每个label为比如[0,0,1,0,0,0,1,0,1,0],就是10类有3类正确 不能用tf.nn.softmax_cross_entropy_with_logits Pytorch使用torch.nn.BCEloss Tensorflow使用tf.losses.sigmoid_cross_entropy
outputs=output)model.compile(optimizer='adam', loss='binary_crossentropy',
size_average=None,reduce=None,reduction='mean'):super(MultiLabelMarginLoss,self).__init__(size_average,reduce,reduction)defforward(self,input,target):returnF.multilabel_margin_loss(input,target,reduction=self.reduction)
1、可以仔细查看公式，两个Loss在BCEWithLogitsLoss的weight为1的时候是一样的 2、可以简单跑一个demo...
Hamming Loss可以说是accuracy的一种呈现。但其实如果只追求hamming loss/accuracy的话,就会出现以下问题:已知MS-COCO里的大多数图片只包含几个(<=4)物体,这样只要把所有图片的标签都设为0,也能达到大约4/80(0.08)的hamming loss。 所以我们需要precision(查准率)和recall(查全率)作为别的metrics去评判一个classifier...
Hamming Loss可以说是accuracy的一种呈现。但其实如果只追求hamming loss/accuracy的话,就会出现以下问题:已知MS-COCO里的大多数图片只包含几个(<=4)物体,这样只要把所有图片的标签都设为0,也能达到大约4/80(0.08)的hamming loss。 所以我们需要precision(查准率)和recall(查全率)作为别的metrics去评判一个classifier...
With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the ...
The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant subset of labels from an extremely large label set. Unfortunately, existing loss functions, such as the Hamming loss, are unsuitable for learning...
当然sigmoid也可以用于多类,也就是博主要解决的问题的方法,可以用sigmoid cross entropy,具体做法就是在最后一层全连接之类的输出过后过一个sigmoid,然后与多类的label求CE loss。这样求出来的结果就是每一类输出概率在0,1之间。比如 人声音乐、舞曲、影视原声、流行歌曲,那么这些类别之间并不是互斥的。例如:一首歌...
I wish to train AlexNet with Cross Entropy Loss, for which every input has multiple label probabilities. Till now, I have been doing this using an HDF5 layer. However, one has to do all sorts of manual pre processing and access to this l...