Deep learning II - I Practical aspects of deep learning - Dropout regularization 随机失活正则化 - 代码天地

Deep learning II - I Practical aspects of deep learning - Dropout regularization 随机失活正则化

其他 2018-06-24 05:13:46 阅读次数: 3

Dropout regularization 随机失活正则化

在训练神经网络时，随机在每层选择一定数量的neuron关闭，从而达到减小过拟合的目的。看起来很疯狂，但是很有效。
这里写图片描述

1. 对应每一层生成以个mask $d^{[3]}$
2. 用mask与该层的neurons $a^{[3]}$ 覆盖，dropout一些neuron
3. 剩余的neurons除以keep-prob，从而保持该层输出 $z^{[3]}$ 的期望值不变，避免在测试的时候出现的缩放问题

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转载自blog.csdn.net/zfcjhdq/article/details/80742912

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