DeepLearning.AI TensorFlow Developer Course 1-4

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[Coursera] DeepLearning.AI TensorFlow Developer ( Tensorflow In Practice ) 강의 정리
Course 1 : Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

  • Week 1 - A New Programming Paradigm
  • Week 2 - Introduction to Computer Vision
  • Week 3 - Enhancing Vision with Convolutional Neural Networks
  • Week 4 - Using Real-world Images

Course 2 : Convolutional Neural Networks in TensorFlow
Course 3 : Natural Language Processing in TensorFlow
Course 4 : Sequences, Time Series and Prediction

Week 4 - Using Real-world Images

Understanding ImageGenerator

ImageGenerator 모듈

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from tensorflow.keras.preprocessing.image
import ImageDataGenerator

Train 폴더

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train_datagen = ImageDataGenerator(rescale=1/255)

train_generator = train_datagen.flow_from_directory(
    train_dir,  
    target_size=(150, 150),  
    batch_size=20,
    class_mode='binary')

상위 디렉토리 위치를 설정해야 함
train_dir   -  train 이미지가 포함 된 하위 디렉토리
target_size=(150, 150)   -  이미지 사이즈 조정
class_mode='binary'   -  이진분류

validation 폴더

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test_datagen = ImageDataGenerator(rescale=1/255)

validation_generator = test_datagen.flow_from_directory( 
    validation_dir,
    target_size=(150, 150),  # 이미지 사이즈 조정
    batch_size=10,
    class_mode='binary')  # 이진분류

validation_dir   -  test 이미지가 포함 된 하위 디렉토리

Defining a ConvNet to use complex images

사람 / 말 분류에 사용한 신경망

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model = tf.keras.models.Sequential([
   tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),
   tf.keras.layers.MaxPooling2D(2,2),
   tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
   tf.keras.layers.MaxPooling2D(2,2),
   tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
   tf.keras.layers.MaxPooling2D(2,2),
   tf.keras.layers.Flatten(),
   tf.keras.layers.Dense(512, activation='relu'),
   tf.keras.layers.Dense(1, activation='sigmoid')
])

이전 강의보다 이미지 복잡성과 크기를 반영하여, 세 개 convolutional, pooling layers 사용
input_shape=(300, 300, 3)   -  300 x 300 사이즈와 컬러이미지(빨간색, 초록색, 파란색)
tf.keras.layers.Dense(1, activation='sigmoid')   -  sigmoid는 0 or 1이므로, 1로 출력

Training the ConvNet with fit_generator

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from tensorflow.keras.optimizers import RMSprop
model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.001),
              metrics=['acc'])

loss='binary_crossentropy'   -  사람 / 말 이진분류로 binary_crossentropy 선택
optimizer=RMSprop(lr=0.001)   -  Learning Rate 조절

이미지 생성은 model.fit() 아닌 model.fit_generator() 사용

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history = model.fit_generator(
      train_generator,		
      steps_per_epoch=8,	
      epochs
      validation_data=validation_generator, 
      validation_steps=8,
      verbose=2)

train_generator   -  위의 train_generator 호출
steps_per_epoch=8   -  train image 1024개.   1024/8로 한 번에 128개 처리
validation_data=validation_generator   -  validation 호출
validation_steps=8   -  test image 256개.   256/8로 한 번에 32개 처리
verbose=2   -  진행사항 시각화 정도

전체코드

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# GRADED FUNCTION: train_happy_sad_model
def train_happy_sad_model():
    # Please write your code only where you are indicated.
    # please do not remove # model fitting inline comments.

    DESIRED_ACCURACY = 0.999

    class myCallback(tf.keras.callbacks.Callback):
         def on_epoch_end(self, epoch, logs={}):
                if(logs.get('acc')>DESIRED_ACCURACY):
                    print('\nacc is high')
                    self.model.stop_training = True

    callbacks = myCallback()
    
    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

    from tensorflow.keras.optimizers import RMSprop

    model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.001),
              metrics=['acc'])
        
    model.summary()
    # This code block should create an instance of an ImageDataGenerator called train_datagen 
    # And a train_generator by calling train_datagen.flow_from_directory

    from tensorflow.keras.preprocessing.image import ImageDataGenerator

    train_datagen = ImageDataGenerator(rescale=1/255)

    # Please use a target_size of 150 X 150.
    train_generator = train_datagen.flow_from_directory(
        "/tmp/h-or-s",
        target_size=(150, 150),
        batch_size=20,
        class_mode='binary'
    )
    # Expected output: 'Found 80 images belonging to 2 classes'

    # This code block should call model.fit_generator and train for
    # a number of epochs.
    # model fitting
    history = model.fit_generator(
        train_generator,
        steps_per_epoch=8,
        epochs=15,
        verbose=1,
        callbacks=[callbacks]    
    )
    # model fitting
    return history.history['acc'][-1]


https://www.coursera.org/professional-certificates/tensorflow-in-practice

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