tensorflow-資料流圖彙總及執行次數(tf.summary)
阿新 • • 發佈:2018-11-27
#!/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(): my_var=tf.Variable(1,dtype=tf.float32) my_step=tf.Variable(1,dtype=tf.int32) varop=tf.assign_add(my_var,tf.pow(my_var,2)) stepop=tf.assign_add(my_step,1) init=tf.initialize_all_variables() addop=tf.group([varop,stepop]) with tf.Session(graph=g1) as sess: sess.run(init) print sess.run([my_step,my_var]) sess.run(addop) print sess.run([my_step,my_var]) sess.run(addop) print sess.run([my_step,my_var]) sess.run(addop) print sess.run([my_step,my_var])
[1, 1.0]
[2, 2.0]
[3, 6.0]
[4, 42.0]
#!/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(): with tf.name_scope("input_Variable"): my_var=tf.Variable(1,dtype=tf.float32) with tf.name_scope("global_step"): my_step=tf.Variable(0,dtype=tf.int32) with tf.name_scope("update"): varop=tf.assign_add(my_var,tf.pow(my_var,2)) stepop=tf.assign_add(my_step,1) addop=tf.group([varop,stepop]) with tf.name_scope("summaries"): tf.summary.scalar('myvar',my_var) with tf.name_scope("global_ops"): init=tf.global_variables_initializer() merged_summaries=tf.summary.merge_all() with tf.Session(graph=g1) as sess: writer=tf.summary.FileWriter('sum_vars',sess.graph) sess.run(init) #---0 step,var,summary=sess.run([my_step,my_var,merged_summaries]) writer.add_summary(summary,global_step=step) print step,var #---1 sess.run(addop) step,var,summary=sess.run([my_step,my_var,merged_summaries]) writer.add_summary(summary,global_step=step) print step,var #--2 sess.run(addop) step,var,summary=sess.run([my_step,my_var,merged_summaries]) writer.add_summary(summary,global_step=step) print step,var #--3 sess.run(addop) step,var,summary=sess.run([my_step,my_var,merged_summaries]) writer.add_summary(summary,global_step=step) print step,var writer.flush() writer.close()
0 1.0
1 2.0
2 6.0
3 42.0