{"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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ndf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = df.drop(['label'], axis = 1)\nY = df['label']\nY","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.1, stratify = Y)\nx_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train / 255.0\nx_test = x_test / 255.0\n\nx_train = x_train.values.astype('float32')\ny_train = y_train.values.astype('float32')\nx_test = x_test.values.astype('float32')\ny_test = y_test.values.astype('float32')\n\nx_train = x_train.reshape(x_train.shape[0], 28, 28,1)\nx_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\nx_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = test / 255.0\ntest = test.values.astype('float32')\ntest = test.reshape(test.shape[0], 28, 28,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class StopTrainingCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs = {}):\n        if (logs.get('accuracy') == 1):\n            print (\"\\nReached 100% accuracy so cancelling training!\")\n            self.model.stop_training = True\n            \ncallbacks = StopTrainingCallback()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.models.Sequential([\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(1024, activation = tf.nn.relu),\n    tf.keras.layers.Dense(10, activation = tf.nn.softmax)    \n])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(\n    optimizer = 'adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics = ['accuracy']\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(x_train, y_train, epochs = 35, callbacks = [callbacks])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.evaluate(x_test, y_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = model.predict_classes(test, verbose=0)\n\nsubmissions=pd.DataFrame({\"ImageId\": list(range(1,len(predictions)+1)),\n                         \"Label\": predictions})\nsubmissions.to_csv(\"DR.csv\", index=False, header=True)","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}