{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":29653,"databundleVersionId":2420395,"sourceType":"competition"},{"sourceId":7771919,"sourceType":"datasetVersion","datasetId":4546744},{"sourceId":11958008,"sourceType":"datasetVersion","datasetId":7518651},{"sourceId":13608975,"sourceType":"datasetVersion","datasetId":8648133},{"sourceId":13650489,"sourceType":"datasetVersion","datasetId":8678039}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"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/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install kaggle\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:47:54.85935Z","iopub.execute_input":"2025-11-04T17:47:54.860146Z","iopub.status.idle":"2025-11-04T17:47:58.763018Z","shell.execute_reply.started":"2025-11-04T17:47:54.860111Z","shell.execute_reply":"2025-11-04T17:47:58.762293Z"},"jupyter":{"outputs_hidden":true},"collapsed":true},"outputs":[{"name":"stdout","text":"Requirement already satisfied: kaggle in /usr/local/lib/python3.11/dist-packages (1.7.4.5)\nRequirement already satisfied: bleach in /usr/local/lib/python3.11/dist-packages (from kaggle) (6.2.0)\nRequirement already satisfied: certifi>=14.05.14 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2025.8.3)\nRequirement already satisfied: charset-normalizer in 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/usr/local/lib/python3.11/dist-packages (from kaggle) (4.67.1)\nRequirement already satisfied: urllib3>=1.15.1 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.5.0)\nRequirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from kaggle) (0.5.1)\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"!pip install nibabel matplotlib scikit-learn tensorflow keras\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:02.982038Z","iopub.execute_input":"2025-11-04T17:48:02.982825Z","iopub.status.idle":"2025-11-04T17:48:06.278098Z","shell.execute_reply.started":"2025-11-04T17:48:02.982795Z","shell.execute_reply":"2025-11-04T17:48:06.277375Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: nibabel in /usr/local/lib/python3.11/dist-packages (5.3.2)\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.7.2)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.2.2)\nRequirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.18.0)\nRequirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (3.8.0)\nRequirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.11/dist-packages (from nibabel) (6.5.2)\nRequirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.11/dist-packages (from nibabel) (1.26.4)\nRequirement already satisfied: packaging>=20 in /usr/local/lib/python3.11/dist-packages (from nibabel) (25.0)\nRequirement already satisfied: typing-extensions>=4.6 in /usr/local/lib/python3.11/dist-packages (from nibabel) (4.15.0)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.2)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.59.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\nRequirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.3.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.9.0.post0)\nRequirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.15.3)\nRequirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.5.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.6.0)\nRequirement already 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tensorflow) (3.20.3)\nRequirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.32.5)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (75.2.0)\nRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.0)\nRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.1.0)\nRequirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.17.2)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.75.1)\nRequirement already satisfied: tensorboard<2.19,>=2.18 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.18.0)\nRequirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.14.0)\nRequirement already satisfied: ml-dtypes<0.5.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.4.1)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\nRequirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras) (14.1.0)\nRequirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras) (0.1.0)\nRequirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras) (0.16.