tensorflow-数据流图汇总及运行次数(tf.summary)
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#!/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
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