https://github.com/nfmcclure/tensorflow_cookbook
全都是.ipynb 文件
pip install --upgrade pip
pip install jupyter notebook
jupyter notebook
参考官方文档
https://www.tensorflow.org/programmers_guide/saved_model?hl=zh-cn
save把变量都存下来
savetest.py
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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(v2-2)
# 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)
print("v1 : %s" % v1.eval())
print("v2 : %s" % v2.eval())
restore把存都变量都取出来
虽然定义来相同的 变量，restore都时候会覆盖掉
如果新程序便利多余save都程序，会报找不到变量都错误
storetest.py
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.reset_default_graph()
# 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)
#v3 = tf.get_variable("v3", shape=[7], initializer = tf.zeros_initializer)
inc_v1 = v1.assign(v1+1)
#dec_v2 = v2.assign(v2-3)
#dec_v3 = v3.assign(v3-7)
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
sess.run(init_op)
#Do some work with the model.
inc_v1.op.run()
#dec_v2.op.run()
#dec_v3.op.run()
# Restore variables from disk.
saver.restore(sess, "./tmp/model.ckpt")
print("Model restored.")
# Check the values of the variables
print("v1 : %s" % v1.eval())
# print("v2 : %s" % v2.eval())
# print("v3 : %s" % v3.eval())
如果想看所有变量，可以打印
# import the inspect_checkpoint library
from tensorflow.python.tools import inspect_checkpoint as chkp
# print all tensors in checkpoint file
chkp.print_tensors_in_checkpoint_file("./tmp/model.ckpt", tensor_name='', all_tensors=True)
print("------")
# tensor_name: v1
# [ 1. 1. 1.]
# tensor_name: v2
# [-1. -1. -1. -1. -1.]
# print only tensor v1 in checkpoint file
chkp.print_tensors_in_checkpoint_file("./tmp/model.ckpt", tensor_name='v1', all_tensors=False)
print("------")
# tensor_name: v1
# tensor_name: v1
# [ 1. 1. 1.]
# print only tensor v2 in checkpoint file
chkp.print_tensors_in_checkpoint_file("./tmp/model.ckpt", tensor_name='v2', all_tensors=False)
# tensor_name: v2
# [-1. -1. -1. -1. -1.]