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy>=1.22->nibabel) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy>=1.22->nibabel) (1.2.4)\nRequirement already satisfied: mkl_umath in 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(2025.8.3)\nRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.8.2)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\nRequirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\nRequirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras) (4.0.0)\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras) (2.19.2)\nRequirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.22->nibabel) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.22->nibabel) (2022.2.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.22->nibabel) (1.4.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.22->nibabel) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.22->nibabel) (2024.2.0)\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"!pip install opencv-python-headless nibabel scikit-image matplotlib pandas Pillow tensorflow keras\n","metadata":{"trusted":true,"collapsed":true,"jupyter":{"outputs_hidden":true},"execution":{"iopub.status.busy":"2025-11-04T17:48:07.989631Z","iopub.execute_input":"2025-11-04T17:48:07.990189Z","iopub.status.idle":"2025-11-04T17:48:15.191727Z","shell.execute_reply.started":"2025-11-04T17:48:07.990164Z","shell.execute_reply":"2025-11-04T17:48:15.190793Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.11/dist-packages (4.12.0.88)\nRequirement already satisfied: nibabel in /usr/local/lib/python3.11/dist-packages (5.3.2)\nRequirement already satisfied: scikit-image in /usr/local/lib/python3.11/dist-packages (0.25.2)\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.7.2)\nRequirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.11/dist-packages (11.3.0)\nRequirement already 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satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras) (14.1.0)\nRequirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras) (0.1.0)\nRequirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras) (0.16.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.3)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.5.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorflow) (2025.8.3)\nRequirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.8.2)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (0.7.2)\nRequirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\nRequirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras) (4.0.0)\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras) (2.19.2)\nRequirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\nDownloading numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.5 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.5/19.5 MB\u001b[0m \u001b[31m91.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: numpy\n  Attempting uninstall: numpy\n    Found existing installation: numpy 1.26.4\n    Uninstalling numpy-1.26.4:\n      Successfully uninstalled numpy-1.26.4\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nbigframes 2.12.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\ngensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.2 which is incompatible.\ngensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.15.3 which is incompatible.\nmkl-umath 0.1.1 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.2 which is incompatible.\nmkl-random 1.2.4 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.2 which is incompatible.\nmkl-fft 1.3.8 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.2 which is incompatible.\ndatasets 4.1.1 requires pyarrow>=21.0.0, but you have pyarrow 19.0.1 which is incompatible.\nonnx 1.18.0 requires protobuf>=4.25.1, but you have protobuf 3.20.3 which is incompatible.\ngoogle-colab 1.0.0 requires google-auth==2.38.0, but you have google-auth 2.40.3 which is incompatible.\ngoogle-colab 1.0.0 requires notebook==6.5.7, but you have notebook 6.5.4 which is incompatible.