TensorFlow with good training model, through saverafter saving, there are often several files corresponding to the path, we combine a look at the following piece of code
import tensorflow as tf
# Create some variables.
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, and save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
inc_v1.op.run()
dec_v2.op.run()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
This code is very short answer, initializes two variables v1, v2, and then were made plus one and minus one operation, and then defines a Saver, the model is saved.
After the general model will save the file as follows:
- checkpoint: All Information checkpoint, the checkpoint information is saved, that is, through it I know a few of the most recently saved version of the model
- xxx.meta: contains all the graph information. This is a sequence of MetaGraphDef protocol buffer, containing a data stream annotations, variables, input pipelines, and other relevant information
- xxx.index： metadata，元数据 [ It’s an immutable table(tensoflow::table::Table). Each key is a name of a Tensor and it’s value is a serialized BundleEntryProto. Each BundleEntryProto describes the metadata of a Tensor]
- xxx.data-00000-of-00001: it contains the values of all variables (weights, biases, placeholders, gradients , hyper-parameters etc), which is a good model training parameters and other values
References:
1, https://www.tensorflow.org/guide/saved_model?hl=zh-cn
