如何在Keras中将损失函数指定为二次加权kappa? [英] How can I specify a loss function to be quadratic weighted kappa in Keras?

问题描述

我的理解是，keras要求损失函数具有签名:

def custom_loss(y_true, y_pred):

我正在尝试使用sklearn.metrics.cohen_kappa_score，这需要 (y1，y2，标签=无，权重=无，sample_weight =无)`

如果我照原样使用它:

model.compile(loss=metrics.cohen_kappa_score,
              optimizer='adam', metrics=['accuracy'])

然后将不会设置weights.我想将其设置为quadtratic.有什么要传递的吗?

解决方案

在Keras中实现参数化的自定义损失函数(cohen_kappa_score)有两个步骤.由于已经实现了满足您需要的功能，因此您无需自己实现它.但是，根据 TensorFlow文档，sklearn.metrics.cohen_kappa_score不支持加权矩阵. 因此，我建议使用TensorFlow的cohen_kappa实现.但是，在Keras中使用TensorFlow并非易事... 根据此问题，他们使用control_dependencies使用TensorFlow Keras中的指标.这是一个示例:

import keras.backend as K
def _cohen_kappa(y_true, y_pred, num_classes, weights=None, metrics_collections=None, updates_collections=None, name=None):
   kappa, update_op = tf.contrib.metrics.cohen_kappa(y_true, y_pred, num_classes, weights, metrics_collections, updates_collections, name)
   K.get_session().run(tf.local_variables_initializer())
   with tf.control_dependencies([update_op]):
      kappa = tf.identity(kappa)
   return kappa

由于 Keras损失函数采用(y_true, y_pred)作为参数，因此您需要一个包装函数来返回另一个函数.这是一些代码:

def cohen_kappa_loss(num_classes, weights=None, metrics_collections=None, updates_collections=None, name=None):
   def cohen_kappa(y_true, y_pred):
      return -_cohen_kappa(y_true, y_pred, num_classes, weights, metrics_collections, updates_collections, name)
   return cohen_kappa

最后，您可以在Keras中按如下方式使用它:

# get the loss function and set parameters
model_cohen_kappa = cohen_kappa_loss(num_classes=3,weights=weights)
# compile model
model.compile(loss=model_cohen_kappa,
          optimizer='adam', metrics=['accuracy'])

关于将Cohen-Kappa度量用作损失函数.通常，可以将加权kappa用作损失函数.这是纸，其中使用加权kappa作为多类分类的损失函数. >

My understanding is that keras requires loss functions to have the signature:

def custom_loss(y_true, y_pred):

I am trying to use sklearn.metrics.cohen_kappa_score, which takes (y1, y2, labels=None, weights=None, sample_weight=None)`

If I use it as is:

model.compile(loss=metrics.cohen_kappa_score,
              optimizer='adam', metrics=['accuracy'])

Then the weights won't be set. I want to set that to quadtratic. Is there some what to pass this through?

解决方案

There are two steps in implementing a parameterized custom loss function (cohen_kappa_score) in Keras. Since there are implemented function for your needs, there is no need for you to implement it yourself. However, according to TensorFlow Documentation, sklearn.metrics.cohen_kappa_score does not support weighted matrix. Therefore, I suggest TensorFlow's implementation of cohen_kappa. However, using TensorFlow in Keras is not that easy... According to this Question, they used control_dependencies to use a TensorFlow metric in Keras. Here is a example:

import keras.backend as K
def _cohen_kappa(y_true, y_pred, num_classes, weights=None, metrics_collections=None, updates_collections=None, name=None):
   kappa, update_op = tf.contrib.metrics.cohen_kappa(y_true, y_pred, num_classes, weights, metrics_collections, updates_collections, name)
   K.get_session().run(tf.local_variables_initializer())
   with tf.control_dependencies([update_op]):
      kappa = tf.identity(kappa)
   return kappa

Since Keras loss functions take (y_true, y_pred) as parameters, you need a wrapper function that returns another function. Here is some code:

def cohen_kappa_loss(num_classes, weights=None, metrics_collections=None, updates_collections=None, name=None):
   def cohen_kappa(y_true, y_pred):
      return -_cohen_kappa(y_true, y_pred, num_classes, weights, metrics_collections, updates_collections, name)
   return cohen_kappa

Finally, you can use it as follows in Keras:

# get the loss function and set parameters
model_cohen_kappa = cohen_kappa_loss(num_classes=3,weights=weights)
# compile model
model.compile(loss=model_cohen_kappa,
          optimizer='adam', metrics=['accuracy'])

Regarding using the Cohen-Kappa metric as a loss function. In general it is possible to use weighted kappa as a loss function. Here is a paper using weighted kappa as a loss function for multi-class classification.

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