本文为大家分享了TensorFLow用Saver保存和恢复变量的具体代码，供大家参考，具体内容如下
建立文件tensor_save.py,保存变量v1,v2的tensor到checkpoint files中，名称分别设置为v3,v4。
import tensorflow as tf
# Create some variables.
v1 = tf.Variable(3,name="v1")
v2 = tf.Variable(4,name="v2")
# Create model
y=tf.add(v1,v2)
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()
# Add ops to save and restore all the variables.
saver = tf.train.Saver({'v3':v1,'v4':v2})
# Later,launch the model,initialize the variables,do some work,save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
print("v1 = ",v1.eval())
print("v2 = ",v2.eval())
# Save the variables to disk.
save_path = saver.save(sess,"f:/tmp/model.ckpt")
print ("Model saved in file: ",save_path)
建立文件tensor_restror.py,将checkpoint files中名称分别为v3,v4的tensor分别恢复到变量v3,v4中。
import tensorflow as tf # Create some variables. v3 = tf.Variable(0,name="v3") v4 = tf.Variable(0,name="v4") # Create model y=tf.mul(v3,v4) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later,use the saver to restore variables from disk,and # do some work with the model. with tf.Session() as sess: # Restore variables from disk. saver.restore(sess,"f:/tmp/model.ckpt") print ("Model restored.") print ("v3 = ",v3.eval()) print ("v4 = ",v4.eval()) print ("y = ",sess.run(y))
