This file contains a serialized [`MetaGraphDef` protocol buffer](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto). The `MetaGraphDef` is designed as a serialization format that includes all of the information required to restore a training or inference process (including the [`GraphDef`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto) that describes the dataflow, and additional annotations that describe the variables, input pipelines, and other relevant information). For example, the `MetaGraphDef` is used by [TensorFlow Serving](https://tensorflow.github.io/serving/serving_basic.html) to start an inference service based on your trained model. We are investigating other tools that could use the `MetaGraphDef` for training.
Assuming that you still have the Python code for your model, you do not need the
MetaGraphDefto restore the model, because you can reconstruct all of the information in the
MetaGraphDefby re-executing the Python code that builds the model. To restore from a checkpoint, you only need the checkpoint files that contain the trained weights, which are written periodically to the same directory.
* **meta file**: describes the saved graph structure, includes GraphDef, SaverDef, and so on; then apply `tf.train.import_meta_graph('/tmp/model.ckpt.meta')`, will restore `Saver` and `Graph`. * **index file**: it is a string-string immutable table(tensorflow::table::Table). Each key is a name of a tensor and its value is a serialized BundleEntryProto. Each BundleEntryProto describes the metadata of a tensor: which of the "data" files contains the content of a tensor, the offset into that file, checksum, some auxiliary data, etc. * **data file**: it is TensorBundle collection, save the values of all variables.