tensorflow-名稱作用域
阿新 • • 發佈:2018-11-30
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Sep 6 10:16:37 2018 @author: myhaspl @email:[email protected] """ import tensorflow as tf with tf.name_scope("Scope_A"): asub=tf.subtract(1,2,name="A_sub") amul=tf.multiply(asub,3,name="B_mul") with tf.name_scope("Scope_B"): badd=tf.add(5,3,name="B_add") bmul=tf.multiply(badd,3,name="B_div") result=tf.add(amul,bmul,name="result") with tf.Session() as sess: sess.run(result) writer=tf.summary.FileWriter("name_scope",graph=sess.graph) writer.close()
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Sep 6 10:16:37 2018 @author: myhaspl @email:[email protected] """ import tensorflow as tf g1=tf.Graph() with g1.as_default(): y=tf.Variable(0.) with tf.name_scope("Scope_C"): a=tf.placeholder(tf.float32,shape=(),name="input_a") b=tf.placeholder(tf.float32,shape=(),name="input_b") with tf.name_scope("Scope_A"): asub=tf.subtract(a,b,name="A_sub") amul=tf.multiply(asub,3,name="B_mul") with tf.name_scope("Scope_B"): badd=tf.add(a,b,name="B_add") bmul=tf.multiply(badd,3,name="B_div") g1res=tf.add(amul,bmul,name="g1result") result=y.assign(y+g1res) init=tf.global_variables_initializer() writer=tf.summary.FileWriter("name_scope") with tf.Session(graph=g1) as sess1: sess1.run(init) print sess1.run(result,feed_dict={a:28,b:9}) writer.add_graph(g1) writer.close()
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