如何为Keras GaussianNoise层选取合适的高斯噪声标准差？

Great question! Let's work through this based on your data's stats and your initial observations:

• Start with your data's inherent spread: Your data has a standard deviation of 2591, so any noise std dev that's a large portion of this will overpower your original data's features. That's why setting it to 1000 (almost 40% of your data's std dev) felt too noisy—it was drowning out the patterns your model needs to learn.

• Your 500 trial is a solid starting point: 500 is roughly 19% of your data's std dev, which falls right in the sweet spot for regularization via noise. Typically, using 10-20% of the data's standard deviation for Gaussian noise balances adding enough perturbation to prevent overfitting without destroying meaningful signals. This should help your model generalize better without distorting the data too much.

• Validate with small grid search: To nail down the optimal value, try a narrow range around 500—like 300, 400, 500, 600. Train your model with each value and compare the validation set performance (loss, accuracy, or whatever metric matters for your task). The best choice will be the one where your validation set performance is strongest, and the gap between training and validation metrics is smallest (that means less overfitting).

• Tune based on training curves: Keep an eye on your model's training vs. validation curves. If the training loss is way lower than validation loss (a classic overfitting sign), you can gradually bump up the noise (e.g., from 500 to 600). If validation performance drops when you increase noise, you've gone too far—stick to a lower value like 400 or 500.

Remember, the GaussianNoise layer only applies noise during training, so your prediction outputs won't be affected by the noise you choose—you just need to find the right balance for regularization.

内容的提问来源于stack exchange，提问作者Harsh Motwani