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mtbookv2
Commits
6c626a4f
Commit
6c626a4f
authored
Nov 06, 2020
by
孟霞
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合并分支 'mengxia' 到 'caorunzhe'
Mengxia 查看合并请求
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parents
6b28ef41
d8b052bd
全部展开
显示空白字符变更
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25 个修改的文件
包含
111 行增加
和
101 行删除
+111
-101
Chapter9/Figures/figure-4-gram.tex
+8
-8
Chapter9/Figures/figure-absolute-loss.tex
+3
-3
Chapter9/Figures/figure-back-propagation-hid.tex
+8
-8
Chapter9/Figures/figure-back-propagation-output1.tex
+4
-4
Chapter9/Figures/figure-back-propagation-output2.tex
+5
-5
Chapter9/Figures/figure-broadcast.tex
+5
-5
Chapter9/Figures/figure-compare.tex
+23
-0
Chapter9/Figures/figure-corresponence-between-matrix-element-and-output.tex
+1
-1
Chapter9/Figures/figure-deep-learning.jpg
+0
-0
Chapter9/Figures/figure-embedding-matrix.tex
+6
-6
Chapter9/Figures/figure-feature-engineering.jpg
+0
-0
Chapter9/Figures/figure-fit.tex
+2
-2
Chapter9/Figures/figure-forward-propagation-hid.tex
+5
-5
Chapter9/Figures/figure-forward-propagation-output.tex
+5
-5
Chapter9/Figures/figure-four-layers-of-neural-network.tex
+3
-3
Chapter9/Figures/figure-parallel.tex
+0
-0
Chapter9/Figures/figure-residual-structure.tex
+4
-4
Chapter9/Figures/figure-rnn-lm.tex
+8
-8
Chapter9/Figures/figure-single-layer-of-neural-network-for-weather-prediction.tex
+3
-3
Chapter9/Figures/figure-tensor-mul.tex
+3
-3
Chapter9/Figures/figure-translation.tex
+2
-2
Chapter9/Figures/figure-two-layer-neural-network.tex
+1
-1
Chapter9/Figures/figure-weather-forward.tex
+5
-5
Chapter9/Figures/figure-weather.tex
+1
-1
Chapter9/chapter9.tex
+6
-19
没有找到文件。
Chapter9/Figures/figure-4-gram.tex
查看文件 @
6c626a4f
%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
\begin{scope}
\node
[anchor=west] (w0) at (0,0)
{
\small
{${
\
bm
o
}_{
i
-
3
}$}}
;
\node
[anchor=west] (w1) at ([xshift=2.8em]w0.east)
{
\small
{${
\
bm
o
}_{
i
-
2
}$}}
;
\node
[anchor=west] (w2) at ([xshift=2.8em]w1.east)
{
\small
{${
\
bm
o
}_{
i
-
1
}$}}
;
\node
[anchor=west] (w0) at (0,0)
{
\small
{${
\
mathbi
{
o
}
}_{
i
-
3
}$}}
;
\node
[anchor=west] (w1) at ([xshift=2.8em]w0.east)
{
\small
{${
\
mathbi
{
o
}
}_{
i
-
2
}$}}
;
\node
[anchor=west] (w2) at ([xshift=2.8em]w1.east)
{
\small
{${
\
mathbi
{
o
}
}_{
i
-
1
}$}}
;
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[anchor=north] (index0) at ([yshift=0.5em]w0.south)
{
\footnotesize
(index)
}
;
\node
[anchor=north] (index1) at ([yshift=0.5em]w1.south)
{
\footnotesize
(index)
}
;
\node
[anchor=north] (index2) at ([yshift=0.5em]w2.south)
{
\footnotesize
(index)
}
;
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[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e0) at ([yshift=1em]w0.north)
{
\footnotesize
{${
\
bm
e
}_
1
=
{
\bm
o
}_{
i
-
3
}
{
\bm
C
}$}}
;
\node
[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e1) at ([yshift=1em]w1.north)
{
\footnotesize
{${
\
bm
e
}_
2
=
{
\bm
o
}_{
i
-
2
}
{
\bm
C
}$}}
;
