-
Notifications
You must be signed in to change notification settings - Fork 0
/
Spe-Separate22.py
53 lines (41 loc) · 1.57 KB
/
Spe-Separate22.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
# coding=utf-8
# https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-06-save/
# 莫烦--saver.save保存和 saver.restore读取
#不能用
import tensorflow as tf
import numpy as np
''' 保存'''
# remember to define the same dtype and shape when restore
W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name='weights')
b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases')
init = tf.global_variables_initializer()
# 保存时, 首先要建立一个 tf.train.Saver() 用来保存, 提取变量. 再创建一个名为my_net的文件夹,
# 用这个 saver 来保存变量到这个目录 "my_net/save_net.ckpt".
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
save_path = saver.save(sess, "./Saver/save_net.ckpt")
print("Save to path: ", save_path)
"""
Save to path: ./Saver/save_net.ckpt
"""
'''提取'''
# 提取时, 先建立零时的W 和 b容器. 找到文件目录, 并用saver.restore()我们放在这个目录的变量.
# 先建立 W, b 的容器
W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights")
b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases")
# 这里不需要初始化步骤 init= tf.initialize_all_variables()
tf.reset_default_graph()
# saver = tf.train.Saver()
with tf.Session() as sess:
# 提取变量
# tf.reset_default_graph()
# sess.run(init)
saver.restore(sess, "./Saver/save_net.ckpt")
print("weights:", sess.run(W))
print("biases:", sess.run(b))
"""
weights: [[ 1. 2. 3.]
[ 3. 4. 5.]]
biases: [[ 1. 2. 3.]]
"""