1. tensorflow模型檔案打包成PB檔案

import tensorflow as tf
from 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',"")

2. PB檔案讀取使用

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)

以上這篇將tensorflow模型打包成PB檔案及PB檔案讀取方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。