No problem Akhilesh.
Please let me know if what I understand is right.
I’m assuming you mean that you want to get the log likelihood on your validation set when the validation error is lowest during training.
You then want to re-train your model (not from scratch?) using a full dataset (train + validation) and train till new log likelihood of the validation set matches your previous minimum. You say that your validation set remains the same. Traditionally you would want your validation set not to include samples from your training set, but when you combine your train + validation set and use the same validation set, the model will be predicting for inputs it has already seen.
I’m curious to why exactly you would want to do something like that.
Nevertheless, I think you can keep a track of your validation_error in your training loop and update the log likelihood whenever you find a lower validation_error. Assume that the log likelihood at the lowest validation error is log_likelihood_target.
Then you would re-train on the combined set till the log_likelihood of your validation set is equal to log_likelihood_target.