{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator, load_img\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nimport tensorflow as tf\nnp.random.seed(0)\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 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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"INPUT_SOURCE = '/kaggle/input/dogs-vs-cats'\nFAST_RUN = False\nIMAGE_WIDTH=224\nIMAGE_HEIGHT=224\nIMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\nIMAGE_CHANNELS=3\nFILE_PATH = '/kaggle/input/dogs-vs-cats-redux-kernels-edition/'","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"loaded = tf.keras.models.load_model(\"/kaggle/input/project-ai-ankk/DogVsCatModelv1\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!rm -rf \"./train\"\n!unzip -q \"/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Preparing the data\nfilenames = os.listdir(\"./train\")\ncategories = []\nfor filename in filenames:\n    category = filename.split('.')[0]\n    if category == 'dog':\n        categories.append(1)\n    else:\n        categories.append(0)\n\ndf = pd.DataFrame({\n    'filename': filenames,\n    'category': categories\n})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['category'] = df[\"category\"].replace({0: 'cat', 1: 'dog'}) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting the data\ntrain_df, val_df = train_test_split(df, test_size=.2, stratify=df[\"category\"], random_state=42)\ntrain_df = train_df.reset_index()\nval_df = val_df.reset_index()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size=32\ntotal_train = train_df.shape[0]\ntotal_validate = val_df.shape[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"validation_datagen = ImageDataGenerator(rescale=1./255)\nvalidation_generator = validation_datagen.flow_from_dataframe(\n    val_df, \n    \"./train/\", \n    x_col='filename',\n    y_col='category',\n    target_size=IMAGE_SIZE,\n    class_mode='categorical',\n    batch_size=batch_size,\n    shuffle = False\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y_pred = loaded.predict_generator(validation_generator)\ny_pred_pob = np.max(Y_pred,axis=-1)\ny_pred = np.argmax(Y_pred, axis=-1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"acc = sum(y_pred==validation_generator.classes)/total_validate\nprint('Accuracy: %s' % acc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"wrong_index_array = []\nright_index_array = []\n\nfor i in range(total_validate):\n    if y_pred[i] != validation_generator.classes[i]:\n        wrong_index_array.append(i)\n    else:\n        right_index_array.append(i)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(wrong_index_array)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(10):\n    ind = np.random.choice(right_index_array)\n    image = load_img(\"./train/\" + val_df['filename'][ind])\n    plt.imshow(image)\n    plt.title((\"Cat: \" if y_pred[ind] == 0 else \"Dog: \") + str(y_pred_pob[ind]))\n    plt.axis(\"off\")\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(10):\n    ind = np.random.choice(wrong_index_array)\n    image = load_img(\"./train/\" + val_df['filename'][ind])\n    plt.imshow(image)\n    plt.title((\"Cat: \" if y_pred[ind] == 0 else \"Dog: \") + str(y_pred_pob[ind]))\n    plt.axis(\"off\")\n    plt.show()","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}