1
$\begingroup$

I was reading up on how to normalize my training, validation, and test sets for a neural network, when I read this snippet:

An important point to make about the preprocessing is that any preprocessing statistics (e.g. the data mean) must only be computed on the training data, and then applied to the validation / test data. E.g. computing the mean and subtracting it from every image across the entire dataset and then splitting the data into train/val/test splits would be a mistake. Instead, the mean must be computed only over the training data and then subtracted equally from all splits (train/val/test).

(source: http://cs231n.github.io/neural-networks-2/)

Does this mean the following?

  1. Split my training set T into training set T1 & validation set V1
  2. Find the mean/var of T1, mean_T1, var_T1
  3. Normalize T1, V1, and my testing set with mean_T1, var_T1.
  4. Train & test accordingly...

Thanks...

$\endgroup$
1
$\begingroup$

Yes, that's what it means. Basically, mean_T1 and var_T1 become part of the model that you're learning. So, same as you'd apply machine learning to the training set to learn a model based on the training set, you'll compute the mean and variance based on the training set.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.