L07_Simple_CNN & InceptionV3.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"FXLIwCGm4RAg"},"source":[]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":529,"status":"ok","timestamp":1720484908889,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"UEvgeIA85Cjb","outputId":"76a1570d-a113-4025-f942-73537834f5ef"},"outputs":[{"data":{"text/plain":["'/Users/seoseondeok/study/ESG_AI'"]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["%pwd"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"AoMqBy-H5HNX"},"outputs":[],"source":["import os\n","import cv2\n","import numpy as np \n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n","\n","from tensorflow.keras.utils import to_categorical\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n","from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["GPU is available\n"]}],"source":["# GPU 설정 및 사용 가능 체크\n","physical_devices = tf.config.list_physical_devices('GPU')\n","if physical_devices:\n","    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n","    print(\"GPU is available\")\n","else:\n","    print(\"GPU is not available, using CPU\")"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["# 그래픽카드 관련\n","import gc\n","class MemoryCleanupCallback(tf.keras.callbacks.Callback):\n","    def on_epoch_end(self, epoch, logs=None):\n","        gc.collect()\n","        tf.keras.backend.clear_session()\n","        print(\"GPU memory was cleaned!\")"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"zg2PmKLB5gbw"},"outputs":[],"source":["def load_and_preprocess_data(base_dir, img_size=(299, 299)):\n","    images = [] # image data를 모으는 빈 리스트 생성\n","    labels = [] # image data의 label(class)를 저장할 리스트 생성\n","    categories = ['NORMAL', 'PNEUMONIA']  # 카테고리 종류\n","\n","    for category in categories: # NORMAL, PENUMONIA\n","        class_num = categories.index(category)  # Normal: 0, Penumonia: 1\n","        base_path = os.path.join(base_dir, category)  # 데이터 구조:\n","        base_files = os.listdir(base_path)  # train폴더에서 category에 맞는 데이터 받아옴\n","        np.random.shuffle(base_files) # 데이터를 랜덤하게 섞음\n","\n","        for idx in range(500):  # 각 카테고리 별 500장씩 받아옴\n","          image_path = os.path.join(base_path, base_files[idx]) #\n","          image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)  # grayscale로 이미지 데이터 받아오기\n","          image = cv2.resize(image, img_size) # 이미지 사이즈 299x299로 resize\n","          images.append(image)  # 'NORMAL' 500장, 'PNEUMONIA' 500장\n","          labels.append(class_num)\n","\n","    # -1: 개수만큼 다 집어넣으라는 의미\n","    # 이미지 사이즈\n","    # grayscale: 1  영상 데이터에서 grayscale의 채널 입력값은 1, rgb라면 3\n","    # images = np.array(images).reshape(-1, 이미지 사이즈, 채널 입력값)\n","    images = np.array(images).reshape(-1, img_size[0], img_size[1], 1)\n","    return images, np.array(labels) # 리스트 데이터를\n","\n","def split_data(X, y, test_size=0.2, val_size=0.5):\n","    X_train, X_val_test, y_train, y_val_test = train_test_split(X,  # image data\n","                                                                y,  # label data\n","                                                                test_size=test_size, # X_train, y_train에 80%의 데이터 입력\n","                                                                stratify=y,  # normal, pneumonia 클래스의 비율을 맞춰서 분할하기 위함\n","                                                                random_state=42)  # 랜덤시드 설정\n","    X_val, X_test, y_val, y_test = train_test_split(X_val_test,\n","                                                    y_val_test,\n","                                                    test_size=val_size,  # 남은 20% 데이터를 1/2로 나눔 -> val:test = 1:1 (10% : 10%)\n","                                                    stratify=y_val_test,\n","                                                    random_state=42)\n","    return X_train, y_train, X_val, y_val, X_test, y_test"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":244552,"status":"ok","timestamp":1720486175254,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"8YcMIJMW5mWT","outputId":"711989f8-48b7-4437-829d-f71f2eb5112e"},"outputs":[{"name":"stdout","output_type":"stream","text":["데이터 