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ZIP Code: Postal Code:
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5088939900 (+1-508-893-9900)
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5088939912 (+1-508-893-9912)
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深度学习中loss和accuracy的关系? - 知乎 loss 的具体形式取决于机器学习任务的类型。 例如，在回归问题中，常用的 loss 函数包括平方损失、绝对损失和对数损失；在分类问题中，常用的 loss 函数包括交叉熵损失和 Hinge 损失。
究竟什么是损失函数 loss function? - 知乎 Focal Loss focal loss出于论文Focal Loss for Dense Object Detection，主要是为了解决one-stage目标检测算法中正负样本比例严重失衡的问题，降低了大量简单负样本在训练中所占的比重，可理解为是一种困难样本挖掘。 focal loss是在交叉熵损失函数上修改的。 具体改进：