added cifar/cars manifests, added model templates
This commit is contained in:
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1d505f644d
commit
cb18ffa937
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cars/allimages.txt
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cars/allimages.txt
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cars/default-split/test.txt
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cars/default-split/train.txt
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cars/default-split/val.txt
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cars/labels.txt
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AM General Hummer SUV 2000
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Acura RL Sedan 2012
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Acura TL Sedan 2012
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Acura TL Type-S 2008
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Acura TSX Sedan 2012
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Acura Integra Type R 2001
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Acura ZDX Hatchback 2012
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Aston Martin V8 Vantage Convertible 2012
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Aston Martin V8 Vantage Coupe 2012
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Aston Martin Virage Convertible 2012
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Aston Martin Virage Coupe 2012
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Audi RS 4 Convertible 2008
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Audi A5 Coupe 2012
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Audi TTS Coupe 2012
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Audi R8 Coupe 2012
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Audi V8 Sedan 1994
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Audi 100 Sedan 1994
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Audi 100 Wagon 1994
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Audi TT Hatchback 2011
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Audi S6 Sedan 2011
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Audi S5 Convertible 2012
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Audi S5 Coupe 2012
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Audi S4 Sedan 2012
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Audi S4 Sedan 2007
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Audi TT RS Coupe 2012
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BMW ActiveHybrid 5 Sedan 2012
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BMW 1 Series Convertible 2012
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BMW 1 Series Coupe 2012
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BMW 3 Series Sedan 2012
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BMW 3 Series Wagon 2012
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BMW 6 Series Convertible 2007
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BMW X5 SUV 2007
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BMW X6 SUV 2012
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BMW M3 Coupe 2012
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BMW M5 Sedan 2010
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BMW M6 Convertible 2010
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BMW X3 SUV 2012
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BMW Z4 Convertible 2012
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Bentley Continental Supersports Conv. Convertible 2012
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Bentley Arnage Sedan 2009
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Bentley Mulsanne Sedan 2011
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Bentley Continental GT Coupe 2012
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Bentley Continental GT Coupe 2007
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Bentley Continental Flying Spur Sedan 2007
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Bugatti Veyron 16.4 Convertible 2009
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Bugatti Veyron 16.4 Coupe 2009
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Buick Regal GS 2012
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Buick Rainier SUV 2007
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Buick Verano Sedan 2012
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Buick Enclave SUV 2012
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Cadillac CTS-V Sedan 2012
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Cadillac SRX SUV 2012
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Cadillac Escalade EXT Crew Cab 2007
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Chevrolet Silverado 1500 Hybrid Crew Cab 2012
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Chevrolet Corvette Convertible 2012
