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
最近在做目标检测,其中关于置信度和类别的预测都用到了F.binary_cross_entropy,这个损失不是经常使用,于是去pytorch 手册看了一下定义。如图。 其中t为标签,只包含0,1,o为输入,包含0～1的小数,两者具有相同的尺寸。 输入两组数据,计算 按照公式可以计算得, 两者结果一致。 根据计算公式,显然可以知道,损失的优化...
# Compute loss. root_mean_squared_error = math.sqrt( metrics.mean_squared_error(predictions, targets)) # Occasionally print the current loss. print(" period %02d : %0.2f" % (period, root_mean_squared_error)) # Add the loss metrics from this period to our list. root_mean_squared_errors...
以下是一个示例文件模板,用于设置项目的基本参数: model:type:"CNN"layers:-type:"Conv2D"filters:32kernel_size:3-type:"Dense"units:64training:loss_function:"BCEWithLogitsLoss"optimizer:"Adam"learning_rate:0.001 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 数学公式推导如下: 损失函数...
binary_cross_entropy loss 在使用 CUDA 时出错怎么解决? 如何避免在使用 CUDA 计算 binary_cross_entropy loss 时出现断言错误? A CUDA assertion error pops up when setting --no_lsgan. It seems it's because there are negative values thrown into the nn.BCELoss(). Get's fixed applying nn.BCEWith...
binary cross entropy loss二值交叉熵损失和交叉熵损失详解以及区别(BE 和 CE) https://www.cnblogs.com/wangguchangqing/p/12068084.html 这个链接也比较详细
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...
in forward size_average=self.size_average) File "/root/miniconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 1179, in binary_cross_entropy return torch._C._nn.binary_cross_entropy(input, target, weight, size_average) RuntimeError: cudaEventSynchronize in future::wait: device...
当使用as loss `binary_crossentropy`时，一个形状为(15000,250)的目标数组被传递为形状(None，1)的...
While reading the documentation for Binary Focal Crossentropy Loss, the example imo seems to lack clarity on how the resultant loss translates to an increased focus onharderexamples, compared to Binary Crossentropy Loss. Thanks for your pull request! It looks like this may be your first contribut...