Generating predictions from trained neural net in simulink

5 views (last 30 days)
Hello,
Background: I have trained a neural network in python using tensorflow and keras, and saved the model in the h5 format. I have succesfully been able to load that model into the matlab environment and make the same predictions using a modified version of the following script:
% Load model into Matlab workspace
loaded_model = importKerasNetwork('Python_Trained_NN.h5');
% Normalize input data
X1_norm = X1 / max(X1);
X2_norm = X2 / max(X2);
X3_norm = X3 / max(X3);
X4_norm = X4 / max(X4);
% Create Input Vector
inputs = [X1_norm, X2_norm, X3_norm, X4_norm];
% Create zeros for predictions with length equal to the length of the time series data I am trying to make predictions on
test_preds = zeros(length(time), 1);
% % Make predictions using inputs
for i = 1:length(time)
test_preds(i) = predict(loaded_model, inputs(i, :));
end
I know there is probably a faster way to do this, but I am limited to matlab 2020a for my application and am unable to upgrade. Also, this works for now which was a good starting point at least. Please feel free to suggest a better way to do this.
Is there any way I can use a matlab function block to get the same behavior in simulink? Again, I am using matlab 2020a, so I do not have access to the full deep learning toolbox library. Right now I am trying the following.
function y = fcn(X1, X2, X3, X4)
%#codegen
inputs = [X1, X2, X3, X4];
y = predict(loaded_model, inputs);
But I get an error saying that 'loaded_model' is an undefined variable even though it is saved in the matlab workspace as a SeriesNetwork object.

Answers (1)

David Willingham
David Willingham on 19 Aug 2022
Hi Mike,
Great to hear your exploring workflows that include TensorFlow and MATLAB/Simulink.
There are more options for running the model in Simulink as of 21a. For example there is a stateful predict for LSTMs: Stateful Predict.
  4 Comments
David Willingham
David Willingham on 22 Aug 2022
I have checked back with R2020b, and this example highlights how you can load your own network using the command in the MATLAB function block:
coder.loadDeepLearningNetwork
Note: this example is different in the current release as there are now dedicated blocks.

Sign in to comment.

Products


Release

R2020a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!