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Chevrolet Corvette ZR1 2012
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Chevrolet Corvette Ron Fellows Edition Z06 2007
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Chevrolet Traverse SUV 2012
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Chevrolet Camaro Convertible 2012
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Chevrolet HHR SS 2010
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Chevrolet Impala Sedan 2007
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Chevrolet Tahoe Hybrid SUV 2012
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Chevrolet Sonic Sedan 2012
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Chevrolet Express Cargo Van 2007
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Chevrolet Avalanche Crew Cab 2012
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Chevrolet Cobalt SS 2010
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Chevrolet Malibu Hybrid Sedan 2010
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Chevrolet TrailBlazer SS 2009
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Chevrolet Silverado 2500HD Regular Cab 2012
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Chevrolet Silverado 1500 Classic Extended Cab 2007
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Chevrolet Express Van 2007
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Chevrolet Monte Carlo Coupe 2007
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Chevrolet Malibu Sedan 2007
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Chevrolet Silverado 1500 Extended Cab 2012
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Chevrolet Silverado 1500 Regular Cab 2012
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Chrysler Aspen SUV 2009
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Chrysler Sebring Convertible 2010
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Chrysler Town and Country Minivan 2012
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Chrysler 300 SRT-8 2010
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Chrysler Crossfire Convertible 2008
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Chrysler PT Cruiser Convertible 2008
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Daewoo Nubira Wagon 2002
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Dodge Caliber Wagon 2012
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Dodge Caliber Wagon 2007
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Dodge Caravan Minivan 1997
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Dodge Ram Pickup 3500 Crew Cab 2010
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Dodge Ram Pickup 3500 Quad Cab 2009
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Dodge Sprinter Cargo Van 2009
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Dodge Journey SUV 2012
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Dodge Dakota Crew Cab 2010
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Dodge Dakota Club Cab 2007
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Dodge Magnum Wagon 2008
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Dodge Challenger SRT8 2011
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Dodge Durango SUV 2012
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Dodge Durango SUV 2007
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Dodge Charger Sedan 2012
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Dodge Charger SRT-8 2009
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Eagle Talon Hatchback 1998
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FIAT 500 Abarth 2012
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FIAT 500 Convertible 2012
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Ferrari FF Coupe 2012
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Ferrari California Convertible 2012
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Ferrari 458 Italia Convertible 2012
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Ferrari 458 Italia Coupe 2012
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Fisker Karma Sedan 2012
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Ford F-450 Super Duty Crew Cab 2012
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Ford Mustang Convertible 2007
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Ford Freestar Minivan 2007
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Ford Expedition EL SUV 2009
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Ford Edge SUV 2012
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Ford Ranger SuperCab 2011
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Ford GT Coupe 2006
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Ford F-150 Regular Cab 2012
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Ford F-150 Regular Cab 2007
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Ford Focus Sedan 2007
