{"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 matplotlib.image as mpimg\nimport seaborn as sns\nimport keras \nimport tensorflow as tf\nfrom keras.optimizers import RMSprop","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest=pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\nxtrain=train.drop(['label'],axis=1)\nytrain=train['label']\nxtrain=xtrain/255.0\ntest=test/255.0\nxtrain=xtrain.values.reshape(-1,28,28,1)\ntest=test.values.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"xtrain.shape\nfrom keras.utils import to_categorical\nytrain=to_categorical(ytrain,num_classes=10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nxtrain,xvalid,ytrain,yvalid=train_test_split(xtrain,ytrain,test_size=0.1,random_state=2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=keras.models.Sequential([keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28,1),padding='same'),\n                              keras.layers.Conv2D(64,(3,3),activation='relu',padding='same'),\n                              keras.layers.MaxPooling2D(2,2),\n                              keras.layers.Dropout(0.1),\n                              keras.layers.Conv2D(128,(3,3),activation='relu',padding='same'),\n                              keras.layers.Conv2D(128,(3,3),activation='relu',padding='same'),\n                              keras.layers.MaxPooling2D(2,2),\n                              keras.layers.Dropout(0.1),\n                              keras.layers.Conv2D(128,(3,3),activation='relu',padding='same'),\n                              keras.layers.Conv2D(128,(3,3),activation='relu',padding='same'),\n                              keras.layers.MaxPooling2D(2,2),\n                              keras.layers.Dropout(0.1),\n                              keras.layers.Flatten(),\n                              keras.layers.Dense(1024,activation='relu'),\n                              keras.layers.Dropout(0.2),\n                              keras.layers.Dense(10,activation='sigmoid')])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer=RMSprop(lr=0.001),loss='categorical_crossentropy',metrics='accuracy')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"h=model.fit(xtrain,ytrain,validation_data=(xvalid,yvalid),epochs=3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(2,1)\nax[0].plot(h.history['loss'], color='b', label=\"Training loss\")\nax[0].plot(h.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\nlegend = ax[0].legend(loc='best', shadow=True)\n\nax[1].plot(h.history['accuracy'], color='b', label=\"Training accuracy\")\nax[1].plot(h.history['val_accuracy'], color='r',label=\"Validation accuracy\")\nlegend = ax[1].legend(loc='best', shadow=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict results\nresults = model.predict(test)\n\n# select the indix with the maximum probability\nresults = np.argmax(results,axis = 1)\n\nresults = pd.Series(results,name=\"Label\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results=results.drop([0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n\nsubmission.to_csv(\"cnn_mnist_datagen.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"z=pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"z['Label']=results","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"z","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"z.to_csv('sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a=pd.read_csv","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}