{"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\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 read-only \"../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# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport keras\nfrom keras.applications import vgg16\nimport numpy as np\nimport pandas as pd\nfrom keras import backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Activation\nfrom keras.layers.core import Dense,Flatten\nfrom keras.optimizers import Adam\nfrom keras.metrics import categorical_crossentropy\nfrom sklearn import preprocessing\nfrom keras.layers.convolutional import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.read_csv(\"../input/digit-recognizer/train.csv\")\ntest=pd.read_csv(\"../input/digit-recognizer/test.csv\")\nsample=pd.read_csv(\"../input/digit-recognizer/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mod = Sequential()\nmod.add(Conv2D(filters=10, kernel_size=3 ,     \n                  input_shape=(28,28,1),kernel_initializer= 'uniform',      \n                  activation= 'relu'))\nmod.add(Conv2D(filters=20, kernel_size=7 ,kernel_initializer= 'uniform',strides=(2,2),      \n                  activation= 'relu'))\nmod.add(MaxPooling2D(pool_size=(2,2)))\nmod.add(Flatten())\nmod.add(Dense(10,activation=\"softmax\"))\nmod.compile(Adam(lr=0.0001),metrics=[\"accuracy\"],loss=\"categorical_crossentropy\")\nmod.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"name=train.columns.values[0]\nytrain=train[name]\nxtrain=train.drop(name,axis=1)\nxtrain=np.array(xtrain)\nytrain=keras.utils.to_categorical(ytrain)\ntest=np.array(test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"l=[]\nfor i in range(xtrain.shape[0]):\n    l.append(np.reshape(xtrain[i],(28,28,1)))\nxtrain=np.array(l)\nxtrain.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"l=[]\nfor i in range(test.shape[0]):\n    l.append(np.reshape(test[i],(28,28,1)))\nxtest=np.array(l)\nxtest.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mod.fit(xtrain,ytrain,batch_size=10,epochs=10,verbose=2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ypred=mod.predict(xtest,batch_size=10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"l=[]\nfor row in ypred:\n    l.append(np.argmax(row)) \nLabel=np.array(l)\nsample[\"Label\"]=Label","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nsample.to_csv(\"op_new.csv\",index=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample","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}