{"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 tensorflow as tf\nimport pandas as pd\nimport numpy as np\nfrom keras.layers import Conv2D,Input,MaxPooling2D,Flatten,Dense\nfrom keras.models import Sequential\nimport keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Before: \\n\",x_train.shape,y_train.shape)\n#scaling\nx_train = x_train.astype(\"float32\") / 255\nx_test = x_test.astype(\"float32\") / 255\n\n#adding channel dimension\nx_train = tf.expand_dims(x_train,-1)\nx_test = tf.expand_dims(x_test,-1)\n\n#one hot encoding\ny_train = tf.keras.utils.to_categorical(y_train)\ny_test = tf.keras.utils.to_categorical(y_test)\n\n#\nprint(\"After: \\ n\",x_train.shape,y_train.shape,x_test.shape,y_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Input(shape = (28,28,1)))\n\nmodel.add(Conv2D(32,kernel_size=(3,3),activation='relu'))\nmodel.add(Conv2D(32,kernel_size=(3,3),activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Conv2D(64,kernel_size=(3,3),activation='relu'))\nmodel.add(Conv2D(64,kernel_size=(3,3),activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Flatten())\nmodel.add(Dense(10,activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size = 128\nepochs = 20","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',optimizer = 'adam',metrics = ['accuracy'])\nmodel.fit(x_train,y_train,batch_size=batch_size,epochs=epochs,validation_data=(x_test,y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pdf = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\nnpdf = pdf.to_numpy()\ns = npdf.shape\nx_pred = tf.expand_dims(npdf.reshape((s[0],28,28)),-1)\nprint(x_pred.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = model.predict(x_pred)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_label = tf.argmax(y_pred,axis=1)\nprint(y_label.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"out_df = pd.DataFrame(y_label)\nout_df['ImageId'] = out_df.index + 1\nout_df['Label'] = out_df[0]\nout_df.drop(columns = [0],inplace = True)\nout_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"out_df.to_csv(\"/kaggle/working/out.csv\")","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}