{"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)\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\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\n\n# reading train data\ntrain=pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\n\n# reading test data\ntest=pd.read_csv('/kaggle/input/digit-recognizer/test.csv') \n\n# X_train, X_test, y_train, y_test = train_test_split(train.drop(['label'], axis=1), train.label, test_size=0.1)\n\nX_train = train.drop(['label'], axis=1)\nX_test = test\ny_train = train.label\n\n#print(X_train)\n#print(y_train)\n\n# print(X_train.shape)\n\nX_train = X_train.to_numpy().reshape(X_train.shape[0], 28, 28, 1)\nX_test = X_test.to_numpy().reshape(X_test.shape[0], 28, 28, 1)\n\n# print(y_train.shape)\n# print(y_train)\n\n\n\nmodel = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu', input_shape=(28,28,1)))\nmodel.add(tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'))\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dense(10, activation='softmax'))\n\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics = ['accuracy'])\n\nmodel.fit(X_train, y_train, epochs=4, batch_size=250)\n\n# model.save('was-steht-dort')\n\n# model = tf.keras.models.load_model('was-steht-dort')\n\n# scores = model.evaluate(X_test, y_test)\nscores = model.predict(X_test)\n# print(scores)\ni=1\nsub=[]\ncursor=[]\nfor score in scores:\n    sub.append(np.argmax(score))\n    cursor.append(i)\n    i=i+1\nsubmission = pd.DataFrame({\n        \"ImageId\": cursor,\n        \"Label\": sub\n    })\n# print(submission)\nsubmission.to_csv('submission.csv', index=False)\nprint(\"Submission saved successfully!\")\n\n\n\n\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-13T12:54:18.079302Z","iopub.execute_input":"2023-03-13T12:54:18.079765Z","iopub.status.idle":"2023-03-13T12:59:25.969595Z","shell.execute_reply.started":"2023-03-13T12:54:18.079726Z","shell.execute_reply":"2023-03-13T12:59:25.968263Z"},"trusted":true},"execution_count":null,"outputs":[]}]}