tensorflow保存 和 加载模型

1、

import tensorflow as tf
import numpy as np

# save to file
W = tf.Variable([[1,2,3],[4,5,6]],dtype = tf.float32,name='weight')
b = tf.Variable([[1,2,3]],dtype = tf.float32,name='biases')

init = tf.initialize_all_variables()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
save_path = saver.save(sess,"my_net/save_net.ckpt")
print ("save to path:",save_path)


说明：保存模型

2、

import tensorflow as tf
import numpy as np

W = tf.Variable(np.arange(6).reshape((2,3)),dtype = tf.float32,name='weight')
b = tf.Variable(np.arange(3).reshape((1,3)),dtype = tf.float32,name='biases')

saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,"my_net/save_net.ckpt")
print ("weights:",sess.run(W))
print ("biases:",sess.run(b))
说明：加载模型