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Ford E-Series Wagon Van 2012
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Ford Fiesta Sedan 2012
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GMC Terrain SUV 2012
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GMC Savana Van 2012
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GMC Yukon Hybrid SUV 2012
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GMC Acadia SUV 2012
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GMC Canyon Extended Cab 2012
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Geo Metro Convertible 1993
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HUMMER H3T Crew Cab 2010
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HUMMER H2 SUT Crew Cab 2009
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Honda Odyssey Minivan 2012
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Honda Odyssey Minivan 2007
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Honda Accord Coupe 2012
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Honda Accord Sedan 2012
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Hyundai Veloster Hatchback 2012
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Hyundai Santa Fe SUV 2012
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Hyundai Tucson SUV 2012
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Hyundai Veracruz SUV 2012
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||||||
|
Hyundai Sonata Hybrid Sedan 2012
|
||||||
|
Hyundai Elantra Sedan 2007
|
||||||
|
Hyundai Accent Sedan 2012
|
||||||
|
Hyundai Genesis Sedan 2012
|
||||||
|
Hyundai Sonata Sedan 2012
|
||||||
|
Hyundai Elantra Touring Hatchback 2012
|
||||||
|
Hyundai Azera Sedan 2012
|
||||||
|
Infiniti G Coupe IPL 2012
|
||||||
|
Infiniti QX56 SUV 2011
|
||||||
|
Isuzu Ascender SUV 2008
|
||||||
|
Jaguar XK XKR 2012
|
||||||
|
Jeep Patriot SUV 2012
|
||||||
|
Jeep Wrangler SUV 2012
|
||||||
|
Jeep Liberty SUV 2012
|
||||||
|
Jeep Grand Cherokee SUV 2012
|
||||||
|
Jeep Compass SUV 2012
|
||||||
|
Lamborghini Reventon Coupe 2008
|
||||||
|
Lamborghini Aventador Coupe 2012
|
||||||
|
Lamborghini Gallardo LP 570-4 Superleggera 2012
|
||||||
|
Lamborghini Diablo Coupe 2001
|
||||||
|
Land Rover Range Rover SUV 2012
|
||||||
|
Land Rover LR2 SUV 2012
|
||||||
|
Lincoln Town Car Sedan 2011
|
||||||
|
MINI Cooper Roadster Convertible 2012
|
||||||
|
Maybach Landaulet Convertible 2012
|
||||||
|
Mazda Tribute SUV 2011
|
||||||
|
McLaren MP4-12C Coupe 2012
|
||||||
|
Mercedes-Benz 300-Class Convertible 1993
|
||||||
|
Mercedes-Benz C-Class Sedan 2012
|
||||||
|
Mercedes-Benz SL-Class Coupe 2009
|
||||||
|
Mercedes-Benz E-Class Sedan 2012
|
||||||
|
Mercedes-Benz S-Class Sedan 2012
|
||||||
|
Mercedes-Benz Sprinter Van 2012
|
||||||
|
Mitsubishi Lancer Sedan 2012
|
||||||
|
Nissan Leaf Hatchback 2012
|
||||||
|
Nissan NV Passenger Van 2012
|
||||||
|
Nissan Juke Hatchback 2012
|
||||||
|
Nissan 240SX Coupe 1998
|
||||||
|
Plymouth Neon Coupe 1999
|
||||||
|
Porsche Panamera Sedan 2012
|
||||||
|
Ram C/V Cargo Van Minivan 2012
|
||||||
|
Rolls-Royce Phantom Drophead Coupe Convertible 2012
|
||||||
|
Rolls-Royce Ghost Sedan 2012
|
||||||
|
Rolls-Royce Phantom Sedan 2012
|
||||||
|
Scion xD Hatchback 2012
|
||||||
|
Spyker C8 Convertible 2009
|
||||||
|
Spyker C8 Coupe 2009
|
||||||
|
Suzuki Aerio Sedan 2007
|
||||||
|
Suzuki Kizashi Sedan 2012
|
||||||
|
Suzuki SX4 Hatchback 2012
|
||||||
|
Suzuki SX4 Sedan 2012
|
||||||
|
Tesla Model S Sedan 2012
|
||||||
|
Toyota Sequoia SUV 2012
|
||||||
|
Toyota Camry Sedan 2012
|
||||||
|
Toyota Corolla Sedan 2012
|
||||||
|
Toyota 4Runner SUV 2012
|
||||||
|
Volkswagen Golf Hatchback 2012
|
||||||
|
Volkswagen Golf Hatchback 1991
|
||||||
|
Volkswagen Beetle Hatchback 2012
|
||||||
|
Volvo C30 Hatchback 2012
|
||||||
|
Volvo 240 Sedan 1993
|
||||||
|
Volvo XC90 SUV 2007
|
||||||
|
smart fortwo Convertible 2012
|
||||||
60000
cifar100/allimages.txt
Normal file
60000
cifar100/allimages.txt
Normal file
File diff suppressed because it is too large
Load Diff
100
cifar100/labels.txt
Normal file
100
cifar100/labels.txt
Normal file
@ -0,0 +1,100 @@
|
|||||||
|
apple
|
||||||
|
aquarium_fish
|
||||||
|
baby
|
||||||
|
bear
|
||||||
|
beaver
|
||||||
|
bed
|
||||||
|
bee
|
||||||
|
beetle
|
||||||
|
bicycle
|
||||||
|
bottle
|
||||||
|
bowl
|
||||||
|
boy
|
||||||
|
bridge
|
||||||
|
bus
|
||||||
|
butterfly
|
||||||
|
camel
|
||||||
|
can
|
||||||
|
castle
|
||||||
|
caterpillar
|
||||||
|
cattle
|
||||||
|
chair
|
||||||
|
chimpanzee
|
||||||
|
clock
|
||||||
|
cloud
|
||||||
|
cockroach
|
||||||
|
couch
|
||||||
|
crab
|
||||||
|
crocodile
|
||||||
|
cup
|
||||||
|
dinosaur
|
||||||
|
dolphin
|
||||||
|
elephant
|
||||||
|
flatfish
|
||||||
|
forest
|
||||||
|
fox
|
||||||
|
girl
|
||||||
|
hamster
|
||||||
|
house
|
||||||
|
kangaroo
|
||||||
|
keyboard
|
||||||
|
lamp
|
||||||
|
lawn_mower
|
||||||
|
leopard
|
||||||
|
lion
