Python PyTorch binary_cross_entropy用法及代码示例本文简要介绍python语言中 torch.nn.functional.binary_cross_entropy 的用法。 用法: torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean')...
nn.BCELoss()函数中weight默认为None, 也就是wi默认设置为1.0
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8个图像样本的二元交叉熵的平均值, loss =F.binary_cross_entropy_with_logits(predict, y, weight, reduction='sum') / batch_size 这里的predict 和 y都是 8×10×...
sum(weight) weight = 5. * K.exp(-5. * K.abs(averaged_mask - 0.5)) w1 = K.sum(weight) weight *= (w0 / w1) loss = weighted_bce_loss(y_true, y_pred, weight) + dice_loss(y_true, y_pred) return loss Dice coeffecient increased and the loss decreased but at every epoch I...
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...
loss3 = binary_cross_entropy_with_logits(preds, target, weight=weight) loss1,loss2, andloss3, which one is the correct usage? On the same subject, I was reading a paper that said: To deal with the unbalanced negative and positive data, we dilate each keypoint by 10 p...
loss = F.binary_cross_entropy(y_pred, y, weight=weight.cuda())else: loss = F.binary_cross_entropy(y_pred, y)returnloss 开发者ID:JiaxuanYou,项目名称:graph-generation,代码行数:20,代码来源:model.py 示例14: extract_grads ▲点赞 6▼ ...
🐛 Bug I'm moving to pytorch 1.0.1 recently. But I got the error below when I use 'binary_cross_entropy_with_logits' RuntimeError: the derivative for 'weight' is not implemented my code is work well with pytorch 0.4.1 I'm used CUDA 9.0.17...
_C._nn.binary_cross_entropy(input, target, weight, size_average) RuntimeError: cudaEventSynchronize in future::wait: device-side assert triggered THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/generic/THCStorage.c line=184 error=59 : device-side assert ...
主要原因是由于Sigmoid + BCE的不稳定性。参考文档和torch社区,我所要做的就是将模型从F.sigmoid(d0)...