understand this code:
```
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import regularizers
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, GlobalAveragePooling2D, MaxPooling2D, BatchNormalization
from tensorflow.keras.applications.mobilenet\_v2 import MobileNetV2
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model 
from tensorflow.lite.python import lite\_constants
import matplotlib.pyplot as plt
from sklearn.model\_selection import train\_test\_split
from sklearn.metrics import classification\_report
import multiprocessing as mp
import numpy as np
import os
import shutil
import random
import math

from google.colab import drive
drive.mount('/content/drive')

# Set up the dataset directory path and train-test split ratio
save\_model\_path = "/content/drive/MyDrive/Colab Notebooks/Models/"
dataset\_path = "/content/drive/MyDrive/Colab Notebooks/Headshots/"
train\_ratio = 0.67
val\_ratio = 0.18
test\_ratio = 0.1
rep\_ratio = 0.05

# Split dataset into train and test sets
train\_path = os.path.join(dataset\_path, 'train')
val\_path = os.path.join(dataset\_path, 'val')
test\_path = os.path.join(dataset\_path, 'test')
rep\_path = os.path.join(dataset\_path, 'rep')

category\_class\_names = ['Family', 'Friend', 'Inhabitant']

# Set up the data generators
batch\_size = 16
image\_size = (128,128)

train\_datagen = ImageDataGenerator(rescale=1./255)

train\_generator = train\_datagen.flow\_from\_directory(
 train\_path,
 target\_size=image\_size,
 batch\_size=batch\_size,
 class\_mode='categorical',
 shuffle=True
)

validation\_datagen = ImageDataGenerator(rescale=1./255)

validation\_generator = validation\_datagen.flow\_from\_directory(
 val\_path,
 target\_size=image\_size,
 batch\_size=batch\_size,
 class\_mode='categorical',
 shuffle=True
)

test\_datagen = ImageDataGenerator(rescale=1./255)

test\_generator = test\_datagen.flow\_from\_directory(
 test\_path,
 target\_size=image\_size,
 batch\_size=batch\_size,
 class\_mode='categorical',
 shuffle=True
)

rep\_datagen = ImageDataGenerator(rescale=1./255)

rep\_generator = rep\_datagen.flow\_from\_directory(
 rep\_path,
 target\_size=image\_size,
 batch\_size=batch\_size,
 class\_mode='categorical',
 shuffle=True
)

# Set up the model
base\_model = MobileNetV2(
 weights='imagenet',
 include\_top=False,
 input\_shape=(128, 128, 3)
)

base\_model.trainable = True

for layer in base\_model.layers[:-20]:
 layer.trainable = False

x = base\_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.2)(x)

# Define the outputs
category\_output = Dense(train\_generator.num\_classes, activation='softmax')(x)

# Build the model
model = Model(inputs=base\_model.input, outputs=category\_output)

# Set up the callbacks
reduce\_lr\_on\_plateau\_callback = ReduceLROnPlateau(
 monitor='val\_loss',
 factor=0.1,
 patience=5,
 verbose=1,
 mode='auto',
 min\_delta=0.0001,
 cooldown=0,
 min\_lr=0
)

earlystop\_callback = EarlyStopping(monitor='val\_loss', min\_delta=0, patience=20, restore\_best\_weights=True)

# Set up the learning rate schedule for optimizer
lr\_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
 initial\_learning\_rate=1e-6,
 decay\_steps=2000,
 decay\_rate=0.9
)

# Compile the model
model.compile(
 optimizer=Adam(learning\_rate=lr\_schedule),
 loss='categorical\_crossentropy',
 metrics=['accuracy']
)

# Set up checkpoints
checkpoint\_filepath = f"{save\_model\_path}/variables/checkpoint"
model\_checkpoint\_callback = tf.keras.callbacks.ModelCheckpoint(
 filepath=checkpoint\_filepath,
 save\_weights\_only=True,
 monitor='val\_accuracy',
 mode='max',
 save\_best\_only=True)

# Train the model
epochs = 15
history = model.fit(
 train\_generator,
