目的

學習tensorflow的目的是能夠訓練的模型，並且利用已經訓練好的模型對新資料進行預測。下文就是一個簡單的儲存模型載入模型的過程。

儲存模型

import tensorflow as tf
import os
import numpy as np
from tensorflow.python.platform import gfile


flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('summaries_dir', '/tmp/save_graph_logs', 'Summaries directory')

data
 
 = np.arange(10,dtype=np.int32)
with tf.Session() as sess:
  print("# build graph and run")
  input1= tf.placeholder(tf.int32, [10], name="input")
  output1= tf.add(input1, tf.constant(100,dtype=tf.int32), name="output") #  data depends on the input data
  saved_result= tf.Variable(data, name="saved_result")
 

  do_save=tf.assign(saved_result,output1)
  tf.initialize_all_variables()
  os.system("rm -rf /tmp/save_graph_logs")
  merged = tf.merge_all_summaries()
  train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir,
                                        sess.graph)
  os.system("rm -rf /tmp/load")
  tf.train.write_graph(sess.graph_def, "/tmp/load"
 
, "test.pb", False) #proto
  # now set the data:
  result,_=sess.run([output1,do_save], {input1: data}) # calculate output1 and assign to 'saved_result'
  saver = tf.train.Saver(tf.all_variables())
  saver.save(sess,"checkpoint.data")

模型圖示

載入模型

with tf.Session() as persisted_sess:
  print("load graph")
  with gfile.FastGFile("/tmp/load/test.pb",'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    persisted_sess.graph.as_default()
    tf.import_graph_def(graph_def, name='')
  print("map variables")
  persisted_result = persisted_sess.graph.get_tensor_by_name("saved_result:0")
  tf.add_to_collection(tf.GraphKeys.VARIABLES,persisted_result)
  try:
    saver = tf.train.Saver(tf.all_variables()) # 'Saver' misnomer! Better: Persister!
  except:pass
  print("load data")
  saver.restore(persisted_sess, "checkpoint.data")  # now OK
  print(persisted_result.eval())
  print("DONE")

顯示結果