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  • tf.losses.mean_squared_error函数浅析

    千次阅读 2021-01-09 01:35:16
    tf.losses.mean_squared_error函数解读 数据 tf.losses.mean_squared_error函数用于求MSE 验证 结论 数据 在实际情况中,假设我们训练得到的label是类似(a, b)的二维坐标点,这里我们用变量labels代表数据原有的标签...

    tf.losses.mean_squared_error函数解读

    数据

    在实际情况中,假设我们训练得到的label是类似(a, b)的二维坐标点,这里我们用变量labels代表数据原有的标签,用pred代表训练得到的输出,数据用array,np.float32的格式表示。

    import numpy as np
    
    #Original labels for data
    labels = np.array([[-3.087136  ,  0.773723  ],
           [ 0.5237208 , -2.3611534 ],
           [ 0.12045471,  0.23965162],
           [ 2.037887  ,  2.9082034 ],
           [ 1.630416  ,  4.253656  ],
           [ 1.581672  , -0.90316653],
           [ 2.1582973 ,  5.3201227 ],
           [-0.6064952 , -0.3148525 ],
           [-3.6611197 ,  1.844364  ],
           [-2.095178  , -4.3820047 ]], dtype=np.float32)
           
    #Predicted result from the model  
    pred = np.array([[-3.6970375 ,  0.61645496],
           [ 0.5356902 , -2.699189  ],
           [ 0.53962135,  0.28041327],
           [ 2.0638177 ,  2.8185306 ],
           [ 1.5602324 ,  4.3334904 ],
           [ 1.8943653 , -1.141346  ],
           [ 2.102385  ,  5.4475656 ],
           [-0.88059306, -0.72823447],
           [-4.00045   ,  1.6507398 ],
           [-2.4099615 , -4.7156816 ]], dtype=np.float32)
    

    tf.losses.mean_squared_error函数用于求MSE

    我们用以下代码求MSE:

    import tensorflow as tf
    
    #Definition
    y = tf.placeholder("float32", [None, 2])
    x = tf.placeholder("float32", [None, 2])
    cost = tf.losses.mean_squared_error(labels=y , predictions=x, weights=1)
    
    #Initializer
    init = tf.global_variables_initializer()
    
    #Session
    with tf.Session() as sess:
        sess.run(init)
        loss = sess.run([cost], feed_dict={y: labels, x: pred})
        print(loss)
    

    运行以上代码,我们得到了这样的输出:

    [0.07457263]
    

    接下来我们自己写一个函数来验证tf.losses.mean_squared_error()函数的功能。

    验证

    这里因为楼主知道数据是二维的,所以写得简单了一些,主要目的是为了让大家更直观理解函数性质。

    def simulated_mean_squared_error(x, y): #For two dimensional data
        m=(x-y)**2
        error=0
        count=0
        for i in m:	#这里因为楼主提前知道数据是二维的,所以写了两个for loop
            for j in i:
             error+=j
             count=count+1
        error=error/count
        return error
    

    然后根据我们的数据,得出模拟的结果:

    loss = simulated_mean_squared_error(pred, labels)
    print(loss)
    
    0.07457262602765695
    

    Exactly, 得到了相同的result。

    结论

    所以,根据比较我们可知,对于类似的像我们这里用的10*2维度的数据来说,tf.losses.mean_squared_error函数的功能就是先对于给定的两组数据做差,然后再进行elementwise的平方,最后进行elementwise相加并除以总element的个数(比如在此例中是除以20)。

    展开全文
  • 回归模型是机器学习中很重要的一类模型,不同于常见的分类模型,回归模型的性能评价指标跟分类...variance_score、mean_absolute_errormean_squared_error、r2_score,详细的解释已经在代码注释中了,就不再多解释...

