What is difference between a regular model checkpoint and a saved model in tensorflow?

Question

There's a fairly clear difference between a model and a frozen model. As described in model_files, relevant part: Freezing ...so there's the freeze_graph.py script that takes a graph definition and a set of checkpoints and freezes them together into a single file.

• Is a "saved_model" most similar to a "frozen_model" (and not a saved "model_checkpoint")?
• Is this defined somewhere in docs I'm missing?
• In prior versions of tensorflow we would save and restore model weights, but this seems to be in context of a "model_checkpoint" not a "saved_model", is that still correct?

I'm asking more for the design overview here, not implementation specifics.

Answer 1

Checkpoint file only contains variables for specific model and should be loaded with either exactly same, predefined graph or with specific assignment_map to load only chosen variables. See https://www.tensorflow.org/api_docs/python/tf/train/init_from_checkpoint

Saved model is more broad cause it contains graph that can be loaded within a session and training could be continued. Frozen graph, however, is serialized and could not be used to continue training.
You can find all the info here https://www.tensorflow.org/guide/saved_model