{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":28618,"sourceType":"datasetVersion","datasetId":22085},{"sourceId":3286242,"sourceType":"kernelVersion"},{"sourceId":144645814,"sourceType":"kernelVersion"}],"dockerImageVersionId":31192,"isInternetEnabled":false,"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},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"1. 导入库与查看文件路径","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nfrom scipy import stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn.model_selection import train_test_split\n\n# 检查一下GPU是否可用\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"当前使用的设备: {device}\")\n\n# 查看一下数据集里的文件名（看看我们有哪些类别）\n# 注意：根据你添加的数据集不同，这里的路径可能要微调，通常在 /kaggle/input 下\nbase_path = '/kaggle/input/quickdraw-npy/'  # 如果报错，请在右侧Input栏查看真实路径复制过来\nprint(\"部分类别文件:\", os.listdir(base_path)[:5])","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}