深入理解tensorflow的session和graph

tensorflow的graph示例

tensorflow作为一个基于图结构的深度学习框架，内部通过session实现图和计算内核的交互，那么这个图是什么样的结构，session的工作原理又是什么样的呢？我们通过几段代码来深入理解一下

tensorflow中的基本数学运算用法

import tensorflow as tf

sess = tf.Session()

a = tf.placeholder("float")

b = tf.placeholder("float")

c = tf.constant(6.0)

d = tf.mul(a, b)

y = tf.mul(d, c)

print sess.run(y, feed_dict={a: 3, b: 3})

A = [[1.1,2.3],[3.4,4.1]]

Y = tf.matrix_inverse(A)

print sess.run(Y)

sess.close()

主要数字运算还包括：

tf.add

tf.sub

tf.mul

tf.div

tf.mod

tf.abs

tf.neg

tf.sign

tf.inv

tf.square

tf.round

tf.sqrt

tf.pow

tf.exp

tf.log

tf.maximum

tf.minimum

tf.cos

tf.sin

主要矩阵运算还包括：

tf.diag生成对角阵

tf.transpose

tf.matmul

tf.matrix_determinant计算行列式的值

tf.matrix_inverse计算矩阵的逆

插播小甜点：tensorboard使用

tensorflow因为代码执行过程是先构建图，然后在执行，所以对中间过程的调试不太方便，所以提供了一个tensorboard工具来便于调试，用法如下：

在训练时会提示写入事件文件到哪个目录(比如：/tmp/tflearn_logs/11U8M4/)

执行如下命令并打开http://192.168.1.101:6006就能看到tensorboard的界面

tensorboard --logdir=/tmp/tflearn_logs/11U8M4/

什么是Graph和Session

为了步入正题，我们通过一段代码来展示Graph和Session的使用

import tensorflow as tf

with tf.Graph().as_default() as g:

with g.name_scope("myscope") as scope: # 有了这个scope，下面的op的name都是类似myscope/Placeholder这样的前缀

sess = tf.Session(target='', graph = g, config=None) # target表示要连接的tf执行引擎

print "graph version:", g.version # 0

a = tf.placeholder("float")

print a.op # 输出整个operation信息，跟下面g.get_operations返回结果一样

print "graph version:", g.version # 1

b = tf.placeholder("float")

print "graph version:", g.version # 2

c = tf.placeholder("float")

print "graph version:", g.version # 3

y1 = tf.mul(a, b) # 也可以写成a * b

print "graph version:", g.version # 4

y2 = tf.mul(y1, c) # 也可以写成y1 * c

print "graph version:", g.version # 5

operations = g.get_operations()

for (i, op) in enumerate(operations):

print "============ operation", i+1, "==========="

print op # 一个结构，包括：name、op、attr、input等,不同op不一样

assert y1.graph is g

assert sess.graph is g

print "================ graph object address ================"

print sess.graph

print "================ graph define ================"

print sess.graph_def

print "================ sess str ================"

print sess.sess_str

print sess.run(y1, feed_dict={a: 3, b: 3}) # 9.0 feed_dictgraph中的元素和值的映射

print sess.run(fetches=[b,y1], feed_dict={a: 3, b: 3}, options=None, run_metadata=None) # 传入的feches和返回值的shape相同

print sess.run({'ret_name':y1}, feed_dict={a: 3, b: 3}) # {'ret_name': 9.0} 传入的feches和返回值的shape相同

assert tf.get_default_session() is not sess

with sess.as_default(): # 把sess作为默认的session，那么tf.get_default_session就是sess, 否则不是

assert tf.get_default_session() is sess

h = sess.partial_run_setup([y1, y2], [a, b, c]) # 分阶段运行，参数指明了feches和feed_dict列表

res = sess.partial_run(h, y1, feed_dict={a: 3, b: 4}) # 12 运行第一阶段

res = sess.partial_run(h, y2, feed_dict={c: res}) # 144.0 运行第二阶段，其中使用了第一阶段的执行结果

print "partial_run res:", res

sess.close()

输出如下：

graph version: 0

name: "myscope/Placeholder"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

graph version: 1

graph version: 2

graph version: 3

graph version: 4

graph version: 5

============ operation 1 ===========

name: "myscope/Placeholder"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

============ operation 2 ===========

name: "myscope/Placeholder_1"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

============ operation 3 ===========

name: "myscope/Placeholder_2"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

============ operation 4 ===========

name: "myscope/Mul"

op: "Mul"

input: "myscope/Placeholder"

input: "myscope/Placeholder_1"

attr {

key: "T"

value {

type: DT_FLOAT

}

}

============ operation 5 ===========

name: "myscope/Mul_1"

op: "Mul"

input: "myscope/Mul"

input: "myscope/Placeholder_2"

attr {

key: "T"

value {

type: DT_FLOAT

}

}

================ graph object address ================

<tensorflow.python.framework.ops.Graph object at 0x1138702d0>

================ graph define ================

node {

name: "myscope/Placeholder"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

}

node {

name: "myscope/Placeholder_1"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

}

node {

name: "myscope/Placeholder_2"

op: "Placeholder"

attr {

key: "dtype"

value {

type: DT_FLOAT

}

}

attr {

key: "shape"

value {

shape {

}

}

}

}

node {

name: "myscope/Mul"

op: "Mul"

input: "myscope/Placeholder"

input: "myscope/Placeholder_1"

attr {

key: "T"

value {

type: DT_FLOAT

}

}

}

node {

name: "myscope/Mul_1"

op: "Mul"

input: "myscope/Mul"

input: "myscope/Placeholder_2"

attr {

key: "T"

value {

type: DT_FLOAT

}

}

}

versions {

producer: 15

}

================ sess str ================

9.0

[array(3.0, dtype=float32), 9.0]

{'ret_name': 9.0}

partial_run res: 144.0

tensorflow的Session是如何工作的

Session是Graph和执行者之间的媒介，Session.run()实际上将graph、fetches、feed_dict序列化到字节数组中，并调用tf_session.TF_Run（参见/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py）

而这里的tf_session.TF_Run实际上调用了动态链接库_pywrap_tensorflow.so中实现的_pywrap_tensorflow.TF_Run接口(参见/usr/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py)，这个动态链接库是tensorflow提供的诸多语言接口中python语言的接口

事实上这里的_pywrap_tensorflow.so和pywrap_tensorflow.py是通过SWIG工具自动生成，大家都知道tensorflow核心语言是c语言，这里是通过SWIG生成了各种脚本语言的接口