로딩 및 전처리 완료\n","훈련 데이터 형태: (800, 299, 299, 1), 레이블 형태: (800,)\n","검증 데이터 형태: (100, 299, 299, 1), 레이블 형태: (100,)\n","테스트 데이터 형태: (100, 299, 299, 1), 레이블 형태: (100,)\n"]}],"source":["base_dir = 'datasets/Pneumonia/train' # Pneumonia의 train 폴더 경로\n","X, y = load_and_preprocess_data(base_dir) # 1000장 데이터 로드\n","\n","X_train, y_train, X_val, y_val, X_test, y_test = split_data(X, y) # train, val, test data split\n","\n","print(\"데이터 로딩 및 전처리 완료\")\n","print(f\"훈련 데이터 형태: {X_train.shape}, 레이블 형태: {y_train.shape}\")\n","print(f\"검증 데이터 형태: {X_val.shape}, 레이블 형태: {y_val.shape}\")\n","print(f\"테스트 데이터 형태: {X_test.shape}, 레이블 형태: {y_test.shape}\")"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"HWm0v9qD8-Rh"},"outputs":[],"source":["def create_simple_cnn(input_shape):\n","    model = Sequential([\n","        Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), # 입력층\n","        MaxPooling2D((2, 2)),   # Maxpooling\n","        Conv2D(64, (3, 3), activation='relu'),\n","        MaxPooling2D((2, 2)),\n","        Conv2D(64, (3, 3), activation='relu'),\n","        Flatten(),  # 가장 큰 특징을 모아서 1차원 배열로 변환\n","        Dense(64, activation='relu'),\n","        Dense(1, activation='sigmoid')  # sigmoid는 이진 분류를 위한 활성화함수(단일클래스)\n","        # 두 개의 클래스의 경우?\n","        # Dense(3, activation='softmax')\n","    ])\n","    return model"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1370,"status":"ok","timestamp":1720487477746,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"okdIeTg0CBmA","outputId":"fe93459b-2ab4-4c49-8869-b40a691af6b5"},"outputs":[{"name":"stderr","output_type":"stream","text":["2024-07-09 16:20:02.233738: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n","2024-07-09 16:20:02.235653: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"]},{"name":"stdout","output_type":"stream","text":["Metal device set to: Apple M2\n","\n","systemMemory: 8.00 GB\n","maxCacheSize: 2.67 GB\n","\n","Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 297, 297, 32)      320       \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 148, 148, 32)     0         \n"," )                                                               \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 146, 146, 64)      18496     \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 73, 73, 64)       0         \n"," 2D)                                                             \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 71, 71, 64)        36928     \n","                                                                 \n"," flatten (Flatten)           (None, 322624)            0         \n","                                                                 \n"," dense (Dense)               (None, 64)                20648000  \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 20,703,809\n","Trainable params: 20,703,809\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["input_shape = (299, 299, 1)  # grayscale: 1\n","model = create_simple_cnn(input_shape)  # 모델 생성 (input_shape에 맞게)\n","\n","model.compile(optimizer='adam', # Optimizer: adam 사용\n","              loss='binary_crossentropy', # sigmoid를 사용했기 때문에 binary loss 사용\n","              metrics=['accuracy']) # 평가 metrics: accuracy\n","\n","early_stopping = EarlyStopping(   # 특정 조건을 만족하면 stop을 하도록 조건을 추가\n","    monitor='val_loss',\n","    patience=10,  # 가중치 변화가 없는 지 확인하는 횟수\n","    verbose=1,\n","    mode='min',   # loss 값은 최소값으로 찾기\n","    restore_best_weights=True\n",")\n","\n","model_checkpoint = ModelCheckpoint(   # 가중치를 저장\n","    'best_model.h5',\n","    monitor='val_accuracy',\n","    mode='max',   # validation 가장 큰 값\n","    verbose=1,\n","    save_best_only=True\n",")\n","\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":42989,"status":"ok","timestamp":1720487822413,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"zsHRcTlHCES8","outputId":"6b9bfa61-4bd1-41ab-a711-100531cf3e6b"},"outputs":[],"source":["history = model.fit(\n","    X_train, y_train, # train data\n","    validation_data=(X_val, y_val), # validation data\n","    epochs=50, # 반복 횟수\n","    batch_size=32,  # 