\node
[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e2) at ([yshift=1em]w2.north)
{
\footnotesize
{${
\
bm
e
}_
3
=
{
\bm
o
}_{
i
-
1
}
{
\bm
C
}$}}
;
\node
[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e0) at ([yshift=1em]w0.north)
{
\footnotesize
{${
\
mathbi
{
e
}}_
1
=
{
\mathbi
{
o
}}_{
i
-
3
}
{
\mathbi
{
C
}
}$}}
;
\node
[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e1) at ([yshift=1em]w1.north)
{
\footnotesize
{${
\
mathbi
{
e
}}_
2
=
{
\mathbi
{
o
}}_{
i
-
2
}
{
\mathbi
{
C
}
}$}}
;
\node
[anchor=south,draw,inner sep=3pt,fill=blue!20!white] (e2) at ([yshift=1em]w2.north)
{
\footnotesize
{${
\
mathbi
{
e
}}_
3
=
{
\mathbi
{
o
}}_{
i
-
1
}
{
\mathbi
{
C
}
}$}}
;
\node
[anchor=south,draw,minimum width=11em,inner sep=3pt,fill=orange!20!white] (h0) at ([yshift=1.5em]e1.north)
{
\footnotesize
{${
\
bm
h
}_
0
=
\textrm
{
Tanh
}
([
{
\bm
e
}_
1
,
{
\bm
e
}_
2
,
{
\bm
e
}_
3
]
{
\bm
H
}
+
{
\bm
d
}
)
$}}
;
\node
[anchor=south,draw,minimum width=9em,inner sep=3pt,fill=orange!20!white] (h1) at ([yshift=1.5em]h0.north)
{
\footnotesize
{${
\
bm
y
}
=
\textrm
{
Softmax
}
(
{
\bm
h
}_
0
{
\bm
U
}
)
$}}
;
\node
[anchor=south,draw,minimum width=11em,inner sep=3pt,fill=orange!20!white] (h0) at ([yshift=1.5em]e1.north)
{
\footnotesize
{${
\
mathbi
{
h
}}_
0
=
\textrm
{
Tanh
}
([
{
\mathbi
{
e
}}_
1
,
{
\mathbi
{
e
}}_
2
,
{
\mathbi
{
e
}}_
3
]
{
\mathbi
{
H
}}
+
{
\mathbi
{
d
}
}
)
$}}
;
\node
[anchor=south,draw,minimum width=9em,inner sep=3pt,fill=orange!20!white] (h1) at ([yshift=1.5em]h0.north)
{
\footnotesize
{${
\
mathbi
{
y
}}
=
\textrm
{
Softmax
}
(
{
\mathbi
{
h
}}_
0
{
\mathbi
{
U
}
}
)
$}}
;
\node
[anchor=south] (ylabel) at ([yshift=1em]h1.north)
{
\small
{$
\funp
{
P
}
(
w
_
i|w
_{
i
-
3
}
w
_{
i
-
2
}
w
_{
i
-
1
}
)
$}}
;
\draw
[->,line width=1pt] ([yshift=0.1em]w0.north) -- ([yshift=-0.1em]e0.south);
...
...
Chapter9/Figures/figure-absolute-loss.tex
查看文件 @
6c626a4f
...
...
@@ -8,12 +8,12 @@
\draw
[->,thick]
(-6,0) -- (5,0);
\draw
[->,thick]
(-5,-4) -- (-5,5);
\draw
[<-] (-2.5,4) -- (-2,5) node [pos=1,right,inner sep=2pt]
{
\footnotesize
{
答案
$
\tilde
{
\
vectorn
{
\emph
{
y
}
}}_
i
$}}
;
\draw
[<-] (-2.5,4) -- (-2,5) node [pos=1,right,inner sep=2pt]
{
\footnotesize
{
答案
$
\tilde
{
\
mathbi
{
y
}}_
i
$}}
;
{
\draw
[<-] (-3,-3) -- (-2.5,-2) node [pos=0,left,inner sep=2pt]
{
\footnotesize
{
预测
${
\
vectorn
{
\emph
{
y
}
}}_
i
$}}
;
}
\draw
[<-] (-3,-3) -- (-2.5,-2) node [pos=0,left,inner sep=2pt]
{
\footnotesize
{
预测
${
\
mathbi
{
y
}}_
i
$}}
;
}
{
\draw
[<-] (2.3,1) -- (3.3,2) node [pos=1,right,inner sep=2pt]
{
\footnotesize
{
偏差
$
|
\tilde
{
\
vectorn
{
\emph
{
y
}}}_
i
-
{
\vectorn
{
\emph
{
y
}
}}_
i|
$}}
;
\draw
[<-] (2.3,1) -- (3.3,2) node [pos=1,right,inner sep=2pt]
{
\footnotesize
{
偏差
$
|
\tilde
{
\
mathbi
{
y
}}_
i
-
{
\mathbi
{
y
}}_
i|
$}}
;
\foreach
\x
in
{
-3.8,-3.7,...,3.0
}{
\pgfmathsetmacro
{
\p
}{
- 1/14 * (
\x
+ 4) * (
\x
+ 1) * (
\x
- 1) * (
\x
- 3)
}
;
\pgfmathsetmacro
{
\q
}{
- 1/14 * (4*
\x*\x*\x
+ 3*
\x*\x
- 26*
\x
- 1)
}
;
...
...
