{"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_minor":4,"nbformat":4,"cells":[{"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 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport os\nimport random\nimport zipfile\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report \nimport tensorflow as tf\nfrom keras.preprocessing.image import load_img, ImageDataGenerator\nfrom keras.models import Sequential, Model\nfrom keras.layers import Conv2D, MaxPool2D, Flatten, Dense, GlobalAveragePooling2D, Dropout, BatchNormalization, Input\nfrom keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n\nbatch_size = 128","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(\"../input/dogs-vs-cats/\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"TRAIN_PATH = \"../input/dogs-vs-cats/train.zip\"\nTEST_PATH = \"../input/dogs-vs-cats/test1.zip\"\n\nFILES = \"/kaggle/files/unzipped/\"\n\nwith zipfile.ZipFile(TRAIN_PATH, 'r') as zipp:\n    zipp.extractall(FILES)\n    \nwith zipfile.ZipFile(TEST_PATH, 'r') as zipp:\n    zipp.extractall(FILES)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.DataFrame({\"file\": os.listdir(\"/kaggle/files/unzipped/train\")})\ntrain_df[\"label\"] = train_df[\"file\"].apply(lambda x: x.split(\".\")[0])\n\ntrain_df.head()","metadata":{},"execution_count":null,"outputs":[]}]}