1. tensorflow模型文件打包成PB文件
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import tensorflow as tffrom tensorflow.python.tools import freeze_graph with tf.Graph().as_default(): with tf.device("/cpu:0"): config = tf.ConfigProto(allow_soft_placement=True) with tf.Session(config=config).as_default() as sess: model = Your_Model_Name() model.build_graph() sess.run(tf.initialize_all_variables()) saver = tf.train.Saver() ckpt_path = "/your/model/path" saver.restore(sess, ckpt_path) graphdef = tf.get_default_graph().as_graph_def() tf.train.write_graph(sess.graph_def,"/your/save/path/","save_name.pb",as_text=False) frozen_graph = tf.graph_util.convert_variables_to_constants(sess,graphdef,['output/node/name']) frozen_graph_trim = tf.graph_util.remove_training_nodes(frozen_graph) freeze_graph.freeze_graph('/your/save/path/save_name.pb','',True, ckpt_path,'output/node/name','save/restore_all','save/Const:0','frozen_name.pb',True,"") |
2. PB文件读取使用
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output_graph_def = tf.GraphDef()with open("your_name.pb","rb") as f: output_graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(output_graph_def, name="") node_in = sess.graph.get_tensor_by_name("input_node_name")model_out = sess.graph.get_tensor_by_name("out_node_name") feed_dict = {node_in:in_data}pred = sess.run(model_out, feed_dict) |
以上这篇将tensorflow模型打包成PB文件及PB文件读取方式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服务器之家。
原文链接：https://blog.csdn.net/hustchenze/article/details/83660960
