在pytorch中,对应的函数为torch.nn.BCELossWithLogits和torch.nn.BCELoss https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a
的确binary_cross_entropy_with_logits不需要sigmoid函数了。 事实上,官方是推荐使用函数带有with_logits的,解释是 This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the ope...
>>> loss = F.binary_cross_entropy_with_logits(input, target) >>> loss.backward() """ if has_torch_function_variadic(input, target, weight, pos_weight): return handle_torch_function( binary_cross_entropy_with_logits, (input, target, weight, pos_weight), input, target, weight=weight, ...
return 1/(1+np.exp(-z)) def costfunction(theta,X,y,learningRate): theta=np.matrix(theta) X=np.matrix(X) y=np.matrix(y) #这里出错了 导致X 和theta的乘法 对不上 艹 first=np.multiply(-y,np.log(sigmoid(X* theta.T))) second=np.multiply((1-y),np.log(1-sigmoid(X* theta.T))...
pred = torch.sigmoid(x) loss = F.binary_cross_entropy(pred, y) loss tensor(0.7739) F.binary_cross_entropy_with_logits Pytorch's single binary_cross_entropy_with_logits function. F.binary_cross_entropy_with_logits(x, y) out: tensor(0.7739)...
loss = self.binary_cross_entropy(logits, labels, weight) return loss 通过源码我们可以看出,BCELoss实际上是对BinaryCrossEntropy的一层封装(weight为None代表各个样本权重值相同)。 2.2 实例验证 下面我们通过一个实例来验证源码解析部分的结论。 实例中我们将weight设置1.0,即各个样本权重相同,等价于BCELoss中参数...
Binary Cross Entropy 常用于二分类问题，当然也可以用于多分类问题，通常需要在网络的最后一层添加sigmoid...
问binary_cross_entropy_with_logits产生负输出EN好奇心重的小伙伴有一种知其然，亦欲知其所以然的特性...
Thanks to that, the proposed binary cross-entropy with dynamical clipping can be used in any model utilizing cross-entropy or focal loss, including pre-trained models. We prove that the proposed loss function is an alpha-calibrated classification loss, implying consistency and robustness to noise ...
-For a binary classification problem->binary\_crossentropy