 steps\_per\_epoch=math.ceil(train\_generator.samples / batch\_size),
 validation\_data=validation\_generator,
 validation\_steps=math.ceil(validation\_generator.samples / batch\_size),
 epochs=epochs,
 callbacks=[reduce\_lr\_on\_plateau\_callback, earlystop\_callback, model\_checkpoint\_callback]
)

# Evaluate the model
loss, accuracy = model.evaluate(test\_generator, steps=math.ceil(test\_generator.samples / batch\_size))
print('Test loss:', loss)
print('Test accuracy:', accuracy)

# Compute mean and standard deviation of training data
def compute\_mean\_std(images):
 mean = np.zeros((3,), dtype=np.float32)
 std = np.zeros((3,), dtype=np.float32)
 for x in images:
 mean += np.mean(x, axis=(0, 1, 2))
 std += np.std(x, axis=(0, 1, 2))
 return mean, std

# Split the data into batches
num\_processes = mp.cpu\_count()
data\_batches = [train\_generator[i:i+batch\_size] for i in range(0, len(train\_generator), batch\_size)]

# Create a process pool and map the batches to the pool
with mp.Pool(processes=num\_processes) as pool:
 results = pool.map(compute\_mean\_std, data\_batches)

print("train\_generator length: " + len(train\_generator))

# Aggregate the results
mean = np.zeros((3,), dtype=np.float32)
std = np.zeros((3,), dtype=np.float32)
for result in results:
 mean += result[0]
 std += result[1]
mean /= len(train\_generator)
std /= len(train\_generator)

# Convert the Keras model to TensorFlow Lite model
converter = tf.lite.TFLiteConverter.from\_keras\_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative\_dataset = rep\_generator
converter.target\_spec.supported\_types = [tf.float16]
converter.inference\_input\_type = tf.float16
converter.inference\_output\_type = tf.float16
converter.quantized\_input\_stats = {"input\_1": (mean.astype(np.float32), std.astype(np.float32))}
tflite\_model = converter.convert()

# Save the Keras model to a file
model.save(f"{save\_model\_path}/shs\_face\_recognition\_mobilenetv2.h5")

# Save the TensorFlow Lite model to a file
with open(f"{save\_model\_path}/shs\_face\_recognition\_mobilenetv2\_quantized.tflite", "wb") as f:
 f.write(tflite\_model)

# Save the model as a .pb file
tf.saved\_model.save(model, f"{save\_model\_path}/protobuf\_model")

# Evaluate the quantized model
interpreter = tf.lite.Interpreter(model\_content=tflite\_model)
interpreter.allocate\_tensors()
input\_details = interpreter.get\_input\_details()
output\_details = interpreter.get\_output\_details()
interpreter.set\_tensor(input\_details[0]['index'], test\_generator[0][0].astype(np.float32))
interpreter.invoke()
predictions = interpreter.get\_tensor(output\_details[0]['index'])
accuracy = np.mean(np.argmax(predictions, axis=1) == test\_generator[0][1])
print('Quantized model accuracy:', accuracy)

# Plot the training and validation accuracy for the category output
plt.plot(history.history['accuracy'])
plt.plot(history.history['val\_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()

# Plot the training and validation loss
plt.plot(history.history['loss'])
plt.plot(history.history['val\_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.show()

# Generate model summary
model.summary()
```
이 코드를 이해합니다:```import tensorflow as tftensorflow.keras.preprocessing.image에서 이미지데이터제너레이터를 임포트합니다.from tensorflow.keras import 정규화기tensorflow.keras.callbacks에서 EarlyStopping, ReduceLROnPlateau, ModelCheckpoint를 가져옵니다.tensorflow.keras.layers에서 Input, Dense, Dropout, Flatten, Conv2D, GlobalAveragePooling2D, MaxPooling2D, BatchNormalization을 가져옵니다.tensorflow.keras.applications.mobilenet\_v2에서 MobileNetV2를 가져옵니다.tensorflow.keras.optimizers에서 Adam을 가져옵니다.tensorflow.keras.models에서 Model을 가져옵니다. tensorflow.lite.python에서 lite\_constants를 가져옵니다.matplotlib.pyplot을 plt로 가져옵니다.sklearn.model\_selection에서 train\_test\_split을 가져옵니다.sklearn.metrics에서 classification\_report를 가져옵니다.