{"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":"","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"import pandas as pd\nDig_MNIST = pd.read_csv(\"../input/Kannada-MNIST/Dig-MNIST.csv\")\nsample_submission = pd.read_csv(\"../input/Kannada-MNIST/sample_submission.csv\")\ntest = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")\ntrain = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Flatten,Conv2D,MaxPooling2D\nfrom keras.layers import Dense\n\nmymodel = Sequential()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#conv layers\n\nmymodel.add(Conv2D(32,(3,3),input_shape = (28,28,1), activation = 'relu'))\nmymodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmymodel.add(Conv2D(32,(3,3), activation = 'relu'))\nmymodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmymodel.add(Conv2D(32,(3,3), activation = 'relu'))\nmymodel.add(MaxPooling2D(pool_size=(2, 2)))\n\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mymodel.add(Flatten())\nmymodel.add(Dense(64,activation='relu'))\nmymodel.add(Dense(units=10,activation='softmax'))\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mymodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mymodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fultr=train.merge(Dig_MNIST,how='outer')\nfux=fultr.drop('label',1).values/255.0\nfuxr=fux.reshape(-1,28,28,1)\nfuy=fultr['label'].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mymodel.fit(fuxr,fuy,epochs=10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mymodel.evaluate(fuxr,fuy)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels=mymodel.predict((test.drop('id',1).values/255.0).reshape(-1,28,28,1))\nlabel=labels.argmax(1)\nlabel","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission['label']=label\nsample_submission.to_csv('submission.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission","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}