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).
Does this mean the following?
- Split my training set T into training set T1 & validation set V1
- Find the mean/var of T1, mean_T1, var_T1
- Normalize T1, V1, and my testing set with mean_T1, var_T1.
- Train & test accordingly...