{"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":"none","dataSources":[{"sourceId":5441,"databundleVersionId":38425,"sourceType":"competition"},{"sourceId":477233,"sourceType":"modelInstanceVersion","modelInstanceId":383801,"modelId":403223}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"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":"2025-07-25T07:41:50.881049Z","iopub.execute_input":"2025-07-25T07:41:50.881369Z","iopub.status.idle":"2025-07-25T07:41:50.890926Z","shell.execute_reply.started":"2025-07-25T07:41:50.881344Z","shell.execute_reply":"2025-07-25T07:41:50.889808Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 1. 必要ライブラリのインポート\nimport numpy as np\nimport pandas as pd\nimport os\nfrom PIL import Image\nfrom tensorflow.keras.models import load_model\nimport zipfile\n\n# zipファイルのパス（Kaggle公式コンペのinput内）\nzip_test_path = '/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip'\n\n# 展開先のパス（/kaggle/working に展開する）\nextract_test_path = '/kaggle/working'\n\nwith zipfile.ZipFile(zip_test_path, 'r') as zip_ref:\n    zip_ref.extractall(extract_test_path)\n\nprint(\"✅ test.zip 展開完了\")\n\n\n# 2. 🔽 モデルのロード ← ココ！！\nmodel = load_model('/kaggle/input/dvc/tensorflow2/default/1/datascience.h5')  # 正しいパスにする\n\n# 3. テスト画像の読み込み＆前処理\ntest_dir = '/kaggle/input/dogs-vs-cats-redux-kernels-edition/test'\ntest_images = []\nimage_ids = []\n\nfor fname in os.listdir(test_dir):\n    img = Image.open(os.path.join(test_dir, fname)).convert('L')\n    img = img.resize((128, 128))\n    img_array = np.array(img) / 255.0\n    img_array = img_array.reshape(128, 128, 1)\n    test_images.append(img_array)\n    image_ids.append(int(fname.split('.')[0]))\n\ntest_images = np.array(test_images).astype(np.float32)\n\n# 4. 予測実行\npreds = model.predict(test_images).flatten()\n\n# 5. 提出用CSV作成\nsubmission = pd.DataFrame({'id': image_ids, 'label': preds})\nsubmission = submission.sort_values('id')\nsubmission.to_csv('submission.csv', index=False)\nprint(\"✅ submission.csv を保存しました\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T07:41:50.892847Z","iopub.execute_input":"2025-07-25T07:41:50.893101Z","iopub.status.idle":"2025-07-25T07:41:55.008238Z","shell.execute_reply.started":"2025-07-25T07:41:50.893081Z","shell.execute_reply":"2025-07-25T07:41:55.006782Z"}},"outputs":[],"execution_count":null}]}