def train(self, train_set, valid_set=None, test_set=None, train_size=None):
        '''We train over mini-batches and evaluate periodically.'''
        iteration = 0
        while True:
            if not iteration % self.config.test_frequency and test_set:
                try:
                    self.test(iteration, test_set)
                except __HOLE__:
                    logging.info('interrupted!')
                    break

            if not iteration % self.validation_frequency and valid_set:
                try:
                    if not self.evaluate(iteration, valid_set):
                        logging.info('patience elapsed, bailing out')
                        break
                except KeyboardInterrupt:
                    logging.info('interrupted!')
                    break

            train_message = ""
            try:
                train_message = self.train_func(train_set)
            except KeyboardInterrupt:
                logging.info('interrupted!')
                break
            if not iteration % self.config.monitor_frequency:
                logging.info('monitor (iter=%i) %s', iteration + 1, train_message)

            iteration += 1
            if hasattr(self.network, "iteration_callback"):
                self.network.iteration_callback()

            yield train_message

        if valid_set:
            self.set_params(self.best_params)
        if test_set:
            self.test(0, test_set)
dataset/ETHPy150Open zomux/deepy/deepy/trainers/customize_trainer.py/CustomizeTrainer.train