问题
I have a model , which I am saving at every 10 iterations . So , i am having following files in my saved directory .
checkpoint model-50.data-00000-of-00001 model-50.index model-50.meta
model-60.data-00000-of-00001 model-60.index model-60.meta
and so on up to 100 . I have to load only the model-50. Because I have got NaN values after 70 iterations. By deafault, when i am restoring the saver will look for the final checkpoint. So, how could I specifically load the model-50. please help, otherwise, i have to run the model gain from scratch, which is time consuming.
回答1:
Since you are using tf.train.Saver's function restore(), you can make use of the last_checkpoints functions to get a list of all available checkpoints. You will see both model-50 and model-60 in this list.
Pick the correct model, and pass it directly to restore() like this,
saver.restore(sess, ckpt_path)
回答2:
I'm not sure if things were different in the past, but at least as of now, you can use tf.train.get_checkpoint_state() to get CheckpointState proto which contains all_model_checkpoint_paths.
回答3:
When you execute the command shown in most of the tutorials about saving/restoring a model saver.restore(sess, tf.train.latest_checkpoint(_dir_models)) the second parameter which you are passing is just a string to model path. This is defined in a saver.restore documentation.
save_path: Path where parameters were previously saved.
So you can path any string there and latest_checkpoint is just a convenient function to extract this path from a checkpoint file. Open this file in a notebook and you will see all the model paths available and what is the latest.
You can substitute that path with any path you want. You can get it from that file (either opening it manually or using get_checkpoin_state which will programmatically do it for you.
来源：https://stackoverflow.com/questions/41455575/load-restore-models-into-tensorflow-at-specific-iteration-or-checkpoint