tensorflow的基本用法——保存神经网络参数和加载神经网络参数

#!/usr/bin/env python
# _*_ coding: utf-8 _*_

import tensorflow as tf
import numpy as np


# 保存神经网络参数
def save_para():
    # 定义权重参数
    W = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype = tf.float32, name = 'weights')
    # 定义偏置参数
    b = tf.Variable([[1, 2, 3]], dtype = tf.float32, name = 'biases')
    # 参数初始化
    init = tf.global_variables_initializer()
    # 定义保存参数的saver
    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(init)
        # 保存session中的数据
        save_path = saver.save(sess, 'my_net/save_net.ckpt')
        # 输出保存路径
        print 'Save to path: ', save_path

# 恢复神经网络参数
def restore_para():
    # 定义权重参数
    W = tf.Variable(np.arange(6).reshape((2, 3)), dtype = tf.float32, name = 'weights')
    # 定义偏置参数
    b = tf.Variable(np.arange(3).reshape((1, 3)), dtype = tf.float32, name = 'biases')
    # 定义提取参数的saver
    saver = tf.train.Saver()

    with tf.Session() as sess:
        # 加载文件中的参数数据，会根据name加载数据并保存到变量W和b中
        save_path = saver.restore(sess, 'my_net/save_net.ckpt')
        # 输出保存路径
        print 'Weights: ', sess.run(W)
        print 'biases:  ', sess.run(b)


# save_para()
restore_para()

# save
Save to path:  my_net/save_net.ckpt


# restore
Weights:  [[ 1.  2.  3.]
 [ 4.  5.  6.]]
biases:   [[ 1.  2.  3.]]