# 基于tensorflow for循环 while循环案例

```import tensorflow as tf

n1 = tf.constant(2)
n2 = tf.constant(3)
n3 = tf.constant(4)

def cond1(i, a, b):
return i < n1

def cond2(i, a, b):
return i < n2

def cond3(i, a, b):
return i < n3

def body(i, a, b):
return i + 1, b, a + b

i1, a1, b1 = tf.while_loop(cond1, body, (2, 1, 1))
i2, a2, b2 = tf.while_loop(cond2, body, (2, 1, 1))
i3, a3, b3 = tf.while_loop(cond3, body, (2, 1, 1))
sess = tf.Session()

print(sess.run(i1))
print(sess.run(a1))
print(sess.run(b1))
print("-")
print(sess.run(i2))
print(sess.run(a2))
print(sess.run(b2))
print("-")
print(sess.run(i3))
print(sess.run(a3))
print(sess.run(b3))
```

print结果：

```2
1
1
-
3
1
2
-
4
2
3```

```xiaojie=1
i=tf.constant(0,dtype=tf.int32)
batch_len=tf.constant(10,dtype=tf.int32)
loop_cond = lambda a,b: tf.less(a,batch_len)
#yy=tf.Print(batch_len,[batch_len],"batch_len:")
yy=tf.constant(0)
loop_vars=[i,yy]
def _recurrence(i,yy):
c=tf.constant(2,dtype=tf.int32)
x=tf.multiply(i,c)
global xiaojie
xiaojie=xiaojie+1
print_info=tf.Print(x,[x],"x:")
yy=yy+print_info
# print (xiaojie)
return i,yy
i,yy=tf.while_loop(loop_cond,_recurrence,loop_vars,parallel_iterations=1)#可以批处理
sess = tf.Session()
print (sess.run(i))
print (xiaojie)```

print (xiaojie)

```def __init__(self):
self.xiaojie=1
def test_RNN(self):
i=tf.constant(0,dtype=tf.int32)
batch_len=tf.constant(10,dtype=tf.int32)
loop_cond = lambda a,b: tf.less(a,batch_len)
#yy=tf.Print(batch_len,[batch_len],"batch_len:")
yy=tf.constant(0)
loop_vars=[i,yy]
def _recurrence(i,yy):
c=tf.constant(2,dtype=tf.int32)
x=tf.multiply(i,c)
self.xiaojie=self.xiaojie+1
print_info=tf.Print(x,[x],"x:")
yy=yy+print_info
return i,yy
i,yy=tf.while_loop(loop_cond,_recurrence,loop_vars,parallel_iterations=1)#可以批处理
sess = tf.Session()
sess.run(yy)
print (self.xiaojie)
if __name__ == "__main__":
model = RNN_Model()#构建树，并且构建词典
model.test_RNN()```