|
||||||
|
lizard
|
||||||
|
lobster
|
||||||
|
man
|
||||||
|
maple_tree
|
||||||
|
motorcycle
|
||||||
|
mountain
|
||||||
|
mouse
|
||||||
|
mushroom
|
||||||
|
oak_tree
|
||||||
|
orange
|
||||||
|
orchid
|
||||||
|
otter
|
||||||
|
palm_tree
|
||||||
|
pear
|
||||||
|
pickup_truck
|
||||||
|
pine_tree
|
||||||
|
plain
|
||||||
|
plate
|
||||||
|
poppy
|
||||||
|
porcupine
|
||||||
|
possum
|
||||||
|
rabbit
|
||||||
|
raccoon
|
||||||
|
ray
|
||||||
|
road
|
||||||
|
rocket
|
||||||
|
rose
|
||||||
|
sea
|
||||||
|
seal
|
||||||
|
shark
|
||||||
|
shrew
|
||||||
|
skunk
|
||||||
|
skyscraper
|
||||||
|
snail
|
||||||
|
snake
|
||||||
|
spider
|
||||||
|
squirrel
|
||||||
|
streetcar
|
||||||
|
sunflower
|
||||||
|
sweet_pepper
|
||||||
|
table
|
||||||
|
tank
|
||||||
|
telephone
|
||||||
|
television
|
||||||
|
tiger
|
||||||
|
tractor
|
||||||
|
train
|
||||||
|
trout
|
||||||
|
tulip
|
||||||
|
turtle
|
||||||
|
wardrobe
|
||||||
|
whale
|
||||||
|
willow_tree
|
||||||
|
wolf
|
||||||
|
woman
|
||||||
|
worm
|
||||||
60000
cifar100/unprocessed/allimages-us.txt
Normal file
60000
cifar100/unprocessed/allimages-us.txt
Normal file
File diff suppressed because it is too large
Load Diff
33
cifar100/unprocessed/processor.py
Normal file
33
cifar100/unprocessed/processor.py
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
"""
|
||||||
|
Turn a combined, unsorted all images list into a sorted list with the right paths
|
||||||
|
"""
|
||||||
|
|
||||||
|
from os import listdir
|
||||||
|
from os.path import isfile, join
|
||||||
|
import random
|
||||||
|
|
||||||
|
ALLFILE="allimages.txt"
|
||||||
|
|
||||||
|
def main():
|
||||||
|
with open("allimages-us.txt",'r') as source:
|
||||||
|
|
||||||
|
data = [ line.split(' ') for line in source ]
|
||||||
|
data = [ (line[0].replace('fine', 'fine_comb'), line[1]) for line in data ]
|
||||||
|
data = [ (line[0].replace('test', 'all_ims'), line[1]) for line in data ]
|
||||||
|
data = [ (line[0].replace('train', 'all_ims'), line[1]) for line in data ]
|
||||||
|
|
||||||
|
data = [ (line[0].split('/'), line[1]) for line in data ]
|
||||||
|
data.sort(key=lambda x: (x[1], x[0][-1]))
|
||||||
|
|
||||||
|
data = [ ('/'.join(line[0]), line[1]) for line in data ]
|
||||||
|
data = [ ' '.join((line[0], line[1])) for line in data ]
|
||||||
|
|
||||||
|
for i in data[:5]:
|
||||||
|
print(i)
|
||||||
|
|
||||||
|
op=open(ALLFILE,'w')
|
||||||
|
for i in data:
|
||||||
|
op.write(i)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
10000
cifar100/unprocessed/test.txt
Normal file
10000
cifar100/unprocessed/test.txt
Normal file
File diff suppressed because it is too large
Load Diff
50000
cifar100/unprocessed/train.txt
Normal file
50000
cifar100/unprocessed/train.txt
Normal file
File diff suppressed because it is too large
Load Diff
50
models/alexnet-template.py
Normal file
50
models/alexnet-template.py
Normal file
@ -0,0 +1,50 @@
|
|||||||
|
# Preferred settings for this model is:
|
||||||
|
# Base Learning Rate = 0.001
|
||||||
|
# Crop Size = 224
|
||||||
|
|
||||||
|
from model import Tower
|
||||||
|
from utils import model_property
|
||||||
|
import tensorflow as tf
|
||||||
|
import tensorflow.contrib.slim as slim
|
||||||
|
import utils as digits
|
||||||
|
|
||||||
|
|
||||||
|
class UserModel(Tower):
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def inference(self):
|
||||||
|
x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
|
||||||
|
with slim.arg_scope([slim.conv2d, slim.fully_connected],
|
||||||
|
weights_initializer=tf.contrib.layers.xavier_initializer(),
|
||||||
|
weights_regularizer=slim.l2_regularizer(1e-6)):
|
||||||
|
|
||||||
|
model = slim.conv2d(x, 96, [11, 11], 4, padding='VALID', scope='conv1')
|
||||||
|
model = slim.max_pool2d(model, [3, 3], 2, scope='pool1')
|
||||||
|
|
||||||
|
model = slim.conv2d(model, 256, [5, 5], 1, scope='conv2')
|
||||||
|
model = slim.max_pool2d(model, [3, 3], 2, scope='pool2')
|
||||||
|
|
||||||
|
model = slim.conv2d(model, 384, [3, 3], 1, scope='conv3')
|
||||||
|
model = slim.conv2d(model, 384, [3, 3], 1, scope='conv4')
|
||||||
|
model = slim.conv2d(model, 256, [3, 3], 1, scope='conv5')
|
||||||
|
model = slim.max_pool2d(model, [3, 3], 2, scope='pool5')
|
||||||
|
|
||||||
|
model = slim.flatten(model)
|
||||||
|
|
||||||
|
model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc1')
|
||||||
|
model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
|
||||||
|
|
||||||
|
model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc2')
|
||||||
|
model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do2')
|
||||||
|
|
||||||
|
model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc3')
|
||||||
|
|
||||||
|
return model
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def loss(self):
|
||||||
|
model = self.inference
|
||||||
|
loss = digits.classification_loss(model, self.y)
|
||||||
|
accuracy = digits.classification_accuracy(model, self.y)
