{"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\ndata=[]\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        data.append(pd.read_csv(os.path.join(dirname, filename)))\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":"train=data[0]\ntest=data[1]\nsub=data[2]\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x=np.array(train.drop('label',axis=1))\ny=train['label']\nprint(x.shape,test.shape)\ntest.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x=x.reshape((42000,28,28,1))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.utils import to_categorical\ny=to_categorical(y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Conv2D,Flatten,Dense,Dropout\nfrom keras.models import Sequential","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=Sequential()\nmodel.add(Conv2D(20, kernel_size=(3, 3),\n                 activation='relu',\n                 input_shape=(28, 28, 1)))\nmodel.add(Dropout(0.5))\nmodel.add(Conv2D(20,kernel_size=3,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Conv2D(18,kernel_size=2,activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Flatten())\nmodel.add(Dense(128))\nmodel.add(Dense(10,activation='softmax'))\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(x,y,batch_size=128,epochs=8,validation_split=0.2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions=np.argmax(model.predict(np.array(test).reshape(28000,28,28,1)),axis=1)\npredictions","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.shape,sub.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub['Label']=predictions","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.to_csv(\"/kaggle/output\",index=False)","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}