate loss and gradients.
loss = elboLoss(Y,X,mu,logSigmaSq);
[gradientsE,gradientsD] = dlgradient(loss,netE.Learnables,netD.Learnables);
end
%% elboloss
function loss = elboLoss(Y,T,mu,logSigmaSq)
% Reconstruction loss.
reconstructionLoss = mse(Y,T);
% KL divergence.
KL = -0.5 * sum(1 + logSigmaSq - mu.^2 - exp(logSigmaSq),1);
KL = mean(KL);
% Combined loss.
loss = reconstructionLoss + KL;
end
%% preprocess minibatch
function X = preprocessMiniBatch(dataX)
% Concatenate.
X = cat(3,dataX{:});
end
%% class
classdef samplingLayerSeq < nnet.layer.Layer
methods
function layer = samplingLayerSeq(args)
% layer = samplingLayer creates a sampling layer for VAEs.
%
% layer = samplingLayer(Name=name) also specifies the layer
% name.
% Parse input arguments.
arguments
args.Name = "";
end
% Layer properties.
layer.Name = args.Name;
layer.Type = "Sampling";
layer.Description = "Mean and log-variance sampling";
layer.OutputNames = ["out" "mean" "log-variance"];
end
function [Z,mu,logSigmaSq] = predict(~,X)
% [Z,mu,logSigmaSq] = predict(~,Z) Forwards input data through
% the layer at prediction and training time and output the
% result.
%
% Inputs:
% X - Concatenated input data where X(1:K,:) and
% X(K+1:end,:) correspond to the mean and
% log-variances, respectively, and K is the number
% of latent channels.
% Outputs:
% Z - Sampled output
% mu - Mean vector.
% logSigmaSq - Log-variance vector
% Data dimensions.
numLatentChannels = size(X,1)/2;
miniBatchSize = size(X,2);
% Split statistics.
mu = X(1:numLatentChannels,:,:);
logSigmaSq = X(numLatentChannels+1:end,:,:);
sz = size(mu);
epsilon =randn(sz);
% Sample output.
% epsilon = randn(numLatentChannels,miniBatchSize,"like",X);
sigma = exp(.5 * logSigmaSq);
Z = epsilon .* sigma + mu;
% Z = dlarray(Z,'CBT');
end
end
end
Illegal use of reserved keyword "classdef".

Answers (1)

Ben
Ben on 5 Jan 2024
Your loss in modelLoss has a non-scalar T dimension since the model outputs sequences. You need to compute a scalar loss to use dlgradient. Standard approaches might be to take a sum or mean over the T dimension, but more intricate losses are common too.

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