使用tensorflow保存和恢复模型saver.restore

import tensorflow as tf
w1 = tf.Variable(变量的初始化, name='w1')
w2 = tf.Variable(变量的初始化, name='w2')
saver = tf.train.Saver([w1,w2],max_to_keep=5, keep_checkpoint_every_n_hours=2)   # 这里是细节部分，可以指定保存的变量，每两小时保存最近的5个模型
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, './checkpoint_dir/MyModel',global_step=step,write_meta_graph=False))   # 因为模型没必要多次保存，所以写为False

import tensorflow as tf
with tf.Session() as sess:    
    saver = tf.train.import_meta_graph(模型路径)  # 模型路径中必须指定到具体的模型下如：xx.ckpt-500.meta，且一般来讲，所有模型都是一样的，如果没有改变模型的条件下。
    # 下面的restore就是在当前的sess下恢复了所有的变量
    saver.restore(sess,数据路径)  # 数据路径也必须指定到具体某个模型的数据，但创建这个路径的方法很多，比如调用最后一个保存的模型tf.train.latest_checkpoint('./checkpoint_dir')，也可以是xx.ckpt-500.data,并且这两个是等效的,如果是xx.ckpt-0.data，就是第一个模型的数据
    print(sess.run('w1:0'))  # 这里的w1必须加上:0

# 建模型
saver = tf.train.Saver()
 
with tf.Session() as sess:
    # 存模型，注意此处的model是文件名，不是路径
    saver.save(sess, "/tmp/model")
 
with tf.Session() as sess:
    # 恢复模型
    saver.restore(sess, "/tmp/model")

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2019/9/9 20:49
# @Author  : ZZL
# @File    : 保存检查点文件，并恢复.py
import tensorflow as tf
# Saving contents and operations.
v1 = tf.placeholder(tf.float32, name="v1")
v2 = tf.placeholder(tf.float32, name="v2")
v3 = tf.multiply(v1, v2)
vx = tf.Variable(10.0, name="vx")
v4 = tf.add(v3, vx, name="v4")
saver = tf.train.Saver([vx])
with tf.Session() as sess:
    with tf.device('/cpu:0'):
        sess.run(tf.global_variables_initializer())
        sess.run(vx.assign(tf.add(vx, vx)))
        result = sess.run(v4, feed_dict={v1: 12.0, v2: 3.3})
        print(result)
        print(saver.save(sess, "./model_ex1"))  # 该方法返回新创建的检查点文件的路径前缀。这个字符串可以直接传递给对“restore()”的调用。

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2019/9/9 20:54
# @Author  : ZZL
# @File    : 恢复文件.py
import  tensorflow as tf
 
saver = tf.train.import_meta_graph("./model_ex1.meta")
sess = tf.Session()
saver.restore(sess, "./model_ex1")
result = sess.run("v4:0", feed_dict={"v1:0": 12.0, "v2:0": 3.3})
print(result)

def test_model():
 
    test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
    loaded_graph = tf.Graph()  # <tensorflow.python.framework.ops.Graph object at 0x0000017CB3702320>
#     print( loaded_graph)
#     print(tf.get_default_graph())  # <tensorflow.python.framework.ops.Graph object at 0x0000017C9A0C0C50>
    with tf.Session(graph=loaded_graph) as sess:
        # 读取模型
        loader = tf.train.import_meta_graph(save_model_path + '.meta')
        print(loader)
        loader.restore(sess, save_model_path)
 
        print(tf.get_default_graph())  # <tensorflow.python.framework.ops.Graph object at 0x0000017CB3702320>
        # 从已经读入的模型中 获取tensors 
        loaded_x = loaded_graph.get_tensor_by_name('x:0')
        loaded_y = loaded_graph.get_tensor_by_name('y:0')
        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
        
        # 获取每个batch的准确率，再求平均值，这样可以节约内存
        test_batch_acc_total = 0
        test_batch_count = 0
        
        for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total += sess.run(
                loaded_acc,
                feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1