        回归模型是机器学习中很重要的一类模型,不同于常见的分类模型,回归模型的性能评价指标跟分类模型也相差很大,这里简单基于工作中的一点实践来记录一下基于sklearn库计算回归模型中常用的四大评价指标主要包括:explained_variance_score、mean_absolute_error、mean_squared_error、r2_score,详细的解释已经在代码注释中了,就不再多解释了,具体实践如下:

    #!usr/bin/env python
    #encoding:utf-8
    from __future__ import division
    
    
    '''
    __Author__:沂水寒城
    功能:计算回归分析模型中常用的四大评价指标
    '''
    
    from sklearn.metrics import explained_variance_score, mean_absolute_error, mean_squared_error, r2_score
    
    
    
    def calPerformance(y_true,y_pred):
        '''
        模型效果指标评估
        y_true:真实的数据值
        y_pred:回归模型预测的数据值
        explained_variance_score:解释回归模型的方差得分,其值取值范围是[0,1],越接近于1说明自变量越能解释因变量
        的方差变化,值越小则说明效果越差。
        mean_absolute_error:平均绝对误差(Mean Absolute Error,MAE),用于评估预测结果和真实数据集的接近程度的程度
        ,其其值越小说明拟合效果越好。
        mean_squared_error:均方差(Mean squared error,MSE),该指标计算的是拟合数据和原始数据对应样本点的误差的
        平方和的均值,其值越小说明拟合效果越好。
        r2_score:判定系数,其含义是也是解释回归模型的方差得分,其值取值范围是[0,1],越接近于1说明自变量越能解释因
        变量的方差变化,值越小则说明效果越差。
        '''
        model_metrics_name=[explained_variance_score, mean_absolute_error, mean_squared_error, r2_score]  
        tmp_list=[]  
        for one in model_metrics_name:  
            tmp_score=one(y_true,y_pred)  
            tmp_list.append(tmp_score)  
        print ['explained_variance_score','mean_absolute_error','mean_squared_error','r2_score']
        print tmp_list
        return tmp_list
    
