{"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":"train_data = pd.read_csv(\"/kaggle/input/Kannada-MNIST/train.csv\")\ntest_data = pd.read_csv(\"/kaggle/input/Kannada-MNIST/test.csv\")\ncross_data = pd.read_csv(\"/kaggle/input/Kannada-MNIST/Dig-MNIST.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_y = train_data['label']\ntrain_X = train_data.drop(['label'], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.models.Sequential()\n\nmodel.add(tf.keras.layers.Dense(784, activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))\nmodel.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))\n\nmodel.add(tf.keras.layers.Dense(10, activation = tf.nn.softmax))\n\nmodel.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(train_X.to_numpy(), train_y.to_numpy(), epochs=5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cross_y = cross_data['label']\ncross_X = cross_data.drop(['label'], axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"val_loss, val_acc = model.evaluate(cross_X.to_numpy(), cross_y.to_numpy())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_label = test_data['id']\ntest_data = test_data.drop(['id'], axis =1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_predictions = model.predict([test_data.to_numpy()])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_predictions_final = []\nfor i in range(0, len(test_predictions)):\n    test_predictions_final.append(np.argmax(test_predictions[i]))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_predictions = pd.DataFrame(\n    {\n        'id' : test_label,\n        'label': test_predictions_final\n    })","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_predictions.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":1}