深度学习之tensorflow框架（上）

 import tensorflow as tf
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL']=''
 def tensorflow_demo():

     #原生python加法运算
     a = 2;
     b=3;
     c=a+b;
     print("普通加法运算的结果:\n",c);
     #tensorflow实现加法运算
     a_t=tf.constant(2)
     b_t=tf.constant(3)
     c_t=a_t+b_t
     print("tensorflow的加法运算结果:\n",c_t)
     #开启会话
     with tf.compat.v1.Session() as sess:
         c_t_value = sess.run(c_t)
         print("c_t_value:\n", c_t_value)
     return None;

 def graph_demo():
     """
     图的演示
     :return:
     """
     #TensorFlow实现加法运算
     a_t = tf.constant(2)
     b_t = tf.constant(3)
     c_t = a_t + b_t
     print("a_t:\n",a_t)
     print("b_t:\n", b_t)
     print("TensorFlow加法运算的结果：\n",c_t)
     #查看默认图
     #方法1:调用方法
     default_g = tf.compat.v1.get_default_graph()
     print("defaut_g:\n", default_g)
     #方法2:查看属性
     print("a_t的图属性:\n",a_t.graph)
     print("c_t的图属性:\n", a_t.graph)

     # 自定义图
     new_g = tf.Graph()
     # 在自己的图中定义数据和操作
     with new_g.as_default():
         a_new = tf.constant(20)
         b_new = tf.constant(30)
         c_new = a_new + b_new
         print("c_new:\n", c_new)
         print("a_new的图属性:\n", a_new.graph)
         print("c_new的图属性:\n", c_new.graph)

     # 开启会话
     with tf.compat.v1.Session() as sess:
         c_t_value = sess.run(c_t)
         print("c_t_value:\n", c_t_value)
         print("sess的图属性:\n", sess.graph)
         # 将图写入本地生成events文件
         tf.compat.v1.summary.FileWriter("./tmp/summary",graph=sess.graph)

     with tf.compat.v1.Session(graph=new_g) as new_sess:
         c_new_value = new_sess.run(c_new)
         print("c_new_value:\n", c_new_value)
         print("new_sess的图属性:\n",new_sess.graph)

     return None
 if __name__ == "__main__":
     #代码1：TensorFlow的基本结构
     #tensorflow_demo()
     #代码2:图的演示
     graph_demo()

代码不同函数里，有图的演示和Tensorflow基本结构