{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":5441,"databundleVersionId":38425,"sourceType":"competition"}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:01:38.660168Z","iopub.execute_input":"2025-07-20T09:01:38.660804Z","iopub.status.idle":"2025-07-20T09:01:38.914551Z","shell.execute_reply.started":"2025-07-20T09:01:38.660762Z","shell.execute_reply":"2025-07-20T09:01:38.913994Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import os\nimport shutil\nimport glob\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\nfrom tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\nfrom tensorflow.keras.optimizers import Adam","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:01:38.915752Z","iopub.execute_input":"2025-07-20T09:01:38.916077Z","iopub.status.idle":"2025-07-20T09:01:57.967912Z","shell.execute_reply.started":"2025-07-20T09:01:38.916059Z","shell.execute_reply":"2025-07-20T09:01:57.967338Z"}},"outputs":[{"name":"stderr","text":"2025-07-20 09:01:41.808542: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1753002102.164611      36 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1753002102.261278      36 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"# データの解凍\nshutil.unpack_archive('/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip', '/kaggle/working')\nshutil.unpack_archive('/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip', '/kaggle/working')\nshutil.copyfile('/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv','/kaggle/working/sample_submission.csv')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:01:57.968651Z","iopub.execute_input":"2025-07-20T09:01:57.969039Z","iopub.status.idle":"2025-07-20T09:02:11.499861Z","shell.execute_reply.started":"2025-07-20T09:01:57.969021Z","shell.execute_reply":"2025-07-20T09:02:11.499277Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/sample_submission.csv'"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"# 犬猫のフォルダを作って振り分け\ndog_dir = '/kaggle/working/train/dog'\ncat_dir = '/kaggle/working/train/cat'\nos.makedirs(dog_dir, exist_ok=True)\nos.makedirs(cat_dir, exist_ok=True)\n\nfiles = glob.glob('/kaggle/working/train/*.jpg')\nfor file in files:\n    file_name = os.path.basename(file)\n    if 'cat' in file_name:\n        shutil.move(file, os.path.join(cat_dir, file_name))\n    else:\n        shutil.move(file, os.path.join(dog_dir, file_name))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:11.501701Z","iopub.execute_input":"2025-07-20T09:02:11.501936Z","iopub.status.idle":"2025-07-20T09:02:12.201434Z","shell.execute_reply.started":"2025-07-20T09:02:11.501919Z","shell.execute_reply":"2025-07-20T09:02:12.200629Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# 設定\nimg_width, img_height = 224, 224\nbatch_size = 32\nepochs = 15\ntrain_dir = '/kaggle/working/train'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:12.202186Z","iopub.execute_input":"2025-07-20T09:02:12.202415Z","iopub.status.idle":"2025-07-20T09:02:12.206301Z","shell.execute_reply.started":"2025-07-20T09:02:12.202398Z","shell.execute_reply":"2025-07-20T09:02:12.205606Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# データ拡張（Augmentation）\ntrain_datagen = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=30,\n    shear_range=0.2,\n    zoom_range=0.2,\n    horizontal_flip=True,\n    validation_split=0.2\n)\ntrain_generator = train_datagen.flow_from_directory(\n    train_dir,\n    target_size=(img_height, img_width),\n    batch_size=batch_size,\n    class_mode='binary',\n    subset='training'\n)\n\nvalidation_generator = train_datagen.flow_from_directory(\n    train_dir,\n    target_size=(img_height, img_width),\n    batch_size=batch_size,\n    class_mode='binary',\n    subset='validation'\n)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:12.207061Z","iopub.execute_input":"2025-07-20T09:02:12.207362Z","iopub.status.idle":"2025-07-20T09:02:12.462965Z","shell.execute_reply.started":"2025-07-20T09:02:12.207339Z","shell.execute_reply":"2025-07-20T09:02:12.46226Z"}},"outputs":[{"name":"stdout","text":"Found 20000 images belonging to 2 classes.\nFound 5000 images belonging to 2 classes.\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# モデル構築（VGG16ベース）\nvgg_base = VGG16(include_top=False, weights='imagenet', input_shape=(img_height, img_width, 3))\nvgg_base.trainable = False  # 全層凍結\n\nmodel = Sequential([\n    vgg_base,\n    Flatten(),\n    Dense(256, activation='relu'),\n    Dropout(0.5),\n    Dense(1, activation='sigmoid')  # 2クラス分類\n])\n\nmodel.