\ngoogle-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\ngoogle-colab 1.0.0 requires requests==2.32.3, but you have requests 2.32.5 which is incompatible.\ngoogle-colab 1.0.0 requires tornado==6.4.2, but you have tornado 6.5.2 which is incompatible.\ndopamine-rl 4.1.2 requires gymnasium>=1.0.0, but you have gymnasium 0.29.0 which is incompatible.\nbigframes 2.12.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.31.0, but you have google-cloud-bigquery 3.25.0 which is incompatible.\nbigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.1.0 which is incompatible.\ngradio 5.38.1 requires pydantic<2.12,>=2.0, but you have pydantic 2.12.0a1 which is incompatible.\nimbalanced-learn 0.13.0 requires scikit-learn<2,>=1.3.2, but you have scikit-learn 1.2.2 which is incompatible.\npandas-gbq 0.29.2 requires google-api-core<3.0.0,>=2.10.2, but you have google-api-core 1.34.1 which is incompatible.\ntransformers 4.53.3 requires huggingface-hub<1.0,>=0.30.0, but you have huggingface-hub 1.0.0rc2 which is incompatible.\nplotnine 0.14.5 requires matplotlib>=3.8.0, but you have matplotlib 3.7.2 which is incompatible.\npylibcugraph-cu12 25.6.0 requires pylibraft-cu12==25.6.*, but you have pylibraft-cu12 25.2.0 which is incompatible.\npylibcugraph-cu12 25.6.0 requires rmm-cu12==25.6.*, but you have rmm-cu12 25.2.0 which is incompatible.\numap-learn 0.5.9.post2 requires scikit-learn>=1.6, but you have scikit-learn 1.2.2 which is incompatible.\nmlxtend 0.23.4 requires scikit-learn>=1.3.1, but you have scikit-learn 1.2.2 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed numpy-2.0.2\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"pip install --upgrade pip","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:20.080137Z","iopub.execute_input":"2025-11-04T17:48:20.080364Z","iopub.status.idle":"2025-11-04T17:48:22.084602Z","shell.execute_reply.started":"2025-11-04T17:48:20.080343Z","shell.execute_reply":"2025-11-04T17:48:22.083805Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: pip in /usr/local/lib/python3.11/dist-packages (25.3)\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"execution_count":7},{"cell_type":"markdown","source":"dataset path","metadata":{}},{"cell_type":"code","source":"TRAIN_DATASET_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:24.332226Z","iopub.execute_input":"2025-11-04T17:48:24.332884Z","iopub.status.idle":"2025-11-04T17:48:24.336584Z","shell.execute_reply.started":"2025-11-04T17:48:24.332853Z","shell.execute_reply":"2025-11-04T17:48:24.335941Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"import nibabel as nib  \ntest_image_flair = nib.load(\"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00000-000/BraTS-GLI-00000-000-seg.nii\").get_fdata()\nprint(\"Shape: \", test_image_flair.shape)\nprint(\"Dtype: \", test_image_flair.dtype)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:27.60468Z","iopub.execute_input":"2025-11-04T17:48:27.605374Z","iopub.status.idle":"2025-11-04T17:48:28.70752Z","shell.execute_reply.started":"2025-11-04T17:48:27.605351Z","shell.execute_reply":"2025-11-04T17:48:28.706827Z"}},"outputs":[{"name":"stdout","text":"Shape:  (240, 240, 155)\nDtype:  float64\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"print(\"Min: \", test_image_flair.min())\nprint(\"Max: \", test_image_flair.max())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T06:41:14.782859Z","iopub.execute_input":"2025-11-04T06:41:14.783459Z","iopub.status.idle":"2025-11-04T06:41:14.806203Z","shell.execute_reply.started":"2025-11-04T06:41:14.783436Z","shell.execute_reply":"2025-11-04T06:41:14.805661Z"}},"outputs":[{"name":"stdout","text":"Min:  0.0\nMax:  3.0\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:46.874694Z","iopub.execute_input":"2025-11-04T17:48:46.875297Z","iopub.status.idle":"2025-11-04T17:48:47.373493Z","shell.execute_reply.started":"2025-11-04T17:48:46.875276Z","shell.execute_reply":"2025-11-04T17:48:47.372856Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"test_image_flair = scaler.fit_transform(test_image_flair.reshape(-1, test_image_flair.shape[-1])).reshape(test_image_flair.