\(
\newcommand{\P}[]{\unicode{xB6}}
\newcommand{\AA}[]{\unicode{x212B}}
\newcommand{\empty}[]{\emptyset}
\newcommand{\O}[]{\emptyset}
\newcommand{\Alpha}[]{Α}
\newcommand{\Beta}[]{Β}
\newcommand{\Epsilon}[]{Ε}
\newcommand{\Iota}[]{Ι}
\newcommand{\Kappa}[]{Κ}
\newcommand{\Rho}[]{Ρ}
\newcommand{\Tau}[]{Τ}
\newcommand{\Zeta}[]{Ζ}
\newcommand{\Mu}[]{\unicode{x039C}}
\newcommand{\Chi}[]{Χ}
\newcommand{\Eta}[]{\unicode{x0397}}
\newcommand{\Nu}[]{\unicode{x039D}}
\newcommand{\Omicron}[]{\unicode{x039F}}
\DeclareMathOperator{\sgn}{sgn}
\def\oiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x222F}\,}{\unicode{x222F}}{\unicode{x222F}}{\unicode{x222F}}}\,}\nolimits}
\def\oiiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x2230}\,}{\unicode{x2230}}{\unicode{x2230}}{\unicode{x2230}}}\,}\nolimits}
\)
Batch Norm in CNNs
See Batch norm in CNN.
In a CNN, the mean and standard deviation are calculated across the batch, width, and height of the features.
# t is still the incoming tensor of shape [B, H, W, C]
# but mean and stddev are computed along (0, 1, 2) axes and have just [C] shape
mean = mean(t, axis=(0, 1, 2))
stddev = stddev(t, axis=(0, 1, 2))
for i in 0..B-1, x in 0..H-1, y in 0..W-1:
out[i,x,y,:] = norm(t[i,x,y,:], mean, stddev)
Resources