다중 처리를 mp로 가져옵니다.numpy를 np로 가져옵니다.import osshutil 가져오기random 가져오기import mathgoogle.colab에서 드라이브 가져오기drive.mount('/content/drive')# 데이터 세트 디렉터리 경로와 훈련-테스트 분할 비율을 설정합니다.save\_model\_path = "/content/drive/MyDrive/Colab Notebooks/Models/"dataset\_path = "/content/drive/MyDrive/Colab Notebooks/Headshots/"train\_ratio = 0.67val\_ratio = 0.18test\_ratio = 0.1rep\_ratio = 0.05# 데이터 세트를 훈련 세트와 테스트 세트로 분할하기train\_path = os.path.join(dataset\_path, 'train')val\_path = os.path.join(dataset\_path, 'val')test\_path = os.path.join(dataset\_path, 'test')rep\_path = os.path.join(dataset\_path, 'rep')category\_class\_names = ['가족', '친구', '거주자']# 데이터 생성기 설정batch\_size = 16image\_size = (128,128)train\_datagen = 이미지데이터제너레이터(rescale=1./255)train\_generator = train\_datagen.flow\_from\_directory( train\_path, target\_size=이미지\_size, 배치\_size=배치\_size, class\_mode='범주형', shuffle=True)validation\_datagen = 이미지데이터제너레이터(rescale=1./255)validation\_generator = validation\_datagen.flow\_from\_directory( val\_path, target\_size=이미지\_size, 배치\_size=배치\_size, class\_mode='범주형', shuffle=True)test\_datagen = 이미지데이터제너레이터(rescale=1./255)test\_generator = test\_datagen.flow\_from\_directory( test\_path, target\_size=이미지\_size, 배치\_size=배치\_size, class\_mode='범주형', shuffle=True)rep\_datagen = 이미지데이터제너레이터(rescale=1./255)rep\_generator = rep\_datagen.flow\_from\_directory( rep\_path, target\_size=이미지\_size, 배치\_size=배치\_size, class\_mode='범주형', shuffle=True)# 모델 설정base\_model = MobileNetV2( 가중치='이미지넷', include\_top=False, input\_shape=(128, 128, 3))base\_model.trainable = Truebase\_model.layers[:-20]의 레이어에 대해: layer.trainable = Falsex = base\_model.outputx = GlobalAveragePooling2D()(x)x = 밀도(128, 활성화='relu')(x)x = 드롭아웃(0.2)(x)# 출력 정의category\_output = Dense(train\_generator.num\_classes, activation='softmax')(x)# 모델 빌드모델 = 모델(입력=base\_model.input, 출력=category\_output)# 콜백 설정reduce\_lr\_on\_plateau\_callback = ReduceLROnPlateau( monitor='val\_loss', factor=0.1, patience=5, verbose=1, mode='auto', min\_delta=0.0001, 쿨다운=0, min\_lr=0)조기중단\_콜백 = 조기중단(monitor='val\_loss', min\_delta=0, patience=20, restore\_best\_weights=True)# 옵티마이저의 학습률 스케줄을 설정합니다.lr\_schedule = tf.keras.optimizers.schedules.ExponentialDecay( initial\_learning\_rate=1e-6, decay\_steps=2000, decay\_rate=0.9)# 모델 컴파일model.compile( optimizer=Adam(learning\_rate=lr\_schedule), loss='범주형\_크로센트로피', metrics=['accuracy']))# 체크포인트 설정체크포인트\_파일경로 = f"{저장\_model\_path}/변수/체크포인트"모델\_체크포인트\_콜백 = tf.keras.callbacks.ModelCheckpoint( 파일경로=체크포인트\_파일경로, save\_weights\_only=True, monitor='val\_accuracy', mode='max', save\_best\_only=True)# 모델 훈련epochs = 15history = model.fit( train\_generator, steps\_per\_epoch=math.ceil(train\_generator.samples / batch\_size), validation\_data=validation\_generator, validation\_steps=math.ceil(validation\_generator.samples / batch\_size), epochs=epochs, callbacks=[reduce\_lr\_on\_plateau\_callback, earlystop\_callback, model\_checkpoint\_callback])# 모델 평가손실, 정확도 = model.evaluate(test\_generator, steps=math.ceil(test\_generator.samples / batch\_size))print('테스트 손실:', loss)print('테스트 정확도:', accuracy)# 훈련 데이터의 평균과 표준 편차 계산하기def compute\_mean\_std(images): mean = np.zeros((3,), dtype=np.float32) std = np.zeros((3,), dtype=np.float32) 이미지의 x에 대해 mean += np.mean(x, axis=(0, 1, 2)) std += np.std(x, axis=(0, 1, 2)) 반환 평균, 표준# 데이터를 일괄 처리로 분할num\_processes = mp.cpu\_count()data\_batches = [train\_generator[i:i+batch\_size] for i in range(0, len(train\_generator), batch\_size)]# 프로세스 풀을 생성하고 배치를 풀에 매핑합니다.mp.Pool(processes=num\_processes)을 풀로 사용합니다: results = pool.map(compute\_mean\_std, d