Troubles with Experiment Manager Setup for LSTM regression
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massimo giannini
on 10 Aug 2024 at 12:00
Commented: massimo giannini
on 12 Aug 2024 at 13:31
I need help!
I am managing Experiment Manager with a very simple exercise. I use data stocks for one-step ahead of the close price with a simple LSTM net. The net works well with trainnest(XTest,YTest,layers,options). I want to calibrate the Epochs parameters with Experiment Manager. I followed any suggestions and tutorials on the web but I can not do it. Xtest is an array with 6 cols (open, close, volum etc.) and 2401 (time steps) rows for a given stock. The reponse is a single 2401 vector containing one-time shifted of the close price. As said. I have no problem with trainnet and the net performs well.
I put data, layers and options in the script for Experiment Manager. Here is my code:
function [XTrain_N,YTrain_N,layers,options] = Experiment1_setup1(params)
load dati_net.mat XTrain_N YTrain_N
num_features = 6;
num_responses = 1;
num_hidden_units = 350;
layers = [
featureInputLayer(6);
lstmLayer(num_hidden_units, 'OutputMode','last')
fullyConnectedLayer(num_responses)
];
%Training Options
options = trainingOptions("adam", ...
MaxEpochs=params.MaxEpochs, ...
SequencePaddingDirection="left",...
InitialLearnRate=0.001,...
Shuffle="every-epoch", ...
ValidationFrequency=50, ...
GradientThreshold=.93, ...
L2Regularization=0.00001, ...
Verbose=false, ...
Metrics="rmse", ...
Plots="training-progress");
end
But when I run the experiment I always get:
The following errors occurred while running the experiment:
Errors occurred while validating the setup function: Caused by: Invalid output arguments from setup function. Third-from-last output of setup function must be a layer array or dlnetwork object, but a value of type 'double' was detected.
I tried dozens of trials but I am able to escape the problem. I attach also my data
Thanks in Advance!
2 Comments
Accepted Answer
Jaimin
on 12 Aug 2024 at 10:37
Edited: Jaimin
on 12 Aug 2024 at 10:49
According to the issue description, you are able to train your model using the“trainnet”function. However, when attempting to tune the epoch hyperparameter value using MATLAB's Experiment Manager app, you encounter an error.
One workaround I found is to use“trainNetwork”instead of“trainnet”in Experiment Manager. To do this
- Click on "New" -> "Project" -> "Blank Project"
- Select the 'Built-In Training' experiment from the 'Blank Built-In trainNetwork Experiment' as shown in the image below.
- After selecting that, configure the parameters as shown in the image below.
- Set all other parameters according to requirements.
Additionally, there is a modification in"Experiment1_setup1". Here is the updated code.
function [XTrain_N, YTrain_N, layers, options] = SequenceRegressionExperiment_setup2(params)
load dati_net.mat XTrain_N YTrain_N
num_features = 6;
num_responses = 1;
num_hidden_units = 350;
layers = [
featureInputLayer(6);
lstmLayer(num_hidden_units, 'OutputMode','last')
fullyConnectedLayer(num_responses)
regressionLayer
];
%Training Options
options = trainingOptions("adam", ...
MaxEpochs=params.MaxEpochs, ...
SequencePaddingDirection="left",...
InitialLearnRate=0.001,...
Shuffle="every-epoch", ...
ValidationFrequency=50, ...
GradientThreshold=.93, ...
L2Regularization=0.00001, ...
Verbose=false, ...
Plots="training-progress");
end
I have attached some resources that will be helpful to you.
Difference between “trainnet” and “trainNetwork”: https://www.mathworks.com/matlabcentral/answers/2060029-difference-between-trainnet-and-trainnetwork
Experiment Manager: https://www.mathworks.com/help/deeplearning/ref/experimentmanager-app.html#mw_92d8f99c-f283-4bc5-a5d7-7d779c4831f7
Training Configurations for LSTM: https://www.mathworks.com/help/deeplearning/ug/experiment-with-training-configurations-for-sequence-regression.html
I hope this helps!
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