Tensorflow系列——Saver的用法

摘抄自：https://blog.csdn.net/u011500062/article/details/51728830/

1、实例

 import tensorflow as tf
 import numpy as np

 x = tf.placeholder(tf.float32, shape=[None, 1])
 y = 4 * x + 4

 w = tf.Variable(tf.random_normal([1], -1, 1))
 b = tf.Variable(tf.zeros([1]))
 y_predict = w * x + b

 loss = tf.reduce_mean(tf.square(y - y_predict))
 optimizer = tf.train.GradientDescentOptimizer(0.5)
 train = optimizer.minimize(loss)

 isTrain = False
 train_steps = 100
 checkpoint_steps = 50
 checkpoint_dir = './checkpoint_dir/'

 saver = tf.train.Saver()  # defaults to saving all variables - in this case w and b
 x_data = np.reshape(np.random.rand(10).astype(np.float32), (10, 1))

 with tf.Session() as sess:
     sess.run(tf.initialize_all_variables())
     if isTrain:
         for i in range(train_steps):
             sess.run(train, feed_dict={x: x_data})
             if (i + 1) % checkpoint_steps == 0:
                 saver.save(sess, checkpoint_dir + 'model.ckpt', global_step=i + 1)
     else:
         ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
         if ckpt and ckpt.model_checkpoint_path:
             saver.restore(sess, ckpt.model_checkpoint_path)
             print("Restore Sucessfully")
         else:
             pass
         print(sess.run(w))
         print(sess.run(b))

2、运行结果

3、解释

训练阶段，每经过checkpoint_steps 步保存一次变量，保存的文件夹为checkpoint_dir

测试阶段，ckpt.model_checkpoint_path：表示模型存储的位置，不需要提供模型的名字，它会去查看checkpoint文件，看看最新的是谁，叫做什么，然后载入变量