train_model.py 文件源码

def __start_training(self):
        tf.logging.set_verbosity(tf.logging.INFO)

        #get batched training training data 
        image, filename,glabels,gbboxes,gdifficults,gclasses, localizations, gscores = self.get_voc_2007_2012_train_data()

        #get model outputs
        predictions, localisations, logits, end_points = g_ssd_model.get_model(image, weight_decay=self.weight_decay, is_training=True)

        #get model training losss
        total_loss = g_ssd_model.get_losses(logits, localisations, gclasses, localizations, gscores)



        global_step = slim.create_global_step()

        # Variables to train.
        variables_to_train = self.__get_variables_to_train()

        learning_rate = self.__configure_learning_rate(self.dataset.num_samples, global_step)
        optimizer = self.__configure_optimizer(learning_rate)


        train_op = slim.learning.create_train_op(total_loss, optimizer, variables_to_train=variables_to_train)

        self.__add_summaries(end_points, learning_rate, total_loss)

        self.setup_debugging(predictions, localizations, glabels, gbboxes, gdifficults)

        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
        config = tf.ConfigProto(log_device_placement=False,
                                gpu_options=gpu_options)

        ###########################
        # Kicks off the training. #
        ###########################

        slim.learning.train(
                train_op,
                self.train_dir,
                train_step_fn=self.train_step,
                saver=tf_saver.Saver(max_to_keep=500),
                init_fn=self.__get_init_fn(),
                number_of_steps=self.max_number_of_steps,
                log_every_n_steps=self.log_every_n_steps,
                save_summaries_secs=self.save_summaries_secs,
#                 session_config=config,
                save_interval_secs=self.save_interval_secs)


        return