TensorFLow用Saver儲存和恢複變數，tensorflowsaver

TensorFLow用Saver儲存和恢複變數，tensorflowsaver

本文為大家分享了TensorFLow用Saver儲存和恢複變數的具體代碼，供大家參考，具體內容如下

建立檔案tensor_save.py, 儲存變數v1,v2的tensor到checkpoint files中，名稱分別設定為v3,v4。

import tensorflow as tf# Create some variables.v1 = tf.Variable(3, name="v1")v2 = tf.Variable(4, name="v2")# Create modely=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)

建立檔案tensor_restror.py, 將checkpoint files中名稱分別為v3,v4的tensor分別恢複到變數v3,v4中。

import tensorflow as tf# Create some variables.v3 = tf.Variable(0, name="v3")v4 = tf.Variable(0, name="v4")# Create modely=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內容，希望對大家的學習有所協助，也希望大家多多支援幫客之家。