{"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 20GB 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":{},"cell_type":"markdown","source":"## import labraries"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## import data"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = pd.read_csv('../input/digit-recognizer/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data = pd.read_csv('../input/digit-recognizer/test.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## preprocessing and reshaping data"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = train_data.iloc[:,1:]\ny_train = train_data.iloc[:,0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.to_numpy().reshape(-1,28,28)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## creating model"},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow import keras","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"class ResidualUnit(keras.layers.Layer):\n    def __init__(self, filters, strides=1, activation=\"relu\", **kwargs):\n        super().__init__(**kwargs)\n        self.activation = keras.activations.get(activation)\n        self.main_layers = [\n            keras.layers.Conv2D(filters, 3, strides=strides,\n            padding=\"same\", use_bias=False),\n            keras.layers.BatchNormalization(),\n            self.activation,\n            keras.layers.Conv2D(filters, 3, strides=1,\n            padding=\"same\", use_bias=False),\n            keras.layers.BatchNormalization()]\n        self.skip_layers = []\n        if strides > 1:\n            self.skip_layers = [\n            keras.layers.Conv2D(filters, 1, strides=strides,\n            padding=\"same\", use_bias=False),\n            keras.layers.BatchNormalization()]\n    def call(self, inputs):\n        Z = inputs\n        for layer in self.main_layers:\n            Z = layer(Z)\n        skip_Z = inputs\n        for layer in self.skip_layers:\n            skip_Z = layer(skip_Z)\n        return self.activation(Z + skip_Z)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = keras.models.Sequential()\nmodel.add(keras.layers.Conv2D(64, 7, strides=2, input_shape=[28, 28, 1],\npadding=\"same\", use_bias=False))\nmodel.add(keras.layers.BatchNormalization())\nmodel.add(keras.layers.Activation(\"relu\"))\nmodel.add(keras.layers.MaxPool2D(pool_size=3, strides=2, padding=\"same\"))\nprev_filters = 64\nfor filters in [64] * 6 + [128] * 8 + [256] * 12 + [512] * 6:\n    strides = 1 if filters == prev_filters else 2\n    model.add(ResidualUnit(filters, strides=strides))\n    prev_filters = filters\nmodel.add(keras.layers.GlobalAvgPool2D())\nmodel.add(keras.layers.Flatten())\nmodel.add(keras.layers.Dense(10, activation=\"softmax\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train = y_train.to_numpy().reshape(-1,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## fitting model"},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X_train,y_train,epochs=100,batch_size=32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data = test_data / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data = test_data.to_numpy().reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = model.predict(test_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_ans = []\nfor i in range(y_pred.shape[0]):\n    final_ans.append(np.argmax(y_pred[i]))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## making submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.DataFrame({'ImageId':range(1,28001),'Label':final_ans}).to_csv('submission.csv',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}