深度学习之tensorflow框架（中）

会话

• 开启会话
  • tf.Session用于完整的程序中
  • tf.InteractiveSession用于交互式上下文中的tensorflow
• 查看张量的值
  • 都必须在会话里面
  • c_new_value=new_sess.run(c_new)
  • print("c_new_value:\n",c_new_value)
  • print("a_new_value:\n",a_new.eval())

  • def session_demo():
        """
        会话的演示
        :return:
        """
        a_t = tf.constant(2, name="a_t")
        b_t = tf.constant(3, name="b_t")
        c_t = tf.add(a_t, b_t, name="c_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("default_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", c_t.graph)
    
        # 开启会话
        with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(allow_soft_placement=True,log_device_placement=True)) as sess:
            # c_t_value = sess.run(c_t)
            # print("c_t_value:\n", c_t_value)
            abc = sess.run([a_t,b_t,c_t])
            print("abc:\n",abc)
            print("sess的图属性：\n", sess.graph)
        return None
    
    
    def feed_demo():
        """
        feed操作
        :return:
        """
        a=tf.compat.v1.placeholder(dtype=tf.float32)
        b=tf.compat.v1.placeholder(dtype=tf.float32)
        sum_ab=tf.add(a,b)
        print("a:\n",a)
        print("b:\n",b)
        print("sum_ab:\n",sum_ab)
    
        with tf.compat.v1.Session() as sess:
            sum_ab_value=sess.run(sum_ab,feed_dict={a:3.9,b:3.5})
            print("sum_ab_value:\n",sum_ab_value)
    
        return None