tf.train.Checkpoint 和 tf.train.Saver 之间的区别
Difference between tf.train.Checkpoint and tf.train.Saver
我发现在 Tensorflow 中 save/restore 模型和变量有不同的方法。这些方式包括：
在tensorflow的文档中，我发现了它们之间的一些区别：
tf.saved_model 是 tf.train.Savertf.train.Checkpoint 支持即时执行但 tf.train.Saver 不支持 .tf.train.Checkpoint 没有创建 .meta 文件但仍然可以加载图结构（这是个大问题！它是怎么做到的？）
tf.train.Checkpoint 如何在没有 .meta 文件的情况下加载图表？或者更一般地说，tf.train.Saver 和 tf.train.Checkpoint 之间有什么区别？
根据 Tensorflow docs:
Checkpoint.save and Checkpoint.restore write and read object-based
checkpoints, in contrast to tf.train.Saver which writes and reads
variable.name based checkpoints. Object-based checkpointing saves a
graph of dependencies between Python objects (Layers, Optimizers,
Variables, etc.) with named edges, and this graph is used to match
variables when restoring a checkpoint. It can be more robust to
changes in the Python program, and helps to support restore-on-create
for variables when executing eagerly. Prefer tf.train.Checkpoint over
tf.train.Saver for new code.
我发现在 Tensorflow 中 save/restore 模型和变量有不同的方法。这些方式包括：
在tensorflow的文档中，我发现了它们之间的一些区别：
tf.saved_model是tf.train.Saver 的薄包装
tf.train.Checkpoint支持即时执行但tf.train.Saver不支持 .tf.train.Checkpoint没有创建.meta文件但仍然可以加载图结构（这是个大问题！它是怎么做到的？）
tf.train.Checkpoint 如何在没有 .meta 文件的情况下加载图表？或者更一般地说，tf.train.Saver 和 tf.train.Checkpoint 之间有什么区别？
根据 Tensorflow docs:
Checkpoint.saveandCheckpoint.restorewrite and read object-based checkpoints, in contrast totf.train.Saverwhich writes and reads variable.name based checkpoints. Object-based checkpointing saves a graph of dependencies between Python objects (Layers, Optimizers, Variables, etc.) with named edges, and this graph is used to match variables when restoring a checkpoint. It can be more robust to changes in the Python program, and helps to support restore-on-create for variables when executing eagerly. Prefertf.train.Checkpointovertf.train.Saverfor new code.