Neural Networkを可視化してみる（数学カフェ忘年会 LT資料）
10
Neural Network 2016/12/11(Sun) LT @kenmatsu4
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Transcript of Neural Networkを可視化してみる（数学カフェ忘年会 LT資料）
Neural Network
2016/12/11(Sun) LT
@kenmatsu4
Neural Network
0
0
1
0
x2
x783
x784
x1
x0
z10u10
z1u1
z2u1
z9u9
z1000
u1000
z999u999
u1z1
u2z2
z0
z1000
u1000
z999u999
u1z1
u2z2
z0
z(l+1) = f(u(l+1))
softmaxy = softmax(z(L)
)
…
0.01
…
0.02
0.92
0.03
u(l+1) = W (l+1)z(l) + b(l+1)
z
(1) = x
(l = 1, 2, · · · , L� 1, L)
x2
x783
x784
x1
x0
z10u10
z1u1
z2u1
z9u9
z1000
u1000
z999u999
u1z1
u2z2
z0
z1000
u1000
z999u999
u1z1
u2z2
z0
z(l+1) = f(u(l+1))
Neural NetworkNeural Networkz
(1) = x
W,b
M
1
N
1
N
M1
N
z(l+1) = f(u(l+1))y = softmax(z(L)
)
softmaxk(z(L)
) =
exp(u(L)k )
PKj=1 exp(u
(L)j )
f(u) =1
1 + exp(�u)
f(u) = tanh(u)
tanh
ReLu
f(u) = max(0, u)
u(l+1) = W (l+1)z(l) + b(l+1)
z(l+1) = f(u(l+1))
z
(1) = x
x2
x783
x784
x1
x0
z10u10
z1u1
z2u1
z9u9
z1000
u1000
z999u999
u1z1
u2z2
z0
z1000
u1000
z999u999
u1z1
u2z2
z0
Neural Networkz
(1) = x
z(l+1) = f(u(l+1))
bWM
1
N
1
N
M1
N
W, b
y = softmax(z(L))
(l = 1, 2, · · · , L� 1, L)
softmaxk(z(L)
) =
exp(u(L)k )
PKj=1 exp(u
(L)j )
L(w) =NY
n=1
KY
k=1
(yk(x,w))
x =
✓x1
x2
◆W (1) =
w(1)
11 w(1)12
w(1)21 w(1)
22
!W (2) =
w(2)
11 w(2)12
w(2)21 w(2)
22
!
b(1) =
b(1)1
b(1)2
!
b(2) =
b(2)1
b(2)2
!
https://github.com/matsuken92/Qiita_Contents/blob/master/General/mathcafe_20161212.ipynb
x =
✓x1
x2
◆W (1) =
w(1)
11 w(1)12
w(1)21 w(1)
22
!W (2) =
w(2)
11 w(2)12
w(2)21 w(2)
22
!
b(1) =
b(1)1
b(1)2
!
b(2) =
b(2)1
b(2)2
!
✔
https://github.com/matsuken92/Qiita_Contents/blob/master/General/mathcafe_20161212.ipynb
http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
https://github.com/matsuken92/Qiita_Contents/blob/master/General/mathcafe_20161212.ipynb
Chainer MNIST
https://github.com/matsuken92/Qiita_Contents/blob/master/chainer-MNIST/chainer-MNIST_forPubs.ipynb
