{"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\nimport tensorflow as tf\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":"from tensorflow.keras.datasets import cifar10","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train, test = cifar10.load_data()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train, y_train = train\nx_test, y_test = test","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train.shape, y_train.shape, x_test.shape, y_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train / 255.0\nx_test = x_test / 255.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train.dtype, y_train.dtype","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train_new, x_val, y_train_new, y_val = train_test_split(x_train, y_train, test_size = 0.2, random_state = 101)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train_new.shape, y_train_new.shape, x_val.shape, y_val.shape, x_test.shape, y_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#x_train.shape[1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#y_train = tf.keras.utils.to_categorical(y_train, num_classes= 10)\ny_train_new = tf.keras.utils.to_categorical(y_train_new, num_classes= 10)\ny_val = tf.keras.utils.to_categorical(y_val, num_classes= 10)\ny_test = tf.keras.utils.to_categorical(y_test, num_classes= 10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_model():\n    \n    model = tf.keras.models.Sequential()\n    \n    model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3,3), padding = \"same\", activation = \"relu\",\n                                    input_shape = (32,32,3)))\n    model.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3,3), padding = \"same\", activation = \"relu\"))\n    model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2)))\n    model.add(tf.keras.layers.Dropout(0.25))\n              \n    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), padding = \"same\", activation = \"relu\"))\n    model.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), padding = \"same\", activation = \"relu\"))\n    model.add(tf.keras.layers.MaxPool2D(pool_size = (2,2)))\n    model.add(tf.keras.layers.Dropout(0.25))\n    \n    model.add(tf.keras.layers.Flatten())\n    model.add(tf.keras.layers.Dense(units = 512, activation = \"relu\"))\n    model.add(tf.keras.layers.Dropout(0.25))\n    model.add(tf.keras.layers.Dense(units = 10, activation = \"softmax\"))\n    \n    return model\n              ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = build_model()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer= \"adam\", loss= \"categorical_crossentropy\", metrics= [\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor= \"val_loss\", patience= 2, verbose= 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"early_stop = tf.keras.callbacks.EarlyStopping(monitor= \"val_loss\", patience= 3, restore_best_weights= True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(x_train_new, y_train_new, batch_size= 32,\n                   epochs= 30, validation_data= (x_val, y_val), shuffle= True, \n                    callbacks= [reduce_lr, early_stop])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history.history['loss'], color='b')\nplt.plot(history.history['val_loss'], color='r')\nplt.show()\nplt.plot(history.history['accuracy'], color='b')\nplt.plot(history.history['val_accuracy'], color='r')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred = model.predict(x_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred = np.argmax(pred, axis= 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_test","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred = tf.keras.utils.to_categorical(pred, num_classes= 10)\n#y_test = tf.keras.utils.to_categorical(y_test, num_classes= 10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred.shape, y_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import classification_report","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_test.shape, pred.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(classification_report(y_test, pred))","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}