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:...
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: ...
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...
Learning Efficient Representations for Keyword Spotting with Triplet Loss We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves...
I've implemented an analog of weighted_cross_entropy_with_logits in my current project. It's useful for working with imbalanced datasets. I want to add it to PyTorch but I'm in doubt if it is really needed for others. For example, my imp...
lossAll = lasagne.objectives.categorical_crossentropy(prediction, Y)#loss functionloss = lossAll.mean() loss = loss + l2_penalty accuracy = T.mean(T.eq(T.argmax(prediction, axis=1), Y), dtype=theano.config.floatX) match = T.eq(T.argmax(prediction, axis=1), Y) ...