I used saver=tf.train.Saver()
to save the model that I trained, and I get three kinds of files named:
the .ckpt file is the old version output of saver.save(sess)
, which is the equivalent of your .ckpt-data
(see below)
the "checkpoint" file is only here to tell some TF functions which is the latest checkpoint file.
.ckpt-meta
contains the metagraph, i.e. the structure of your computation graph, without the values of the variables (basically what you can see in tensorboard/graph).
.ckpt-data
contains the values for all the variables, without the structure. To restore a model in python, you'll usually use the meta and data files with (but you can also use the .pb
file):
saver = tf.train.import_meta_graph(path_to_ckpt_meta)
saver.restore(sess, path_to_ckpt_data)
I don't know exactly for .ckpt-index
, I guess it's some kind of index needed internally to map the two previous files correctly. Anyway it's not really necessary usually, you can restore a model with only .ckpt-meta
and .ckpt-data
.
the .pb
file can save your whole graph (meta + data). To load and use (but not train) a graph in c++ you'll usually use it, created with freeze_graph, which creates the .pb
file from the meta and data. Be careful, (at least in previous TF versions and for some people) the py function provided by freeze_graph
did not work properly, so you'd have to use the script version. Tensorflow also provides a tf.train.Saver.to_proto()
method, but I don't know what it does exactly.
There are a lot of questions here about how to save and restore a graph. See the answer here for instance, but be careful that the two cited tutorials, though really helpful, are far from perfect, and a lot of people still seem to struggle to import a model in c++.
EDIT: it looks like you can also use the .ckpt files in c++ now, so I guess you don't necessarily need the .pb file any more.