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:12.463815Z","iopub.execute_input":"2025-07-20T09:02:12.46441Z","iopub.status.idle":"2025-07-20T09:02:16.214263Z","shell.execute_reply.started":"2025-07-20T09:02:12.464389Z","shell.execute_reply":"2025-07-20T09:02:16.21365Z"}},"outputs":[{"name":"stderr","text":"I0000 00:00:1753002134.022985      36 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13942 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\nI0000 00:00:1753002134.023676      36 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13942 MB memory:  -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n","output_type":"stream"},{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"# コールバック設定\ncheckpoint_cb = ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True, verbose=1)\nearly_cb = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True, verbose=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:16.214952Z","iopub.execute_input":"2025-07-20T09:02:16.215171Z","iopub.status.idle":"2025-07-20T09:02:16.219074Z","shell.execute_reply.started":"2025-07-20T09:02:16.215155Z","shell.execute_reply":"2025-07-20T09:02:16.218383Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"# 学習\nhistory = model.fit(\n    train_generator,\n    epochs=epochs,\n    validation_data=validation_generator,\n    callbacks=[checkpoint_cb, early_cb]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T09:02:16.219894Z","iopub.execute_input":"2025-07-20T09:02:16.22015Z","iopub.status.idle":"2025-07-20T10:11:55.02635Z","shell.execute_reply.started":"2025-07-20T09:02:16.220127Z","shell.execute_reply":"2025-07-20T10:11:55.025789Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n  self._warn_if_super_not_called()\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/15\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1753002139.552985     111 service.cc:148] XLA service 0x7e1b4c00d6d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1753002139.554463     111 service.cc:156]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\nI0000 00:00:1753002139.554486     111 service.cc:156]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5\nI0000 00:00:1753002140.026485     111 cuda_dnn.cc:529] Loaded cuDNN version 90300\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m  1/625\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:34:44\u001b[0m 15s/step - accuracy: 0.4375 - loss: 0.7643","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1753002151.762553     111 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356ms/step - accuracy: 0.8176 - loss: 0.3823\nEpoch 1: val_loss improved from inf to 0.24874, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m301s\u001b[0m 458ms/step - accuracy: 0.8176 - loss: 0.3822 - val_accuracy: 0.8894 - val_loss: 0.2487\nEpoch 2/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step - accuracy: 0.9020 - loss: 0.2373\nEpoch 2: val_loss improved from 0.24874 to 0.21644, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m285s\u001b[0m 456ms/step - accuracy: 0.9020 - loss: 0.2373 - val_accuracy: 0.9080 - val_loss: 0.2164\nEpoch 3/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.9074 - loss: 0.2211\nEpoch 3: val_loss improved from 0.21644 to 0.21087, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m277s\u001b[0m 443ms/step - accuracy: 0.9074 - loss: 0.2211 - val_accuracy: 0.9090 - val_loss: 0.2109\nEpoch 4/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355ms/step - accuracy: 0.9090 - loss: 0.2120\nEpoch 4: val_loss did not improve from 0.21087\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m277s\u001b[0m 443ms/step - accuracy: 0.9090 - loss: 0.2120 - val_accuracy: 0.9098 - val_loss: 0.2163\nEpoch 5/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 352ms/step - accuracy: 0.9173 - loss: 0.2019\nEpoch 5: val_loss did not improve from 0.21087\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m276s\u001b[0m 441ms/step - accuracy: 0.9173 - loss: 0.2019 - val_accuracy: 0.9004 - val_loss: 0.2226\nEpoch 6/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.9174 - loss: 0.1992\nEpoch 6: val_loss improved from 0.21087 to 0.19587, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m281s\u001b[0m 449ms/step - accuracy: 0.9174 - loss: 0.1992 - val_accuracy: 0.9168 - val_loss: 0.1959\nEpoch 7/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 351ms/step - accuracy: 0.9243 - loss: 0.1842\nEpoch 7: val_loss improved from 0.19587 to 0.19157, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m275s\u001b[0m 439ms/step - accuracy: 0.9243 - loss: 0.1842 - val_accuracy: 0.9182 - val_loss: 0.1916\nEpoch 