张量流中类不平衡二元分类器的损失函数 [英] Loss function for class imbalanced binary classifier in Tensor flow

问题描述

我正在尝试将深度学习应用于目标类别（500k，31k）之间类别高度不平衡的二进制分类问题。我想编写一个自定义损失函数，该函数应类似于：
minimum（100-（（predicted_smallerclass）/（total_smallerclass））* 100）

I am trying to apply deep learning for a binary classification problem with high class imbalance between target classes (500k, 31K). I want to write a custom loss function which should be like: minimize(100-((predicted_smallerclass)/(total_smallerclass))*100)

感谢任何有关如何构建此逻辑的指针。

Appreciate any pointers on how I can build this logic.

推荐答案

您可以通过乘以logit将类权重添加到损失函数中。
正态交叉熵损失是这样的：

You can add class weights to the loss function, by multiplying logits. Regular cross entropy loss is this:

loss(x, class) = -log(exp(x[class]) / (\sum_j exp(x[j])))
               = -x[class] + log(\sum_j exp(x[j]))

在加权情况下：

loss(x, class) = weights[class] * -x[class] + log(\sum_j exp(weights[class] * x[j]))

因此，通过乘以logit，您可以按类别权重重新调整每个类别的预测。

So by multiplying logits, you are re-scaling predictions of each class by its class weight.

例如：

ratio = 31.0 / (500.0 + 31.0)
class_weight = tf.constant([ratio, 1.0 - ratio])
logits = ... # shape [batch_size, 2]
weighted_logits = tf.mul(logits, class_weight) # shape [batch_size, 2]
xent = tf.nn.softmax_cross_entropy_with_logits(
  weighted_logits, labels, name="xent_raw")

现在有一个标准损失函数支持每批重量：

There is a standard losses function now that supports weights per batch:

tf.losses.sparse_softmax_cross_entropy(labels=label, logits=logits, weights=weights)

权重应该从类转换的地方权重等于每个示例的权重（形状为[batch_size]）。请参见此处的文档。

Where weights should be transformed from class weights to a weight per example (with shape [batch_size]). See documentation here.

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