{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","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.\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras.models import Sequential\nfrom sklearn.utils import shuffle\nfrom keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","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')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = train.loc[:,'pixel0':].values\ntrain_data = train_data/255.0\ntrain_data=train_data.reshape(-1,28,28,1)\ntest_data = test.loc[:,'pixel0':].values\ntest_data = test_data/255.0\ntest_data=test_data.reshape(-1,28,28,1)\ntrain_label=train['label']\ntrain_label=train_label.values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data, train_label = shuffle(train_data, train_label, random_state = 5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"input_shape = (28, 28, 1)\n# Creating a Sequential Model and adding the layers\nmodel = Sequential()\nmodel.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten()) # Flattening the 2D arrays for fully connected layers\nmodel.add(Dense(128, activation=tf.nn.relu))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(10,activation=tf.nn.softmax))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam', \n              loss='sparse_categorical_crossentropy', \n              metrics=['accuracy'])\nmodel.fit(x=train_data,y=train_label, epochs=10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.evaluate(train_data, train_label)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = model.predict(test_data)\nprediction1 = np.argmax(predictions,axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Create a  DataFrame\nsubmission = pd.DataFrame({'id':test['id'],'label':prediction1.astype(int)})\n\n#Convert DataFrame to a csv file that can be uploaded\n#This is saved in the same directory as your notebook\nfilename = 'mnist2.csv'\n\nsubmission.to_csv(filename,index=False)\n\nprint('Saved file: ' + filename)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"}},"nbformat":4,"nbformat_minor":1}