深度学习常用函数记录（tensorflow）

loss_mse = tf.reduce_mean(tf.square(y_-y))

loss_custom = tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*COST,(y_-y)*PROFIT))

tf.losses.categorical_crossentropy(y_,y)#tensorflow内置的

loss_crossentropy =- tf.reduce_sum(y_*tf.math.log(tf.clip_by_value(y,0.1,1.0)))#根据公式实现的

import tensorflow as tf
y_ = [[1,0],[2,3]]
y = [[0.6,0.4],[0.3,0.7]]
# loss_ce1 = tf.losses.categorical_crossentropy([1, 0], [0.6, 0.4])
loss_ce1 = tf.losses.categorical_crossentropy(y_,y)
loss_ce11 = -tf.reduce_sum(y_*tf.math.log(tf.clip_by_value(y,0.1,1.0)),axis=1)
print("loss_ce1:", loss_ce1)
print(loss_ce11)
输出
loss_ce1: tf.Tensor([0.5108256 3.4779706], shape=(2,), dtype=float32)
loss_ce11:tf.Tensor([0.5108256 3.4779706], shape=(2,), dtype=float32)

tf.nn.softmax_cross_entropy_with_logits(y_,y)

 1 # softmax与交叉熵损失函数的结合
 2 import tensorflow as tf
 3 import numpy as np
 4 
 5 y_ = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 0]])
 6 y = np.array([[12, 3, 2], [3, 10, 1], [1, 2, 5], [4, 6.5, 1.2], [3, 6, 1]])
 7 y_pro = tf.nn.softmax(y)
 8 loss_ce1 = tf.losses.categorical_crossentropy(y_,y_pro)
 9 loss_ce2 = tf.nn.softmax_cross_entropy_with_logits(y_, y)
10 
11 print('分步计算的结果:\n', loss_ce1)
12 print('结合计算的结果:\n', loss_ce2)
输出
分步计算的结果:
 tf.Tensor(
[1.68795487e-04 1.03475622e-03 6.58839038e-02 2.58349207e+00
 5.49852354e-02], shape=(5,), dtype=float64)
结合计算的结果:
 tf.Tensor(
[1.68795487e-04 1.03475622e-03 6.58839038e-02 2.58349207e+00
 5.49852354e-02], shape=(5,), dtype=float64)