```_recurrence: 2
10
2```

tf.while_loop操纵全局变量和类成员变量总结

self.L与loop_vars中变量有依赖关系，但是并没有真正被修改。

```#IIII通过计算将非叶子节点的词向量也放入nodes_tensor中。
iiii=tf.constant(0,dtype=tf.int32)
loop____cond = lambda a,b,c,d,e: tf.less(a,self.sentence_length-1)#iiii的范围是0到sl-2。注意，不包括sl-1。这是因为只需要计算sentence_length-1次，就能构建出一颗树
loop____vars=[iiii,columnLinesOfL,node_tensors_cost_tensor,nodes_tensor,tfPrint]
def ____recurrence(iiii,columnLinesOfL,node_tensors_cost_tensor,nodes_tensor,tfPrint):#循环的目的是实现Greedy算法
###
#Greedy的主要目标就是确立树结构。
###
c1 = self.L[:,0:columnLinesOfL-1]#这段代码是从RvNN的matlab的源码中复制过来的，但是Matlab的下标是从1开始，并且Matlab中1:2就是1和2，而python中1:2表示的是1，不包括2,所以，有很大的不同。
c2 = self.L[:,1:columnLinesOfL]
c=tf.concat([c1,c2],axis=0)
p=tf.tanh(tf.matmul(self.W1,c)+tf.tile(self.b1,[1,columnLinesOfL-1]))
p_normalization=self.normalization(p)
y=tf.tanh(tf.matmul(self.U,p_normalization)+tf.tile(self.bs,[1,columnLinesOfL-1]))#根据Matlab中的源码来的，即重构后，也有一个激活的过程。
#将Y矩阵拆分成上下部分之后，再分别进行标准化。
columnlines_y=columnLinesOfL-1
(y1,y2)=self.split_by_row(y,columnlines_y)
y1_normalization=self.normalization(y1)
y2_normalization=self.normalization(y2)
#论文中提出一种计算重构误差时要考虑的权重信息。具体见论文，这里暂时不实现。
#这个权重是可以修改的。
alpha_cat=1
bcat=1
#计算重构误差矩阵
##    constant1=tf.constant([[1.0,2.0,3.0],[4.0,5.0,6.0],[7.0,8.0,9.0]])
##    constant2=tf.constant([[1.0,2.0,3.0],[1.0,4.0,2.0],[1.0,6.0,1.0]])
##    constructionErrorMatrix=self.constructionError(constant1,constant2,alpha_cat,bcat)
y1c1=tf.subtract(y1_normalization,c1)
y2c2=tf.subtract(y2_normalization,c2)
constructionErrorMatrix=self.constructionError(y1c1,y2c2,alpha_cat,bcat)
################################################################################
print_info=tf.Print(iiii,[iiii],"\niiii:")#专门为了调试用，输出相关信息。
tfPrint=print_info+tfPrint
print_info=tf.Print(columnLinesOfL,[columnLinesOfL],"\nbefore modify. columnLinesOfL:")#专门为了调试用，输出相关信息。
tfPrint=print_info+tfPrint
print_info=tf.Print(constructionErrorMatrix,[constructionErrorMatrix],"\nbefore modify. constructionErrorMatrix:",summarize=100)#专门为了调试用，输出相关信息。
tfPrint=tf.to_int32(print_info[0])+tfPrint#一种不断输出tf.Print的方式，注意tf.Print的返回值。
################################################################################
J_minpos=tf.to_int32(tf.argmin(constructionErrorMatrix))#如果不转换的话，下面调用delete_one_column中，会调用tf.slice，之后tf.slice的参数中的类型必须是一样的。
J_min=constructionErrorMatrix[J_minpos]
#一共要进行sl-1次循环。因为是从sl个叶子节点，两两结合sl-1次，才能形成一颗完整的树，而且是采用Greedy的方式。
#所以，需要为下次循环做准备。
#第一步，从该sentence的词向量矩阵中删除第J_minpos+1列，因为第J_minpos和第J_minpos+1列对应的单词要合并为一个新的节点,这里就是修改L
################################################################################
print_info=tf.Print(self.L,[self.L[0]],"\nbefore modify. L row 0:",summarize=100)#专门为了调试用，输出相关信息。
tfPrint=tf.to_int32(print_info[0][0])+tfPrint
print_info=tf.Print(self.L,[tf.shape(self.L)],"\nbefore modify. L shape:")#专门为了调试用，输出相关信息。
tfPrint=tf.to_int32(print_info[0][0])+tfPrint
################################################################################
deleteColumnIndex=J_minpos+1
self.L=self.delete_one_column(self.L,deleteColumnIndex,self.numlinesOfL,columnLinesOfL)
columnLinesOfL=tf.subtract(columnLinesOfL,1) #列数减去1.
################################################################################
print_info=tf.Print(deleteColumnIndex,[deleteColumnIndex],"\nbefore modify. deleteColumnIndex:")#专门为了调试用，输出相关信息。
tfPrint=print_info+tfPrint
print_info=tf.Print(self.L,[self.L[0]],"\nafter modify. L row 0:",summarize=100)#专门为了调试用，输出相关信息。
tfPrint=tf.to_int32(print_info[0][0])+tfPrint

print_info=tf.Print(self.L,[tf.shape(self.L)],"\nafter modify. L shape:")#专门为了调试用，输出相关信息。
tfPrint=tf.to_int32(print_info[0][0])+tfPrint
print_info=tf.Print(columnLinesOfL,[columnLinesOfL],"\nafter modify. columnLinesOfL:")#专门为了调试用，输出相关信息。
tfPrint=print_info+tfPrint
################################################################################

#第二步，将新的词向量赋值给第J_minpos列
columnTensor=p_normalization[:,J_minpos]
new_column_tensor=tf.expand_dims(columnTensor,1)
self.L=self.modify_one_column(self.L,new_column_tensor,J_minpos,self.numlinesOfL,columnLinesOfL)
#第三步，同时将新的非叶子节点的词向量存入nodes_tensor
nodes_tensor=self.modify_one_column(nodes_tensor,new_column_tensor,modified_index_tensor,self.numlines_tensor,self.numcolunms_tensor)
#第四步：记录合并节点的最小损失，存入node_tensors_cost_tensor
J_min_tensor=tf.expand_dims(tf.expand_dims(J_min,0),1)
node_tensors_cost_tensor=self.modify_one_column(node_tensors_cost_tensor,J_min_tensor,iiii,self.numlines_tensor2,self.numcolunms_tensor2)
####进入下一次循环
print_info=tf.Print(J_minpos,[J_minpos,J_minpos+1],"node:")#专门为了调试用，输出相关信息。
tfPrint=tfPrint+print_info
#    columnLinesOfL=tf.subtract(columnLinesOfL,1) #在上面的循环体中已经执行了，没有必要再执行。
return iiii,columnLinesOfL,node_tensors_cost_tensor,nodes_tensor,tfPrint
iiii,columnLinesOfL,node_tensors_cost_tensor,nodes_tensor,tfPrint=tf.while_loop(loop____cond,____recurrence,loop____vars,parallel_iterations=1)
pass```

