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Error in LSTM layer architecture

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Chuck Noise
Chuck Noise on 14 Sep 2023
Answered: Ben on 18 Sep 2023
Hi
I am playing around with a LSTM network and trying out different things. I am an amateur regarding this and I am just figuring things out as I go along.
I have created a regression network that takes in a sequence and tries to estimate certain parameters based on the sequence.
Not being able to get a decent network running with just one layer I have implemented a second lstmLayer, and this is where I have some troubles.
When I do a sequence-to-sequence regressions network with these settings:
layers = [
sequenceInputLayer(3501)
lstmLayer(12, 'OutputMode', 'sequence')
lstmLayer(12, 'OutputMode', 'sequence')
fullyConnectedLayer(1)
regressionLayer
];
options = trainingOptions('adam', ...
'MaxEpochs',150, ...
'GradientThreshold',1, ...
'InitialLearnRate',0.01, ...
'ValidationData',{XVal, YVal}, ...
'ValidationFrequency',5, ...
'Verbose',1, ...
'Plots', 'training-progress');
I get a trainable network, but when I try changing the second lstmLayer to just output the last time step of the sequence, i.e.
layers = [
sequenceInputLayer(3501)
lstmLayer(12, 'OutputMode', 'sequence')
lstmLayer(12, 'OutputMode', 'last')
fullyConnectedLayer(1)
regressionLayer
];
I get the error:
"Error using trainNetwork
Invalid training data. For regression tasks, responses must be a vector, a matrix, or a 4-D array of real numeric responses.
Responses must not contain NaNs."
I guess it has something to do with the output of the second lstmLayer vs. the input of the fullyConnectedLayer, and I've been reading documentation for the last hour, but I simply can't figure out why there should be a problem. Can anyone enlighten me here?
Thanks

Answers (1)

Ben
Ben on 18 Sep 2023
It looks like the issue is the data you pass to trainNetwork. When you swap the 2nd lstmLayer to have OutputMode="last" then the network only outputs that last LSTM state, the fullyConnectedLayer only operates on that last state, and the loss computed in regressionLayer only uses that last state, and the "actual targets" passed to trainNetwork.
Here's an example:
sequenceLength = 5;
x = {randn(1,sequenceLength)};
y = x+1;
layers = [
sequenceInputLayer(1)
lstmLayer(1)
regressionLayer];
opts = trainingOptions("adam");
net = trainNetwork(x,y,layers,opts);
This LSTM takes in a sequence, outputs a sequence, and trainNetwork trains the network by minimizing the loss between the network output and the target data y. We usually call this sequence-to-sequence.
Now if you instead just want a sequence-to-one case you can do something like:
sequenceLength = 5;
x = {randn(1,sequenceLength)};
y = sum(x);
layers = [
sequenceInputLayer(1)
lstmLayer(1,OutputMode="last")
regressionLayer];
opts = trainingOptions("adam");
net = trainNetwork(x,y,layers,opts);
The various supported response types for trainNetwork are described here https://uk.mathworks.com/help/deeplearning/ref/trainnetwork.html?s_tid=doc_ta#mw_d0b3a2e4-09a0-42f9-a273-2bb25956fe66

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