|
||||||
|
self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
|
||||||
|
return loss
|
||||||
228
models/googlenet-template.py
Normal file
228
models/googlenet-template.py
Normal file
@ -0,0 +1,228 @@
|
|||||||
|
# Preferred settings for this model is:
|
||||||
|
# Training epochs = 80
|
||||||
|
# Crop Size = 224
|
||||||
|
# Learning Rate = 0.001
|
||||||
|
# Under advanced learning rate options:
|
||||||
|
# Step Size = 10.0
|
||||||
|
# Gamma = 0.96
|
||||||
|
|
||||||
|
# The auxillary branches as spcified in the original googlenet V1 model do exist in this implementation of
|
||||||
|
# googlenet but it is not used. To use it, be sure to check self.is_training to ensure that it is only used
|
||||||
|
# during training.
|
||||||
|
|
||||||
|
from model import Tower
|
||||||
|
from utils import model_property
|
||||||
|
import tensorflow as tf
|
||||||
|
import utils as digits
|
||||||
|
|
||||||
|
|
||||||
|
class UserModel(Tower):
|
||||||
|
|
||||||
|
all_inception_settings = {
|
||||||
|
'3a': [[64], [96, 128], [16, 32], [32]],
|
||||||
|
'3b': [[128], [128, 192], [32, 96], [64]],
|
||||||
|
'4a': [[192], [96, 208], [16, 48], [64]],
|
||||||
|
'4b': [[160], [112, 224], [24, 64], [64]],
|
||||||
|
'4c': [[128], [128, 256], [24, 64], [64]],
|
||||||
|
'4d': [[112], [144, 288], [32, 64], [64]],
|
||||||
|
'4e': [[256], [160, 320], [32, 128], [128]],
|
||||||
|
'5a': [[256], [160, 320], [32, 128], [128]],
|
||||||
|
'5b': [[384], [192, 384], [48, 128], [128]]
|
||||||
|
}
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def inference(self):
|
||||||
|
# rescale to proper form, really we expect 224 x 224 x 1 in HWC form
|
||||||
|
model = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
|
||||||
|
|
||||||
|
conv_7x7_2s_weight, conv_7x7_2s_bias = self.create_conv_vars([7, 7, self.input_shape[2], 64], 'conv_7x7_2s')
|
||||||
|
model = self.conv_layer_with_relu(model, conv_7x7_2s_weight, conv_7x7_2s_bias, 2)
|
||||||
|
|
||||||
|
model = self.max_pool(model, 3, 2)
|
||||||
|
|
||||||
|
# model = tf.nn.local_response_normalization(model)
|
||||||
|
|
||||||
|
conv_1x1_vs_weight, conv_1x1_vs_bias = self.create_conv_vars([1, 1, 64, 64], 'conv_1x1_vs')
|
||||||
|
model = self.conv_layer_with_relu(model, conv_1x1_vs_weight, conv_1x1_vs_bias, 1, 'VALID')
|
||||||
|
|
||||||
|
conv_3x3_1s_weight, conv_3x3_1s_bias = self.create_conv_vars([3, 3, 64, 192], 'conv_3x3_1s')
|
||||||
|
model = self.conv_layer_with_relu(model, conv_3x3_1s_weight, conv_3x3_1s_bias, 1)
|
||||||
|
|
||||||
|
# model = tf.nn.local_response_normalization(model)
|
||||||
|
|
||||||
|
model = self.max_pool(model, 3, 2)
|
||||||
|
|
||||||
|
inception_settings_3a = InceptionSettings(192, UserModel.all_inception_settings['3a'])
|
||||||
|
model = self.inception(model, inception_settings_3a, '3a')
|
||||||
|
|
||||||
|
inception_settings_3b = InceptionSettings(256, UserModel.all_inception_settings['3b'])
|
||||||
|
model = self.inception(model, inception_settings_3b, '3b')
|
||||||
|
|
||||||
|
model = self.max_pool(model, 3, 2)
|
||||||
|
|
||||||
|
inception_settings_4a = InceptionSettings(480, UserModel.all_inception_settings['4a'])
|
||||||
|
model = self.inception(model, inception_settings_4a, '4a')
|
||||||
|
|
||||||
|
# first auxiliary branch for making training faster
|
||||||
|
# aux_branch_1 = self.auxiliary_classifier(model, 512, "aux_1")
|
||||||
|
|
||||||
|
inception_settings_4b = InceptionSettings(512, UserModel.all_inception_settings['4b'])
|
||||||
|
model = self.inception(model, inception_settings_4b, '4b')
|
||||||
|
|
||||||
|
inception_settings_4c = InceptionSettings(512, UserModel.all_inception_settings['4c'])
|
||||||
|
model = self.inception(model, inception_settings_4c, '4c')
