SlideShare a Scribd company logo
1ST
2018.06.28.
iOS
1ST
1ST
1ST
1ST
1ST
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User

Data
1ST
1ST
User

Data
1ST
https://developer.apple.com/videos/play/wwdc2017/703/
1ST
0.1 s 😎
1 s
5 s 😫
😯
1ST
Geekbench 4 - cross-platform processor benchmark
1ST
1ST
https://developer.apple.com/videos/play/wwdc2017/703/
😄
1ST
Classification
Object Detection
Regression
Decision Tree
Classifier
Logistic
Regression
Random
Forest Regression
Linear
Regression
Random
Forest Classifier
SVM
1ST
CoreML Store
https://coreml.store
1ST
AgeNet
Input: Image
Output: String
43.5 MB
https://github.com/cocoa-ai/FacesVisionDemo
1ST
DocumentClassification
Input: String
Output: String
1.4 MB
https://github.com/toddkramer/DocumentClassifier
1ST
Exermote
Input: Accelerations Output: Array
80 KB
https://github.com/Lausbert/Exermote/tree/master/ExermoteInference
1ST
Food101
Input: Image
Output: String
83.3 MB
https://github.com/ph1ps/Food101-CoreML
1ST
RN1015k500
Input: Image
Output: Dictionary
284.4 MB
https://github.com/awslabs/MXNet2CoreML_iOS_sample_app
1ST
1ST
1ST
1ST
http://www.ritchieng.com/neural-networks-learning/
1ST
“Don’t be a hero!”
-Andrej Karpathy
1ST
1ST
1ST
1ST
1ST
1ST
https://developer.apple.com/videos/play/wwdc2017/703/
1ST
https://developer.apple.com/videos/play/wwdc2017/703/
1ST
🤯
Rectangle Circle Triangle
1ST
1ST
Xcode
Swift
1ST
1ST
Rectangle Circle Triangle
1ST
1ST
1ST
1ST
from keras.preprocessing.image import ImageDataGenerator
# 1.
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
‘data_set/train’, //
target_size=(28, 28), //
batch_size=10, //
class_mode=‘categorical') //
: https://tykimos.github.io/
1ST
from keras.preprocessing.image import ImageDataGenerator
# 1.
train_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.7,
zoom_range=[0.9, 2.2],
horizontal_flip=True,
vertical_flip=True,
fill_mode='nearest')
: https://tykimos.github.io/
1ST
# 2.
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
model = Sequential() //
model.add(Conv2D(32, //
kernel_size=(3, 3), //
activation=‘relu’, //
input_shape=(28, 28, 3)) // ( , )
model.add(Conv2D(64, (3, 3), activation=‘relu')) //
model.add(MaxPooling2D(pool_size=(2, 2))) //
model.add(Flatten()) // ->
model.add(Dense(128, activation=‘relu’)) //
model.add(Dense(3, activation=‘softmax')) // : https://tykimos.github.io/
1ST
# 3.
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=[‘accuracy']) //
# 4.
model.fit_generator(
train_generator, //
steps_per_epoch=60, // / (= 600/10)
epochs=150) //
# 5.
score=model.evaluate_generator(test_generator, steps=3)
: https://tykimos.github.io/
1ST
1ST
# 6. coreml
import coremltools
coreml_model = coremltools.converters.keras.convert(model,
input_names=‘image’, // =
output_names=‘class',
image_input_names = ‘image',
class_labels = ['circle', 'rectangle', ‘triangle'], //
is_bgr=True)
coreml_model.save(‘shape_detect_with_keras.mlmodel') //
1ST
1ST
1ST
# 7. coreml
import CoreML
let model = shape_detect_with_keras()
guard let output: shape_detect_with_kerasOutput = try?
model.prediction(image: image) else { return }
https://github.com/MijeongJeon
1ST
1ST
1ST
1ST
Apple
IBM
Microsoft
1ST
Apple
CoreML 2CoreML 1
1ST
Apple
iOS 12(9 )
CreateML
1ST
Apple
1ST
Apple
1ST
Apple
Image Classification
Text Meaning
Find Relationship
1ST
Apple
1ST
IBM
IBM
Watson
Apple
CoreML
1ST
IBM
1ST
IBM
1ST
Microsoft
1ST
Microsoft
1ST
1ST
class func compileModel(at: URL) -> URL
convert_neural_network_spec_weights_to_fp16(model_spec)
1ST
1ST
IBM
1ST
Apple
CreateML
1ST
1ST
1ST
🤓

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