将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6): ''' Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions. Assumes the `channels_last` format. # Arguments ...
将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6):'''Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.Assumes the `channels_last` format.# Argumentsy_true:...
将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6):'''Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.Assumes the `channels_last` format.# Argumentsy_true:...
损失函数（loss function）是用来评测模型的预测值f(x)与真实值Y的相似程度，损失函数越小，就代表模型...
Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection...
focal loss的设计很巧妙,就是在cross entropy的基础上加上权重,让模型注重学习难以学习的样本,训练数据不均衡中占比较少的样本,相对放大对难分类样本的梯度,相对降低对易分类样本的梯度,并在一定程度上解决类别不均衡问题。 如果将cross loss定义为: ...
6 Application to weighted cross entropy losses In the following, we show that the well-known (weighted) Cross Entropy (wCE) loss [1] can be included in our framework as a particular wSOL. To observe this, let us consider the following admissible weight function $$\begin{aligned} \begin{al...
可以看出,当权重为1时就是不加权的Loss。 二、实现Python SigmoidCrossEntropyWeightLossLayer import caffe import numpy as npclassSigmoidCrossEntropyWeightLossLayer(caffe.Layer):defsetup(self,bottom,top):# check for all inputsparams=eval(self.param_str)self.cls_weight=float(params["cls_weight"])iflen...
The exponentially weighted cross-entropy (EWCE) loss function is designed to address the problem of inaccurate recognition of small-scale imbalanced underwater acoustic datasets. Compared with the cross-entropy loss, the EWCE loss down-weights the loss of the correctly predicted samples and focuses on...
For this purpose, an exponentially weighted cross-entropy loss is proposed as the convolutional neural network's loss function, which adds an impact factor to the standard cross-entropy loss according to the prediction probability of each sample. The proposed approach is evaluated on imbalanced under...