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T06:41:18.303825Z","iopub.execute_input":"2025-11-04T06:41:18.304677Z","iopub.status.idle":"2025-11-04T06:41:18.501481Z","shell.execute_reply.started":"2025-11-04T06:41:18.304647Z","shell.execute_reply":"2025-11-04T06:41:18.50067Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"print(\"Min: \", test_image_flair.min())\nprint(\"Max: \", test_image_flair.max())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T06:41:20.291034Z","iopub.execute_input":"2025-11-04T06:41:20.291293Z","iopub.status.idle":"2025-11-04T06:41:20.313928Z","shell.execute_reply.started":"2025-11-04T06:41:20.291272Z","shell.execute_reply":"2025-11-04T06:41:20.313302Z"}},"outputs":[{"name":"stdout","text":"Min:  0.0\nMax:  1.0\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"import os\nimport nibabel as nib\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\n\n# ✅ Path to your BRATS dataset\nDATASET_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"\nscaler = MinMaxScaler()\n\n# --- Helper: Explore dataset structure ---\ndef explore_brats_dataset(path=DATASET_PATH):\n    patients = sorted([p for p in os.listdir(path) if p.startswith(\"BraTS-GLI\")])\n    print(f\"Total Patients Found: {len(patients)}\\n\")\n    \n    # check modality availability\n    records = []\n    for pid in patients[:5]:  # explore first 5\n        ppath = os.path.join(path, pid)\n        files = os.listdir(ppath)\n        records.append({\n            \"Patient_ID\": pid,\n            \"Files\": files\n        })\n    df = pd.DataFrame(records)\n    print(df)\n    return patients\n\npatients = explore_brats_dataset()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:48:52.063432Z","iopub.execute_input":"2025-11-04T17:48:52.064165Z","iopub.status.idle":"2025-11-04T17:48:52.086987Z","shell.execute_reply.started":"2025-11-04T17:48:52.064143Z","shell.execute_reply":"2025-11-04T17:48:52.086428Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"Total Patients Found: 1251\n\n            Patient_ID                                              Files\n0  BraTS-GLI-00000-000  [BraTS-GLI-00000-000-t1n.nii, BraTS-GLI-00000-...\n1  BraTS-GLI-00002-000  [BraTS-GLI-00002-000-t1c.nii, BraTS-GLI-00002-...\n2  BraTS-GLI-00003-000  [BraTS-GLI-00003-000-seg.nii, BraTS-GLI-00003-...\n3  BraTS-GLI-00005-000  [BraTS-GLI-00005-000-seg.nii, BraTS-GLI-00005-...\n4  BraTS-GLI-00006-000  [BraTS-GLI-00006-000-t1n.nii, BraTS-GLI-00006-...\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"def load_and_rescale_modalities(patient_id, base_path=DATASET_PATH):\n    \"\"\"\n    Load t1n, t1c, t2f, t2w, seg from a BraTS 2023 patient folder.\n    Rescale each image to [0,1] using MinMaxScaler.\n    Returns: 4 modalities + segmentation (numpy arrays)\n    \"\"\"\n    pdir = os.path.join(base_path, patient_id)\n\n    def rescale_volume(img_path):\n        img = nib.load(img_path).get_fdata()\n        flat = img.reshape(-1, 1)\n        scaled = scaler.fit_transform(flat).reshape(img.shape)\n        return scaled.astype(np.float32)\n\n    # load modalities according to new naming\n    t1n = rescale_volume(os.path.join(pdir, f\"{patient_id}-t1n.nii\"))\n    t1c = rescale_volume(os.path.join(pdir, f\"{patient_id}-t1c.nii\"))\n    t2f = rescale_volume(os.path.join(pdir, f\"{patient_id}-t2f.nii\"))\n    t2w = rescale_volume(os.path.join(pdir, f\"{patient_id}-t2w.nii\"))\n    seg = nib.load(os.path.join(pdir, f\"{patient_id}-seg.nii\")).get_fdata().astype(np.uint8)\n\n    return t1n, t1c, t2f, t2w, seg\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:00.008561Z","iopub.execute_input":"2025-11-04T17:49:00.009142Z","iopub.status.idle":"2025-11-04T17:49:00.014511Z","shell.execute_reply.started":"2025-11-04T17:49:00.00912Z","shell.execute_reply":"2025-11-04T17:49:00.013717Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# Pick one case for inspection\npid = patients[0]\nprint(f\"\\nInspecting patient: {pid}\")\n\nt1n, t1c, t2f, t2w, seg = load_and_rescale_modalities(pid)\n\nprint(\"Shapes:\")\nprint(\"T1n:\", t1n.shape)\nprint(\"T1c:\", t1c.shape)\nprint(\"T2f:\", t2f.shape)\nprint(\"T2w:\", t2w.shape)\nprint(\"Seg:\", seg.shape)\n\nprint(\"\\nValue