{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":11848,"databundleVersionId":862157,"sourceType":"competition"},{"sourceId":9937982,"sourceType":"datasetVersion","datasetId":6109719}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom sklearn.model_selection import train_test_split\nimport tifffile as tiff\nimport pickle\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-11-18T07:03:07.049131Z","iopub.execute_input":"2024-11-18T07:03:07.050095Z","iopub.status.idle":"2024-11-18T07:03:23.645046Z","shell.execute_reply.started":"2024-11-18T07:03:07.050045Z","shell.execute_reply":"2024-11-18T07:03:23.643858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with open('/kaggle/input/cancer-model-v1/train_history.pkl', 'rb') as file: \n    history = pickle.load(file)\n    \ncnn = load_model('/kaggle/input/cancer-model-v1/cnn_model.h5')","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:03:23.647231Z","iopub.execute_input":"2024-11-18T07:03:23.647846Z","iopub.status.idle":"2024-11-18T07:03:24.031192Z","shell.execute_reply.started":"2024-11-18T07:03:23.647806Z","shell.execute_reply":"2024-11-18T07:03:24.029988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_datagen = ImageDataGenerator(rescale=1/255)","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:03:24.032700Z","iopub.execute_input":"2024-11-18T07:03:24.033149Z","iopub.status.idle":"2024-11-18T07:03:24.038346Z","shell.execute_reply.started":"2024-11-18T07:03:24.033108Z","shell.execute_reply":"2024-11-18T07:03:24.037007Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/histopathologic-cancer-detection/sample_submission.csv\")\ntest['id'] = test['id'].apply(lambda x:f'{x}.tif')\n\ntest_generator = test_datagen.flow_from_dataframe(\n    dataframe = test,\n    directory = '/kaggle/input/histopathologic-cancer-detection/test',\n    x_col = \"id\",\n    y_col = None,\n    batch_size = 100,\n    seed = 1,\n    shuffle = False,\n    class_mode = None,\n    target_size = (96,96)\n)","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:03:24.039755Z","iopub.execute_input":"2024-11-18T07:03:24.040150Z","iopub.status.idle":"2024-11-18T07:06:38.550274Z","shell.execute_reply.started":"2024-11-18T07:03:24.040113Z","shell.execute_reply":"2024-11-18T07:06:38.549107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_pred = np.argmax(cnn.predict(test_generator), axis=-1)","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:06:38.552840Z","iopub.execute_input":"2024-11-18T07:06:38.553247Z","iopub.status.idle":"2024-11-18T07:14:40.796017Z","shell.execute_reply.started":"2024-11-18T07:06:38.553207Z","shell.execute_reply":"2024-11-18T07:14:40.794625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Submission = test\nSubmission['label'] = test_pred\nSubmission['id'] = Submission['id'].str.replace(r'.tif$', '',regex=True)","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:14:40.797672Z","iopub.execute_input":"2024-11-18T07:14:40.798156Z","iopub.status.idle":"2024-11-18T07:14:40.826771Z","shell.execute_reply.started":"2024-11-18T07:14:40.798113Z","shell.execute_reply":"2024-11-18T07:14:40.825170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Submission","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:33:14.921811Z","iopub.execute_input":"2024-11-18T07:33:14.922563Z","iopub.status.idle":"2024-11-18T07:33:15.252436Z","shell.execute_reply.started":"2024-11-18T07:33:14.922523Z","shell.execute_reply":"2024-11-18T07:33:15.251250Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Submission.to_csv('submission.csv',index=False,header=True)","metadata":{"execution":{"iopub.status.busy":"2024-11-18T07:14:40.828465Z","iopub.execute_input":"2024-11-18T07:14:40.828833Z","iopub.status.idle":"2024-11-18T07:14:40.954160Z","shell.execute_reply.started":"2024-11-18T07:14:40.828794Z","shell.execute_reply":"2024-11-18T07:14:40.952908Z"},"trusted":true},"execution_count":null,"outputs":[]}]}