```before modify. columnLinesOfL:[31]
iiii:[0]

after modify. columnLinesOfL:[30]

before modify. L shape:[300 31]

before modify. L row 0:[0.126693 -0.013654 -0.166731 -0.13703 -0.261395 0.11459 0.016001 0.016001 0.144603 0.05588 0.171787 0.016001 1.064545 0.144603 0.130615 -0.13703 -0.261395 1.064545 -0.261395 0.144603 0.036626 1.064545 0.188871 0.201198 0.05588 0.203795 0.201198 0.03536 0.089345 0.083778 0.103635]
node:[0][1]

before modify. constructionErrorMatrix:[3.0431733686706206 11.391056715427794 19.652819956115856 13.713453313903868 11.625973829805879 12.827533320819564 9.7513513723204746 13.009151292890811 13.896089243289065 10.649829109971648 9.45239374745086 15.704486086921641 18.274065790781862 12.447866299915024 15.302996103637689 13.713453313903868 14.295549844738751 13.779406175789358 11.625212314259059 16.340507223201449 19.095964364689717 15.10149194936319 11.989443162329437 13.436654650354058 11.120373311110505 12.39345317975002 13.568052800712424 10.998430341124633 8.3223909323599869 6.8896857405641851]

after modify. L shape:[300 30]

after modify. L row 0:[0.126693 -0.166731 -0.13703 -0.261395 0.11459 0.016001 0.016001 0.144603 0.05588 0.171787 0.016001 1.064545 0.144603 0.130615 -0.13703 -0.261395 1.064545 -0.261395 0.144603 0.036626 1.064545 0.188871 0.201198 0.05588 0.203795 0.201198 0.03536 0.089345 0.083778 0.103635]

before modify. deleteColumnIndex:[1]

before modify. columnLinesOfL:[30]

iiii:[1]

before modify. L shape:[300 31]

after modify. columnLinesOfL:[29]

before modify. L row 0:[0.126693 -0.013654 -0.166731 -0.13703 -0.261395 0.11459 0.016001 0.016001 0.144603 0.05588 0.171787 0.016001 1.064545 0.144603 0.130615 -0.13703 -0.261395 1.064545 -0.261395 0.144603 0.036626 1.064545 0.188871 0.201198 0.05588 0.203795 0.201198 0.03536 0.089345 0.083778 0.103635]

before modify. deleteColumnIndex:[1]
node:[0][1]

before modify. constructionErrorMatrix:[3.0431733686706206 11.391056715427794 19.652819956115856 13.713453313903868 11.625973829805879 12.827533320819564 9.7513513723204746 13.009151292890811 13.896089243289065 10.649829109971648 9.45239374745086 15.704486086921641 18.274065790781862 12.447866299915024 15.302996103637689 13.713453313903868 14.295549844738751 13.779406175789358 11.625212314259059 16.340507223201449 19.095964364689717 15.10149194936319 11.989443162329437 13.436654650354058 11.120373311110505 12.39345317975002 13.568052800712424 10.998430341124633 8.3223909323599869]

after modify. L shape:[300 29]

after modify. L row 0:[0.126693 -0.166731 -0.13703 -0.261395 0.11459 0.016001 0.016001 0.144603 0.05588 0.171787 0.016001 1.064545 0.144603 0.130615 -0.13703 -0.261395 1.064545 -0.261395 0.144603 0.036626 1.064545 0.188871 0.201198 0.05588 0.203795 0.201198 0.03536 0.089345 0.083778]

before modify. columnLinesOfL:[29]

iiii:[2]```

self.L=self.modify_one_column(self.L,new_column_tensor,J_minpos,self.numlinesOfL,columnLinesOfL)

before modify. L shape:[300 31]