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,FSBT5FOTPS'MPXϞσϧͷ
࣮૷ɾίʔσΟϯά5VUPSJBM
ษ‫ڧ‬ձ
˙໨త%FFQ-FBSOJOHϞσϧͷ࣮૷ํ๏ΛֶͿ
%FFQ-FBSOJOH‫ܥ‬ͷ‫͍͓ͯʹڀݚ‬ɺϞσϧͷ࣮૷͕‫ٻ‬ΊΒΕΔ৔໘
ɾఏҊϞσϧͷཧղ
ɾ࿦จͰఏҊ͞ΕͨϞσϧͷ࣮૷
ɾΦϦδφϧϞσϧͷ࣮૷
ɾ‫ط‬ଘϞσϧͷվྑ
˙಺༰7((
3FT/FU
౳ͷϞσϧͷ࣮૷ɾղઆ
• ࿦จͰఏҊ͞Εͨ؆୯ͳϞσϧͷ࣮૷
VGG, Unet ͳͲ
• ෼‫ذ‬΍ෳࡶͳϞσϧͷ࣮૷ํ๏
ex) Resnet model, GANͳͲ
DPEJOH
ίʔυ
࿦จͷϞσϧਤɾϞσϧ֓ཁ

͸͡Ίʹ
໨తɾ಺༰
͸͡Ίʹ
‫ػ‬ցֶशͷྲྀΕ
σʔλ
४උ
σʔλ
લॲཧ
Ϟσϧ
࣮૷
Ϟσϧ
ֶश
ධՁ
ɾίʔσΟϯάͰͷ࣮૷
ɾQQMJDBUJPO1*ͷར༻	,FSBTͷ࣮૷ࡁΈϞσϧͳͲ

ɾ1VCMJD$PEFͷར༻	(JUIVCͳͲ
%FFQ-FBSOJOHϑϨʔϜϫʔΫ
͸͡Ίʹ

74
5FOTPSqPX
ɾ(PPHMF͕։ൃͨ͠ϑϨʔϜϫʔΫ
,FSBT
ɾ5FOTPSqPXʹಉࠝ
ɾ༰қͳϞσϧߏஙʢॳ৺ऀ޲͚ʣ
ɾ'BDFCPPLͷਓ޻஌ೳάϧʔϓʹΑΔ։ൃ
ɾϞσϧͷΧελϚΠζੑ͕ߴ͍
ɾ‫ऀڀݚ‬ͷؒͰਓ‫ؾ‬
ʢ࿦จͰൃද͞ΕͨϞσϧͷ࣮ࡍͷίʔυ͸ɺ
1Z5PSDIͰॻ͔Εͨ΋ͷ͕ൺֱత‫͔ͭݟ‬Γ΍͍͢ʣ
,FSBTϞσϧͷ࡞Γํ

inputs = Input(shape=(2,))
x = Dense(256, activation='relu')(inputs)
x = Dense(256, activation='relu')(x)
outputs = Dense(1, activation='linear')(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=['accuracy'])

model = Sequential()
model.add(Dense(256, activation=‘relu’,
input_shape=(2,))
model.add(Dense(256, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=['accuracy'])
4FRVFOUJBM.PEFM 'VODUJPOBM1*
ɾBEEϝιουͰ
ϨΠϠʔΛ௥Ճ͍ͯ͘͠
ɾ෼‫ذ‬΍݁߹ͳͲͷ֦ு͕Ͱ͖ͳ͍
ɾϨΠϠʔͷग़ྗΛ࣍ͷϨΠϠʔͷೖྗʹࢦఆ͠

ɹ૚Λ࿈͍݁ͤͯ͘͞
ɾॊೈͳϞσϧʢ෼‫ذ‬΍݁߹ͳͲʣͷ࣮૷͕Մೳ
,FSBT
,FSBTϞσϧ࡞੒
,FSBTϞσϧͷ࡞੒खॱ
,FSBTϞσϧͷ࡞੒
 ೖྗ૚ͷ࡞੒ɾఆٛ
 தؒ૚ͷ࡞੒ɾఆٛ
 ग़ྗ૚ͷ࡞੒ɾఆٛ
 Ϟσϧͷఆٛ
*OQVUɾೖྗ૚
*OUFSNFEJBUFɾதؒ૚
0VUQVUɾग़ྗ૚
Y
*OQVU
Y
%FOTF
$POW%
Y
%FOTF
$POW%
UGLFSBT.PEFMͷఆٛ
ᶃ
ᶄ
ᶅ
ᶆ
,FSBT
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(2,))
# தؒ૚ͷఆٛ
x = Dense(256, activation=‘relu')(inputs)
x = Dense(256, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(1, activation=‘linear')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=[‘accuracy'])
VOJUT VOJUT
PVUQVUTIBQF
VOJU
JOQVUTIBQF
	

ઢ‫ܗ‬
ม‫׵‬
SFMV
ؔ਺
,FSBT
ଟ૚ύʔηϓτϩϯ(MLP)

