TensorFLow用Saver保存和恢复变量,tensorflowsaver
TensorFLow用Saver保存和恢复变量,tensorflowsaver
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, launch the model, 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))
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持帮客之家。
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