    
    if __name__=='__main__':
        y_pred=[22, 21, 21, 21, 22, 26, 28, 28, 33, 41, 93, 112, 119, 132, 126, 120, 101, 56, 58, 58, 57, 57, 53, 52, 52, 51, 50, 49, 49, 50, 54, 58, 85, 115, 125, 131, 135, 137, 135, 126, 109, 80, 83, 83, 77, 75, 74, 73, 73, 69, 67, 64, 64, 65, 69, 72, 93, 126, 141, 145, 126, 107, 65, 67, 70, 73, 77, 80, 82, 82, 79, 77, 72, 69, 67, 65, 64, 67, 75, 88, 105, 107, 101, 102, 100, 84, 85, 56, 50, 44, 41, 40, 36, 36, 35, 35, 34, 37, 37, 37, 38, 40, 41, 41, 44, 74, 98, 89, 89, 95, 101, 117, 115, 75, 47, 41, 40, 40, 37, 38, 40, 37, 37, 42, 41, 43, 39, 40, 44, 47, 54, 59, 70, 83, 83, 77, 58, 48, 50, 47, 44, 43, 42, 42, 41, 39, 39, 39, 40, 40, 40, 40, 44, 46, 46, 47, 45, 44, 43, 38, 34, 32, 32, 36, 35, 33, 34, 33, 29, 29, 30, 29, 28, 27, 25, 24, 23, 22, 23, 22, 25, 27, 25, 24, 22, 26, 30, 32, 34, 35, 34, 34, 36, 35, 36, 35, 37, 41, 40, 40, 44, 52, 74, 79, 75, 67, 49, 46, 40, 42, 40, 42, 44, 44, 46, 45, 44, 44, 42, 44, 45, 45, 46, 48, 72, 100, 101, 106, 104, 106, 78, 53, 51, 51, 52, 53, 53, 54, 58, 58, 59, 58, 58, 60, 59, 57, 56, 57, 98, 106, 114, 119, 114, 92, 52, 46, 42, 40, 40, 35, 35, 35, 33, 32, 33, 36, 40, 46, 54, 59, 74, 117, 132, 138, 122, 102, 93, 65, 44, 42, 40, 38, 39, 39, 38, 39, 39, 41, 41, 43, 46, 51, 53, 53, 54, 51, 52, 49, 45, 45, 45, 42, 40, 41, 45, 49, 54, 53, 53, 52, 52, 52, 52, 51, 51, 53, 55, 56, 55, 52, 52, 50, 48, 45, 43, 43, 47, 49, 46, 44, 49, 52, 53, 55, 58, 59, 60, 60, 61, 65, 71, 73, 73, 106, 115, 111, 100, 104, 106, 95, 54, 53, 54, 58, 56, 54, 55, 54, 53, 54, 56, 58, 60, 63, 61, 56, 57, 64, 102, 112, 117, 122, 120, 114, 111, 75, 48, 39, 40, 41, 40, 38, 39, 39, 41, 44, 42, 46, 45, 40, 41, 39, 37, 32, 32, 27, 27, 25, 23, 21, 20, 18, 18, 18, 17, 14, 15, 15, 16, 16, 14, 13, 18, 23, 23, 32, 29, 45, 45, 43, 49, 47, 45, 39, 35, 32, 26, 23, 20, 19, 21, 23, 22, 24, 25, 28, 26, 25, 29, 30, 30, 32, 30, 30, 32, 33, 34, 33, 33, 32, 31, 30, 30, 29, 28, 28, 27, 28, 28, 30, 29, 30, 32, 33, 29, 28, 36, 36, 40, 42, 41, 42, 41, 39, 32, 34, 32, 33, 36, 40, 38, 42, 43, 44, 46, 46, 51, 50, 49, 48, 46, 40, 36, 36, 31, 27, 24, 23, 21, 18, 19, 17, 16, 15, 16, 14, 13, 13, 13, 15, 20, 23, 25, 31, 29, 29, 27, 30, 29, 19, 21, 23, 25, 28, 29, 28, 28, 31, 30, 32, 35, 33, 30, 30, 33, 33, 32, 33, 36, 36, 34, 31, 30, 29, 28, 28, 28, 19, 18, 17, 18, 17, 18, 19, 20, 21, 20, 25, 28, 30, 29, 29, 28, 26, 24, 22, 23, 22, 22, 24, 22, 23, 25, 23, 22, 23, 21, 25, 27, 30, 29, 32, 46, 73, 90, 73, 77, 54, 51, 46, 46, 47, 49, 47, 45, 42, 43, 41, 36, 35, 33, 32, 36, 41, 48, 51, 55, 55, 56, 73, 80, 68, 59, 59, 56, 59, 64, 59, 55, 48, 29, 30, 32, 30, 30, 32, 35, 29, 29, 32, 44, 52, 53, 52, 52, 46, 42, 38, 33, 30, 32, 32, 35, 34, 36, 39, 42, 39, 45, 48, 48, 42, 47, 52, 54, 54, 