笔者在重构mindspore/models下stargan代码时,发现了ClassificationLoss类,其代码如下: class ClassificationLoss(nn.Cell): """Define classification loss for StarGAN""" def __init__(self, dataset='CelebA'): super().__init__() self.BCELoss = P.BinaryCrossEntropy(reduction='sum') self.cross_entropy ...
LOSS FUNCTIONS FOR BINARY CLASSIFICATION AND CLASS PROBABILITY ESTIMATION YI SHEN A DISSERTATION IN STATISTICS For the Graduate Group in Managerial Science and Applied Economics Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor...
SphereFace2: Binary Classification is All You Need for Deep Face Recognition [pdf] 简要概述: 目前基于分类的人脸识别训练主要使用基于softmax的损失函数进行多分类的训练,这存在几个问题: 训练时候采用多分类,需要明确每个样本所属类别,数据收集困难,且容易受到类别噪声的干扰; 测试的时候只需要判断两张人脸...
答案是logloss/cros-entropy L=N∑i=1yi∗log(pi)+(1−yi)∗log(1−pi)(1)(1)L=∑i=1Nyi∗log(pi)+(1−yi)∗log(1−pi) 我们可以从两个角度来理解为什么logloss是对真实概率的估计 从极大似然估计的角度 logloss可以由极大似然函数取对数得到,最小化logloss对应的最大化似然函数。p...
从信息论的角度 不熟悉信息论的同学看这里Intro to Information Theorylogloss也叫cross-entropy(交叉熵),用来衡量两个分布的相似程度。 交叉熵本身可以分解为P本身的信息熵+分布P和分布q之间的距离。这里P是样本的真实分布信息,信息熵一定。所以最小化交叉熵就变成了最小化分布p和q之间的距离,也就是样本分布和模...
https://www.kaggle.com/c/quora-insincere-questions-classification/overview/evaluation accuracy https://www.kaggle.com/c/titanic/overview/evaluation 不过开始用到和阈值相关的评价指标有时是在模型已经确定以后。第一步在确定模型时,往往还是需要一些可以综合衡量模型整体表现的指标。简单!粗暴!别整啥曲线阈值的,...
Loss functions for binary class probability estimation and classification: structure and application What are the natural loss functions or fitting criteria for binary class probability estimation? This question has a simple answer: so-called "proper scoring rules", that is, functions that score ...
Again the action space has two elements: (say a1:‘diagnosis of disease’ and a0:‘diagnosis of no disease’), and the structure of the loss function is analogous to Table 2. A typical situation occurs when the DM observes data on the attributes x and correct disease classification y of ...
Deep neural networks have demonstrated remarkable efficacy in diverse classification tasks. In this paper, we specifically focus on the predictive performance in deep binary classification problems with the sigmoid loss. Given that sigmoid loss is categorized as a non-convex and bounded loss function, ...
CalibratedBinaryClassificationMetrics 屬性 C# 閱讀英文版本 儲存 新增至集合 新增至計劃 分享方式: Facebookx.comLinkedIn電子郵件 列印 參考 意見反應 定義 命名空間: Microsoft.ML.Data 組件: Microsoft.ML.Data.dll 套件: Microsoft.ML v3.0.1 取得分類器的記錄遺失。 對數損失會測量分類器的效能,相對於預測...