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python博客。