54, 49, 53, 55, 50, 49, 47, 46, 43, 43, 51, 49, 51, 50, 51, 52, 53, 52, 53, 73, 82, 88, 100, 83, 89, 103, 110, 110, 106, 79, 63, 68, 55, 50, 47, 50, 54, 58, 59, 58, 51, 41, 38, 37, 40, 40, 40, 47, 51, 51, 49, 50, 48, 46, 43, 43, 42, 42, 41, 39, 42, 42, 38, 38, 36, 33, 33, 34, 33, 34, 36, 35, 29, 28, 30, 34, 37, 42, 44, 47, 48, 51, 52, 52, 50, 44, 43, 44, 41, 37, 34, 34, 30, 30, 34, 28, 27, 25, 26, 25, 23, 22, 23, 23, 24, 23, 24, 26, 28, 29, 28, 26, 26, 26, 27, 27, 28, 28, 26, 29, 30, 28, 28, 25, 22, 22, 22, 20, 20, 20, 20, 21, 26, 24, 24, 24, 26, 31, 33, 35, 35, 34, 33, 30, 30, 28, 29, 28, 26, 25, 23, 22, 23, 23, 22, 22, 26, 26, 26, 25, 27, 34, 37, 39, 41, 38, 35, 34, 35, 37, 38, 35, 30, 26, 24, 23, 21, 19, 21, 23, 22, 21, 21, 24, 24, 28, 35, 36, 35, 35, 32, 27, 36, 39, 38, 38, 30, 32, 30, 29, 26, 24, 21, 21, 23, 23, 23, 23, 24, 26, 30, 35, 39, 38, 35, 34, 48, 52, 42, 35, 34, 35, 38, 36, 34, 33, 33, 35, 37, 30, 29, 33, 37, 39, 40, 37, 38, 41, 43, 49, 53, 56, 56, 40, 38, 35, 35, 36, 36, 36, 38, 41, 45, 41, 36, 39, 40, 36, 34, 35, 36, 36, 36, 35, 37, 37, 39, 38, 40, 42, 46, 51, 54, 59, 62, 64, 65, 79, 88, 79, 72, 70, 68, 67, 58, 58, 59, 60, 62, 62, 62, 61, 63, 63, 62, 63, 65, 67, 69, 70, 69, 108, 118, 123, 122, 127, 129, 129, 114, 79, 70, 67, 70, 71, 71, 71, 72, 73, 73, 75, 76, 78, 81, 79, 78, 105, 113, 116, 109, 107, 90, 90, 94, 102, 105, 95, 94, 90, 80, 82, 77, 64, 52, 49]
        y_true=[23, 23, 23, 22, 23, 26, 28, 28, 32, 37, 56, 64, 68, 74, 75, 71, 66, 55, 59, 59, 58, 58, 52, 51, 50, 49, 48, 47, 47, 48, 53, 59, 73, 83, 84, 86, 87, 90, 90, 88, 84, 78, 83, 83, 77, 75, 75, 73, 73, 69, 66, 64, 65, 68, 72, 77, 84, 96, 100, 102, 92, 79, 65, 66, 71, 75, 79, 82, 84, 84, 82, 79, 73, 70, 68, 65, 66, 70, 79, 93, 98, 85, 77, 76, 76, 72, 74, 57, 48, 44, 41, 40, 37, 36, 36, 35, 35, 37, 37, 37, 37, 39, 40, 40, 41, 53, 63, 60, 61, 64, 70, 84, 86, 66, 47, 41, 40, 40, 38, 39, 40, 38, 37, 41, 40, 42, 39, 40, 43, 46, 47, 47, 49, 56, 58, 56, 51, 47, 48, 46, 43, 42, 42, 41, 40, 39, 39, 39, 40, 40, 39, 40, 43, 44, 42, 42, 43, 43, 42, 38, 35, 33, 33, 37, 36, 34, 34, 34, 31, 31, 32, 31, 30, 29, 27, 26, 26, 25, 25, 24, 25, 25, 25, 24, 24, 28, 31, 33, 34, 35, 34, 34, 36, 35, 36, 35, 37, 40, 40, 40, 42, 45, 53, 54, 53, 52, 48, 46, 41, 43, 41, 42, 44, 44, 45, 44, 43, 43, 41, 43, 44, 43, 45, 46, 55, 65, 66, 71, 72, 73, 63, 52, 50, 50, 51, 52, 52, 53, 58, 58, 58, 57, 57, 59, 58, 57, 56, 56, 70, 71, 73, 76, 78, 70, 52, 45, 43, 41, 41, 36, 35, 35, 34, 33, 33, 36, 39, 44, 52, 59, 69, 88, 91, 96, 85, 70, 65, 54, 44, 42, 40, 38, 39, 39, 38, 39, 39, 40, 40, 42, 44, 48, 51, 51, 52, 49, 48, 44, 41, 42, 42, 41, 40, 41, 44, 47, 53, 52, 52, 51, 51, 51, 51, 50, 50, 52, 55, 57, 55, 51, 47, 44, 43, 42, 42, 42, 46, 47, 45, 