{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14774,"databundleVersionId":875431,"sourceType":"competition"},{"sourceId":187731,"sourceType":"datasetVersion","datasetId":80814},{"sourceId":2285981,"sourceType":"datasetVersion","datasetId":1377250}],"dockerImageVersionId":28755,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Block1- Importing the necessary libraries","metadata":{}},{"cell_type":"code","source":"import json\nimport math\nimport os\n\nimport cv2\nfrom PIL import Image\nimport numpy as np\nfrom keras import layers\nfrom keras.applications import DenseNet121\nfrom keras.callbacks import Callback, ModelCheckpoint\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import cohen_kappa_score, accuracy_score\nimport scipy\nimport tensorflow as tf\nfrom tqdm import tqdm\n\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-03-31T12:14:46.446232Z","iopub.execute_input":"2023-03-31T12:14:46.446568Z","iopub.status.idle":"2023-03-31T12:14:49.207613Z","shell.execute_reply.started":"2023-03-31T12:14:46.446503Z","shell.execute_reply":"2023-03-31T12:14:49.206798Z"},"trusted":true},"outputs":[{"name":"stderr","text":"Using TensorFlow backend.\n","output_type":"stream"}],"execution_count":1},{"cell_type":"markdown","source":"## Set random seed for reproducibility.","metadata":{}},{"cell_type":"code","source":"np.random.seed(2019)\ntf.set_random_seed(2019)","metadata":{"execution":{"iopub.status.busy":"2023-03-31T12:15:11.747726Z","iopub.execute_input":"2023-03-31T12:15:11.74802Z","iopub.status.idle":"2023-03-31T12:15:11.752198Z","shell.execute_reply.started":"2023-03-31T12:15:11.747969Z","shell.execute_reply":"2023-03-31T12:15:11.751495Z"},"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"# Block2- Loading & Exploration","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_excel('../input/d/deepakhiremat/deepak/all type.xlsx')\ntest_df = pd.read_excel('../input/d/deepakhiremat/deepak/test3.xlsx')\nprint(train_df.shape)\nprint(test_df.shape)\ntrain_df.head()\ntest_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-31T12:15:16.081114Z","iopub.execute_input":"2023-03-31T12:15:16.081434Z","iopub.status.idle":"2023-03-31T12:15:16.474131Z","shell.execute_reply.started":"2023-03-31T12:15:16.081379Z","shell.execute_reply":"2023-03-31T12:15:16.47323Z"},"trusted":true},"outputs":[{"name":"stdout","text":"(950, 2)\n(400, 1)\n","output_type":"stream"},{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" id_code\n0 0005cfc8afb6\n1 003f0afdcd15\n2 006efc72b638\n3 00836aaacf06\n4 009245722fa4","text/html":"\n\n
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