ranges:\")\nprint(\"T1n:\", (np.min(t1n), np.max(t1n)))\nprint(\"Seg unique labels:\", np.unique(seg))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:03.455671Z","iopub.execute_input":"2025-11-04T17:49:03.456224Z","iopub.status.idle":"2025-11-04T17:49:05.0667Z","shell.execute_reply.started":"2025-11-04T17:49:03.456202Z","shell.execute_reply":"2025-11-04T17:49:05.065828Z"}},"outputs":[{"name":"stdout","text":"\nInspecting patient: BraTS-GLI-00000-000\nShapes:\nT1n: (240, 240, 155)\nT1c: (240, 240, 155)\nT2f: (240, 240, 155)\nT2w: (240, 240, 155)\nSeg: (240, 240, 155)\n\nValue ranges:\nT1n: (0.0, 1.0)\nSeg unique labels: [0 1 2 3]\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# Stack modalities into one 4-channel 3D array (C, H, W, D)\nimage_4ch = np.stack([t1n, t1c, t2f, t2w], axis=0)\nprint(\"\\nStacked image shape:\", image_4ch.shape)\n\n# save temporarily as npy for faster reuse\nos.makedirs(\"/content/preprocessed\", exist_ok=True)\nnp.save(f\"/content/preprocessed/{pid}_img.npy\", image_4ch)\nnp.save(f\"/content/preprocessed/{pid}_mask.npy\", seg)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:05.741839Z","iopub.execute_input":"2025-11-04T17:49:05.742158Z","iopub.status.idle":"2025-11-04T17:49:05.897093Z","shell.execute_reply.started":"2025-11-04T17:49:05.742135Z","shell.execute_reply":"2025-11-04T17:49:05.896433Z"}},"outputs":[{"name":"stdout","text":"\nStacked image shape: (4, 240, 240, 155)\n","output_type":"stream"}],"execution_count":14},{"cell_type":"code","source":"import nibabel as nib\nimport matplotlib.pyplot as plt\n\n# ✅ Set slice number\nslice_num = 95\nprint(f\"Slice Number: {slice_num}\")\n\n# ✅ Define patient folder (adjust if needed)\npatient_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00011-000\"\n\n# ✅ Load NIfTI files (convert to numpy arrays)\nt1n  = nib.load(f\"{patient_path}/BraTS-GLI-00011-000-t1n.nii\").get_fdata()\nt1c  = nib.load(f\"{patient_path}/BraTS-GLI-00011-000-t1c.nii\").get_fdata()\nt2f  = nib.load(f\"{patient_path}/BraTS-GLI-00011-000-t2f.nii\").get_fdata()\nt2w  = nib.load(f\"{patient_path}/BraTS-GLI-00011-000-t2w.nii\").get_fdata()\nmask = nib.load(f\"{patient_path}/BraTS-GLI-00011-000-seg.nii\").get_fdata()\n\n# ✅ Plot each modality and segmentation mask\nplt.figure(figsize=(12, 8))\n\nplt.subplot(2, 3, 1)\nplt.imshow(t1n[:, :, slice_num], cmap='gray')\nplt.title('T1n (Native)')\n\nplt.subplot(2, 3, 2)\nplt.imshow(t1c[:, :, slice_num], cmap='gray')\nplt.title('T1c (Contrast Enhanced)')\n\nplt.subplot(2, 3, 3)\nplt.imshow(t2f[:, :, slice_num], cmap='gray')\nplt.title('T2-FLAIR')\n\nplt.subplot(2, 3, 4)\nplt.imshow(t2w[:, :, slice_num], cmap='gray')\nplt.title('T2-weighted')\n\nplt.subplot(2, 3, 5)\nplt.imshow(mask[:, :, slice_num])\nplt.title('Segmentation Mask')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:29.433749Z","iopub.execute_input":"2025-11-04T17:49:29.434251Z","iopub.status.idle":"2025-11-04T17:49:30.294152Z","shell.execute_reply.started":"2025-11-04T17:49:29.434228Z","shell.execute_reply":"2025-11-04T17:49:30.293334Z"}},"outputs":[{"name":"stdout","text":"Slice Number: 95\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 5 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"import nibabel as nib\n\n# Define paths\nmodality_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00011-000/BraTS-GLI-00011-000-t1n.nii\"\nseg_path      = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00011-000/BraTS-GLI-00011-000-seg.nii\"\n\n# Load NIfTI files\nmodality_img = nib.load(modality_path).get_fdata()\nseg_img = nib.load(seg_path).get_fdata()\n\n# Print shapes\nprint(\"Modality shape:\", modality_img.shape)\nprint(\"Segmentation shape:\", seg_img.shape)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:37.536952Z","iopub.execute_input":"2025-11-04T17:49:37.537705Z","iopub.status.idle":"2025-11-04T17:49:37.594589Z","shell.execute_reply.started":"2025-11-04T17:49:37.537681Z","shell.execute_reply":"2025-11-04T17:49:37.593913Z"}},"outputs":[{"name":"stdout","text":"Modality shape: (240, 240, 155)\nSegmentation shape: (240, 240, 155)\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"import nibabel as