43, 47, 51, 52, 55, 59, 61, 62, 61, 63, 68, 77, 79, 76, 84, 84, 80, 71, 72, 73, 70, 53, 52, 54, 59, 56, 54, 55, 53, 52, 54, 57, 59, 61, 66, 63, 57, 58, 64, 80, 86, 89, 91, 92, 90, 88, 70, 48, 39, 41, 42, 41, 39, 40, 40, 41, 43, 42, 45, 44, 40, 41, 39, 38, 34, 33, 29, 29, 27, 25, 24, 23, 21, 21, 21, 20, 17, 18, 18, 19, 19, 17, 16, 19, 22, 23, 33, 28, 41, 38, 40, 48, 45, 45, 40, 34, 31, 28, 25, 23, 22, 23, 25, 24, 26, 27, 29, 27, 26, 29, 30, 30, 32, 30, 30, 32, 33, 33, 32, 32, 32, 31, 30, 30, 29, 28, 28, 27, 28, 28, 29, 29, 30, 31, 32, 29, 29, 36, 36, 39, 40, 39, 40, 39, 38, 33, 34, 33, 33, 35, 38, 37, 40, 41, 42, 43, 44, 48, 47, 46, 45, 44, 39, 36, 36, 32, 29, 26, 25, 23, 21, 21, 20, 19, 18, 18, 17, 16, 16, 16, 18, 22, 24, 26, 30, 30, 30, 28, 31, 30, 22, 23, 25, 27, 29, 30, 29, 29, 32, 31, 32, 35, 33, 31, 31, 33, 32, 30, 31, 35, 35, 33, 31, 30, 29, 28, 28, 28, 21, 20, 20, 20, 20, 20, 21, 22, 22, 22, 23, 24, 24, 25, 24, 25, 24, 24, 23, 24, 23, 23, 25, 23, 24, 25, 24, 23, 24, 22, 25, 27, 29, 28, 31, 39, 50, 57, 53, 56, 49, 48, 45, 45, 46, 47, 46, 44, 42, 42, 41, 37, 36, 34, 33, 36, 40, 46, 49, 55, 56, 57, 62, 64, 58, 56, 59, 57, 60, 65, 60, 55, 48, 30, 29, 30, 28, 29, 31, 35, 29, 29, 31, 37, 44, 43, 44, 45, 45, 43, 39, 34, 32, 33, 33, 35, 34, 36, 38, 40, 38, 43, 46, 46, 41, 42, 42, 42, 42, 42, 41, 43, 45, 46, 47, 46, 44, 42, 42, 48, 46, 48, 47, 49, 50, 51, 50, 51, 58, 58, 60, 64, 57, 58, 65, 71, 74, 73, 66, 63, 66, 54, 48, 46, 48, 53, 58, 59, 58, 49, 40, 38, 37, 39, 40, 40, 45, 48, 48, 47, 47, 46, 44, 42, 42, 43, 41, 40, 39, 41, 41, 37, 39, 36, 34, 34, 34, 34, 34, 36, 35, 30, 29, 31, 34, 37, 41, 42, 45, 46, 48, 50, 50, 48, 43, 41, 42, 40, 37, 34, 34, 31, 30, 30, 29, 28, 27, 27, 26, 25, 24, 25, 24, 25, 24, 25, 26, 28, 29, 28, 26, 26, 26, 26, 26, 27, 27, 26, 29, 30, 28, 28, 26, 23, 23, 23, 21, 21, 21, 21, 22, 25, 24, 24, 24, 26, 27, 27, 28, 28, 28, 29, 30, 30, 29, 29, 28, 27, 26, 25, 24, 24, 24, 23, 23, 26, 26, 26, 26, 27, 28, 30, 31, 32, 32, 31, 32, 34, 36, 36, 34, 30, 27, 25, 25, 23, 21, 22, 24, 23, 22, 22, 25, 25, 28, 30, 29, 29, 30, 29, 28, 35, 37, 37, 37, 30, 32, 30, 29, 27, 25, 23, 23, 24, 24, 24, 24, 25, 25, 27, 31, 32, 33, 32, 34, 46, 50, 41, 35, 34, 35, 38, 36, 34, 33, 33, 35, 36, 30, 29, 30, 31, 31, 34, 36, 37, 40, 42, 46, 51, 56, 56, 40, 38, 35, 35, 36, 36, 36, 38, 40, 43, 40, 36, 36, 36, 36, 34, 35, 36, 36, 36, 35, 37, 37, 38, 37, 39, 41, 44, 48, 52, 58, 63, 65, 68, 71, 71, 65, 60, 58, 58, 59, 57, 57, 59, 60, 62, 62, 62, 61, 64, 64, 63, 65, 67, 70, 72, 73, 70, 81, 82, 84, 85, 88, 91, 93, 89, 80, 74, 70, 74, 75, 76, 76, 77, 79, 79, 82, 82, 86, 89, 87, 86, 93, 95, 94, 90, 87, 82, 83, 85, 94, 103, 101, 101, 96, 85, 86, 80, 66, 52, 48]
        calPerformance(y_true,y_pred)
    