|
||||||
|
|
||||||
|
inception_settings_4d = InceptionSettings(512, UserModel.all_inception_settings['4d'])
|
||||||
|
model = self.inception(model, inception_settings_4d, '4d')
|
||||||
|
|
||||||
|
# second auxiliary branch for making training faster
|
||||||
|
# aux_branch_2 = self.auxiliary_classifier(model, 528, "aux_2")
|
||||||
|
|
||||||
|
inception_settings_4e = InceptionSettings(528, UserModel.all_inception_settings['4e'])
|
||||||
|
model = self.inception(model, inception_settings_4e, '4e')
|
||||||
|
|
||||||
|
model = self.max_pool(model, 3, 2)
|
||||||
|
|
||||||
|
inception_settings_5a = InceptionSettings(832, UserModel.all_inception_settings['5a'])
|
||||||
|
model = self.inception(model, inception_settings_5a, '5a')
|
||||||
|
|
||||||
|
inception_settings_5b = InceptionSettings(832, UserModel.all_inception_settings['5b'])
|
||||||
|
model = self.inception(model, inception_settings_5b, '5b')
|
||||||
|
|
||||||
|
model = self.avg_pool(model, 7, 1, 'VALID')
|
||||||
|
|
||||||
|
fc_weight, fc_bias = self.create_fc_vars([1024, self.nclasses], 'fc')
|
||||||
|
model = self.fully_connect(model, fc_weight, fc_bias)
|
||||||
|
|
||||||
|
# if self.is_training:
|
||||||
|
# return [aux_branch_1, aux_branch_2, model]
|
||||||
|
|
||||||
|
return model
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def loss(self):
|
||||||
|
model = self.inference
|
||||||
|
loss = digits.classification_loss(model, self.y)
|
||||||
|
accuracy = digits.classification_accuracy(model, self.y)
|
||||||
|
self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
|
||||||
|
return loss
|
||||||
|
|
||||||
|
def inception(self, model, inception_setting, layer_name):
|
||||||
|
weights, biases = self.create_inception_variables(inception_setting, layer_name)
|
||||||
|
conv_1x1 = self.conv_layer_with_relu(model, weights['conv_1x1_1'], biases['conv_1x1_1'], 1)
|
||||||
|
|
||||||
|
conv_3x3 = self.conv_layer_with_relu(model, weights['conv_1x1_2'], biases['conv_1x1_2'], 1)
|
||||||
|
conv_3x3 = self.conv_layer_with_relu(conv_3x3, weights['conv_3x3'], biases['conv_3x3'], 1)
|
||||||
|
|
||||||
|
conv_5x5 = self.conv_layer_with_relu(model, weights['conv_1x1_3'], biases['conv_1x1_3'], 1)
|
||||||
|
conv_5x5 = self.conv_layer_with_relu(conv_5x5, weights['conv_5x5'], biases['conv_5x5'], 1)
|
||||||
|
|
||||||
|
conv_pool = self.max_pool(model, 3, 1)
|
||||||
|
conv_pool = self.conv_layer_with_relu(conv_pool, weights['conv_pool'], biases['conv_pool'], 1)
|
||||||
|
|
||||||
|
final_model = tf.concat([conv_1x1, conv_3x3, conv_5x5, conv_pool], 3)
|
||||||
|
|
||||||
|
return final_model
|
||||||
|
|
||||||
|
def create_inception_variables(self, inception_setting, layer_name):
|
||||||
|
model_dim = inception_setting.model_dim
|
||||||
|
conv_1x1_1_w, conv_1x1_1_b = self.create_conv_vars([1, 1, model_dim, inception_setting.conv_1x1_1_layers],
|
||||||
|
layer_name + '-conv_1x1_1')
|
||||||
|
conv_1x1_2_w, conv_1x1_2_b = self.create_conv_vars([1, 1, model_dim, inception_setting.conv_1x1_2_layers],
|
||||||
|
layer_name + '-conv_1x1_2')
|
||||||
|
conv_1x1_3_w, conv_1x1_3_b = self.create_conv_vars([1, 1, model_dim, inception_setting.conv_1x1_3_layers],
|
||||||
|
layer_name + '-conv_1x1_3')
|
||||||
|
conv_3x3_w, conv_3x3_b = self.create_conv_vars([3, 3, inception_setting.conv_1x1_2_layers,
|
||||||
|
inception_setting.conv_3x3_layers],
|
||||||
|
layer_name + '-conv_3x3')
|
||||||
|
conv_5x5_w, conv_5x5_b = self.create_conv_vars([5, 5, inception_setting.conv_1x1_3_layers,
|
||||||
|
inception_setting.conv_5x5_layers],
|
||||||
|
layer_name + '-conv_5x5')
|
||||||
|
conv_pool_w, conv_pool_b = self.create_conv_vars([1, 1, model_dim, inception_setting.conv_pool_layers],
|