nib\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import rotate\n\n# Load a T1CE example\nt1ce_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00000-000/BraTS-GLI-00000-000-t1c.nii\"\ntest_image_t1ce = nib.load(t1ce_path).get_fdata()\n\n# Choose a slice index\nslice_num = 95\nprint(f\"Slice number: {slice_num}\")\n\nplt.figure(figsize=(15, 5))\n\n# --- 1️⃣ Axial (Transverse) View ---\nplt.subplot(1, 3, 1)\nplt.imshow(test_image_t1ce[:, :, slice_num], cmap='gray')\nplt.title('Axial (Transverse) View')\nplt.axis('off')\n\n# --- 2️⃣ Coronal (Frontal) View ---\nplt.subplot(1, 3, 2)\nplt.imshow(rotate(test_image_t1ce[:, slice_num, :], 90, reshape=True), cmap='gray')\nplt.title('Coronal (Frontal) View')\nplt.axis('off')\n\n# --- 3️⃣ Sagittal View ---\nplt.subplot(1, 3, 3)\nplt.imshow(rotate(test_image_t1ce[slice_num, :, :], 90, reshape=True), cmap='gray')\nplt.title('Sagittal View')\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:39.416574Z","iopub.execute_input":"2025-11-04T17:49:39.416849Z","iopub.status.idle":"2025-11-04T17:49:39.744328Z","shell.execute_reply.started":"2025-11-04T17:49:39.416828Z","shell.execute_reply":"2025-11-04T17:49:39.743539Z"}},"outputs":[{"name":"stdout","text":"Slice number: 95\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x500 with 3 Axes>","image/png":"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nibabel as nib\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import rotate\nfrom skimage.util import montage\n\n# Load a T1CE volume (example)\nt1ce_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00000-000/BraTS-GLI-00000-000-t1c.nii\"\ntest_image_t1ce = nib.load(t1ce_path).get_fdata()\n\n# Create montage from all slices\nmontage_img = montage(test_image_t1ce, grid_shape=None, padding_width=2)\n\n# Plot rotated montage\nplt.figure(figsize=(10, 10))\nplt.imshow(rotate(montage_img, 90, reshape=True), cmap='gray')\nplt.title('Montage of T1CE Slices (All Slices)')\nplt.axis('off')\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:42.456587Z","iopub.execute_input":"2025-11-04T17:49:42.457292Z","iopub.status.idle":"2025-11-04T17:49:45.05937Z","shell.execute_reply.started":"2025-11-04T17:49:42.457268Z","shell.execute_reply":"2025-11-04T17:49:45.058573Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nplt.imshow(rotate(montage(test_image_t1ce[50:-50,:,:]), 90, reshape=True), cmap='gray')\nplt.title('Cropped Montage (Slices 50–105)')\nplt.axis('off')\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:47.615642Z","iopub.execute_input":"2025-11-04T17:49:47.616208Z","iopub.status.idle":"2025-11-04T17:49:49.103711Z","shell.execute_reply.started":"2025-11-04T17:49:47.616184Z","shell.execute_reply":"2025-11-04T17:49:49.102958Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"!pip install matplotlib\n!pip install scipy\n!pip install scikit-image\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:51.867702Z","iopub.execute_input":"2025-11-04T17:49:51.868293Z","iopub.status.idle":"2025-11-04T17:49:57.482317Z","shell.execute_reply.started":"2025-11-04T17:49:51.868269Z","shell.execute_reply":"2025-11-04T17:49:57.481528Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.7.2)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.2)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.59.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\nRequirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.0.2)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (25.0)\nRequirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.3.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.9.0.post0)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\nRequirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.15.3)\nRequirement already satisfied: numpy<2.5,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (2.0.2)\nRequirement already satisfied: scikit-image in /usr/local/lib/python3.11/dist-packages (0.25.2)\nRequirement already satisfied: numpy>=1.24 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2.0.2)\nRequirement already satisfied: scipy>=1.11.