배치 사이즈\n","    callbacks=[early_stopping, model_checkpoint]  # 학습 stop 조건, 가중치 파일 저장\n",")"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"XPKue5klD4sO"},"outputs":[],"source":["model = tf.keras.models.load_model('best_model.h5') # 학습 후 가장 높은 성능을 보이는 가중치를 가져오기"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1169,"status":"ok","timestamp":1720488041588,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"RjlpSjGQE28M","outputId":"08fa97d5-7cd0-4d08-c42c-d9e613051dd7"},"outputs":[{"name":"stderr","output_type":"stream","text":["2024-07-09 16:23:01.254686: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"]},{"name":"stdout","output_type":"stream","text":["4/4 [==============================] - 1s 73ms/step - loss: 0.4450 - accuracy: 0.8600\n","Test Accuracy: 0.860000\n"]}],"source":["test_loss, test_acc = model.evaluate(X_test, y_test)\n","print(f\"Test Accuracy: {test_acc:4f}\")"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":497,"status":"ok","timestamp":1720488148137,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"cTHLgNaNFBVh","outputId":"70dbb0a0-0853-4b11-eb08-9e6395d4947f"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/4 [======>.......................] - ETA: 0s"]},{"name":"stderr","output_type":"stream","text":["2024-07-09 16:23:05.070797: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"]},{"name":"stdout","output_type":"stream","text":["4/4 [==============================] - 1s 147ms/step\n"]}],"source":["y_pred = model.predict(X_test)\n","y_pred_classes = (y_pred > 0.5).astype(int).flatten() # 확률값이 0.5 초과는 1로 저장할 것"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"elapsed":1408,"status":"ok","timestamp":1720488354400,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"uooWnFFEFbg3","outputId":"c0d4db85-c5d5-4c46-f66d-fcb0e5ccfbfb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# confusion matrix\n","cm = confusion_matrix(y_test, y_pred_classes)\n","\n","plt.figure(figsize=(5,5))\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n","plt.title(\"Confusion Matrix\")\n","plt.xlabel('True')\n","plt.ylabel('Predicted')\n","plt.show()"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":1501,"status":"ok","timestamp":1720488534128,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"CJIH2PhPGNo8","outputId":"cc74a25a-5f27-4d37-cd6e-650975a3913e"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x400 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Model Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history.history['loss'], label='Training Loss')\n","plt.plot(history.history['val_loss'], label='Validation Loss')\n","plt.title('Model Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"GinynwIqHylP"},"source":["### InceptionV3"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TgqnQoaxG5gi"},"outputs":[],"source":["from tensorflow.keras.applications import InceptionV3\n","from tensorflow.keras.layers import GlobalAveragePooling2D\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M-NDFz9UIKBb"},"outputs":[],"source":["# Inception 모델은 channel value가 3이어야 함(rgb)\n","# 데이터가 grayscale이라면? -> r,g,b 값에 모두 동일한 grayscale 값을 넣어주는 방법\n","def create_inceptionv3_model(input_shape):\n","    base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=input_shape)\n","    # include_top: 전이 학습\n","    # True: 시작부터 완전 연결 계층까지 그대로 가져다 사용\n","    # False: 그대로 사용하지 않겠다는 의미\n","    base_model.trainable = False\n","\n","    # 완전 연결 계층(inference)\n","    x = base_model.output\n","    x = GlobalAveragePooling2D()(x)\n","    x = Dense(1024, activation='relu')(x)\n","    output = Dense(1, activation='sigmoid')(x)\n","\n","    model = Model(inputs=base_model.input, outputs=output)\n","    return model"]},{"cell_type":"markdown","metadata":{"id":"m0oEAMDuJxhq"},"source":["### Inception data 처리\n","- channel 1 -> 3"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21601,"status":"ok","timestamp":1720490892327,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"xeQ1JdGGIKSP","outputId":"f70961d2-fc51-4544-e559-1fcdce6083f0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n","87910968/87910968 [==============================] - 