1、我就废话不多说了，直接上代码吧！

# Set up a RunConfig to only save checkpoints once per training cycle. run_config = tf.estimator.RunConfig(save_checkpoints_secs=1e9,keep_checkpoint_max = 10) model = tf.estimator.Estimator( model_fn=deeplab_model_focal_class_imbalance_loss_adaptive.deeplabv3_plus_model_fn, model_dir=FLAGS.model_dir, config=run_config, params={ 'output_stride': FLAGS.output_stride, 'batch_size': FLAGS.batch_size, 'base_architecture': FLAGS.base_architecture, 'pre_trained_model': FLAGS.pre_trained_model, 'batch_norm_decay': _BATCH_NORM_DECAY, 'num_classes': _NUM_CLASSES, 'tensorboard_images_max_outputs': FLAGS.tensorboard_images_max_outputs, 'weight_decay': FLAGS.weight_decay, 'learning_rate_policy': FLAGS.learning_rate_policy, 'num_train': _NUM_IMAGES['train'], 'initial_learning_rate': FLAGS.initial_learning_rate, 'max_iter': FLAGS.max_iter, 'end_learning_rate': FLAGS.end_learning_rate, 'power': _POWER, 'momentum': _MOMENTUM, 'freeze_batch_norm': FLAGS.freeze_batch_norm, 'initial_global_step': FLAGS.initial_global_step })

以上这篇在tensorflow中设置保存checkpoint的最大数量实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。