           结果如下:

    ['explained_variance_score', 'mean_absolute_error', 'mean_squared_error', 'r2_score']
    [0.709825411300075, 4.719, 112.613, 0.6725361793319165]

    展开全文
  • sklearn.metrics.mean_squared_error

    万次阅读 2019-01-09 11:07:53
    sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’) 参数: y_true:真实值。 y_pred:预测值。 sample_weight:样本权值。 multioutput:多维输入输出,...

    计算均方误差回归损失
    格式:
    sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
    参数:
    y_true:真实值。
    y_pred:预测值。
    sample_weight:样本权值。
    multioutput:多维输入输出,默认为’uniform_average’,计算所有元素的均方误差,返回为一个标量;也可选‘raw_values’,计算对应列的均方误差,返回一个与列数相等的一维数组。
    示例:

    from sklearn.metrics import mean_squared_error
    y_true = [3, -1, 2, 7]
    y_pred = [2, 0.0, 2, 8]
    mean_squared_error(y_true, y_pred)
    # 结果为:0.75
    y_true = [[0.5, 1],[-1, 1],[7, -6]]
    y_pred = [[0, 2],[-1, 2],[8, -5]]
    mean_squared_error(y_true, y_pred)
    # 结果为:0.7083333333333334
    mean_squared_error(y_true, y_pred, multioutput='raw_values')
    # 结果为:array([0.41666667, 1.        ])
    mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
    # 结果为:0.825
    # multioutput=[0.3, 0.7]返回将array([0.41666667, 1.        ])按照0.3*0.41666667+0.7*1.0计算所得的结果
    mean_squared_error(y_true, y_pred, multioutput='uniform_average')
    # 结果为:0.7083333333333334

    参考链接:https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html

    展开全文
  • 总的来说:keras.metrics下面的指标是累积的,在当前batch上的结果会和之前的batch做平均。而keras.losses下面的不会。 具体举例说明: # metric使用 metric = keras....而keras.losses.mean_squared_error是不累积的。

    总的来说:keras.metrics下面的指标是累积的,在当前batch上的结果会和之前的batch做平均。而keras.losses下面的不会。

    具体举例说明:

    # metric使用
    
    metric = keras.metrics.MeanSquaredError()
    print(metric([5.], [2.]))
    print(metric([0.], [1.]))
    print(metric.result())
    
    metric.reset_states()
    metric([1.], [3.])
    print(metric.result())

    输出:

    tf.Tensor(9.0, shape=(), dtype=float32)
    tf.Tensor(5.0, shape=(), dtype=float32)
    tf.Tensor(5.0, shape=(), dtype=float32)
    tf.Tensor(4.0, shape=(), dtype=float32)

