tensorflow随笔-新的计算图
生成新的计算图，并完成常量初始化，在新的计算 图中完成加法计算
import tensorflow as tf
g1=tf.Graph()
with g1.as_default():
value=[1.,2.,3.,4.,5.,6.]
init = tf.constant_initializer(value)
x=tf.get_variable("x",initializer=init,shape=[2,3])
y=tf.get_variable("y",shape=[2,3],initializer=tf.ones_initializer())
result=tf.add(x,y,name="myadd")
with tf.Session(graph=g1) as sess:
tf.global_variables_initializer().run()
with tf.variable_scope("",reuse=True):
print(sess.run(tf.get_variable("x")))
print(sess.run(tf.get_variable("y")))
print(sess.run(result))
A）tf.Graph.as_default()会创建一个新图，这个图成为当前线程的默认图。
B)在相同进程中创建多个计算图使用tf.Graph.as_default()。如果不创建新的计算图，默认的计算图将被自动创建。
C）如果创建一个新线程，想使用该线程的默认计算图，使用tf.Graph.as_default()，这个函数返回一个上下文管理器（ context manager），它能够在这个上下文里面覆盖默认的计算图。在代码务必使用with。
# -*- coding: utf-8 -*-
"""
Spyder Editor
生成新的计算图，并完成常量初始化
[代码1]
[email protected]
"""
import tensorflow as tf
g = tf.Graph()
with g.as_default():
c = tf.constant(5.0)
assert c.graph is g
print "ok"
[代码2]
# -*- coding: utf-8 -*-
"""
Spyder Editor
生成新的计算图，并完成常量初始化
[email protected]
"""
import tensorflow as tf
with tf.Graph().as_default() as g:
c = tf.constant(5.0)
assert c.graph is g
print "ok"
[代码3]
# -*- coding: utf-8 -*-
"""
Spyder Editor
生成新的计算图，并完成常量初始化
[email protected]
"""
import tensorflow as tf
with tf.Graph().as_default() as g:
c = tf.constant(5.0)
assert c.graph is g
print "ok"
sess=tf.Session(graph=g)
print sess.run(c)
sess.close()
[代码4]
# -*- coding: utf-8 -*-
"""
Spyder Editor
生成新的计算图，并完成常量初始化
[email protected]
"""
import tensorflow as tf
g=tf.get_default_graph()#默认计算图会自动注册
c = tf.constant(4.0)
result=c*c
assert result.graph is g#验证是否result操作属于g这个计算图
print "ok1"
with tf.Graph().as_default() as g1:
c = tf.constant(5.0)
assert c.graph is g1
print "ok2"
assert c.graph is g
print "ok3"
sess=tf.Session(graph=g1)
print sess.run(c)
sess.close()
运行：
输出验证失败
ok1
ok2
…
assert c.graph is g
AssertionError
…
[代码5]
# -*- coding: utf-8 -*-
"""
Spyder Editor
生成新的计算图，并完成常量初始化，在新的计算 图中完成加法计算
[email protected]
"""
import tensorflow as tf
g1=tf.Graph()
with g1.as_default():
value=[1.,2.,3.,4.,5.,6.]
init = tf.constant_initializer(value)
x=tf.get_variable("x",initializer=init,shape=[2,3])
y=tf.get_variable("y",shape=[2,3],initializer=tf.ones_initializer())
result=tf.add(x,y,name="myadd")
assert result.graph is g1#验证是否result操作属于g1这个计算图
print "ok"
with tf.Session(graph=g1) as sess:
tf.global_variables_initializer().run()
with tf.variable_scope("",reuse=True):
print(sess.run(tf.get_variable("x")))
print(sess.run(tf.get_variable("y")))
print(sess.run(result))