{"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"}},"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","execution":{"iopub.status.busy":"2023-03-15T03:36:04.72548Z","iopub.execute_input":"2023-03-15T03:36:04.726269Z","iopub.status.idle":"2023-03-15T03:37:36.345875Z","shell.execute_reply.started":"2023-03-15T03:36:04.726228Z","shell.execute_reply":"2023-03-15T03:37:36.344938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"articles = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/articles.csv\")\narticles\n","metadata":{"execution":{"iopub.status.busy":"2023-03-15T03:38:36.185462Z","iopub.execute_input":"2023-03-15T03:38:36.186799Z","iopub.status.idle":"2023-03-15T03:38:36.922374Z","shell.execute_reply.started":"2023-03-15T03:38:36.186737Z","shell.execute_reply":"2023-03-15T03:38:36.920969Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"transactions_train = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv\")\ntransactions_train","metadata":{"execution":{"iopub.status.busy":"2023-03-15T03:39:38.084679Z","iopub.execute_input":"2023-03-15T03:39:38.085884Z","iopub.status.idle":"2023-03-15T03:40:50.952199Z","shell.execute_reply.started":"2023-03-15T03:39:38.085833Z","shell.execute_reply":"2023-03-15T03:40:50.950971Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\n\nprint(\"--> Checking for physical Tensorflow devices\")\nfor device in tf.config.list_physical_devices():\n    print(\": {}\".format(device.name))","metadata":{"execution":{"iopub.status.busy":"2023-03-15T03:50:16.308Z","iopub.execute_input":"2023-03-15T03:50:16.309842Z","iopub.status.idle":"2023-03-15T03:50:25.75574Z","shell.execute_reply.started":"2023-03-15T03:50:16.309786Z","shell.execute_reply":"2023-03-15T03:50:25.75391Z"},"trusted":true},"execution_count":null,"outputs":[]}]}