调用tf.softmax_cross_entropy_with_logits函数出错解决

运行一个程序时提示出错如下：

Traceback (most recent call last):
  File "/MNIST/softmax.py", line 12, in <module>
    cross_entropy2=tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits, y_))#dont forget tf.reduce_sum()!!
  File "C:\python35\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1578, in softmax_cross_entropy_with_logits
    labels, logits)
  File "C:\python35\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1533, in _ensure_xent_args
    "named arguments (labels=..., logits=..., ...)" % name)
ValueError: Only call `softmax_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

原来这个函数，不能按以前的方式进行调用了，只能使用命名参数的方式来调用。原来是这样的：

tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))

因此修改需要成这样：

tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_))

例子完整代码如下：

#python 3.5.3      
#2017-03-09 蔡军生  http://blog.csdn.net/caimouse    
#
import tensorflow as tf  
  
#our NN's output  
logits = tf.constant([[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]])

#step1:do softmax  
y = tf.nn.softmax(logits)

#true label  
y_ = tf.constant([[0.0,0.0,1.0],[0.0,0.0,1.0],[0.0,0.0,1.0]])  
#step2:do cross_entropy  
cross_entropy = -tf.reduce_sum(y_*tf.log(y))  
#do cross_entropy just one step  
cross_entropy2 = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_))
  
with tf.Session() as sess:  
    softmax=sess.run(y)  
    c_e = sess.run(cross_entropy)  
    c_e2 = sess.run(cross_entropy2)  
    print("step1:softmax result=")  
    print(softmax)  
    print("step2:cross_entropy result=")  
    print(c_e)  
    print("Function(softmax_cross_entropy_with_logits) result=")  
    print(c_e2)  

输出结果：

step1:softmax result=
[[ 0.09003057  0.24472848  0.66524094]
 [ 0.09003057  0.24472848  0.66524094]
 [ 0.09003057  0.24472848  0.66524094]]
step2:cross_entropy result=
1.22282
Function(softmax_cross_entropy_with_logits) result=
1.22282

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