Chapter9/Figures/figure-back-propagation-hid.tex
查看文件 @
6c626a4f
%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
\begin{scope}
\node
[anchor=center,draw,fill=red!20,minimum height=1.8em,minimum width=2.5em] (h) at (0,0)
{${
\
bm
h
}^{
k
-
1
}$}
;
\node
[anchor=west,draw,fill=blue!20,minimum height=1.8em,minimum width=2.5em] (s) at ([xshift=6em]h.east)
{${
\
bm
s
}^{
k
}$}
;
\node
[anchor=west,draw,fill=green!20,minimum height=1.8em,minimum width=2.5em] (h2) at ([xshift=6em]s.east)
{${
\
bm
h
}^{
k
}$}
;
\node
[anchor=center,draw,fill=red!20,minimum height=1.8em,minimum width=2.5em] (h) at (0,0)
{${
\
mathbi
{
h
}
}^{
k
-
1
}$}
;
\node
[anchor=west,draw,fill=blue!20,minimum height=1.8em,minimum width=2.5em] (s) at ([xshift=6em]h.east)
{${
\
mathbi
{
s
}
}^{
k
}$}
;
\node
[anchor=west,draw,fill=green!20,minimum height=1.8em,minimum width=2.5em] (h2) at ([xshift=6em]s.east)
{${
\
mathbi
{
h
}
}^{
k
}$}
;
\node
[anchor=east] (prev) at ([xshift=-2em]h.west)
{
...
}
;
\node
[anchor=west] (next) at ([xshift=2em]h2.east)
{
...
}
;
\draw
[->,thick] ([xshift=0.1em]prev.east) -- ([xshift=-0.1em]h.west);
\draw
[->,thick] ([xshift=0.1em]h.east) -- ([xshift=-0.1em]s.west) node [pos=0.5,below]
{
\scriptsize
{${
\
bm
s
}^
k
=
{
\bm
h
}^{
k
-
1
}{
\bm
W
}^
k
$}}
;
\draw
[->,thick] ([xshift=0.1em]s.east) -- ([xshift=-0.1em]h2.west) node [pos=0.5,below]
{
\scriptsize
{${
\
bm
h
}^
k
=
f
^
k
(
{
\bm
s
}^{
k
}
)
$}}
;
\draw
[->,thick] ([xshift=0.1em]h.east) -- ([xshift=-0.1em]s.west) node [pos=0.5,below]
{
\scriptsize
{${
\
mathbi
{
s
}}^
k
=
{
\mathbi
{
h
}}^{
k
-
1
}{
\mathbi
{
W
}
}^
k
$}}
;
\draw
[->,thick] ([xshift=0.1em]s.east) -- ([xshift=-0.1em]h2.west) node [pos=0.5,below]
{
\scriptsize
{${
\
mathbi
{
h
}}^
k
=
f
^
k
(
{
\mathbi
{
s
}
}^{
k
}
)
$}}
;
\draw
[->,thick] ([xshift=0.1em]h2.east) -- ([xshift=-0.1em]next.west);
{
...
...
@@ -29,15 +29,15 @@
}
{
\node
[anchor=south] (h2label) at (h2.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
bm
h
}^{
k
}}$}
;
\node
[anchor=south] (h2label) at (h2.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
mathbi
{
h
}
}^{
k
}}$}
;
}
{
\node
[anchor=south] (slabel) at (s.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
bm
s
}^{
k
}}$}
;
\node
[anchor=south] (slabel) at (s.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
mathbi
{
s
}
}^{
k
}}$}
;
}
{
\node
[anchor=south] (hlabel) at (h.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
bm
h
}^{
k
-
1
}}$
,
$
\frac
{
\partial
L
}{
\partial
{
\bm
W
}^{
k
}}$}
;
\node
[anchor=south] (hlabel) at (h.north)
{$
\frac
{
\partial
L
}{
\partial
{
\
mathbi
{
h
}}^{
k
-
1
}}$
,
$
\frac
{
\partial
L
}{
\partial
{
\mathbi
{
W
}
}^{
k
}}$}
;
}
\end{scope}
...
...
Chapter9/Figures/figure-back-propagation-output1.tex
查看文件 @
6c626a4f
%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
\begin{scope}
\node
[anchor=west,minimum height=1.7em,fill=blue!20,draw] (s) at (0,0)
{$
{
\bm
s
}^{
K
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=green!20,draw] (h2) at ([xshift=5.5em]s.east)
{$
{
\bm
h
}^{
K
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=blue!20,draw] (s) at (0,0)
{$
\mathbi
{
s
}^{
K
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=green!20,draw] (h2) at ([xshift=5.5em]s.east)
{$
\mathbi
{
h
}^{
K
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=orange!20,draw] (l) at ([xshift=5.5em]h2.east)
{$
L
$}
;
\draw
[->] (s.east) -- (h2.west);
\draw
[->] (h2.east) -- (l.west);
\draw
[->,very thick,red] ([yshift=1em,xshift=-0.1em]l.north) -- ([yshift=1em,xshift=0.1em]h2.north) node [pos=0.5,above]
{
\scriptsize
{
求梯度
{$
\frac
{
\partial
L
}{
\partial
{
\
bm
h
}^
K
}
=
?
