내 손 위의 딥러닝_iOS에 딥러닝 심기
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The document discusses various machine learning tools and models, highlighting resources for implementing Core ML in iOS development. It mentions several GitHub repositories, tutorials, and benchmarks for object detection and classification. Key topics include model creation, training with Keras, and integration with Apple's Core ML framework.
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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/](https://image.slidesharecdn.com/20180628dlcat-180629114027/85/_iOS-43-320.jpg)
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# 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/](https://image.slidesharecdn.com/20180628dlcat-180629114027/85/_iOS-45-320.jpg)
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# 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') //](https://image.slidesharecdn.com/20180628dlcat-180629114027/85/_iOS-47-320.jpg)
내 손 위의 딥러닝_iOS에 딥러닝 심기
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- 12. 1ST Geekbench 4 - cross-platform processor benchmark
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- 15. 1ST Classification Object Detection Regression Decision Tree Classifier Logistic Regression Random Forest Regression Linear Regression Random Forest Classifier SVM
- 17. 1ST AgeNet Input: Image Output: String 43.5 MB https://github.com/cocoa-ai/FacesVisionDemo
- 18. 1ST DocumentClassification Input: String Output: String 1.4 MB https://github.com/toddkramer/DocumentClassifier
- 19. 1ST Exermote Input: Accelerations Output: Array 80 KB https://github.com/Lausbert/Exermote/tree/master/ExermoteInference
- 20. 1ST Food101 Input: Image Output: String 83.3 MB https://github.com/ph1ps/Food101-CoreML
- 21. 1ST RN1015k500 Input: Image Output: Dictionary 284.4 MB https://github.com/awslabs/MXNet2CoreML_iOS_sample_app
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- 26. 1ST “Don’t be a hero!” -Andrej Karpathy
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- 42. 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/
- 43. 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/
- 44. 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/
- 45. 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/
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- 47. 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') //
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- 50. 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
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- 59. 1ST Apple Image Classification Text Meaning Find Relationship
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- 67. 1ST class func compileModel(at: URL) -> URL convert_neural_network_spec_weights_to_fp16(model_spec)
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