,FSBTϞσϧͷ࡞੒
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(2,))
# தؒ૚ͷఆٛ
x = Dense(256, activation='relu')(inputs)
x = Dense(256, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(1, activation=‘linear')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=[‘accuracy'])
,FSBT
■ શ݁߹χϡʔϥϧωοτϫʔΫϨΠϠʔ
Dense(units, activation=None)
ɾunits:ग़ྗۭؒͷ࣍‫਺ݩ‬
ɾactivation:࢖༻͢Δ‫׆‬ੑԽؔ਺໊
■ ೖྗ૚ϨΠϠʔ
Input(shape=())
ɾshape:ೖྗσʔλͷ‫ܗ‬ঢ়
ςʔϒϧσʔλ: (࣍‫ݩ‬,)
ը૾σʔλ: (ॎ, ԣ, νϟωϧ)
■ ModelΫϥεAPI: ModelΫϥεͷΠϯελϯεԽΛߦ͏
Model(inputs=, outputs=)
ɾinputs: ఆٛͨ͠ೖྗ
ɾoutputs: ఆٛͨ͠ग़ྗ
ଟ૚ύʔηϓτϩϯ(MLP)

,FSBTϞσϧͷ࡞੒
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(2,))
# தؒ૚ͷఆٛ
x = Dense(256, activation='relu')(inputs)
x = Dense(256, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(1, activation=‘linear')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=[‘accuracy'])
VOJUT VOJUT
PVUQVUTIBQF
VOJU
JOQVUTIBQF
	

ઢ‫ܗ‬
ม‫׵‬
SFMV
ؔ਺
,FSBT
ଟ૚ύʔηϓτϩϯ(MLP)

,FSBTϞσϧͷ࡞੒
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(2,))
# தؒ૚ͷఆٛ
x = Dense(256, activation='relu')(inputs)
x = Dense(256, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(1, activation=‘linear')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘mse',
metrics=[‘accuracy'])
VOJUT VOJUT
PVUQVUTIBQF
VOJU
JOQVUTIBQF
	

ઢ‫ܗ‬
ม‫׵‬
SFMV
ؔ਺
,FSBT
ଟ૚ύʔηϓτϩϯ(MLP)

,FSBTϞσϧͷ࡞੒
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(32,32,1))
# தؒ૚ͷఆٛ
# block 1
x = Conv2D(filters=16, kernel_size=3, strides=1,
padding='same', activation='relu')(inputs)
x = MaxPooling2D(pool_size=2)(x)
# block 2
x = Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', activation='relu')(x)
x = MaxPooling2D(pool_size=2)(x)
# block 3
x = Flatten()(x)
x = Dense(128, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(5, activation='softmax')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘categorical_crossentropy',
metrics=['accuracy'])
,FSBT
৞ΈࠐΈNN(CNN)
CMPDL CMPDL
CMPDL

,FSBTϞσϧͷ࡞੒
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(32,32,1))
# தؒ૚ͷఆٛ
# block 1
x = Conv2D(filters=16, kernel_size=3, strides=1,
padding='same', activation='relu')(inputs)
x = MaxPooling2D(pool_size=2)(x)
# block 2
x = Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', activation='relu')(x)
x = MaxPooling2D(pool_size=2)(x)
# block 3
x = Flatten()(x)
x = Dense(128, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(5, activation='softmax')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘categorical_crossentropy',
metrics=['accuracy'])
,FSBT
৞ΈࠐΈNN(CNN)
■ ϓʔϦϯάϨΠϠʔ(MaxPool2D)
MaxPool2D(pool_size)
ɾpool_size: ϓʔϦϯά΢Οϯυ΢αΠζͷ෯ͱߴ͞
■ ৞ΈࠐΈϨΠϠʔ
Conv2D(filters, kernel_size, strides, padding)
ɾfilters: ग़ྗۭؒͷ࣍‫ݩ‬ʢग़ྗϑΟϧλͷ਺ʣ
ɾkernel_size: 2࣍‫ݩ‬ͷ৞ΈࠐΈ΢Οϯυ΢ͷ෯ͱߴ͞
ɾstrides: ৞ΈࠐΈͷॎͱԣͷετϥΠυ
ɾpadding: ύσΟϯάͷద༻Λࢦఆ
■ ςϯιϧͷฏୱԽϨΠϠʔ
Flatten()
※ ࠓճ͸,ग़ྗۭؒͷ෯ͱߴ͞ͷ‫ؔ͢ʹࢉܭ‬Δ࿩͸লུ͢Δ.