$}}}
;
\draw
[->,very thick,red] ([yshift=1em,xshift=-0.1em]h2.north) -- ([yshift=1em,xshift=0.1em]s.north) node [pos=0.5,above]
{
\scriptsize
{
求梯度
{$
\frac
{
\partial
f
^
K
(
{
\
bm
s
}^
K
)
}{
\partial
{
\bm
s
}^
K
}
=
?
$}}}
;
\draw
[->,very thick,red] ([yshift=1em,xshift=-0.1em]l.north) -- ([yshift=1em,xshift=0.1em]h2.north) node [pos=0.5,above]
{
\scriptsize
{
求梯度
{$
\frac
{
\partial
L
}{
\partial
{
\
mathbi
{
h
}
}^
K
}
=
?
$}}}
;
\draw
[->,very thick,red] ([yshift=1em,xshift=-0.1em]h2.north) -- ([yshift=1em,xshift=0.1em]s.north) node [pos=0.5,above]
{
\scriptsize
{
求梯度
{$
\frac
{
\partial
f
^
K
(
{
\
mathbi
{
s
}}^
K
)
}{
\partial
{
\mathbi
{
s
}
}^
K
}
=
?
$}}}
;
\draw
[-,very thick,red] ([yshift=0.5em]l.north) -- ([yshift=1.5em]l.north);
\draw
[-,very thick,red] ([yshift=0.5em]h2.north) -- ([yshift=1.5em]h2.north);
\draw
[-,very thick,red] ([yshift=0.5em]s.north) -- ([yshift=1.5em]s.north);
...
...
Chapter9/Figures/figure-back-propagation-output2.tex
查看文件 @
6c626a4f
%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
\begin{scope}
\node
[anchor=center,minimum height=1.7em,fill=yellow!20,draw] (h) at (0,0)
{${
\
bm
h
}^{
K
-
1
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=blue!20,draw] (s) at ([xshift=6.0em]h.east)
{${
\
bm
s
}^{
K
}$}
;
\node
[anchor=center,minimum height=1.7em,fill=yellow!20,draw] (h) at (0,0)
{${
\
mathbi
{
h
}
}^{
K
-
1
}$}
;
\node
[anchor=west,minimum height=1.7em,fill=blue!20,draw] (s) at ([xshift=6.0em]h.east)
{${
\
mathbi
{
s
}
}^{
K
}$}
;
\draw
[->] (h.east) -- (s.west);
\node
[anchor=south west,inner sep=2pt] (step100) at ([xshift=0.5em,yshift=-0.8em]h.north east)
{
\scriptsize
{${
\
bm
s
}^
K
=
{
\bm
h
}^{
K
-
1
}
{
\bm
W
}^
K
$}}
;
\node
[anchor=south west,inner sep=2pt] (step100) at ([xshift=0.5em,yshift=-0.8em]h.north east)
{
\scriptsize
{${
\
mathbi
{
s
}}^
K
=
{
\mathbi
{
h
}}^{
K
-
1
}
{
\mathbi
{
W
}
}^
K
$}}
;
\node
[anchor=south west] (slabel) at ([yshift=1em,xshift=0.3em]s.north)
{
\scriptsize
{
\textbf
{{
已经得到:
${
\bm
\pi
}^
K
=
\frac
{
\partial
L
}{
\partial
{
\
bm
s
}^
K
}$}}}}
;
\node
[anchor=south west] (slabel) at ([yshift=1em,xshift=0.3em]s.north)
{
\scriptsize
{
\textbf
{{
已经得到:
${
\bm
\pi
}^
K
=
\frac
{
\partial
L
}{
\partial
{
\
mathbi
{
s
}
}^
K
}$}}}}
;
\draw
[->] ([yshift=0.3em]slabel.south) .. controls +(south:0.5) and +(north:0.5) .. ([xshift=0.5em,yshift=0.1em]s.north);
{
\draw
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Chapter9/Figures/figure-broadcast.tex
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@@ -8,7 +8,7 @@
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@@ -20,7 +20,7 @@
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@@ -34,7 +34,7 @@
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...
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@@ -50,7 +50,7 @@
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...
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@@ -62,7 +62,7 @@
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Chapter9/Figures/figure-compare.tex
0 → 100644
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%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
{
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%%%------------------------------------------------------------------------------------------------------------
Chapter9/Figures/figure-corresponence-between-matrix-element-and-output.tex
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@@ -10,7 +10,7 @@
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[anchor=east] (x0) at ([xshift=-6em]neuron00.west)
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Chapter9/Figures/figure-deep-learning.jpg
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Chapter9/Figures/figure-embedding-matrix.tex
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Chapter9/Figures/figure-feature-engineering.jpg
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Chapter9/Figures/figure-fit.tex
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...
...
@@ -11,7 +11,7 @@
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...
...
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Chapter9/Figures/figure-forward-propagation-hid.tex
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...
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Chapter9/Figures/figure-forward-propagation-output.tex
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[anchor=south west,inner sep=2pt] (step201) at ([xshift=1em]step200.north west)
{
\tiny
{
激活函数
}}
;
\node
[anchor=south,inner sep=1pt] (outputlabel) at ([yshift=0.0em]h2.north)
{
\tiny
{
\textbf
{
输出层
}}}
;
...
