TensorFlow图中还原变量的错误输出

我目前正在忙于保存和恢复变量。为此,我创建了两个脚本。其中一个保存一个简单的图形,而另一个则恢复它。这里是保存图表的测试脚本:

import tensorflow as tf

a = tf.Variable(3.0, name='a')
b = tf.Variable(5.0, name='b')

b = tf.assign_add(b, a)

n_steps = 5

global_step = tf.Variable(0, name='global_step', trainable=False)

saver = tf.train.Saver()

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    for step in range(n_steps):
        print(sess.run(b))

        global_step.assign_add(1).eval()
        print(global_step.eval())

        saver.save(sess, './my_test_model', global_step=global_step)

基本上,我想运行5次循环,每次我这样做,我补充说 a 到 b . 我还想通过 global_step . 这是按预期工作的。输出为:

8.0     # value of b
1       # step
11.0
2
14.0
3
17.0
4
20.0
5

现在,当恢复变量时,我尝试获取所有三个变量。脚本是:

import tensorflow as tf

from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file

# List ALL tensors.
print_tensors_in_checkpoint_file(tf.train.latest_checkpoint('./'), all_tensors=True, tensor_name='')

tf.reset_default_graph()

a = tf.get_variable('a', shape=[])
b = tf.get_variable('b', shape=[])
global_step = tf.get_variable('global_step', shape=[])

saver = tf.train.Saver()

with tf.Session() as sess:

    ckpt = tf.train.latest_checkpoint('./')
    if ckpt:
        print(ckpt)

        saver.restore(sess, ckpt)

    else:
        print('Nothing restored')

    print(a.eval())
    print(b.eval())
    print(global_step.eval())

它的输出是

tensor_name:  a
3.0
tensor_name:  b
20.0
tensor_name:  global_step
5
./my_test_model-5
INFO:tensorflow:Restoring parameters from ./my_test_model-5
3.0
20.0
7e-45

全局步骤的值是如何正确存储在检查点中的,但是经过评估,我得到的值很小 7E-45 ?而且,在恢复之后,我似乎无法定义任何附加变量,因为它声明它在检查点中找不到该变量。例如,如何定义变量并将其添加到 乙 恢复的图形?

谢谢你的帮助!