{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"}],"dockerImageVersionId":30746,"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},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport matplotlib.pyplot as plt","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tf.__version__","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\ntrain","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nX_train = train.iloc[:,1:-1]\ny = train.iloc[:,0]\nX_train[1]","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:51:28.639397Z","iopub.execute_input":"2024-08-11T16:51:28.639946Z","iopub.status.idle":"2024-08-11T16:51:28.864104Z","shell.execute_reply.started":"2024-08-11T16:51:28.639906Z","shell.execute_reply":"2024-08-11T16:51:28.861981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = tf.keras.utils.normalize(X_train,axis =1)\nX_test = tf.keras.utils.normalize(X_test,axis =1)\nX_train","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:40:12.865577Z","iopub.execute_input":"2024-08-11T16:40:12.866032Z","iopub.status.idle":"2024-08-11T16:40:13.132585Z","shell.execute_reply.started":"2024-08-11T16:40:12.865999Z","shell.execute_reply":"2024-08-11T16:40:13.131080Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dense(128,activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(128,activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(10,activation = tf.nn.softmax))\n\nmodel.compile(optimizer = 'adam',\n             loss = 'sparse_categorical_crossentropy',\n             metrics = ['accuracy'])\n\nmodel.fit(X_train,y,epochs = 5)","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:46:29.550401Z","iopub.execute_input":"2024-08-11T16:46:29.550968Z","iopub.status.idle":"2024-08-11T16:47:01.742204Z","shell.execute_reply.started":"2024-08-11T16:46:29.550925Z","shell.execute_reply":"2024-08-11T16:47:01.740725Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred = model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:48:20.848086Z","iopub.execute_input":"2024-08-11T16:48:20.848560Z","iopub.status.idle":"2024-08-11T16:48:21.069539Z","shell.execute_reply.started":"2024-08-11T16:48:20.848525Z","shell.execute_reply":"2024-08-11T16:48:21.067174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}