1、文件组织
2、打开tensorboard服务
3、查看运行流程
4、代码
import tensorflow as tf # 通过声明一个默认的Graph对象,然后定义张量内容,在后面可以调用或保存 # 1. 它可以通过tensorboard用图形化界面展示出来流程结构 # 2. 它可以整合一段代码为一个整体存在于一个图中 graph = tf.Graph() with graph.as_default(): in_1 = tf.placeholder(tf.float32,shape=[],name="imput_a") in_2 = tf.placeholder(tf.float32,shape=[],name="imput_b") const = tf.constant(3,dtype=tf.float32,name="static_value") with tf.name_scope("Transformation"): with tf.name_scope("A"): A_mul = tf.multiply(in_1,const) A_out = tf.subtract(A_mul,in_1) with tf.name_scope("B"): B_mul = tf.multiply(in_2,const) B_out = tf.subtract(B_mul,in_2) with tf.name_scope("C"): C_div = tf.div(A_out,B_out) C_out = tf.add(C_div,const) with tf.name_scope("D"): D_div = tf.div(B_out,A_out) D_out = tf.add(D_div,const) out = tf.maximum(C_out,D_out,name="max") writer = tf.summary.FileWriter("./board/name_scope2",graph=graph) writer.close()