...
Chapter9/Figures/figure-four-layers-of-neural-network.tex
查看文件 @
6c626a4f
...
...
@@ -19,7 +19,7 @@
\node
[anchor=north] (x
\n
) at ([yshift=-2em]neuron0
\n
.south)
{$
x
_
\n
$}
;
}
\node
[anchor=west] (w1label) at ([xshift=-0.5em,yshift=0.8em]x5.north east)
{${
\
vectorn
{
\emph
{
W
}
}}^{
[
1
]
}$}
;
\node
[anchor=west] (w1label) at ([xshift=-0.5em,yshift=0.8em]x5.north east)
{${
\
mathbi
{
W
}}^{
[
1
]
}$}
;
\begin{pgfonlayer}
{
background
}
\node
[rectangle,inner sep=0.2em,fill=red!20] [fit = (neuron01) (neuron05)] (layer01)
{}
;
...
...
@@ -47,7 +47,7 @@
}
}
\node
[anchor=west] (w2label) at ([xshift=-2.5em,yshift=5.4em]x5.north east)
{${
\
vectorn
{
\emph
{
W
}
}}^{
[
2
]
}$}
;
\node
[anchor=west] (w2label) at ([xshift=-2.5em,yshift=5.4em]x5.north east)
{${
\
mathbi
{
W
}}^{
[
2
]
}$}
;
\begin{pgfonlayer}
{
background
}
{
...
...
@@ -77,7 +77,7 @@
\draw
[<-,thick] ([yshift=1.1em]neuron2
\n
.north) -- (neuron2
\n
.north);
}
\node
[anchor=west] (w3label) at ([xshift=-2.5em,yshift=9.5em]x5.north east)
{${
\
vectorn
{
\emph
{
W
}
}}^{
[
3
]
}$}
;
\node
[anchor=west] (w3label) at ([xshift=-2.5em,yshift=9.5em]x5.north east)
{${
\
mathbi
{
W
}}^{
[
3
]
}$}
;
\begin{pgfonlayer}
{
background
}
{
...
...
Chapter9/Figures/figure-parallel.tex
查看文件 @
6c626a4f
差异被折叠。
点击展开。
Chapter9/Figures/figure-residual-structure.tex
查看文件 @
6c626a4f
...
...
@@ -10,15 +10,15 @@
\node
[anchor=north,draw,thick](node2)at ([yshift=-1.2em]node3.south)
{
\small
{
weight layer
}}
;
\draw
[->,thick]
(node2.north)--([yshift=0.35em]node3.south);
\node
[anchor=west]
(node2-1) at ([xshift=2.1em,yshift=1.2em]node2.east)
{$
{
\bm
x
}$}
;
\node
[anchor=west]
(node2-1) at ([xshift=2.1em,yshift=1.2em]node2.east)
{$
\mathbi
{
x
}$}
;
\node
[anchor=north]
(node2-2) at ([xshift=0.2em,yshift=-0.3em]node2-1.south)
{
\footnotesize
{$
\rm
{
identity
}$}}
;
\node
[anchor=east](node4) at ([xshift=-0.2em]node2.west)
{$
\textrm
{
F
}
(
{
\bm
x
}
)
$}
;
\node
[anchor=east](node5) at ([xshift=-0.3em]node3.west)
{$
\textrm
{
F
}
(
{
\bm
x
}
)+
{
\bm
x
}$}
;
\node
[anchor=east](node4) at ([xshift=-0.2em]node2.west)
{$
\textrm
{
F
}
(
\mathbi
{
x
}
)
$}
;
\node
[anchor=east](node5) at ([xshift=-0.3em]node3.west)
{$
\textrm
{
F
}
(
\mathbi
{
x
}
)+
\mathbi
{
x
}$}
;
\node
[anchor=north](node1) at ([yshift=-1.8em]node2.south)
{}
;
\draw
[->,thick]
([yshift=0.0em]node1.north)--(node2.south);
\node
[anchor=east](node1-1) at ([xshift=1em,yshift=0.4em]node1.east)
{$
{
\bm
x
}$}
;
\node
[anchor=east](node1-1) at ([xshift=1em,yshift=0.4em]node1.east)
{$
\mathbi
{
x
}$}
;
\draw
[->,thick]
([xshift=-1.3em,yshift=0.8em]node1-1.east)--([xshift=2.7em,yshift=0.8em]node1-1.east)--([xshift=2.7em,yshift=5.35em]node1-1.east)--([xshift=-0.4em]node3.east);
...
...
Chapter9/Figures/figure-rnn-lm.tex
查看文件 @
6c626a4f
...
...
