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  • 通常,mean_squared_error越小越好.当我使用sklearn指标包时,它在文档页面中显示:...

    通常,mean_squared_error越小越好.

    当我使用sklearn指标包时,它在文档页面中显示:http://scikit-learn.org/stable/modules/model_evaluation.html

    All scorer objects follow the convention that higher return values are

    better than lower return values. Thus metrics which measure the

    distance between the model and the data, like

    metrics.mean_squared_error, are available as neg_mean_squared_error

    which return the negated value of the metric.

    7XYb7.png

    但是,如果我去:http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error

    它说它是均方误差回归损失,并没有说它被否定了.

    如果我查看源代码并检查那里的例子:https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/metrics/regression.py#L183它正在进行正常的均方误差,即越小越好.

    所以我想知道我是否错过了关于文档中否定部分的任何内容.谢谢!

    实际的函数“mean_squared_error”没有关于负面部分的任何内容.但是当您尝试’neg_mean_squared_error’时实现的功能将返回分数的否定版本.

    请查看源代码,了解其在the source code中的定义:

    neg_mean_squared_error_scorer = make_scorer(mean_squared_error,

    greater_is_better=False)

    观察param greater_is_better如何设置为False.

    现在所有这些得分/损失都用于其他各种事情,如cross_val_score,cross_val_predict,GridSearchCV等.例如,在’accuracy_score’或’f1_score’的情况下,得分越高越好,但是如果出现损失(错误),则降低得分更好.要以相同的方式处理它们,它返回负数.

    因此,该实用程序用于以相同方式处理分数和损失,而无需更改特定丢失或分数的源代码.

    所以,你没有错过任何东西.您只需要处理要使用损失函数的场景.如果您只想计算mean_squared_error,则只能使用mean_squared_error.但是,如果您想使用它来调整模型,或者使用scikit中的实用程序进行cross_validate,请使用’neg_mean_squared_error’.

    也许添加一些细节,我会解释更多.

    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
  • 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
  • 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...mean_absolute_error:平均绝对误差(Mean Absolute ...

    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)

    x=[]

    x.append(tmp_list)

    f1 = pd.DataFrame(x,columns=['explained_variance_score', 'mean_absolute_error', 'mean_squared_error', 'r2_score'])

    print(f1)

    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)

    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]...
  • from sklearn.metrics import mean_absolute_percentage_error
  • 在使用mean_absolute_percentage_error时,导入模块报错...ImportError: cannot import name 'mean_absolute_percentage_error' from 'sklearn.metrics' (/root/anaconda3/lib/python3.7/site-packages/sklearn/metrics
  • 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, ...
  • Mean squared error MSE即方差

    万次阅读 2017-11-04 09:55:53
    均方误差 (Mean Squared Error)均方误差  MSE是网络的性能函数,网络的均方误差,叫"Mean Square Error"。比如有n对输入输出数据,每对为[Pi,Ti],i=1,2,...,n.网络通过训练后有网络输出,记为Yi。 在相同测量...
  • 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...
  • 文章目录一、简介二、数学推导2.1 导数计算首先求解 bbb:然后求解 www:三、代码实现3.1 python 代码实现3.2 torch 代码实现参考资料 一、简介 欧氏距离损失经常用在线性回归问题(求解的是连续问题)。 回归问题...
  • Pytorch 损失函数 Mean Squared Error

    千次阅读 2019-07-29 17:59:36
    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...
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  • tensorflow--均方误差(MSE, mean squared error)表示方法

    万次阅读 多人点赞 2018-04-24 20:56:13
    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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