{"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)\nimport tensorflow as tf \nfrom os import path, getcwd, chdir \npath = f\"{getcwd()}/../tmp2/mnist.npz\"\nfrom sklearn.model_selection import train_test_split\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 20GB 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":"\ndef train_mnist():\n\n    mnist = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\n    x_train, x_test, y_train, y_test = train_test_split(mnist.drop('label',axis = 1), mnist.label, \n                                                 test_size=10000, random_state=123, \n                                                 stratify=mnist.label)\n\n#     X_train, X_test = X_train.to_numpy(), X_test.to_numpy()\n\n#     (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)\n    x_train, x_test = x_train / 255.0, x_test / 255.0\n\n    model = tf.keras.models.Sequential([\n        tf.keras.layers.Flatten(input_shape=(28, 28)),\n        tf.keras.layers.Dense(512, activation=tf.nn.relu),\n        tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n    ])\n\n    model.compile(optimizer='adam',\n                  loss='sparse_categorical_crossentropy',\n                  metrics=['accuracy'])\n    \n\n    history = model.fit(x_train, y_train , epochs=10\n    )\n\n    return history.epoch, history.history","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\nx_train, x_test, y_train, y_test = train_test_split(mnist.drop('label',axis = 1), mnist.label, \n                                                 test_size=10000, random_state=123, \n                                                 stratify=mnist.label) \nx_train, x_test = x_train / 255.0, x_test / 255.0\n\nmodel = tf.keras.models.Sequential([\n        tf.keras.layers.Flatten(input_shape=(28, 28)),\n        tf.keras.layers.Dense(512, activation=tf.nn.relu),\n        tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n    ])\n\nmodel.compile(optimizer='adam',\n                  loss='sparse_categorical_crossentropy',\n                  metrics=['accuracy']) \n\nhistory = model.fit(x_train, y_train , epochs=10\n   )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\nmnist_test = mnist_test.to_numpy()\nmnist_test = [np.reshape(x, (28,28)) for x in mnist_test]\nmnist_test = tf.data.Dataset.from_tensor_slices(mnist_test)\nmnist_test = mnist_test.batch(32)\nmnist_test","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"prediction = model.predict(mnist_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = [np.argmax(x) for x in prediction]\nImageId = [x+1 for x in range(len(prediction))]\n\nsubmission = pd.DataFrame({'ImageId': ImageId,\n                          'Label' : labels}, index = None)\nsubmission","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":"","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}