    从上面的结果可以看到当运行“print(metric([5.], [2.]))”时,输出结果为“9”,(即:(5-2)**2 / 1 = 9),而运行第二句时,如果不累加的话,应该为1。((0-1)**2 / 1 = 1), 但是可以看到,输出并不是1,而是5,原因就是keras.metrics下面的指标是累积的,即((9+1)/ 2 = 5),想要清除累积,需要执行“metric.reset_states()”即可。而keras.losses.mean_squared_error是不累积的。

    展开全文
  • 2、cross_val_score()中的‘neg_mean_squared_error’使用的是metrics.mean_absolute_error,参考mse的计算公式,都已经平方了为什么还会有负数?? 背景: MSE的计算公式: ![图片说明]...
  • mean_squared_error ( test_data [ 'C' ] , ret ) rmse = mse ** ( 1 / 2 ) sklearn标准化 from sklearn . preprocessing import StandarScater data [ features ] = StandardScaler ( ) . fit...
  • from sklearn.metrics import mean_absolute_percentage_error
  • Keras : KeyError: 'val_mean_absolute_error' 把训练轮次改的小一点,然后添加下面代码: print(history.history.keys()) 就可以打印出有哪些key: dict_keys(['loss', 'mae', 'val_loss', 'val_mae']) 发现并...
  • from sklearn.metrics import mean_absolute_percentage_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_absolute_percentage_error(y_true, y_pred) 的时候,报以下错误’ -------------------...
  • sklearn.metrics.mean_absolute_error

    千次阅读 2018-10-30 18:01:00
    注意多维数组 MAE 的计算方法 * ... from sklearn.metrics import mean_absolute_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_error(y_true, ...
  • 1. 均方根误差 RMSE(Root Mean Squared Error) 2. 均方根对数误差 RMSLE(Root Mean Squared Logarithmic Error) 使用 RMSLE 的优点 1.RMSLE 惩罚欠预测大于过预测,适用于某些需要欠预测损失更大的场景,如...
  • 下面我们用三种方法来计算这两个数组的均方误差(MSE, mean squared error),具体公式为 1. tf.square()与tf.reduce_mean()组合 c = tf .square (a - b) mse = tf .reduce _mean(c) with tf .Session () as ...
  • Pytorch 损失函数 Mean Squared Error

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    Mean Squared Error(MSE)即均方误差,常用在数值型输出上: 其中θ是网络的参数,取决于使用的网络结构,例如如果只是普通的线性感知器,那么: 注意MSE和L2范数相比,L2范数是做了开平方操作的,所以如果要使用...
  • np.sum((y_hat - y)) / num_train 3.2 torch 代码实现 import torch loss_fn = torch.nn.MSELoss(reduce=False, size_average=False,reduction='mean') loss = loss_fn(input.float(), target.float()) ''' ...
  • 1.均方差损失函数(Mean Squared Error) 均方差损失函数是预测数据和原始数据对应点误差的平方和的均值。计算方式也比较简单 MSE=1N(y^−y)2MSE = \frac{1}{N}(\hat y - y) ^ 2MSE=N1​(y^​−y)2 其中,N为样本个数...
  • 回归分析评估指标均方对数误差(MSLE)详解及其意义:Mean Squared Log Error 目录 回归分析评估指标均方对数误差(MSLE)详解及其意义:Mean Squared Log Error 均方对数误差(MSLE) 何时使用MSLE MSLE示例 ...
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    下面我们用三种方法来计算这两个数组的均方误差(MSE, mean squared error),具体公式为 1. tf.square()与tf.reduce_mean()组合 c = tf .square (a - b) mse = tf .reduce _mean(c) with tf .Session () as...
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    均方误差(mean squared error)是回归问题中最常用的性能度量。 【计算公式】: E ( f ; D ) = 1 m ∑ i = 1 m ( f ( x i ) − y i ) 2 E(f; D) = \frac{1}{m}\sum_{i=1}^{m}(f(x_i) - y_i)^2 E ( f ; D ) = ...
  • Root Mean Squared Error (RMSE)

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    Root Mean Squared Error (RMSE) The square root of the mean/average of the square of all of the error. The use of RMSE is very common and it makes an excellent general purpose error metric for nu

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