{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":" import os\nfrom glob import glob\nimport random\nimport time\nimport tensorflow\nimport datetime\nos.environ['KERAS_BACKEND'] = 'tensorflow'\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 3 = INFO, WARNING, and ERROR messages are not printed\n\nfrom tqdm import tqdm\n\nimport numpy as np\nimport pandas as pd\nfrom IPython.display import FileLink\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore')\nimport seaborn as sns \n%matplotlib inline\nfrom IPython.display import display, Image\nimport matplotlib.image as mpimg\nimport cv2\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_files \nfrom keras.utils import np_utils\nfrom sklearn.utils import shuffle\nfrom sklearn.metrics import log_loss\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.preprocessing import image\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nfrom keras.applications.vgg16 import VGG16\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-03T17:26:32.231139Z","iopub.execute_input":"2023-03-03T17:26:32.231828Z","iopub.status.idle":"2023-03-03T17:26:38.790739Z","shell.execute_reply.started":"2023-03-03T17:26:32.231723Z","shell.execute_reply":"2023-03-03T17:26:38.789898Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"Dataset description","metadata":{}},{"cell_type":"code","source":"dataset = pd.read_csv('../input/state-farm-distracted-driver-detection/driver_imgs_list.csv')\ndataset.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-03-03T17:26:38.793102Z","iopub.execute_input":"2023-03-03T17:26:38.793967Z","iopub.status.idle":"2023-03-03T17:26:38.841458Z","shell.execute_reply.started":"2023-03-03T17:26:38.793927Z","shell.execute_reply":"2023-03-03T17:26:38.840566Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":" subject classname img\n0 p002 c0 img_44733.jpg\n1 p002 c0 img_72999.jpg\n2 p002 c0 img_25094.jpg\n3 p002 c0 img_69092.jpg\n4 p002 c0 img_92629.jpg\n5 p002 c0 img_3370.jpg\n6 p002 c0 img_67639.jpg\n7 p002 c0 img_58560.jpg\n8 p002 c0 img_35779.jpg\n9 p002 c0 img_10012.jpg","text/html":"\n\n
\n \n \n | \n subject | \n classname | \n img | \n
\n \n \n \n | 0 | \n p002 | \n c0 | \n img_44733.jpg | \n
\n \n | 1 | \n p002 | \n c0 | \n img_72999.jpg | \n
\n \n | 2 | \n p002 | \n c0 | \n img_25094.jpg | \n
\n \n | 3 | \n p002 | \n c0 | \n img_69092.jpg | \n
\n \n | 4 | \n p002 | \n c0 | \n img_92629.jpg | \n
\n \n | 5 | \n p002 | \n c0 | \n img_3370.jpg | \n
\n \n | 6 | \n p002 | \n c0 | \n img_67639.jpg | \n
\n \n | 7 | \n p002 | \n c0 | \n img_58560.jpg | \n
\n \n | 8 | \n p002 | \n c0 | \n img_35779.jpg | \n
\n \n | 9 | \n p002 | \n c0 | \n img_10012.jpg | \n
\n \n
\n