{"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":"train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = train_df['label'].to_numpy()\nlabels","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = train_df.iloc[:, 1:]\nX = X.to_numpy()\nX = X.reshape(X.shape[0], 28, 28, 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X / 255.0\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\nx_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train.shape\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu', input_shape = (28, 28, 1)),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(32, kernel_size=5, strides=2, padding='same', activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Dropout(0.4),\n\n    tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Conv2D(64, kernel_size=5, strides=2, padding='same', activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Dropout(0.4),\n\n    tf.keras.layers.Conv2D(128, kernel_size=4, activation='relu'),\n    tf.keras.layers.BatchNormalization(),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dropout(0.4),\n    tf.keras.layers.Dense(512, activation='relu'),\n    tf.keras.layers.Dense(10, activation='softmax'),\n])\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class MyCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if logs.get('acc') >= 0.999:\n            pass\n            #self.model.stop_training = True\ncallback = MyCallback()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(x_train, y_train, epochs=30, batch_size=32, callbacks=[callback], shuffle=True)","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":"import matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\ntest_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = test_df.to_numpy()\nX_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\nX_test = X_test / 255.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = model.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = np.argmax(y_pred, axis=1)\ny_pred.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.DataFrame({\n    'ImageId': range(1, len(y_pred)+1),\n    'Label': y_pred\n})","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}