8/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349ms/step - accuracy: 0.9228 - loss: 0.1829\nEpoch 8: val_loss did not improve from 0.19157\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m272s\u001b[0m 435ms/step - accuracy: 0.9228 - loss: 0.1829 - val_accuracy: 0.9142 - val_loss: 0.2033\nEpoch 9/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356ms/step - accuracy: 0.9229 - loss: 0.1869\nEpoch 9: val_loss improved from 0.19157 to 0.18786, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m278s\u001b[0m 444ms/step - accuracy: 0.9229 - loss: 0.1869 - val_accuracy: 0.9206 - val_loss: 0.1879\nEpoch 10/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 351ms/step - accuracy: 0.9270 - loss: 0.1817\nEpoch 10: val_loss did not improve from 0.18786\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m275s\u001b[0m 440ms/step - accuracy: 0.9270 - loss: 0.1817 - val_accuracy: 0.9222 - val_loss: 0.1955\nEpoch 11/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362ms/step - accuracy: 0.9261 - loss: 0.1772\nEpoch 11: val_loss did not improve from 0.18786\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m282s\u001b[0m 451ms/step - accuracy: 0.9261 - loss: 0.1772 - val_accuracy: 0.9188 - val_loss: 0.1996\nEpoch 12/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.9256 - loss: 0.1788\nEpoch 12: val_loss improved from 0.18786 to 0.18218, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m277s\u001b[0m 443ms/step - accuracy: 0.9256 - loss: 0.1788 - val_accuracy: 0.9230 - val_loss: 0.1822\nEpoch 13/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.9298 - loss: 0.1684\nEpoch 13: val_loss did not improve from 0.18218\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m277s\u001b[0m 443ms/step - accuracy: 0.9298 - loss: 0.1684 - val_accuracy: 0.9218 - val_loss: 0.1854\nEpoch 14/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.9311 - loss: 0.1655\nEpoch 14: val_loss did not improve from 0.18218\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 439ms/step - accuracy: 0.9311 - loss: 0.1655 - val_accuracy: 0.9202 - val_loss: 0.1828\nEpoch 15/15\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 348ms/step - accuracy: 0.9365 - loss: 0.1531\nEpoch 15: val_loss improved from 0.18218 to 0.17967, saving model to best_model.h5\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m273s\u001b[0m 436ms/step - accuracy: 0.9365 - loss: 0.1531 - val_accuracy: 0.9264 - val_loss: 0.1797\nRestoring model weights from the end of the best epoch: 15.\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"# グラフ表示\nplt.plot(history.history['loss'], label='loss', marker='o')\nplt.plot(history.history['val_loss'], label='val_loss', marker='x')\nplt.xlabel('Epoch'); plt.ylabel('Loss'); plt.legend(); plt.grid(); plt.show()\n\nplt.plot(history.history['accuracy'], label='accuracy', marker='o')\nplt.plot(history.history['val_accuracy'], label='val_accuracy', marker='x')\nplt.xlabel('Epoch'); plt.ylabel('Accuracy'); plt.legend(); plt.grid(); plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T10:11:55.028244Z","iopub.execute_input":"2025-07-20T10:11:55.028982Z","iopub.status.idle":"2025-07-20T10:11:55.511471Z","shell.execute_reply.started":"2025-07-20T10:11:55.028962Z","shell.execute_reply":"2025-07-20T10:11:55.510836Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"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size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"# テスト画像から予測（ファイル名にID含む前提）\ntest_dir = '/kaggle/working/test'\ntest_files = sorted(glob.glob(os.path.join(test_dir, '*.jpg')))\n\nmodel = load_model('best_model.h5')\n\nresults = []\nfor file in test_files:\n    img = load_img(file, target_size=(img_height, img_width))\n    x = img_to_array(img)\n    x = np.expand_dims(x, axis=0)\n    x = x / 255.0\n    pred = model.predict(x)[0][0]\n    image_id = int(os.path.basename(file).split('.')[0])\n    results.append([image_id, pred])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T10:11:55.512229Z","iopub.execute_input":"2025-07-20T10:11:55.512496Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 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history.history['val_accuracy'][-1])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-20T10:43:57.825682Z","iopub.execute_input":"2025-07-20T10:43:57.825954Z","iopub.status.idle":"2025-07-20T10:43:57.830613Z","shell.execute_reply.started":"2025-07-20T10:43:57.825933Z","shell.execute_reply":"2025-07-20T10:43:57.829908Z"}},"outputs":[{"name":"stdout","text":"最終訓練精度: 0.9325000047683716\n最終検証精度: 0.9264000058174133\n","output_type":"stream"}],"execution_count":15}]}