CNN Classification Different Results When Shuffled Columns

Hello everyone,

I have applied 1D CNN on speech recognition, when I shuffled columns I got diffrent results, for example, using matrix(:,[1 2 3]) gives different classification results than matrix(:,[2 3 1]) which should be the same, I have tried rng("default") but it hasn't resolved the issue. Can someone please assist me with this?

Thank you in advance!

Answer 1

[Shubham]

Hi Hamza,

The issue you're facing with different classification results when shuffling columns in your 1D CNN for speech recognition can be attributed to the sensitivity of the CNN model to the input data arrangement. Although shuffling the columns should not affect the final results, there could be other factors at play.

Here are a few suggestions to help you troubleshoot and resolve the issue:

1. Check the architecture and configuration of your CNN model: Ensure that the model architecture, layer configurations, and hyperparameters are consistent across different runs. Even a slight difference in the model setup can lead to different results.
2. Verify the data preprocessing steps: Make sure that the data preprocessing steps, such as normalization, feature extraction, and encoding, are applied consistently before training and testing the model. Any inconsistency in these steps can lead to different results.
3. Randomness in the training process: If your model incorporates any randomization, such as dropout or weight initialization, it can lead to different results. Set the random seed explicitly using rng(seed_value) to ensure reproducibility.
4. Check the evaluation process: Ensure that the evaluation process, such as calculating accuracy or loss, is consistent across different runs. Double-check the code for any discrepancies or variations in the evaluation process.
5. Examine the data: Analyze the data itself to see if there are any patterns or characteristics that could explain the differences in results. It's possible that the order of the columns influences the model's ability to learn certain features.
6. Perform cross-validation: Instead of relying on a single train-test split, perform cross-validation to assess the model's performance more robustly. This involves splitting the data into multiple folds and averaging the results across different splits.