{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 5GB 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","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\nprint(train_data.shape)\nprint(test_data.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data.head","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = train_data.drop('label', axis=1)\ny_train = train_data['label']\n\nX_train /= 255.0\ntest_data /= 255.0\n\nX_train = X_train.values.reshape(X_train.shape[0], 28, 28, 1)\ntest_data = test_data.values.reshape(test_data.shape[0], 28, 28, 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = keras.Sequential([\n    \n    keras.layers.Conv2D(16, (3, 3), padding = 'same', activation = 'relu', input_shape = (28, 28, 1)),\n    keras.layers.MaxPooling2D(2,2),\n    \n    keras.layers.Conv2D(32, (3, 3), padding = 'same', activation = 'relu'),\n    keras.layers.MaxPooling2D(2,2),\n    \n    keras.layers.Conv2D(64, (3, 3), padding = 'same', activation = 'relu'),\n    keras.layers.Conv2D(128, (3, 3), padding = 'same', activation = 'relu'),\n    keras.layers.MaxPooling2D(2,2),\n    \n    keras.layers.Flatten(),\n    keras.layers.Dense(256, activation='relu'),\n    keras.layers.Dropout(0.3),\n    keras.layers.Dense(64, activation='relu'),\n    keras.layers.Dropout(0.3),\n    keras.layers.Dense(10, activation='softmax')\n])\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X_train, y_train, epochs=100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"list = model.predict(test_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.argmax(list[1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"list = np.argmax(list,axis = 1)\nlist = pd.Series(list,name=\"Label\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"result = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),list],axis = 1)\nresult.to_csv(\"result.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}