{"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.\nimport matplotlib.pyplot as plt\nimport matplotlib.image as maping\nimport seaborn as sns\n\nnp.random.seed(2)\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nimport itertools\n\nfrom keras.utils.np_utils import to_categorical \nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.optimizers import RMSprop\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import GridSearchCV\n\nmnist_train = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")\n\nmnist_test = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")\n\nY_train = mnist_train[\"label\"]\n\nX_train = mnist_train.drop(labels = [\"label\"], axis=1)\n\nmnist_test = mnist_test.drop(labels = [\"id\"], axis=1)\n\nX_train = X_train/255.0\nmnist_test = mnist_test/255.0\n\nX_train = X_train.values.reshape(-1,28,28,1)\n\nY_train = to_categorical(Y_train, num_classes=10)\n\nX_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=2)\n\nmodel = Sequential()\nmodel.add(Conv2D(filters = 64, kernel_size = (5,5),padding = 'Same', \n                     activation ='relu', input_shape = (28,28,1)))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.4))\nmodel.add(Conv2D(filters = 64, kernel_size = (5,5),padding = 'Same', \n                     activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.4))\nmodel.add(Flatten())\nmodel.add(Dense(10, activation = \"softmax\"))\n\nmodel.summary()\n\noptimizer = RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0)\n\nmodel.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n\nmodel.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=10, batch_size=200, verbose=2)\n\nresults = model.predict(mnist_test.values.reshape(-1,28,28,1)) \n\nresults = np.argmax(results,axis = 1)\n\nresults = pd.Series(results,name=\"label\")\n\nsubmission = pd.concat([pd.Series(range(0,mnist_test.shape[0]),name = \"id\"),results],axis = 1)\n\nsubmission.to_csv(\"submission.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"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}