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"这个加权二元交叉熵损失公式是否正确？"

[英]Is this formula for weighted binary cross entropy loss correct?

原文 2022-02-06 13:17:27 8 1 python/ machine-learning/ cross-entropy

Here it is the formula I built: My equation for weighted binary cross entropy loss<\/a>这是我建立的公式：我的加权二元交叉熵损失方程<\/a>

Where:在哪里：

alpha(i) = #Negative samples \/ Total samples if y(i)=1 alpha(i) = #Negative samples \/ Total samples if y(i)=1

alpha(i) = #Positive samples \/ Total samples if y(i)=0 alpha(i) = #Positive samples \/ Total samples if y(i)=0

Is it correct?这是正确的吗？ I cannot find any similar formula on the internet.我在互联网上找不到任何类似的公式。 Thank you!谢谢！

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1 个解决方案

The alpha(i) value inside the summation block seems misplaced.求和块内的 alpha(i) 值似乎放错了位置。 The binary loss value is calculated for each sample which is then summated to get the total binary log loss\/binary cross entropy.为每个样本计算二进制损失值，然后将其相加得到总二进制对数损失\/二进制交叉熵。 But the alpha(i) does not belong to the sample, it is an aggregate property.但是 alpha(i) 不属于样本，它是一个聚合属性。 Hence the formula is not logical.所以这个公式是不合逻辑的。 Refer -> https:\/\/www.analyticsvidhya.com\/blog\/2021\/03\/binary-cross-entropy-log-loss-for-binary-classification\/<\/a>参考-> https:\/\/www.analyticsvidhya.com\/blog\/2021\/03\/binary-cross-entropy-log-loss-for-binary-classification\/<\/a>

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如何创建加权交叉熵损失？ - How to create weighted cross entropy loss?

如何在 keras 中创建自定义损失函数？（自定义加权二元交叉熵） - How can I create a custom loss function in keras ? (Custom Weighted Binary Cross Entropy)

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为什么 Binary_Cross-entropy Loss 给出负值？ - Why the Binary_Cross-entropy Loss is giving negative values?

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