import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
 
# ตั้งค่าไดเรกทอรีของข้อมูล
train_dir = "train"
validation_dir = "val"
test_dir = "test"
 
# เพิ่ม Data Augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    vertical_flip=True,  # เพิ่มการพลิกในแนวตั้ง
    brightness_range=[0.8, 1.2],  # ปรับความสว่าง
    fill_mode='nearest'
)
 
validation_datagen = ImageDataGenerator(rescale=1./255)
 
# โหลดข้อมูล
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)
 
validation_generator = validation_datagen.flow_from_directory(
    validation_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary'
)
 
# ปรับปรุงโครงสร้างโมเดล
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    layers.BatchNormalization(),  # เพิ่ม Batch Normalization
    layers.MaxPooling2D(2, 2),
    layers.Dropout(0.25),  # เพิ่ม Dropout
 
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.BatchNormalization(),
    layers.MaxPooling2D(2, 2),
    layers.Dropout(0.25),
 
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.BatchNormalization(),
    layers.MaxPooling2D(2, 2),
    layers.Dropout(0.25),
 
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.BatchNormalization(),
    layers.Dropout(0.5),  # เพิ่ม Dropout ใน Fully Connected Layer
    layers.Dense(1, activation='sigmoid')
])
 
# กำหนดออปติไมเซอร์และค่าพารามิเตอร์
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='binary_crossentropy',
    metrics=['accuracy']
)
 
# ใช้ ReduceLROnPlateau เพื่อลด Learning Rate เมื่อโมเดลเริ่มหยุดพัฒนา
reduce_lr = keras.callbacks.ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.5,
    patience=3,
    min_lr=1e-6
)
 
# ฝึกโมเดล
history = model.fit(
    train_generator,
    validation_data=validation_generator,
    epochs=30,
    callbacks=[reduce_lr]
)
 
# แสดงกราฟผลลัพธ์
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.legend()
plt.title('Accuracy')
 
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.title('Loss')
 
plt.show()

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