Run the main training loop.

        Args:
            steps_per_epoch, starting_epoch, max_epoch (int):

def main_loop(self, steps_per_epoch, starting_epoch, max_epoch):
        """
        Run the main training loop.

        Args:
            steps_per_epoch, starting_epoch, max_epoch (int):
        """
        with self.sess.as_default():
            self.loop.config(steps_per_epoch, starting_epoch, max_epoch)
            self.loop.update_global_step()
            try:
                self._callbacks.before_train()
                # refresh global step (might have changed by callbacks) TODO ugly
                # what if gs is changed later?
                self.loop.update_global_step()
                for self.loop._epoch_num in range(
                        self.loop.starting_epoch, self.loop.max_epoch + 1):
                    logger.info("Start Epoch {} ...".format(self.loop.epoch_num))
                    self._callbacks.before_epoch()
                    start_time = time.time()
                    for self.loop._local_step in range(self.loop.steps_per_epoch):
                        if self.hooked_sess.should_stop():
                            return
                        self.run_step()  # implemented by subclass
                        self._callbacks.trigger_step()
                    self._callbacks.after_epoch()
                    logger.info("Epoch {} (global_step {}) finished, time:{}.".format(
                        self.loop.epoch_num, self.loop.global_step, humanize_time_delta(time.time() - start_time)))

                    # trigger epoch outside the timing region.
                    self._callbacks.trigger_epoch()
                logger.info("Training has finished!")
            except (StopTraining, tf.errors.OutOfRangeError) as e:
                logger.info("Training was stopped by exception {}.".format(str(e)))
            except KeyboardInterrupt:
                logger.info("Detected Ctrl-C and exiting main loop.")
                raise
            finally:
                self._callbacks.after_train()
                self.hooked_sess.close()