{"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/working/test1'):\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":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport cv2\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout, Activation, Conv2D, MaxPooling2D\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input/dogs-vs-cats\"))\n\nimport zipfile\n\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\",\"r\") as z:\n    z.extractall(\".\")\n    \nwith zipfile.ZipFile(\"../input/dogs-vs-cats/test1.zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Ignore  the warnings\nimport warnings\nwarnings.filterwarnings('always')\nwarnings.filterwarnings('ignore')\n\n# data visualisation and manipulation\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport seaborn as sns\n \n#configure\n# sets matplotlib to inline and displays graphs below the corressponding cell.\n%matplotlib inline  \nstyle.use('fivethirtyeight')\nsns.set(style='whitegrid',color_codes=True)\n\n#model selection\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_curve,roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.preprocessing import LabelEncoder\n\n#preprocess.\nfrom keras.preprocessing.image import ImageDataGenerator\n\n#dl libraraies\nfrom keras import backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop\nfrom keras.utils import to_categorical\nfrom keras.callbacks import ReduceLROnPlateau\n\n# specifically for cnn\nfrom keras.layers import Dropout, Flatten,Activation\nfrom keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n \nimport tensorflow as tf\nimport random as rn\n\n# specifically for manipulating zipped images and getting numpy arrays of pixel values of images.\nimport cv2                  \nimport numpy as np  \nfrom tqdm import tqdm\nimport os                   \nfrom random import shuffle  \nfrom zipfile import ZipFile\nfrom PIL import Image\n\n#TL pecific modules\nfrom keras.applications.vgg16 import VGG16","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=[]\nZ=[]\nIMG_SIZE=50\nTRAIN_DIR = '/kaggle/working/train'\n# TEST_DIR = '/kaggle/working/test1'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def make_train_data(DIR):\n    for img in tqdm(os.listdir(DIR)):\n        label = [1, 0]\n        if img.split('.')[0] == 'cat':\n            label=[0, 1]\n        path = os.path.join(DIR,img)\n        img = cv2.imread(path,cv2.IMREAD_COLOR)\n        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n        \n        X.append(np.array(img))\n        Z.append((label))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"make_train_data(TRAIN_DIR)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(X[677])\nprint(Z[677])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=np.array(X)\nX=X/255\nZ = np.array(Z)\nnp.save('X.npy', X)\nnp.save('Z.npy', Z)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train,x_test,y_train,y_test=train_test_split(X,Z,test_size=0.25,random_state=42)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.random.seed(42)\nrn.seed(42)\n# tf.set_random_seed(42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_model=VGG16(include_top=False, weights=None,input_shape=(150,150,3), pooling='avg')\n 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