F.binary_cross_entropy_with_logits函数和 F.binary_cross_entropy函数的reduction 参数都默认是‘mean’模式,直接使用默认值的话,结果是320个样本点的二元交叉熵的平均值, 若要计算8个图像样本的二元交叉熵的平均值,可以设置reduction=‘sum’ ,这样能得到320个样本点的二元交叉熵的和,然后除以batch_size 就能得到...
binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None) 参数: input-任意形状的张量作为非标准化分数(通常称为 logits)。 target-与输入具有相同形状的张量,其值介于 0 和 1 之间 weight(Tensor,可选的) -手动重新调整权重(如果...
binary_cross_entropy_with_logits 接受任意形状的输入,target要求与输入形状一致。切记:target的值必须在[0,N-1]之间,其中N为类别数,否则会出现莫名其妙的错误,比如loss为负数。 计算其实就是交叉熵,不过输入不要求在0,1之间,该函数会自动添加sigmoid运算 默认的reduction方式为mean 下面的实现代码中loss2是根据公式...
reduction_enum = F._Reduction.get_enum(reduction)# none: 0, mean:1, sum: 2ifreduction_enum ==0:returnlosselifreduction_enum ==1:returnloss.mean()elifreduction_enum ==2:returnloss.sum() 开发者ID:dingjiansw101,项目名称:AerialDetection,代码行数:23,代码来源:losses.py 示例4: train ▲...
loss = F.binary_cross_entropy_with_logits(ouputs[i], Y, weight=Mask, reduction='sum') / Mask.sum() This is how i am doing testing ouput = model(X) ouput_sig = torch.sigmoid(ouput) plot_voxel2d(ouput_soft1) The exact same model, inputs, targets work if i use the mse los...
self.binary_cross_entropy = P.BinaryCrossEntropy(reduction=reduction) self.weight_one = weight is None if not self.weight_one: self.weight = weight else: self.ones = P.OnesLike() def construct(self, logits, labels): _check_is_tensor('logits', logits, self.cls_name) ...
What are their differences? The explanation is quite vague. If I understands correctly, weight is individual weight for each pixel (class), wheres pos_weight is the weight for everything that's not background (negative pixel/zero)? What if I set both parameters? For example:...
return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum) RuntimeError: the derivative for 'weight' is not implemented Environment Please copy and paste the output from our environment collection script ...
reduction=reduction, pos_weight=self.pos_weight, ) ) paddle.core._set_prim_all_enabled(True) static_result = paddle.jit.to_static( paddle.nn.functional.binary_cross_entropy_with_logits, full_graph=True, )( self.logits, self.labels, weight=self.weight, reduction=reduction, pos_weight=self....
好奇心重的小伙伴有一种知其然,亦欲知其所以然的特性,我们在spring事务应用中会接触到aop技术,而...