Example of using dataset and iterators to the train and val

UsingIterators.py

if __name__=="__main__":
        #make the iterators and next element op 
        next_element, training_init_op, validation_init_op = prepare_dataset_iterators(batch_size=32)
        ...

        for epoch in range(1000):
            #Initialize the iterator to consume training data
            sess.run(training_init_op)
            while True:
                #As long as the iterator is not empty
                try:
                    _, summary,gs = sess.run([M.train,M.write_op,M.gs],feed_dict={M.lr: lr, M.keep_prob:keep_prob})
                except tf.errors.OutOfRangeError:
                    #Do stuff at the end of a training epoch here
                    break

            #Intiialize the iterator to provide validation data
            sess.run(validation_init_op)
            #We'll store the losses from each batch to get an average
            while True:
                # As long as the iterator is not empty
                try:
                    loss,summary,gs,_ = sess.run([M.total_loss,M.write_op,M.gs,M.increment_gs],feed_dict={M.lr: lr,M.keep_prob:1})
                except tf.errors.OutOfRangeError:
                    #Do stuff at the end of a validation run here
                    break