TensorFlow保存模型代码

import tensorflow as tffrom tensorflow.python.framework import graph_utilvar1 = tf.Variable(1.0, dtype=tf.float32, name='v1')var2 = tf.Variable(2.0, dtype=tf.float32, name='v2')var3 = tf.Variable(2.0, dtype=tf.float32, name='v3')x = tf.placeholder(dtype=tf.float32, shape=None, name='x')x2 = tf.placeholder(dtype=tf.float32, shape=None, name='x2')addop = tf.add(x, x2, name='add')addop2 = tf.add(var1, var2, name='add2')addop3 = tf.add(var3, var2, name='add3')initop = tf.global_variables_initializer()model_path = './Test/model.pb'with tf.Session() as sess: sess.run(initop) print(sess.run(addop, feed_dict={x: 12, x2: 23})) output_graph_def = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['add', 'add2', 'add3']) # 将计算图写入到模型文件中 model_f = tf.gfile.FastGFile(model_path, mode="wb") model_f.write(output_graph_def.SerializeToString())

读取模型代码

import tensorflow as tfwith tf.Session() as sess: model_f = tf.gfile.FastGFile("./Test/model.pb", mode='rb') graph_def = tf.GraphDef() graph_def.ParseFromString(model_f.read()) c = tf.import_graph_def(graph_def, return_elements=["add2:0"]) c2 = tf.import_graph_def(graph_def, return_elements=["add3:0"]) x, x2, c3 = tf.import_graph_def(graph_def, return_elements=["x:0", "x2:0", "add:0"]) print(sess.run(c)) print(sess.run(c2)) print(sess.run(c3, feed_dict={x: 23, x2: 2}))

以上这篇TensorFlow实现保存训练模型为pd文件并恢复就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。