Upload train_efficientnetb0_finetuned.py

train_efficientnetb0_finetuned.py

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+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetB0
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
+import numpy as np
+
+TRAIN_DIR = r'C:\Python_CV\mritest\Training'
+TEST_DIR = r'C:\Python_CV\mritest\Testing'
+IMG_SIZE = (224, 224)
+BATCH_SIZE = 16  # ลด Batch Size ลงเพื่อเพิ่ม Generalization
+
+# 1. Data Augmentation แบบเข้มข้น
+train_datagen = ImageDataGenerator(
+    preprocessing_function=preprocess_input,
+    rotation_range=15,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    shear_range=0.1,
+    zoom_range=0.15,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
+
+train_gen = train_datagen.flow_from_directory(
+    TRAIN_DIR, target_size=IMG_SIZE, batch_size=BATCH_SIZE, class_mode='categorical', shuffle=True
+)
+
+test_gen = test_datagen.flow_from_directory(
+    TEST_DIR, target_size=IMG_SIZE, batch_size=BATCH_SIZE, class_mode='categorical', shuffle=False
+)
+
+# 2. สร้างโครงสร้างโมเดล
+base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+
+# Unfreeze 40 ชั้นสุดท้ายของ EfficientNetB0 เพื่อ Fine-Tune
+base_model.trainable = True
+for layer in base_model.layers[:-40]:
+    layer.trainable = False
+
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+x = BatchNormalization()(x)
+x = Dense(256, activation='relu')(x)
+x = Dropout(0.4)(x)  # ลด Overfitting
+outputs = Dense(4, activation='softmax')(x)
+
+model = Model(inputs=base_model.input, outputs=outputs)
+
+# 3. คอมไพล์โมเดลด้วย Learning Rate ต่ำสำหรับ Fine-tuning
+model.compile(
+    optimizer=Adam(learning_rate=1e-4),
+    loss='categorical_crossentropy',
+    metrics=['accuracy']
+)
+
+# 4. Callbacks ปรับ Learning Rate อัตโนมัติเมื่อ Accuracy เริ่มนิ่ง
+callbacks = [
+    ReduceLROnPlateau(monitor='val_accuracy', factor=0.3, patience=3, verbose=1, min_lr=1e-6),
+    EarlyStopping(monitor='val_accuracy', patience=7, restore_best_weights=True)
+]
+
+# 5. เทรนโมเดล
+print("Starting Fine-Tuning Training...")
+history = model.fit(
+    train_gen,
+    epochs=25,
+    validation_data=test_gen,
+    callbacks=callbacks
+)
+
+# 6. ประเมินผลความแม่นยำ
+test_loss, test_acc = model.evaluate(test_gen)
+print(f"\n>>> Final Test Accuracy: {test_acc * 100:.2f}% <<<")
+
+# บันทึกโมเดลไว้ในโฟลเดอร์โครงการ
+model.save(r'C:\Python_CV\mritest\efficientnetb0_finetuned_brain_mri.keras')
+print("เซฟโมเดลแบบ .keras เรียบร้อยแล้ว!")
+
+from sklearn.metrics import classification_report, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+# 1. พยากรณ์ผลบนชุด Testing Data
+test_gen.reset()
+y_pred_prob = model.predict(test_gen, verbose=1)
+y_pred = np.argmax(y_pred_prob, axis=1)
+y_true = test_gen.classes
+class_labels = list(test_gen.class_indices.keys())
+
+# 2. พิมพ์รายงาน Classification Report (Precision, Recall, F1-score)
+print("\n================ Classification Report ================")
+print(classification_report(y_true, y_pred, target_names=class_labels))
+
+# 3. วาดกราฟ Confusion Matrix
+cm = confusion_matrix(y_true, y_pred)
+plt.figure(figsize=(8, 6))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
+            xticklabels=class_labels, yticklabels=class_labels)
+plt.title('Fine-Tuned EfficientNetB0 - Confusion Matrix')
+plt.xlabel('Predicted Label')
+plt.ylabel('True Label')
+plt.show()
+
+# พล็อต กราฟ Loss & Accuracy
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs_range = range(len(acc))
+
+plt.figure(figsize=(12, 5))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation/Test Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation/Test Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+