tf.nn.sigmoid_cross_entropy_with_logits 分类

tf.nn.sigmoid_cross_entropy_with_logits(_sentinel=None,,labels=None,logits=None,name=None)

logits和labels必须有相同的类型和大小

参数：
_sentinel：内部的并不使用
labels：和logits的shape和type一样
logits：类型为float32或者float64
name：操作的名称，可省

返回的是：一个张量，和logits的大小一致。是逻辑损失

sample

import numpy as np
import tensorflow as tf
 
labels=np.array([[1.,0.,0.],[0.,1.,0.],[0.,0.,1.]])
logits=np.array([[11.,8.,7.],[10.,14.,3.],[1.,2.,4.]])

y_pred=tf.math.sigmoid(logits)
prob_error1=-labels*tf.math.log(y_pred)-(1-labels)*tf.math.log(1-y_pred)
 
labels1=np.array([[0.,1.,0.],[1.,1.,0.],[0.,0.,1.]])#不一定只属于一个类别
logits1=np.array([[1.,8.,7.],[10.,14.,3.],[1.,2.,4.]])
y_pred1=tf.math.sigmoid(logits1)
prob_error11=-labels1*tf.math.log(y_pred1)-(1-labels1)*tf.math.log(1-y_pred1)
 
with tf.compat.v1.Session() as sess:
    print("1:")
    print(sess.run(prob_error1))
    print("2:")
    print(sess.run(prob_error11))
    print("3:")
    print(sess.run(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,logits=logits)))
    print("4:")
    print(sess.run(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels1,logits=logits1)))

output

1和3，2和4结果一样
1:
[[1.67015613e-05 8.00033541e+00 7.00091147e+00]
 [1.00000454e+01 8.31528373e-07 3.04858735e+00]
 [1.31326169e+00 2.12692801e+00 1.81499279e-02]]
2:
[[1.31326169e+00 3.35406373e-04 7.00091147e+00]
 [4.53988992e-05 8.31528373e-07 3.04858735e+00]
 [1.31326169e+00 2.12692801e+00 1.81499279e-02]]
3:
[[1.67015613e-05 8.00033541e+00 7.00091147e+00]
 [1.00000454e+01 8.31528373e-07 3.04858735e+00]
 [1.31326169e+00 2.12692801e+00 1.81499279e-02]]
4:
[[1.31326169e+00 3.35406373e-04 7.00091147e+00]
 [4.53988992e-05 8.31528373e-07 3.04858735e+00]
 [1.31326169e+00 2.12692801e+00 1.81499279e-02]]