||||||
|
layer_name + '-conv_pool')
|
||||||
|
|
||||||
|
weights = {
|
||||||
|
'conv_1x1_1': conv_1x1_1_w,
|
||||||
|
'conv_1x1_2': conv_1x1_2_w,
|
||||||
|
'conv_1x1_3': conv_1x1_3_w,
|
||||||
|
'conv_3x3': conv_3x3_w,
|
||||||
|
'conv_5x5': conv_5x5_w,
|
||||||
|
'conv_pool': conv_pool_w
|
||||||
|
}
|
||||||
|
|
||||||
|
biases = {
|
||||||
|
'conv_1x1_1': conv_1x1_1_b,
|
||||||
|
'conv_1x1_2': conv_1x1_2_b,
|
||||||
|
'conv_1x1_3': conv_1x1_3_b,
|
||||||
|
'conv_3x3': conv_3x3_b,
|
||||||
|
'conv_5x5': conv_5x5_b,
|
||||||
|
'conv_pool': conv_pool_b
|
||||||
|
}
|
||||||
|
|
||||||
|
return weights, biases
|
||||||
|
|
||||||
|
def auxiliary_classifier(self, model, input_size, name):
|
||||||
|
aux_classifier = self.avg_pool(model, 5, 3, 'VALID')
|
||||||
|
|
||||||
|
conv_weight, conv_bias = self.create_conv_vars([1, 1, input_size, input_size], name + '-conv_1x1')
|
||||||
|
aux_classifier = self.conv_layer_with_relu(aux_classifier, conv_weight, conv_bias, 1)
|
||||||
|
|
||||||
|
fc_weight, fc_bias = self.create_fc_vars([4*4*input_size, self.nclasses], name + '-fc')
|
||||||
|
aux_classifier = self.fully_connect(aux_classifier, fc_weight, fc_bias)
|
||||||
|
|
||||||
|
aux_classifier = tf.nn.dropout(aux_classifier, 0.7)
|
||||||
|
|
||||||
|
return aux_classifier
|
||||||
|
|
||||||
|
def conv_layer_with_relu(self, model, weights, biases, stride_size, padding='SAME'):
|
||||||
|
new_model = tf.nn.conv2d(model, weights, strides=[1, stride_size, stride_size, 1], padding=padding)
|
||||||
|
new_model = tf.nn.bias_add(new_model, biases)
|
||||||
|
new_model = tf.nn.relu(new_model)
|
||||||
|
return new_model
|
||||||
|
|
||||||
|
def max_pool(self, model, kernal_size, stride_size, padding='SAME'):
|
||||||
|
new_model = tf.nn.max_pool(model, ksize=[1, kernal_size, kernal_size, 1],
|
||||||
|
strides=[1, stride_size, stride_size, 1], padding=padding)
|
||||||
|
return new_model
|
||||||
|
|
||||||
|
def avg_pool(self, model, kernal_size, stride_size, padding='SAME'):
|
||||||
|
new_model = tf.nn.avg_pool(model, ksize=[1, kernal_size, kernal_size, 1],
|
||||||
|
strides=[1, stride_size, stride_size, 1], padding=padding)
|
||||||
|
return new_model
|
||||||
|
|
||||||
|
def fully_connect(self, model, weights, biases):
|
||||||
|
fc_model = tf.reshape(model, [-1, weights.get_shape().as_list()[0]])
|
||||||
|
fc_model = tf.matmul(fc_model, weights)
|
||||||
|
fc_model = tf.add(fc_model, biases)
|
||||||
|
fc_model = tf.nn.relu(fc_model)
|
||||||
|
return fc_model
|
||||||
|
|
||||||
|
def create_conv_vars(self, size, name):
|
||||||
|
weight = self.create_weight(size, name + '_W')
|
||||||
|
bias = self.create_bias(size[3], name + '_b')
|
||||||
|
return weight, bias
|
||||||
|
|
||||||
|
def create_fc_vars(self, size, name):
|
||||||
|
weight = self.create_weight(size, name + '_W')
|
||||||
|
bias = self.create_bias(size[1], name + '_b')
|
||||||
|
return weight, bias
|
||||||
|
|
||||||
|
def create_weight(self, size, name):
|
||||||
|
weight = tf.get_variable(name, size, initializer=tf.contrib.layers.xavier_initializer())
|
||||||
|
return weight
|
||||||
|
|
||||||
|
def create_bias(self, size, name):
|
||||||
|
bias = tf.get_variable(name, [size], initializer=tf.constant_initializer(0.2))
|
||||||
|
return bias
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionSettings():
|
||||||
|
|
||||||
|
def __init__(self, model_dim, inception_settings):
|
||||||
|
self.model_dim = model_dim
|
||||||
|
self.conv_1x1_1_layers = inception_settings[0][0]
|
||||||
|
self.conv_1x1_2_layers = inception_settings[1][0]
|
||||||
|
self.conv_1x1_3_layers = inception_settings[2][0]
|
||||||
|
self.conv_3x3_layers = inception_settings[1][1]
|
||||||
|
