Komachi Lab

M2

2016/06/14

Chainer

back propagation

Komachi Lab 2

Komachi Lab

Neural Net

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

3

SGD

Komachi Lab

Neural Net2. Loss Loss

3.

4W

Loss

Loss

Komachi Lab

Neural Net

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

5

SGD

W1 W2

predictinput

Komachi Lab

Neural Net

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

6

SGD

Loss func

correct

W1 W2

predictinput

Komachi Lab

Neural Net

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

7

SGD

gW1 gW2

Loss func

correct

W1 W2

predictinput

Komachi Lab

Neural Net

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

8

SGD

gW1 gW2

Loss func

correct

W1 W2

predictinput

Komachi Lab 9

Komachi Lab

1. forward

2. Loss Loss

3.

4. (AdaGrad )

5. 1

10

gW1 gW2

Loss func

correct

W1 W2

predictinput

Komachi Lab

✤

11

Komachi Lab 12

Komachi Lab

✤ W1 W2

✤ x, h, y, t

✤ Loss

13

(i train set index)

correct

W1 W2

predictinput :x

hidden:h:y :t

Komachi Lab

✤

14

W1, W2

correct

W1 W2

predictinput :x

hidden:h:y :t

Komachi Lab

✤

i

15

forward

correct

W1 W2

predictinput :x

hidden:h:y :t

Komachi Lab 16

gW2

gW1

Komachi Lab 17

zW4

xW3

hW1

yW2

Komachi Lab 18

(Chainer )

Komachi Lab 19

Komachi Lab

on Chainer

20

Komachi Lab

Chainer

✤ PFN DNN

http://chainer.org/

✤ Define-by-Run

Define-and-Run

✤

TensorFlow, Theano, Torch, Keras, Caffe, etc...

21

Komachi Lab

on Chainer

22

Komachi Lab 23

loss.backward()

Komachi Lab 24

loss.backward()

loss backward

Komachi Lab

MeanSquaredError

25

x0, x1 y, t

backward gy 1backward forward inputs gy

backward gy

Komachi Lab

Linear

26

grad_outputs Mean Squared Error backward

gW

backward

Komachi Lab

Linear

27

grad_outputs Linear backward

Komachi Lab

on Chainer✤

✤

✤

✤ W

28

tanh forward backwardchainer.functions.activation.tanh

forward Chain backward ChainChainer

Komachi Lab

✤

-

✤ Chainer

-

-

29

Komachi Lab

LSTM

30

forwarda, i, f, o

Linear

backward

gy