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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)

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