Sulaf Waiss
nderstand Normalization: Grasp how normalization is done in the Excel file, whether it's scaling input features or normalizing target outputs. Load Excel File: Import data from the Excel file into MATLAB using functions like xlsread, readtable, or importdata. Extract Parameters: Get the normalization parameters (mean, standard deviation, etc.) used in the Excel file. Normalize Input Data: Apply the same normalization process to input data as in Excel using extracted parameters. Reimplement Model: Implement the deep learning model in MATLAB using TensorFlow, PyTorch, or MATLAB's deep learning toolbox. Make Predictions: Use the model to make predictions on the normalized input data. Reverse Normalization: If post-prediction normalization was done in Excel, reverse it in MATLAB. Validation: Thoroughly validate predictions by comparing, checking metrics, and ensuring accuracy.
If you have a deep learning model implemented in an Excel file and you want to use it for predictions in MATLAB, you'll need to follow a few steps to ensure proper normalization and integration into MATLAB. Here's a general guideline:
Remember, understanding the specifics of normalization in your Excel file is crucial for a successful integration with MATLAB for predictions.
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