softmax_cross_entropy.py

import tensorflow as tf

#Output from a neural network
logits = [1.0,2.0,3.0,4.0,5.0,1.0,1.0]

#Expected output
expected = [1.0,0.0,0.0,0.0,0.0,1.0,1.0]

output1 = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=expected)

#This is equivalent to softmax_cross_entropy_with_logits
softmax = tf.nn.softmax(logits)
output2 = -tf.reduce_sum(expected * tf.log(softmax))

with tf.Session() as sess:
  print(output1.eval())
  print(output2.eval())