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) 1.TensorFlow入门基本教程http://edu.csdn.net/course/detail/4369
2. C++标准模板库从入门到精通http://edu.csdn.net/course/detail/3324
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