Currently I am using the trainNetwork command to train my network model. I want to save the model with the best running validation loss. For example, let us say at epoch 10, my validation loss is 0.2 and that is the lowest validation loss up to that point, then I would save that network model. Then, we reach epoch 11, where the validation loss reaches 0.1, we would also save this model (i.e. running best validation loss model).
My network contains batchNormalization layers, and as a result, I cannot use the models saved at checkpoints as the batchNormalization layers' parameters TrainedMean and TrainedVariance are not initialized.
Is there a work around for this? I know that tensorflow/Keras supports saving models with the best validation loss that do contain batchNormalization layers.