@@ -7,14 +7,14 @@
\node
[anchor=west,rnnnode] (node13) at ([xshift=2em]node12.east)
{
\scriptsize
{
RNN Cell
}}
;
\node
[anchor=west,rnnnode] (node14) at ([xshift=2em]node13.east)
{
\scriptsize
{
RNN Cell
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;
}
\node
[anchor=north,rnnnode,fill=red!30!white] (e1) at ([yshift=-1.2em]node11.south)
{
\tiny
{${
\
bm
e
}_
1
=
{
\bm
o
}_
1
{
\bm
C
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;
\node
[anchor=north,rnnnode,fill=red!30!white] (e2) at ([yshift=-1.2em]node12.south)
{
\tiny
{${
\
bm
e
}_
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=
{
\bm
wo
}_
2
{
\bm
C
}$}}
;
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[anchor=north,rnnnode,fill=red!30!white] (e3) at ([yshift=-1.2em]node13.south)
{
\tiny
{${
\
bm
e
}_
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=
{
\bm
o
}_
3
{
\bm
C
}$}}
;
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[anchor=north,rnnnode,fill=red!30!white] (e4) at ([yshift=-1.2em]node14.south)
{
\tiny
{${
\
bm
e
}_
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=
{
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o
}_
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{
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[anchor=north] (w1) at ([yshift=-1em]e1.south)
{
\footnotesize
{${
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bm
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}_
1
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[anchor=north] (w2) at ([yshift=-1em]e2.south)
{
\footnotesize
{${
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bm
o
}_
2
$}}
;
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[anchor=north] (w3) at ([yshift=-1em]e3.south)
{
\footnotesize
{${
\
bm
o
}_
3
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;
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[anchor=north] (w4) at ([yshift=-1em]e4.south)
{
\footnotesize
{${
\
bm
o
}_
4
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;
\node
[anchor=north,rnnnode,fill=red!30!white] (e1) at ([yshift=-1.2em]node11.south)
{
\tiny
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\
mathbi
{
e
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=
{
\mathbi
{
o
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\mathbi
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C
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[anchor=north,rnnnode,fill=red!30!white] (e2) at ([yshift=-1.2em]node12.south)
{
\tiny
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\
mathbi
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\mathbi
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o
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\mathbi
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C
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[anchor=north,rnnnode,fill=red!30!white] (e3) at ([yshift=-1.2em]node13.south)
{
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\
mathbi
{
e
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=
{
\mathbi
{
o
}}_
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{
\mathbi
{
C
}
}$}}
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[anchor=north,rnnnode,fill=red!30!white] (e4) at ([yshift=-1.2em]node14.south)
{
\tiny
{${
\
mathbi
{
e
}}_
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=
{
\mathbi
{
o
}}_
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{
\mathbi
{
C
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{
\footnotesize
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mathbi
{
o
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;
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{
\footnotesize
{${
\
mathbi
{
o
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;
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[anchor=north] (w3) at ([yshift=-1em]e3.south)
{
\footnotesize
{${
\
mathbi
{
o
}
}_
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$}}
;
\node
[anchor=north] (w4) at ([yshift=-1em]e4.south)
{
\footnotesize
{${
\
mathbi
{
o
}
}_
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$}}
;
\draw
[->,thick] ([yshift=0.1em]w1.north)--([yshift=-0.1em]e1.south);
\draw
[->,thick] ([yshift=0.1em]w2.north)--([yshift=-0.1em]e2.south);
...
...
Chapter9/Figures/figure-single-layer-of-neural-network-for-weather-prediction.tex
查看文件 @
6c626a4f
...
...
@@ -9,7 +9,7 @@
\node
[anchor=center,neuronnode] (neuron02) at ([yshift=-3em]neuron01)
{}
;
\node
[anchor=east] (x0) at ([xshift=-6em]neuron00.west)
{$
x
_
1
$}
;
\node
[anchor=east] (x1) at ([xshift=-6em]neuron01.west)
{$
x
_
2
$}
;
\node
[anchor=east] (x2) at ([xshift=-6em]neuron02.west)
{$
{
\vectorn
{
\emph
{
b
}}
}$}
;
\node
[anchor=east] (x2) at ([xshift=-6em]neuron02.west)
{$
\mathbi
{
b
}$}
;
\node
[anchor=west] (y0) at ([xshift=4em]neuron00.east)
{$
y
_
1
$}
;
\draw
[->] (x0.east) -- (neuron00.180) node [pos=0.1,above]
{
\tiny
{$
w
_{
11
}$}}
;
...
...
@@ -29,10 +29,10 @@
\draw
[->] (x2.east) -- (neuron02.180) node [pos=0.3,below]
{
\tiny
{$
b
_{
3
}$}}
;
\draw
[->] (neuron02.east) -- (y2.west);
\node
[anchor=east,align=left] (inputlabel) at ([xshift=-0.1em]x1.west)
{
\scriptsize
{
输入向量
}
:
\\\small
{$
{
\vectorn
{
\emph
{
x
}}
}
=(
x
_
1
,x
_
2
)
$}}
;
\node
[anchor=east,align=left] (inputlabel) at ([xshift=-0.1em]x1.west)
{
\scriptsize
{
输入向量
}
:
\\\small
{$
\mathbi
{
x
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=(
x
_
1
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_
2
)
$}}
;
\node
[anchor=west,align=left] (outputlabel) at ([xshift=0.1em]y1.east)
{
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输出向量
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{$
{
\vectorn
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\emph
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y
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=(
y
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1
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_
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3
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;
\node
[anchor=west,align=left] (outputlabel) at ([xshift=0.1em]y1.east)
{
\scriptsize
{
输出向量
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:
\\\small
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{
y
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y
_
1
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_
2
,y
_
3
)
$}}
;
\begin{pgfonlayer}
{
background
}
...
