TensorFlow不同交叉熵计算方式

import tensorflow as tf
#our NN's output
logits=tf.constant([[1.0,3.0,2.0],[3.0,2.0,1.0],[1.0,2.0,3.0]])
#step1:do softmax
y=tf.nn.softmax(logits)
#true label
y_=tf.constant([[0.0,1.0,0.0],[1.0,0.0,0.0],[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_))#dont forget tf.reduce_sum()!!
lab = tf.constant([1,0,2])
cross_entropy3 = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=lab))
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)
c_e3 = sess.run(cross_entropy3)
print("using sparse cross_entropy")
print(c_e3)