,FSBTϞσϧͷ࡞੒
.BY1PPMJOH
# ೖྗ૚ͷఆٛ
inputs = Input(shape=(32,32,1))
# தؒ૚ͷఆٛ
# block 1
x = Conv2D(filters=16, kernel_size=3, strides=1,
padding='same', activation='relu')(inputs)
x = MaxPooling2D(pool_size=2)(x)
# block 2
x = Conv2D(filters=32, kernel_size=3, strides=1,
padding='same', activation='relu')(x)
x = MaxPooling2D(pool_size=2)(x)
# block 3
x = Flatten()(x)
x = Dense(128, activation=‘relu')(x)
# ग़ྗ૚ͷఆٛ
outputs = Dense(5, activation='softmax')(x)
# Ϟσϧͷఆٛ
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=‘adam',
loss=‘categorical_crossentropy',
metrics=['accuracy'])
,FSBT
৞ΈࠐΈNN(CNN)
CMPDL CMPDL
CMPDL

,FSBTϞσϧͷ࡞੒
Flatten()
ςϯιϧͷฏୱԽϨΠϠʔ
MaxPool2D()
ϓʔϦϯάϨΠϠʔ
Dense()
શ݁߹૚
Conv2D()
৞ΈࠐΈϨΠϠʔ
,FSBTϞσϧ࡞੒ͷ࣮ԋ
VGG16, VGG19
VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
“conv receptive field size-number of channels” .
The ReLU activation function is not shown for brevity.
Deep Residual Learning for Image Recognition
ResNet34
4LJQ$POOFDUJPO

Y TIPSUDVU
Y
Convolution arithmetic tutorial
৞ΈࠐΈԋࢉͷಈ࡞ྫ
/P[FSPQBEEJOH
TUSJEFT
;FSPQBEEJOH
TUSJEFT
;FSPQBEEJOH
TUSJEFT
QPPMJOHͱಉ༷ʹ
ग़ྗαΠζ͕ഒ
LFSOFMTJ[FʹΑͬͯ
ग़ྗαΠζ͕ॖখ
ೖྗαΠζͱ
ग़ྗαΠζ͕ಉ͡ʹ
ࣗ‫߸ූݾ‬Խ‫(ث‬AutoEncoder,AE)
Autoencoders, Unsupervised Learning, and Deep Architectures
ࣗ‫߸ූݾ‬Խ‫(ث‬AutoEncoder,AE)
Autoencoders, Unsupervised Learning, and Deep Architectures
TLJQߏ଄

Y
Y
Y
Unet ʢলུʣ
U-Net: Convolutional Networks for Biomedical Image Segmentation
ɿࣝผ‫͕ث‬ຊ෺ͷσʔλͱࣝผͰ͖ͳ͍Α͏ͳσʔλΛੜ੒͢Δ
ɿຊ෺ͷσʔλͱੜ੒‫͕ث‬ੜ੒ͨ͠σʔλΛࣝผ͢Δ
ੜ੒‫ث‬
ࣝผ‫ث‬
ੜ੒‫ث‬
ࣝผ‫ث‬
જࡏม਺
D
G
ຊ෺ِ͔෺͔൑அ
ύϥϝʔλߋ৽ʢֶशʣ
‫܇‬࿅σʔλ X
ੜ੒σʔλ X*
Z
(FOFSBUPS %JTDSJNJOBUPS
GAN (Generative Adversarial Networks)
Generative Adversarial Nets
Ϟσϧ࣮૷ͷͦͷଞ
࠷‫ʹޙ‬
ΦϦδφϧʢ$VTUPNʣͰ࡞੒Ͱ͖ΔϞσϧͷߏ੒ཁૉ
ɾ૚ʢϨΠϠʔʣ
ɾ‫׆‬ੑԽؔ਺
ɾଛࣦؔ਺
Ϟσϧͷ࡞੒ํ๏
ɾϝιουͰ‫ͼݺ‬ग़͔͢
ɾϞσϧΫϥεͰఆٛ͢Δ͔
Ϟσϧͷೖग़ྗ
ɾෳ਺ೖྗ
ෳ਺ग़ྗ
ɾதؒ૚ͷग़ྗ
Ϟσϧͷ౷߹
ࢀߟจ‫ݙ‬
෇࿥

ɾ5FOTPS'MPX
,FSBTͷ‫ج‬ຊతͳ࢖͍ํʢϞσϧߏஙɾ‫܇‬࿅ɾධՁɾ༧ଌʣ
ɾʲ,FSBT%PDVNFOUBUJPOʳ4FRVFOUJBMϞσϧͷΨΠυ
ɾ$POWPMVUJPOBSJUINFUJDUVUPSJBM
ɾఆ൪ͷ$POWPMVUJPOBM/FVSBM/FUXPSLΛθϩ͔Βཧղ͢Δ
ɾLFSBTͰNVMUJQMF	ෳ਺ͷ
ೖྗग़ྗଛࣦؔ਺Λѻ͏࣌ͷ5JQTΛ·ͱΊΔ
ɾ5FOTPS'MPX,FSBTͰ࿦จͷϞσϧΛ࠶‫͢ݱ‬Δํ๏
ɾΧελϚΠζ͢ΔͨΊͷɺ5FOTPS'MPX࠷৽ͷॻ͖ํೖ໳

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