tensorflow中交叉熵损失函数预测酸奶价格

「爱情、让人受尽委屈。」 2023-02-12 08:25 14阅读 0赞

# tensorflow中交叉熵损失函数 #

> # 引用API：tensorflow.kreas.losses.categorical\_crossentropy #
> 
> # ![H(y\\\_,y)=-\\sum y\\\_\*lny][H_y_y_-_sum y_lny] #

![20200525145910388.png][]

> # 则，tf.losses.categorical\_crossentropy(y\_, y) \#y\_为真实值，y为预测值 #

> ##  交叉熵是说明两个概率分布之间的距离，距离越小，说明二者分布越接近 ##
> 
> ## 例如：已知真实值y\_(1, 1),预测值y1(0.8, 0.6),预测值y2(0.2, 0.4)。这两个预测值哪一个距离真实值最近。 ##
> 
> ## 可得：H1((1, 1), (0.8, 0.5)) = -(1 \* ln(0.8) + 1 \* ln(0.5)) ~0.69 ##
> 
> ##            H2((1, 2), (0.2, 0.4)) = -(1 \* ln(0.2) + 1 \* ln(0.4)) ~0.92 ##
> 
> ## 因为H1<H2，所以H1更接近真实值 ##

#  tensorflow中交叉熵损失函数预测酸奶价格 #

import tensorflow as tf
    import numpy as np
    
    seed = 23456 #定义种子数值
    
    rdm = np.random.RandomState(seed) #生成[0, 1)之间的小数,由于固定seed数值，每次生成的数值都一样
    
    x = rdm.rand(32, 2) #生成32行2列的输入特征x
    
    np.set_printoptions(threshold=6) #概略显示
    
    print("32行2列的特征xs为：", x)  # 打印输入特征，概略显示5行
    
    y_ = [[x1 + x2 + (rdm.rand()  / 10.0 - 0.05)] for (x1, x2) in x] #从x中取出x1,x2相加，并且加上-0.05到+0.05随机噪声，得到标准答案y_
    
    x = tf.cast(x, dtype = tf.float32) #转换x数据类型
    
    print("转换数据类型后x:", x) #打印转换书籍类型后的x
    
    w1 = tf.Variable(tf.random.normal([2, 1], stddev = 1, seed = 1)) #初始化参数w1，生成两行一列的可训练参数
    
    print("w1为：", w1)
    
    epoch = 15000 #迭代次数为15000次
    
    lr = 0.002 #学习率为0.002
    
    for epoch in range(epoch):
        with tf.GradientTape() as tape: #用with上下文管理器调用GradientTape()方法
            y = tf.matmul(x, w1) #求前向传播计算结果y
            # loss_mse = tf.reduce_mean(tf.square(y_ - y)) #求均方误差loss_mean
            loss_mse = tf.losses.categorical_crossentropy(y_, y)  # 求交叉熵loss_mean
    # print("y为：", y)
    # print("loss_mean为：", loss_mse)
        grads = tape.gradient(loss_mse, w1) #损失函数对待训练参数w1求偏导
        w1.assign_sub(lr * grads) #更新参数w1
    
        if epoch % 500 == 0: #每迭代500轮
            print("After %d training steps, w1 is " % epoch) #每迭代500轮打印当前w1参数
            print(w1.numpy(), "\n") #打印当前w1参数
    
    print("Final w1 is :", w1.numpy()) #打印最终w1

## 结果为： ##

> E:\\Anaconda3\\envs\\TF2\\python.exe C:/Users/Administrator/PycharmProjects/untitled8/酸奶价格预测.py  
> 32行2列的特征xs为： \[\[0.32180029 0.32730047\]  
>  \[0.92742231 0.31169778\]  
>  \[0.16195411 0.36407808\]  
>  ...  
>  \[0.83554249 0.59131388\]  
>  \[0.2829476  0.05663651\]  
>  \[0.2916721  0.33175172\]\]  
> 转换数据类型后x: tf.Tensor(  
> \[\[0.3218003  0.32730046\]  
>  \[0.9274223  0.31169778\]  
>  \[0.1619541  0.36407807\]  
>  ...  
>  \[0.8355425  0.5913139 \]  
>  \[0.2829476  0.05663651\]  
>  \[0.29167208 0.33175173\]\], shape=(32, 2), dtype=float32)  
> w1为： <tf.Variable 'Variable:0' shape=(2, 1) dtype=float32, numpy=  
> array(\[\[-0.8113182\],  
>        \[ 1.4845988\]\], dtype=float32)>  
> After 0 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 1000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 1500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 2000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 2500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 3000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 3500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 4000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 4500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 5000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 5500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 6000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 6500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 7000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 7500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 8000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 8500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 9000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 9500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 10000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 10500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 11000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 11500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 12000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 12500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 13000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 13500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 14000 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> After 14500 training steps, w1 is   
> \[\[-0.8113182\]  
>  \[ 1.4845988\]\] 
> 
> Final w1 is : \[\[-0.8113182\]  
>  \[ 1.4845988\]\]
> 
> Process finished with exit code 0  
>

[H_y_y_-_sum y_lny]: https://private.codecogs.com/gif.latex?H%28y%5C_%2Cy%29%3D-%5Csum%20y%5C_*lny
[20200525145910388.png]: https://img-blog.csdnimg.cn/20200525145910388.png