import tensorflow as tf
import numpy as np

labels = tf.convert_to_tensor([1], dtype=tf.int64)
logits = tf.convert_to_tensor([[0,1,2]], dtype=tf.float32)
r = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)

# 如果label为one_hot，那么用softmax_cross_entropy_with_logits

with tf.Session() as sess:
	print ('tf calc: ', sess.run(r)[0])

x = [0, 1, 2]
exp_x = list(map(lambda x: np.exp(x), x))
softmax_x = list(map(lambda x: x / sum(exp_x), exp_x))

print (softmax_x)
one_hot_label = [0, 1, 0]

loss = -np.log(np.dot(softmax_x, one_hot_label))
print ('manual calc: ', loss)