5s 0us/step\n"]}],"source":["# 데이터 차원을 3차원으로 수정\n","X_train_rgb = np.repeat(X_train, 3, axis=-1)\n","X_val_rgb = np.repeat(X_val, 3, axis=-1)\n","X_test_rgb = np.repeat(X_test, 3, axis=-1)\n","\n","# Inception용 전처리\n","X_train_preprocessed = tf.keras.applications.inception_v3.preprocess_input(X_train_rgb)\n","X_val_preprocessed = tf.keras.applications.inception_v3.preprocess_input(X_val_rgb)\n","X_test_preprocessed = tf.keras.applications.inception_v3.preprocess_input(X_test_rgb)\n","\n","input_shape = (299,299,3)\n","model = create_inceptionv3_model(input_shape)\n","model.compile(optimizer=Adam(learning_rate=0.001),\n","              loss='binary_crossentropy',\n","              metrics=['accuracy'])\n","\n","callbacks = [\n","    tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),\n","    tf.keras.callbacks.ModelCheckpoint('best_inception_model.h5', save_best_only=True)\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":138022,"status":"ok","timestamp":1720491086286,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"DU-5fgypLp41","outputId":"7c786c38-32c1-4117-b377-878caaef27e9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/50\n","25/25 [==============================] - ETA: 0s - loss: 0.7319 - accuracy: 0.7412"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]},{"name":"stdout","output_type":"stream","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r25/25 [==============================] - 18s 353ms/step - loss: 0.7319 - accuracy: 0.7412 - val_loss: 0.2643 - val_accuracy: 0.8900\n","Epoch 2/50\n","25/25 [==============================] - 5s 192ms/step - loss: 0.2085 - accuracy: 0.9038 - val_loss: 0.2197 - val_accuracy: 0.9100\n","Epoch 3/50\n","25/25 [==============================] - 5s 197ms/step - loss: 0.1674 - accuracy: 0.9262 - val_loss: 0.2060 - val_accuracy: 0.9200\n","Epoch 4/50\n","25/25 [==============================] - 5s 188ms/step - loss: 0.1318 - accuracy: 0.9500 - val_loss: 0.1953 - val_accuracy: 0.9200\n","Epoch 5/50\n","25/25 [==============================] - 5s 187ms/step - loss: 0.1112 - accuracy: 0.9538 - val_loss: 0.1651 - val_accuracy: 0.9200\n","Epoch 6/50\n","25/25 [==============================] - 4s 169ms/step - loss: 0.1046 - accuracy: 0.9650 - val_loss: 0.2044 - val_accuracy: 0.9200\n","Epoch 7/50\n","25/25 [==============================] - 4s 149ms/step - loss: 0.0884 - accuracy: 0.9638 - val_loss: 0.2308 - val_accuracy: 0.9300\n","Epoch 8/50\n","25/25 [==============================] - 5s 204ms/step - loss: 0.0853 - accuracy: 0.9638 - val_loss: 0.1344 - val_accuracy: 0.9300\n","Epoch 9/50\n","25/25 [==============================] - 4s 181ms/step - loss: 0.0644 - accuracy: 0.9750 - val_loss: 0.1440 - val_accuracy: 0.9400\n","Epoch 10/50\n","25/25 [==============================] - 5s 191ms/step - loss: 0.0519 - accuracy: 0.9837 - val_loss: 0.1312 - val_accuracy: 0.9300\n","Epoch 11/50\n","25/25 [==============================] - 4s 154ms/step - loss: 0.0467 - accuracy: 0.9900 - val_loss: 0.1320 - val_accuracy: 0.9500\n","Epoch 12/50\n","25/25 [==============================] - 5s 203ms/step - loss: 0.0414 - accuracy: 0.9887 - val_loss: 0.1208 - val_accuracy: 0.9400\n","Epoch 13/50\n","25/25 [==============================] - 4s 155ms/step - loss: 0.0346 - accuracy: 0.9925 - val_loss: 0.1697 - val_accuracy: 0.9400\n","Epoch 14/50\n","25/25 [==============================] - 4s 149ms/step - loss: 0.0254 - accuracy: 0.9975 - val_loss: 0.1237 - val_accuracy: 0.9400\n","Epoch 15/50\n","25/25 [==============================] - 4s 156ms/step - loss: 0.0206 - accuracy: 0.9987 - val_loss: 0.1657 - val_accuracy: 0.9400\n","Epoch 16/50\n","25/25 [==============================] - 4s 157ms/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.1652 - val_accuracy: 0.9500\n","Epoch 17/50\n","25/25 [==============================] - 6s 225ms/step - loss: 0.0216 - accuracy: 0.9950 - val_loss: 0.1081 - val_accuracy: 0.9300\n","Epoch 18/50\n","25/25 [==============================] - 5s 191ms/step - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9400\n","Epoch 19/50\n","25/25 [==============================] - 5s 184ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9400\n","Epoch 20/50\n","25/25 [==============================] - 5s 200ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9400\n","Epoch 21/50\n","25/25 [==============================] - 5s 183ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9400\n","Epoch 22/50\n","25/25 [==============================] - 5s 187ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9400\n","Epoch 23/50\n","25/25 [==============================] - 5s 209ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9400\n","Epoch 24/50\n","25/25 [==============================] - 4s 164ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9300\n","Epoch 25/50\n","25/25 [==============================] - 4s 156ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9400\n","Epoch 26/50\n","25/25 [==============================] - 4s 153ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9400\n","Epoch 27/50\n","25/25 [==============================] - 4s 167ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9400\n"]}],"source":["history = model.fit(\n","    X_train_preprocessed, y_train, # train data\n","    validation_data=(X_val_preprocessed, y_val), # validation data\n","    epochs=50, # 반복 횟수\n","    batch_size=32,  # 배치 사이즈\n","    callbacks=callbacks\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3UnDbrKGQr7t"},"outputs":[],"source":["model = tf.keras.models.load_model('best_inception_model.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3579,"status":"ok","timestamp":1720491168184,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"Q3eF3LKMQHQy","outputId":"0371cc83-9def-4af9-9a67-a5f6a36f4f5e"},"outputs":[{"name":"stdout","output_type":"stream","text":["4/4 [==============================] - 3s 113ms/step - loss: 0.1729 - accuracy: 0.9300\n","Test accuracy: 0.9300\n"]}],"source":["test_loss, test_acc = model.evaluate(X_test_preprocessed, y_test)\n","print(f\"Test accuracy: {test_acc:.4f}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":505},"executionInfo":{"elapsed":5576,"status":"ok","timestamp":1720491247435,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"onY_ypeYQ8Ft","outputId":"6a3c54af-ef70-4d80-b56b-11206efc0900"},"outputs":[{"name":"stdout","output_type":"stream","text":["4/4 [==============================] - 4s 144ms/step\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["y_pred = model.predict(X_test_preprocessed)\n","y_pred_classes = (y_pred>0.5).astype(int)\n","\n","# confusion matrix\n","cm = confusion_matrix(y_test, y_pred_classes)\n","\n","plt.figure(figsize=(5, 5))\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n","plt.title(\"Confusion Matrix\")\n","plt.xlabel('True')\n","plt.ylabel('Predicted')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":2012,"status":"ok","timestamp":1720491306240,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"-F-N2UCaRO8t","outputId":"07d11709-dd33-4311-ccaf-80a1db83d1cb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x400 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Model Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(history.history['loss'], label='Training Loss')\n","plt.plot(history.history['val_loss'], label='Validation Loss')\n","plt.title('Model Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":705,"status":"ok","timestamp":1720491363121,"user":{"displayName":"서선덕","userId":"08439344677461994483"},"user_tz":-540},"id":"QSOFXQF4ReKu","outputId":"00f8ac2f-e37f-49a9-95de-9cb34c095e95"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from sklearn.metrics import roc_curve, auc\n","\n","# ROC 곡선 계산\n","fpr, tpr, _ = roc_curve(y_test, y_pred)\n","roc_auc = auc(fpr, tpr)\n","\n","# ROC 곡선 그리기\n","plt.figure()\n","plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n","plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver Operating Characteristic (ROC) Curve')\n","plt.legend(loc=\"lower right\")\n","plt.show()"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOsJEE2RoeCHXwcIJ/HgU1F","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.13"}},"nbformat":4,"nbformat_minor":0}