如何將tensorflow訓練好的參數凍結成pb文件

# -*- coding: utf-8 -*-
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("E:/mnist/", one_hot=True)

def weight(shape):
    return tf.Variable(tf.truncated_normal(shape, stddev=0.1),#truncated_nomal表示從截斷的正態分佈中輸出隨機值，stddev是標準差，shape表示生成長量的維度。
                       name ='W')

def bias(shape):
    return tf.Variable(tf.constant(0.1, shape=shape) #bias的常數設置爲0.1
                       , name = 'b')

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], #x是輸入，W是filter，strides卷積時在圖像每一維的步長，這是一個一維的向量，[ 1, strides, strides, 1]，第一位和最後一位固定必須是1
                        padding='SAME') #‘SAME’是考慮邊界，不足的時候用0去填充周圍，‘VALID’則不考慮

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], 
                          strides=[1,2,2,1], 
                          padding='SAME')

with tf.name_scope('Input_Layer'):
    x = tf.placeholder("float",shape=[None, 784] #shape=[None,784] 表示列是784，行數不定，shape是數據形狀
                       ,name="x")    
    x_image = tf.reshape(x, [-1, 28, 28, 1])

with tf.name_scope('C1_Conv'):
    W1 = weight([5,5,1,16])
    b1 = bias([16])
    Conv1=conv2d(x_image, W1)+ b1
    C1_Conv = tf.nn.relu(Conv1 )

with tf.name_scope('C1_Pool'):
    C1_Pool = max_pool_2x2(C1_Conv)

with tf.name_scope('C2_Conv'):
    W2 = weight([5,5,16,36])
    b2 = bias([36])
    Conv2=conv2d(C1_Pool, W2)+ b2
    C2_Conv = tf.nn.relu(Conv2)

with tf.name_scope('C2_Pool'):
    C2_Pool = max_pool_2x2(C2_Conv) 

with tf.name_scope('D_Flat'):
    D_Flat = tf.reshape(C2_Pool, [-1, 1764])#7*7*56=1766

with tf.name_scope('D_Hidden_Layer'):
    W3= weight([1764, 128])
    b3= bias([128])
    D_Hidden = tf.nn.relu(
                  tf.matmul(D_Flat, W3)+b3)
    D_Hidden_Dropout= tf.nn.dropout(D_Hidden, ####若在結構上不定義keep_prob,則需要在結構中定義keep_prob = tf.placeholder(tf.float32)，###在session()中用feed_dict()喂參數進去
                                keep_prob=0.8)####在結構上直接定義參數大小，直接賦值

with tf.name_scope('Output_Layer'):
    W4 = weight([128,10])
    b4 = bias([10])
    y_predict= tf.nn.softmax(
                 tf.matmul(D_Hidden_Dropout,
                           W4)+b4)

with tf.name_scope("optimizer"):
    
    y_label = tf.placeholder("float", shape=[None, 10], 
                              name="y_label")
    
    loss_function = tf.reduce_mean(
                      tf.nn.softmax_cross_entropy_with_logits
                         (logits=y_predict , 
                          labels=y_label))
    
    optimizer = tf.train.AdamOptimizer(learning_rate=0.0001) \
                    .minimize(loss_function)
    #saver_path=saver.save(sess, "E:/123456789")##保存模型  

with tf.name_scope("evaluate_model"):
    correct_prediction = tf.equal(tf.argmax(y_predict, 1),
                                  tf.argmax(y_label, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

trainEpochs = 30##30個週期，每個週期對訓練的樣本55000樣本進行訓練
batchSize = 100#批尺寸100
totalBatchs = int(mnist.train.num_examples/batchSize)##55000/100=550
epoch_list=[];accuracy_list=[];loss_list=[];
from time import time
startTime=time()

log_dir = "D:/Temp/logs4/"

with tf.Session() as sess:
    
    train_writer = tf.summary.FileWriter(log_dir + "train/", sess.graph)  # 記錄默認圖
    test_writer = tf.summary.FileWriter(log_dir + "test/")

    sess.run(tf.global_variables_initializer())
 
    for epoch in range(trainEpochs):    
        for i in range(totalBatchs):
            batch_x, batch_y = mnist.train.next_batch(batchSize)
            sess.run(optimizer,feed_dict={x: batch_x,
                                      y_label: batch_y})
           
        loss,acc = sess.run([loss_function,accuracy],
                        feed_dict={x: mnist.validation.images, 
                                   y_label: mnist.validation.labels})
 
        epoch_list.append(epoch)
        loss_list.append(loss);accuracy_list.append(acc)    
    
        print("Train Epoch:", '%02d' % (epoch+1), \
              "Loss=","{:.9f}".format(loss)," Accuracy=",acc)
       
    constant_graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ["evaluate_model/Mean"])#convert_variables_to_constants函數，會將計算圖中的變量取值以常量的形式保存。在保存模型文件的時候，我們只是導出了GraphDef部分，GraphDef保存了從輸入層到輸出層的計算過程。在保存的時候，通過convert_variables_to_constants函數來指定保存的節點名稱而不是張量的名稱，“add:0”是張量的名稱而"add"表示的是節點的名稱。

    with tf.gfile.FastGFile("D:/wsj.pb", mode='wb') as f: #這兩行可以改爲：model_f=tf.gfile.FastGFile("D:/wsj.pb", mode='wb')
        f.write(constant_graph.SerializeToString())       #model_f.write(constant_graph.SerializeToString())
#duration =time()-startTime
#print("Train Finished takes:",duration)         

直接貼代碼，先記錄下來，防止以後忘了，以後研究好了，繼續不出這篇文章。