{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd \n\nimport os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras import layers, optimizers, backend, models, callbacks\nfrom keras.layers import Dense, Dropout\nfrom keras.models import Sequential","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_raw = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\ntest_raw = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\n\nprint(f\"Training size : {train_raw.shape[0]}*{train_raw.shape[1]}\\n\")\nprint(f\"Test size : {test_raw.shape[0]}*{test_raw.shape[1]}\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = train_raw.drop(\"label\", axis=1)/255\ny = train_raw[\"label\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2)\nprint(f\"Training size : {X_train.shape[0]}*{X_train.shape[1]}\\n\")\nprint(f\"Validation size : {X_valid.shape[0]}*{X_valid.shape[1]}\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = models.Sequential()\nmodel.add(Dense(400, input_shape=[784],activation=\"relu\"))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(200, activation=\"relu\"))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(100, activation=\"relu\"))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(10, activation=\"softmax\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plt.figure(figsize=(40,40))\nplt.plot(history.history[\"accuracy\"], label=\"MSE(training data)\")\nplt.plot(history.history[\"val_accuracy\"], label=\"MSE(validation data)\")\nplt.legend([\"train\", \"test\"], loc=\"lower right\")\nplt.xlabel(\"epoch\")\nplt.ylabel(\"accuracy\")\nplt.show();","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = test_raw/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred = model.predict_classes(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame({\"ImageId\": np.array(range(1,28001)), \"Label\": pred})\nsubmission.to_csv(\"/kaggle/working/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}