在keras中创建自定义损失函数

本文介绍了在keras中创建自定义损失函数的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我一直在尝试在keras中为dice_error_coefficient建立一个自定义损失函数.它在 tensorboard 中有其实现，我尝试在tensorflow的keras中使用相同的功能，但是当我使用 model.train_on_batch 时，它始终返回 NoneType .或 model.fit ，因为它在模型的指标中使用时会给出正确的值.可以请别人帮我做什么吗?我尝试过使用ahundt编写的Keras-FCN之类的库，其中他使用了自定义损失函数，但似乎都不起作用.代码的目标和输出分别是keras的loss.py文件中使用的y_true和y_pred.

Hi I have been trying to make a custom loss function in keras for dice_error_coefficient. It has its implementations in tensorboard and I tried using the same function in keras with tensorflow but it keeps returning a NoneType when I used model.train_on_batch or model.fit where as it gives proper values when used in metrics in the model. Can please someone help me out with what should i do? I have tried following libraries like Keras-FCN by ahundt where he has used custom loss functions but none of it seems to work. The target and output in the code are y_true and y_pred respectively as used in the losses.py file in keras.

def dice_hard_coe(target, output, threshold=0.5, axis=[1,2], smooth=1e-5): """References ----------- - `Wiki-Dice <en.wikipedia/wiki/Sørensen–Dice_coefficient>`_ """ output = tf.cast(output > threshold, dtype=tf.float32) target = tf.cast(target > threshold, dtype=tf.float32) inse = tf.reduce_sum(tf.multiply(output, target), axis=axis) l = tf.reduce_sum(output, axis=axis) r = tf.reduce_sum(target, axis=axis) hard_dice = (2. * inse + smooth) / (l + r + smooth) hard_dice = tf.reduce_mean(hard_dice) return hard_dice

推荐答案

在Keras中实现参数化的自定义损失函数有两个步骤.首先，编写系数/度量的方法.其次，编写包装函数以格式化Keras需要的格式.

There are two steps in implementing a parameterized custom loss function in Keras. First, writing a method for the coefficient/metric. Second, writing a wrapper function to format things the way Keras needs them to be.

使用Keras后端代替tensorflow直接运行简单的自定义损失函数(如DICE)实际上要干净得多.这是以这种方式实现的系数的示例:

It's actually quite a bit cleaner to use the Keras backend instead of tensorflow directly for simple custom loss functions like DICE. Here's an example of the coefficient implemented that way: import keras.backend as K def dice_coef(y_true, y_pred, smooth, thresh): y_pred = y_pred > thresh y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

现在是棘手的部分. Keras损失函数只能将(y_true，y_pred)作为参数.因此，我们需要一个单独的函数来返回另一个函数.

Now for the tricky part. Keras loss functions must only take (y_true, y_pred) as parameters. So we need a separate function that returns another function.

def dice_loss(smooth, thresh): def dice(y_true, y_pred) return -dice_coef(y_true, y_pred, smooth, thresh) return dice

最后，您可以在Keras编译中按如下方式使用它.

Finally, you can use it as follows in Keras compile.

# build model model = my_model() # get the loss function model_dice = dice_loss(smooth=1e-5, thresh=0.5) # compile model modelpile(loss=model_dice)