Sách Deep Learning cơ bản

11.1 Transfer learning 157
# Load model VGG 16 của ImageNet dataset, include_top=False để bỏ phần Fully connected lay
baseModel = VGG16(weights='imagenet', include_top=False, \
input_tensor=Input(shape=(224, 224, 3)))
# Xây thêm các layer
# Lấy output của ConvNet trong VGG16
fcHead = baseModel.output
# Flatten trước khi dùng FCs
fcHead = Flatten(name='flatten')(fcHead)
# Thêm FC
fcHead = Dense(256, activation='relu')(fcHead)
fcHead = Dropout(0.5)(fcHead)
# Output layer với softmax activation
fcHead = Dense(17, activation='softmax')(fcHead)
# Xây dựng model bằng việc nối ConvNet của VGG16 và fcHead
model = model = Model(inputs=baseModel.input, outputs=fcHead)
# Chia traing set, test set tỉ lệ 80-20
X_train, X_test, y_train, y_test = train_test_split(list_image, labels, \
test_size=0.2, random_state=42)
# augmentation cho training data
aug_train = ImageDataGenerator(rescale=1./255, rotation_range=30, \
width_shift_range=0.1, height_shift_range=0.1, \
shear_range=0.2, zoom_range=0.2, horizontal_flip=True, \
fill_mode='nearest')
# augementation cho test
aug_test= ImageDataGenerator(rescale=1./255)
# freeze VGG model
for layer in baseModel.layers:
layer.trainable = False
opt = RMSprop(0.001)
model.compile(opt, 'categorical_crossentropy', ['accuracy'])
numOfEpoch = 25
H = model.fit_generator(aug_train.flow(X_train, y_train, batch_size=32),
steps_per_epoch=len(X_train)//32,
validation_data=(aug_test.flow(X_test, y_test, batch_size=32)),
validation_steps=len(X_test)//32,
epochs=numOfEpoch)
# unfreeze some last CNN layer:
for layer in baseModel.layers[15:]:
layer.trainable = True