...
Chapter9/Figures/figure-tensor-mul.tex
查看文件 @
6c626a4f
...
...
@@ -49,7 +49,7 @@
\node
[fill=red!20,inner sep=0pt,minimum height=0.49cm,minimum width=0.49cm] at (
\x
,
\y
)
{$
\number\value
{
mycount
4
}$}
;
\addtocounter
{
mycount4
}{
1
}
;
}
\node
[anchor=north] (xlabel) at (0,-1.2)
{$
\
vectorn
{
\emph
{
x
}
}$}
;
\node
[anchor=north] (xlabel) at (0,-1.2)
{$
\
mathbi
{
x
}$}
;
}
\node
[anchor=south west] (label14) at (-1.3,0.9)
{
\footnotesize
{
\ding
{
175
}}}
;
\end{scope}
...
...
@@ -65,7 +65,7 @@
\node
[fill=black!20,inner sep=0pt,minimum height=0.49cm,minimum width=0.49cm] at (0.25,0.25)
{
\small
{$
-
1
$}}
;
\node
[fill=black!20,inner sep=0pt,minimum height=0.49cm,minimum width=0.49cm] at (0.25,-0.25)
{$
1
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;
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[fill=black!20,inner sep=0pt,minimum height=0.49cm,minimum width=0.49cm] at (0.25,-0.75)
{$
0
$}
;
\node
[anchor=north] (xlabel) at (0,-1.2)
{$
\
vectorn
{
\emph
{
W
}
}$}
;
\node
[anchor=north] (xlabel) at (0,-1.2)
{$
\
mathbi
{
W
}$}
;
}
{
\draw
[->,thick,dashed] (-1.5in+2em+1.5em,-0.3) .. controls +(east:2) and +(west:1) .. (-0.55,0.8) node [pos=0.5,left]
{
\scriptsize
{
\textbf
{
矩阵乘
}}}
;
}
...
...
@@ -129,7 +129,7 @@
}
\node
[anchor=south west] (label24) at (-0.8,0.9)
{
\footnotesize
{
\ding
{
175
}}}
;
{
\node
[anchor=north] (xlabel) at (0,-1.2)
{${
\
vectorn
{
\emph
{
x
}}}
\cdot
{
\vectorn
{
\emph
{
W
}
}}$}
;
\node
[anchor=north] (xlabel) at (0,-1.2)
{${
\
mathbi
{
x
}}
\cdot
{
\mathbi
{
W
}}$}
;
\node
[anchor=center] (elabel) at (-0.7in,0)
{
\Huge
{$
\textbf
{
=
}$}}
;
}
\end{scope}
...
...
Chapter9/Figures/figure-translation.tex
查看文件 @
6c626a4f
...
...
@@ -19,7 +19,7 @@
\node
[above]
at ([xshift=2em,yshift=1em]a2.west)
{
1
}
;
\node
[below]
at ([xshift=-0.5em,yshift=0em]a2.west)
{
-1
}
;
\node
[anchor=west] (x) at ([xshift=-3.5cm,yshift=2em]a2.north)
{
\scriptsize
{
$
{
\vectorn
{
\emph
{
W
}}
}
=
\begin
{
pmatrix
}
$
\mathbi
{
W
}
=
\begin
{
pmatrix
}
1
&
0
&
0
\\
0
&
-
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&
0
\\
0
&
0
&
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...
...
@@ -44,7 +44,7 @@
\node
[anchor=west] (x) at ([xshift=-4cm,yshift=2em]a3.north)
{
\scriptsize
{
$
{
\vectorn
{
\emph
{
b
}}
}
=
\begin
{
pmatrix
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$
\mathbi
{
b
}
=
\begin
{
pmatrix
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0
.
5
&
0
&
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\\
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&
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&
0
\\
0
&
0
&
0
...
...
Chapter9/Figures/figure-two-layer-neural-network.tex
查看文件 @
6c626a4f
...
...
@@ -11,7 +11,7 @@
\draw
[-,ublue] (n10.west) -- (n10.east);
\draw
[-,ublue] (n11.west) -- (n11.east);
\node
[anchor=north] (x1) at ([yshift=-4em]n11.south)
{$
x
_
1
$}
;
\node
[anchor=north] (b) at ([yshift=-4em]n10.south)
{$
\
vectorn
{
\emph
{
b
}
}$}
;
\node
[anchor=north] (b) at ([yshift=-4em]n10.south)
{$
\
mathbi
{
b
}$}
;
{
\draw
[->,thick] (b.north) -- ([yshift=-0.1em]n10.south);
\draw
[->,thick] (x1.north) -- ([yshift=-0.1em]n10.290);
...
