{"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":"import tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/Kannada-MNIST/train.csv')\ndf_test = pd.read_csv('../input/Kannada-MNIST/test.csv')\ndf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = df.iloc[:,1:].values.reshape(df.shape[0],28,28)\nx_test = df_test.iloc[:,1:].values.reshape(df_test.shape[0],28,28)\ny_train = df.iloc[:,0].values\nid_test = df_test.iloc[:,0].values\nx_train,x_val, y_train,y_val = train_test_split(x_train,y_train,stratify=y_train,test_size=0.1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train/255\nx_val = x_val/255\nx_test = x_test/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\nx_val = x_val.reshape(x_val.shape[0], 28, 28, 1)\nx_test = x_test.reshape(x_test.shape[0],28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), strides=(1,1),padding='same',activation='relu', input_shape=(28, 28,1)),\n    tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), strides=(2,2),padding='same',activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    # 2nd layer\n    tf.keras.layers.Conv2D(128,kernel_size=(3,3), strides=(1,1),padding='same', activation='relu'),\n    tf.keras.layers.Conv2D(128,kernel_size=(3,3), strides=(2,2),padding='same', activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    # 3rd layer\n    tf.keras.layers.Conv2D(256,kernel_size=(3,3), strides=(1,1),padding='same', activation='relu'),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(512, activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(128, activation='relu'),    \n    tf.keras.layers.Dense(10, activation='softmax')\n\n])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(x_train,y_train,validation_data=(x_val,y_val),epochs=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"val_loss,val_acc = model.evaluate(x_val,y_val)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_test_pred = np.argmax(model.predict(x_test),axis=1)\nresult = pd.DataFrame([id_test,y_test_pred],index=['id','label']).T\nresult = result.set_index('id')\nresult.to_csv('submission.csv')j","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}