saver实例代码：
- ## Save to file
- # remember to define the same dtype and shape when restore
- W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name='weights')
- b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases')
- init= tf.global_variables_initialize()
- 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)
restore实例代码：
- # restore variables
- # redefine the same shape and same type for your variables
- W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights")
- b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases")
- # not need init step
- 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))
转自：http://blog.csdn.net/smf0504/article/details/56666305
