深度学习之tensorflow框架（上）

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
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:
",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:
",c_t)
    #开启会话
    with tf.compat.v1.Session() as sess:
        c_t_value = sess.run(c_t)
        print("c_t_value:
", 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:
",a_t)
    print("b_t:
", b_t)
    print("TensorFlow加法运算的结果：
",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:
", default_g)
    #方法2:查看属性
    print("a_t的图属性:
",a_t.graph)
    print("c_t的图属性:
", 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:
", c_new)
        print("a_new的图属性:
", a_new.graph)
        print("c_new的图属性:
", 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:
", c_t_value)
        print("sess的图属性:
", 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:
", c_new_value)
        print("new_sess的图属性:
",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