Labview下使用的深度学习PB文件

import tensorflow as tf import os from tensorflow.python.framework import graph_util pb_file_path = os.getcwd() with tf.Session(graph=tf.Graph()) as sess: x = tf.placeholder(tf.int32, name='x') y = tf.placeholder(tf.int32, name='y') b = tf.Variable(1, name='b') xy = tf.multiply(x, y) # 这里的输出需要加上name属性 op = tf.add(xy, b, name='op_to_store') sess.run(tf.global_variables_initializer()) # convert_variables_to_constants 需要指定output_node_names，list()，可以多个 constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['op_to_store']) # 测试 OP feed_dict = {x: 10, y: 3} print(sess.run(op, feed_dict)) # 写入序列化的 PB 文件 with tf.gfile.FastGFile(pb_file_path+'model.pb', mode='wb') as f: f.write(constant_graph.SerializeToString()) # 输出 # INFO:tensorflow:Froze 1 variables. # Converted 1 variables to const ops. # 31