我以这种方式解决了这个问题：首先，我在图形“输出”中命名所需的计算，然后将此模型保存在下面的代码中…
import tensorflow as tfx = tf.placeholder(dtype=tf.float64, shape=[], name="input")a = tf.Variable(111, name="var1", dtype=tf.float64)b = tf.Variable(-666, name="var2", dtype=tf.float64)y = tf.add(x, a, name="output")saver = tf.train.Saver()with tf.Session() as sess: tf.initialize_all_variables().run() print(sess.run(y, feed_dict={x: 555})) save_path = saver.save(sess, "model.ckpt", meta_graph_suffix='meta', write_meta_graph=True) print("Model saved in file: %s" % save_path)其次，我需要在图形中运行某些操作，我将其称为“输出”。因此，我只是用另一个代码还原模型，并通过使用名称为“ input”和“
output”的必要图形部分来运行还原的计算：
import tensorflow as tf# Restore graph to another graph (and make it default graph) and variablesgraph = tf.Graph()with graph.as_default(): saver = tf.train.import_meta_graph("model.ckpt.meta") y = graph.get_tensor_by_name("output:0") x = graph.get_tensor_by_name("input:0") with tf.Session() as sess: saver.restore(sess, "model.ckpt") print(sess.run(y, feed_dict={x: 888})) # Variable out: for var in tf.all_variables(): print("%s %.2f" % (var.name, var.eval()))
