{"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 pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\ntrain_data=pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest_data=pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\nX=train_data.drop('label',axis=1)\ny=train_data['label']\nX=X/255.0\ntest_data=test_data/255.0\nX=X.values.reshape(-1,28,28,1)\ntest_data=test_data.values.reshape(-1,28,28,1)\nfrom tensorflow.keras.utils import to_categorical\ny_train=to_categorical(y,num_classes=10)\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y_train, test_size=0.1, random_state=42)\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n\n\nmodel = Sequential()\n\nmodel.add(Conv2D(filters = 32,\n                 kernel_size = (5,5),\n                 padding = 'Same', \n                 activation ='relu',\n                 input_shape = (28,28,1)))\nmodel.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\n\nmodel.add(Conv2D(filters = 64,\n                 kernel_size = (3,3),\n                 padding = 'Same', \n                 activation ='relu'))\nmodel.add(Conv2D(filters = 64,\n                 kernel_size = (3,3),\n                 padding = 'Same', \n                 activation ='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\nmodel.add(Dropout(0.5))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation = \"relu\"))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation = \"softmax\"))\n\n\nmodel.compile(optimizer = 'adam' , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\nfrom tensorflow.keras.callbacks import EarlyStopping\nearly_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=2)\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimage_gen=ImageDataGenerator(rotation_range=10,\n                            width_shift_range=0.1,\n                            height_shift_range=0.1,\n                            shear_range=0.15,\n                            zoom_range=0.15,\n                            horizontal_flip=False,\n                            vertical_flip=False, \n                            fill_mode='nearest')\nimage_gen.fit(X_train)\nhistory = model.fit_generator(image_gen.flow(X_train,y_train, batch_size=16),\n                              epochs = 8, validation_data = (X_test,y_test),\n                              callbacks=[early_stop])\nresults = model.predict(test_data)\nresults = np.argmax(results,axis = 1)\n\nresults = pd.Series(results,name=\"Label\")\nsubmission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n\nsubmission.to_csv(\"digit_recognizer.csv\",index=False)","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}