In Deep Learning models, ‘cell-arrays’ are used to manage input and output data.
You can also use ‘dlarray’ object in MATLAB for handling data in DL tasks, since it is well-integrated with MATLAB's Deep Learning Toolbox, making it easier to work with complex data structures. It stores data with optional data format labels for custom training loops and enables functions to compute and use derivatives through automatic differentiation.
You can control the time-steps at each layer using the time dimension – denoted as “T” in the ‘fmt’ (Data Format) input argument for ‘dlarray()’.
Refer to the following MathWorks documentation links to learn more about ‘dlarray’ in MATLAB:
To address the error message indicating the presence of ‘NaN’s in the dataset, check for these ‘NaN’s before training the network. You can use the following snippet in your code to check for ‘NaN’s in the dataset:
inputHasNaNs = any(isnan(cell2mat(X)));
if inputHasNaNs
error('Input data contains NaNs.');
end
The above snippet throws an error if ‘NaN’s are detected, ensuring that the network training process only begins after the data is cleaned.
Additionally, you can refer to following resource to know more about ‘Sequence-to-Sequence Regression Using Deep Learning’ in MATLAB:
