{"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":"import numpy as np\nimport pandas as pd\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Dropout,Conv2D,MaxPool2D,Flatten\nfrom keras.optimizers import Adam\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"load data\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\ntest = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')\nsubmission = pd.read_csv('/kaggle/input/Kannada-MNIST/sample_submission.csv')\ndisplay(train.shape,test.shape)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Id = test['id']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"prepare train and test\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train, x_test, y_train, y_test = train_test_split(train.iloc[:, 1:], train.iloc[:, 0], test_size=0.2)\ndisplay(x_train.shape,y_train.shape,x_test.shape,y_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_test.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Data preprocessing"},{"metadata":{"trusted":true},"cell_type":"code","source":"# (60000,784)->(60000,28,28,1)\nx_train = x_train.values.reshape(-1,28,28,1)/255.0\nx_test = x_test.values.reshape(-1,28,28,1)/255.0\n\ny_train = np_utils.to_categorical(y_train,num_classes=10)\ny_test = np_utils.to_categorical(y_test,num_classes=10)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Prepare the CNN model"},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(input_shape = (28,28,1),filters = 32,kernel_size = 5,strides = 1,padding = 'same',activation = 'relu'))\nmodel.add(MaxPool2D(pool_size = 2,strides = 2,padding = 'same'))\nmodel.add(Conv2D(64,5,strides=1,padding='same',activation = 'relu'))\nmodel.add(MaxPool2D(2,2,'same'))\nmodel.add(Flatten())\n\nmodel.add(Dense(1024,activation = 'relu'))\n# Dropout\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(10,activation='softmax'))\n\nadam = Adam(lr=1e-4)\n\nmodel.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])\n\nmodel.fit(x_train,y_train,batch_size=32,epochs=10)\n\nloss,accuracy = model.evaluate(x_test,y_test)\n\nprint('test loss',loss)\nprint('test accuracy',accuracy)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = test.drop(['id'],axis=1)\ntest = test.values.reshape(test.shape[0],28,28,1)/255.0\nFINAL_PREDS = model.predict_classes(test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame({ 'id': Id,\n                            'label': FINAL_PREDS })\nsubmission.to_csv(path_or_buf =\"submission.csv\", index=False)\nsubmission.head()","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}