小编D.S*_*H.J的帖子

当试图用sigmoid激活函数得到交叉熵时,两者之间存在差异

1. loss1 = -tf.reduce_sum(p*tf.log(q), 1)
2. loss2 = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=p, logits=logit_q),1)

但是当使用softmax激活功能时,它们是相同的.

以下是示例代码:

import tensorflow as tf

sess2 = tf.InteractiveSession()
p = tf.placeholder(tf.float32, shape=[None, 5])
logit_q = tf.placeholder(tf.float32, shape=[None, 5])
q = tf.nn.sigmoid(logit_q)
sess.run(tf.global_variables_initializer())

feed_dict = {p: [[0, 0, 0, 1, 0], [1,0,0,0,0]], logit_q: [[0.2, 0.2, 0.2, 0.2, 0.2], [0.3, 0.3, 0.2, 0.1, 0.1]]}
loss1 = -tf.reduce_sum(p*tf.log(q),1).eval(feed_dict)
loss2 = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=p, logits=logit_q),1).eval(feed_dict)

print(p.eval(feed_dict), "\n", q.eval(feed_dict))
print("\n",loss1, "\n", loss2)