{"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":{"trusted":true},"cell_type":"code","source":"from matplotlib import pyplot as plt\nfrom tensorflow.keras import backend as K\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Importing all required files"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"dig_mnist = pd.read_csv(\"/kaggle/input/Kannada-MNIST/Dig-MNIST.csv\")\ntrain_data = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\ntest_data = pd.read_csv(\"/kaggle/input/Kannada-MNIST/test.csv\")\ntest_Y = pd.read_csv(\"/kaggle/input/Kannada-MNIST/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_X = np.float64(train_data.loc[:, train_data.columns != 'label'].to_numpy()) / 255\ntrain_X = train_X.reshape(-1, 28, 28, 1)\ntrain_Y = tf.keras.utils.to_categorical(train_data.label, num_classes = 10)\n\ntest_X = np.float64(test_data.loc[:, test_data.columns != 'id'].to_numpy()) / 255\ntest_X = test_X.reshape(-1, 28, 28, 1)\ntest_Y = test_Y.merge(test_data, on = 'id').label","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def display_image(images, size):\n     images = images.squeeze()\n     plt.figure(figsize = (6, 6))\n     n = len(images)\n     plt.figure()\n     plt.gca().set_axis_off()\n     im = np.vstack([np.hstack([images[np.random.choice(n)] for i in range(size)])\n     for i in range(size)])\n     plt.imshow(im)\n     plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"display_image(train_X, 5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Image Augumentation\nImage"},{"metadata":{"trusted":true},"cell_type":"code","source":"datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n     featurewise_center=True,\n    featurewise_std_normalization=True,\n    rotation_range=20,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    horizontal_flip=True, \n    rescale = 1./255\n)\n\ndatagen.fit(train_X.reshape(-1, 28, 28, 1))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_gen = tf.keras.preprocessing.image.ImageDataGenerator(\n    rescale = 1./255, \n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1 = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(filters = 64, kernel_size = (3, 3), input_shape = [28, 28, 1], activation = 'relu'), \n    tf.keras.layers.Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu'), \n    tf.keras.layers.MaxPool2D(), \n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Flatten(), \n#     tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(128, activation = 'relu'), \n    tf.keras.layers.Dense(10, activation = 'softmax')\n])\nmodel1.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1.fit_generator(datagen.flow(train_X.reshape(-1, 28, 28, 1), train_Y, batch_size=32),\n                    steps_per_epoch=len(train_X) / 32, epochs=5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1_pred = model1.predict_classes(test_X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.read_csv(\"/kaggle/input/Kannada-MNIST/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.drop(['label'], axis = 'columns', inplace = True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission['label'] = model1_pred","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv(\"submission.csv\", index = False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1_pred","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":1}