...
Chapter9/Figures/figure-weather-forward.tex
查看文件 @
6c626a4f
...
...
@@ -15,10 +15,10 @@
\draw
[-,thick](part3.south)--(part4.north);
\node
[anchor=north,minimum width=1.5em,minimum height=1.5em] (part4-2) at ([xshift=-1.2em,yshift=-0.2em]part4.south)
{
\scriptsize
{$
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\times
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$}}
;
\node
[anchor=north,minimum width=4.0em,minimum height=1.5em] (part5) at ([yshift=-1.4em]part4.south)
{
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{$
{
\vectorn
{
\emph
{
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;
\node
[anchor=north,minimum width=4.0em,minimum height=1.5em] (part5) at ([yshift=-1.4em]part4.south)
{
\footnotesize
{$
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{
a
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;
\draw
[-,thick](part4.south)--([yshift=-0.1em]part5.north);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\node
[anchor=west,minimum width=2.0em,minimum height=1.5em] (part5-3) at ([xshift=-0.8em,yshift=0.2em]part5.east)
{
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vectorn
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}}^{
[
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}$}}
;
\node
[anchor=west,minimum width=2.0em,minimum height=1.5em] (part5-3) at ([xshift=-0.8em,yshift=0.2em]part5.east)
{
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{${
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{
W
}}^{
[
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}$}}
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[anchor=west,minimum width=2.0em,minimum height=1.5em] (part5-4) at ([xshift=2.0em,yshift=0.0em]part5-3.east)
{
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{$
b
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;
\draw
[-,thick]
(part4.south)--([xshift=-0.5em]part5-3.north);
\draw
[-,thick]
(part3.south)--(part5-4.north);
...
...
@@ -38,11 +38,11 @@
\draw
[-,thick](part7.south)--(part8.north);
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[anchor=north,minimum width=1.5em,minimum height=1.5em] (part8-2) at ([xshift=-1.2em,yshift=-0.2em]part8.south)
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[anchor=north,minimum width=4.0em,minimum height=1.5em] (part9) at ([yshift=-1.4em]part8.south)
{
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{$
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{
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;
\draw
[-,thick](part8.south)--([yshift=-0.1em]part9.north);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\node
[anchor=west,minimum width=2.0em,minimum height=1.5em] (part9-3) at ([xshift=-0.8em,yshift=0.1em]part9.east)
{
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{${
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\emph
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{
\scriptsize
{$
3
\times
2
$}}
;
...
...
Chapter9/Figures/figure-weather.tex
查看文件 @
6c626a4f
%%%------------------------------------------------------------------------------------------------------------
\begin{tikzpicture}
\tikzstyle
{
neuronnode
}
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Chapter9/chapter9.tex
查看文件 @
6c626a4f
...
...
@@ -129,27 +129,14 @@
\parinterval
端到端学习使机器学习不再依赖传统的特征工程方法,因此也不需要繁琐的数据预处理、特征选择、降维等过程,而是直接利用人工神经网络自动从输入数据中提取、组合更复杂的特征,大大提升了模型能力和工程效率。以图
\ref
{
fig:9-2
}
中的图像分类为例,在传统方法中,图像分类需要很多阶段的处理。首先,需要提取一些手工设计的图像特征,在将其降维之后,需要利用SVM等分类算法对其进行分类。与这种多阶段的流水线似的处理流程相比,端到端深度学习只训练一个神经网络,输入就是图片的像素表示,输出直接是分类类别。
%------------------------------------------------------------------------------
\begin{figure}
[htp]
\centering
\subfigcapskip
=8pt
\subfigure
[基于特征工程的机器学习方法做图像分类]
{
\begin{minipage}
{
.9
\textwidth
}
\centering
\includegraphics
[width=8cm]
{
./Chapter9/Figures/figure-feature-engineering.jpg
}
\end{minipage}
%
}
\vfill
\subfigure
[端到端学习方法做图像分类]
{
\begin{minipage}
{
.9
\textwidth
}
\centering
\includegraphics
[width=8cm]
{
./Chapter9/Figures/figure-deep-learning.jpg
}
\end{minipage}
%
}
%----------------------------------------------
\begin{figure}
[htp]
\centering
\input
{
./Chapter9/Figures/figure-compare
}
\caption
{
特征工程
{
\small\sffamily\bfseries
{
vs
}}
端到端学习
}
\label
{
fig:9-2
}
\end
{
figure
}
%----------------------------------------------
--------------------------------
\end{figure}
%----------------------------------------------
\parinterval
传统的机器学习需要人工定义特征,这个过程往往需要对问题的隐含假设。这种方法存在三方面的问题:
...
...
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