Error using predictAndUpdateState (LSTM NN)
18 views (last 30 days)
I am trying to conduct a regression analysis using a LSTM neural network.
I am using 8 variables as input, and obtaining 1 output.
My knowledge in Deep Learning Toolbox is limited, therefore I have used Neural Network Fitting App to create the network.
Once exported, I am trying to predict into the future using the function predictAndUpdateState. However, I keep getting the same error message:
% Xnew is a cell array with the 8 inputs I want to use to predict.
>> for i = 2:numTimeStepsTest
v = Xnew(:,i);
[net1,score] = predictAndUpdateState(net1,v);
scores(:,i) = score;
Undefined function 'predictAndUpdateState' for input arguments of type 'network'.
As I understand, a LSTM network is a recurrent neural network, therefore I don't know where the mistake could be.
As I said, my knowledge is very limited, so I would appreciate any help on this matter.
Vineet Joshi on 26 Oct 2021
In the following code I have used the command line equivalent of 'Neural Network Fitting App' to create a simple network.
trainFcn = 'trainlm';
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);
As you can see the 'fitnet' returns a network of type 'network'.
From the error shared by you, it looks like your case is same as well since input argument is of type 'network'.
Understanding the Error:
The documentation of predictAndUpdateState states that the input network can be of two types only. It can either be a SeriesNetwork object or a DAGNetwork object.
The most strightforward workaround is to create a SeriesNetwork object or a DAGNetwork object. Attaching a few links to help you with this.