I am training a model to classify the traffic signs with CNN.
1. Training data: 32x32 photo of traffic signs
Calculate the train_mean and train_std with all the training data. Then nor_x = (x - train_mean)/train_std
3. Training CNN model with nor_x.
4. Validating the model. Here is a strange thing I found. I preprocess the validation data with train_mean and train_std, but the result is slightly worse than using validation data's valid_mean and valid_std. I believe I should get a better result with train_mean and train_std since it is used to train a model.
Or it is a normal phenomenon and there are more other factors effecting this experiment.
Any comment or resource is welcomed. Thanks.
This question is probably duplicated of this question. But what I concerned is the result is not consistent with the correct way(the result of using training data's mean and std is worse than using validation data's mean and std).