(Tensorflow 2.x) CNN 3탄 (Transfer Learning)

Garbage Classification + Transfer Learning 

Feature extractor : imagenet 데이터셋으로 사전훈련된 VGG16 모델

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import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout from tensorflow.keras.datasets import cifar10 from tensorflow.keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator import matplotlib.pyplot as plt import os, shutil from tensorflow.keras.applications import VGG16   # 데이터셋 생성   dataset_dir = './data/Garbage classification/'   train_dir = os.path.join(dataset_dir, 'train') validation_dir = os.path.join(dataset_dir, 'val')     train_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,                                     horizontal_flip=True, zoom_range=0.3, rescale=1./255) val_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,                                     horizontal_flip=True, zoom_range=0.3, rescale=1./255)   train_generator = train_gen.flow_from_directory(train_dir, target_size=(240, 240), batch_size=32, class_mode='sparse') validation_generator = train_gen.flow_from_directory(validation_dir, target_size=(240, 240), batch_size=32, class_mode='sparse')     # 모델 생성 feature_extractor = VGG16(weights='imagenet', include_top=False, input_shape=(240, 240, 3)) # include_top=False, 최상위 dense layer 포함여부 feature_extractor.trainable = False   model = Sequential() model.add(feature_extractor) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(6, activation='softmax'))     # 모델 컴파일 및 훈련시키기 model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])   history = model.fit_generator(train_generator, steps_per_epoch=10, epochs=100,                               validation_data=validation_generator, validation_steps=10)     # 훈련의 정확도 확인 plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.xlabel('epoch') plt.ylabel('accuracy') plt.legend(['train', 'validation'], loc='best') plt.show()         Colored by Color Scripter

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위의 소스코드를 실행시키면 아래와 같은 결과값을 얻을 수 있다. 

 

파인 튜닝 : 특성 추출에 사용했던 모델의 상위 층 몇 개를 동결에서 해제하고, 새로 추가한 층(완전 연결 분류기)과 함께 훈련시킴

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import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout from tensorflow.keras.datasets import cifar10 from tensorflow.keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator import matplotlib.pyplot as plt import os, shutil from tensorflow.keras.applications import VGG16   # 데이터셋 생성   dataset_dir = './data/Garbage classification/'   train_dir = os.path.join(dataset_dir, 'train') validation_dir = os.path.join(dataset_dir, 'val')     train_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,                                     horizontal_flip=True, zoom_range=0.3, rescale=1./255) val_gen = ImageDataGenerator(rotation_range=60, width_shift_range=0.3, shear_range=0.3,                                     horizontal_flip=True, zoom_range=0.3, rescale=1./255)   train_generator = train_gen.flow_from_directory(train_dir, target_size=(240, 240), batch_size=32, class_mode='sparse') validation_generator = train_gen.flow_from_directory(validation_dir, target_size=(240, 240), batch_size=32, class_mode='sparse')     # 모델 생성 feature_extractor = VGG16(weights='imagenet', include_top=False, input_shape=(240, 240, 3)) # include_top=False, 최상위 dense layer 포함여부 feature_extractor.trainable = True   train_flag = False for layer in feature_extractor.layers:     if layer.name == 'block5_con1':         train_flag = True     if train_flag:         layer.trainable = True     else:         layer.trainable = False            model = Sequential() model.add(feature_extractor) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(6, activation='softmax'))     # 모델 컴파일 및 훈련시키기 model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])   history = model.fit_generator(train_generator, steps_per_epoch=10, epochs=100,                               validation_data=validation_generator, validation_steps=10)     # 훈련의 정확도 확인 plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.xlabel('epoch') plt.ylabel('accuracy') plt.legend(['train', 'validation'], loc='best') plt.show()     Colored by Color Scripter

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