{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"%matplotlib inline\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/Kannada-MNIST/train.csv')\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train = np.array(train['label'])\ny_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = train.drop(['label'],1)\nX_train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(np.array(X_train.iloc[4]).reshape(28,28),cmap='gray')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.utils import np_utils\n\ny_train = np_utils.to_categorical(y_train, 10)\nprint(y_train[:10])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = np.array(X_train/255)\n\n# define the model\nmodel = Sequential()\nmodel.add(Dense(1024, input_dim = X_train.shape[1], activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1024, activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(10, activation='softmax'))\n\n# summarize the model\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# compile the model\nmodel.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.callbacks import ModelCheckpoint   \n\n# train the model\n# checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', \n#                                verbose=1, save_best_only=True)\n# hist = model.fit(X_train, y_train, batch_size=128, epochs=10,\n#           validation_split=0.2, callbacks=[checkpointer],\n#           verbose=1, shuffle=True)\n\nhist = model.fit(X_train, y_train, batch_size=128, epochs=10,\n          validation_split=0.2,\n          verbose=1, shuffle=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.read_csv('../input/Kannada-MNIST/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = test.drop(['id'],1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = np.array(test/255)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction = model.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"result = []\nfor i in range(0,prediction.shape[0]):\n    y = np.where(prediction[i] == max(prediction[i]))[0][0]\n    result.append(y)\nresult","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"snn = pd.DataFrame({'label':result}).reset_index().rename(columns = {'index':'id'})\nsnn.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"snn.to_csv('snn.csv',index = False)","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":1}