self.conv_5x5_layers = inception_settings[2][1]
|
||||||
|
self.conv_pool_layers = inception_settings[3][0]
|
||||||
34
models/lenet-template.py
Normal file
34
models/lenet-template.py
Normal file
@ -0,0 +1,34 @@
|
|||||||
|
from model import Tower
|
||||||
|
from utils import model_property
|
||||||
|
import tensorflow as tf
|
||||||
|
import tensorflow.contrib.slim as slim
|
||||||
|
import utils as digits
|
||||||
|
|
||||||
|
|
||||||
|
class UserModel(Tower):
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def inference(self):
|
||||||
|
x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
|
||||||
|
# scale (divide by MNIST std)
|
||||||
|
x = x * 0.0125
|
||||||
|
with slim.arg_scope([slim.conv2d, slim.fully_connected],
|
||||||
|
weights_initializer=tf.contrib.layers.xavier_initializer(),
|
||||||
|
weights_regularizer=slim.l2_regularizer(0.0005)):
|
||||||
|
model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='conv1')
|
||||||
|
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
|
||||||
|
model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
|
||||||
|
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
|
||||||
|
model = slim.flatten(model)
|
||||||
|
model = slim.fully_connected(model, 500, scope='fc1')
|
||||||
|
model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
|
||||||
|
model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc2')
|
||||||
|
return model
|
||||||
|
|
||||||
|
@model_property
|
||||||
|
def loss(self):
|
||||||
|
model = self.inference
|
||||||
|
loss = digits.classification_loss(model, self.y)
|
||||||
|
accuracy = digits.classification_accuracy(model, self.y)
|
||||||
|
self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
|
||||||
|
return loss
|
||||||
52
split.py
Executable file
52
split.py
Executable file
@ -0,0 +1,52 @@
|
|||||||
|
from os import listdir
|
||||||
|
from os.path import isfile, join
|
||||||
|
import random
|
||||||
|
import sys
|
||||||
|
|
||||||
|
ALLFILE="allimages.txt"
|
||||||
|
TRAINFILE="train.txt"
|
||||||
|
VALIDFILE="val.txt"
|
||||||
|
TESTFILE="test.txt"
|
||||||
|
|
||||||
|
# Change this from 1.0 to some lower fraction to subsample the data
|
||||||
|
# e.g. 0.05 will use 5 percent of all the data
|
||||||
|
SUBSAMP=1.0
|
||||||
|
|
||||||
|
def main(path, TRAINPERC, VALIDPERC):
|
||||||
|
|
||||||
|
with open(join(path, ALLFILE),'r') as source:
|
||||||
|
|
||||||
|
data = [ (random.random(), line) for line in source ]
|
||||||
|
data.sort()
|
||||||
|
train=open(join(path, TRAINFILE),'w')
|
||||||
|
valid=open(join(path, VALIDFILE),'w')
|
||||||
|
test=open(join(path, TESTFILE),'w')
|
||||||
|
|
||||||
|
count=len(data)
|
||||||
|
cumlvalid=int(TRAINPERC*count)
|
||||||
|
cumltest=cumlvalid+int(VALIDPERC*count)
|
||||||
|
|
||||||
|
print("Total records = %d" % count)
|
||||||
|
print("Train %d%% = %d" % (round(TRAINPERC*100), cumlvalid) )
|
||||||
|
print("Valid %d%% = %d" % (round(VALIDPERC*100), cumltest-cumlvalid) )
|
||||||
|
print("Test %d%% = %d" % (round((1-TRAINPERC-VALIDPERC)*100), count-cumltest))
|
||||||
|
|
||||||
|
didwrite=0
|
||||||
|
ctr=0
|
||||||
|
for _, line in data:
|
||||||
|
if (ctr>=cumltest):
|
||||||
|
if (random.uniform(0,1)<SUBSAMP):
|
||||||
|
test.write( line )
|
||||||
|
elif (ctr>=cumlvalid):
|
||||||
|
if (random.uniform(0,1)<SUBSAMP):
|
||||||
|
valid.write( line )
|
||||||
|
else:
|
||||||
|
if (random.uniform(0,1)<SUBSAMP):
|
||||||
|
train.write( line )
|
||||||
|
didwrite=didwrite+1
|
||||||
|
ctr=ctr+1
|
||||||
|
|
||||||
|
print('Wrote training data %d' % didwrite)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main(input('Path: '), float(input('Train: ')), float(input('Val: ')))
|
||||||
Loading…
Reference in New Issue
Block a user