{"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":{"trusted":true},"cell_type":"code","source":"data = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")\ndata.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras import utils","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = pd.DataFrame([data[\"pixel\" + str(i)] for i in range(0, 784)]).T\nX = X / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.max()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = utils.to_categorical(data.label, 10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Dense(900, input_dim = 784, activation=\"relu\"))\nmodel.add(Dense(1000, activation=\"relu\"))\nmodel.add(Dense(1500, activation=\"relu\"))\nmodel.add(Dense(2000, activation=\"relu\"))\nmodel.add(Dense(10, activation=\"softmax\"))\n\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])\nprint(model.summary())\n\nhistory = model.fit(X, y, batch_size=60, epochs=5, validation_split=0.3, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(history.history.keys())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history_model = pd.DataFrame([history.history[\"accuracy\"],history.history[\"val_accuracy\"],history.history[\"loss\"],history.history[\"val_loss\"]]).T\nhistory_model.rename(columns={0:\"accuracy\", 1: \"val_accuracy\", 2:\"loss\", 3:\"val_loss\"})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10, 5))\nsns.lineplot(data=history_model)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pr_X = model.predict(X)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.argmax(pr_X[0]) == np.argmax(y[0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_finish = Sequential()\n\nmodel_finish.add(Dense(900, input_dim = 784, activation=\"relu\"))\nmodel_finish.add(Dense(1000, activation=\"relu\"))\nmodel_finish.add(Dense(1500, activation=\"relu\"))\nmodel_finish.add(Dense(2000, activation=\"relu\"))\nmodel_finish.add(Dense(10, activation=\"softmax\"))\n\nmodel_finish.compile(loss=\"categorical_crossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])\nprint(model.summary())\n\nmodel_finish.fit(X, y, batch_size=60, epochs=2, verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub = pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')\ndig_mnist = pd.read_csv('../input/Kannada-MNIST/Dig-MNIST.csv')\ntest = pd.read_csv('../input/Kannada-MNIST/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dig_mnist.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = test.drop(\"id\", axis=\"columns\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = X_test / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = model_finish.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = np.argmax(y_pred, axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub['label'] = y_pred\nsample_sub.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}