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Deep learning II - I Practical aspects of deep learning - Normalizing inputs 输入归一化
其他
2018-06-24 05:13:46
阅读次数: 2
Normalizing inputs 输入归一化
1. 输入归一化能够加快训练速度
2. 首先,将输入减去均值
μ
=
1
m
∑
i
=
1
m
x
(
i
)
μ
=
1
m
∑
i
=
1
m
x
(
i
)
x
:
=
x
−
μ
x
:=
x
−
μ
3. 其次,方差归一化
σ
2
=
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m
∑
i
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i
)
∗
∗
2
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∗
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)
σ
2
=
1
m
∑
i
=
1
m
x
(
i
)
∗
∗
2
(
∗
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m
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a
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x
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/
σ
2
4. 使用在test set得到的
μ
,
σ
2
μ
,
σ
2
来处理dev set 或者test set
如果输入的变量范围差别很大(0-1,0-1000),那就很有必要使用上述方法
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转载自
blog.csdn.net/zfcjhdq/article/details/80743621
Deep learning II - I Practical aspects of deep learning - Normalizing inputs 输入归一化
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