{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras import layers, models\nfrom tensorflow import keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = pd.read_csv(\"/kaggle/input/Kannada-MNIST/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"target = train_data['label']\ntrain_vars = train_data.drop(['label'],axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = train_vars/255\ny = target","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.values.reshape(X_train.shape[0],28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = models.Sequential()\nmodel.add(layers.Conv2D(64, (4,4),activation='relu',input_shape=(28,28,1)))\nmodel.add(layers.Dropout(0.2, noise_shape=None, seed=None))\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(32, (7,7),activation='relu'))\nmodel.add(layers.Dropout(0.2, noise_shape=None, seed=None))\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(32, (2,2),activation='relu'))\nmodel.add(layers.Dropout(0.2, noise_shape=None, seed=None))\n\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(32,activation='relu'))\nmodel.add(layers.Dense(10,activation='softmax'))\n\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n\nes = EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=8)\nlr_reduction = ReduceLROnPlateau(monitor='val_loss',patience=5,verbose=1,factor=0.2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X_train,np.array(y),\n          epochs=50,validation_split=0.2,\n         batch_size=128, shuffle=True,callbacks =[lr_reduction,es])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')\nImageId = test_data['id']\nNN_test = test_data.drop(['id'],axis=1)\nNN_test = NN_test/255\nNN_test = NN_test.values.reshape(NN_test.shape[0],28,28,1)\n\npredictions = model.predict_classes(NN_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.DataFrame({'id':ImageId, 'label':predictions})\nsub.to_csv(\"submission.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":1}