I am working on a 2d time series problem with vector size 140*6 for binary classification using CNN. I have not used any scaling and normalising techniques instead directly fed data to CNN with 3 hidden layers and Batch Normalisation layers with batch size 256 during training .Since I have to test it at real time as well with batch size 1 how would batch Normalisation work then having not calculated any mean or std deviation for any training layer.And also should batch normalisation later be used for forward pass during final testing or the mean and std deviation only should be calculated for training layers and used.
Batch normalization is not used during testing. The reason for that being is batch normalization is used to alleviate the problem of covariance shift between different batches in training data. The covariance shift leads to bad models getting trained, thus, we use it. It has no role to play during testing.
And if you have used batch normalization with batch size 1, then, that is simply instance normalization.