No, you should not do that because your model will be biased. It means that in epoch 1 some of the data will be used to train, but then in the second epoch that data may be part of the validation. The model will perform very well on that data then as it has seen it before, which you do not want. The validation set should always be data that the model has never seen during training.
What you seem to want is something like k-fold cross-validation. You can Google it, there’s quite a lot of information about it on the internet.