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (1.15.3)\nRequirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (3.5)\nRequirement already satisfied: pillow>=10.1 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (11.3.0)\nRequirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2.37.0)\nRequirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2025.6.11)\nRequirement already satisfied: packaging>=21 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (25.0)\nRequirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (0.4)\n","output_type":"stream"}],"execution_count":21},{"cell_type":"code","source":"import nibabel as nib\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import rotate\nfrom skimage.util import montage\n\n# --- Load the segmentation volume ---\nseg_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00000-000/BraTS-GLI-00000-000-seg.nii\"\ntest_image_seg = nib.load(seg_path).get_fdata()\n\n# --- Create and visualize montage (skip empty slices) ---\nplt.figure(figsize=(10, 10))\nplt.imshow(rotate(montage(test_image_seg[50:-50, :, :]), 90, reshape=True), cmap='gray')\nplt.title('Segmentation Mask Montage (Slices 50–105)')\nplt.axis('off')\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:49:58.249315Z","iopub.execute_input":"2025-11-04T17:49:58.249839Z","iopub.status.idle":"2025-11-04T17:49:59.867724Z","shell.execute_reply.started":"2025-11-04T17:49:58.249811Z","shell.execute_reply":"2025-11-04T17:49:59.866948Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 1 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\n"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"import nibabel as nib\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\n\n# Load segmentation if not already loaded\nseg_path = \"/kaggle/input/brats2023-full/BraTS2023/BraTS-GLI-00011-000/BraTS-GLI-00011-000-seg.nii\"\ntest_image_seg = nib.load(seg_path).get_fdata()\n\n# Custom colormap for BraTS segmentation labels\n# Labels: 0=background, 1=necrotic, 2=edema, 4=enhancing tumor\ncmap = mcolors.ListedColormap(['#440054', '#3b528b', '#18b880', '#e6d74f'])\nbounds = [-0.5, 0.5, 1.5, 2.5, 4.5]\nnorm = mcolors.BoundaryNorm(bounds, cmap.N)\n\n# Select slice\nslice_num = 95\n\nplt.figure(figsize=(6, 6))\nplt.imshow(test_image_seg[:, :, slice_num], cmap=cmap, norm=norm)\ncbar = plt.colorbar(ticks=[0, 1, 2, 4])\ncbar.ax.set_yticklabels(['Background', 'Necrotic', 'Edema', 'Enhancing'])\nplt.title(f'Segmentation Mask (Slice {slice_num})')\nplt.axis('off')\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:02.402395Z","iopub.execute_input":"2025-11-04T17:50:02.402881Z","iopub.status.idle":"2025-11-04T17:50:02.567014Z","shell.execute_reply.started":"2025-11-04T17:50:02.402861Z","shell.execute_reply":"2025-11-04T17:50:02.566425Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x600 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Choose a slice index\nslice_num = 95\n\n# --- Isolate each segmentation class ---\nseg_0 = test_image_seg.copy()\nseg_0[seg_0 != 0] = np.nan\n\nseg_1 = test_image_seg.copy()\nseg_1[seg_1 != 1] = np.nan\n\nseg_2 = test_image_seg.copy()\nseg_2[seg_2 != 2] = np.nan\n\nseg_4 = test_image_seg.copy()\nseg_4[seg_4 != 4] = np.nan\n\n# --- Define legend ---\nclass_names = ['Background (0)', 'Non-Enhancing (1)', 'Edema (2)', 'Enhancing (4)']\nlegend = [plt.Rectangle((0, 0), 1, 1, color=cmap(i), label=class_names[i]) for i in range(len(class_names))]\n\n# --- Plot ---\nfig, ax = plt.subplots(1, 5, figsize=(20, 5))\n\nax[0].imshow(test_image_seg[:, :, slice_num], cmap=cmap, norm=norm)\nax[0].set_title('Original Segmentation')\nax[0].legend(handles=legend, loc='lower left', fontsize=8)\n\nax[1].imshow(seg_0[:, :, slice_num], cmap=cmap, norm=norm)\nax[1].set_title('Background (Class 0)')\n\nax[2].imshow(seg_1[:, :, slice_num], cmap=cmap, norm=norm)\nax[2].set_title('Non-Enhancing (Class 1)')\n\nax[3].imshow(seg_2[:, :, slice_num], cmap=cmap, norm=norm)\nax[3].set_title('Edema (Class 2)')\n\nax[4].imshow(seg_4[:, :, slice_num], cmap=cmap, norm=norm)\nax[4].set_title('Enhancing (Class 4)')\n\nfor a in ax:\n    a.axis('off')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:05.127487Z","iopub.execute_input":"2025-11-04T17:50:05.127978Z","iopub.status.idle":"2025-11-04T17:50:05.878714Z","shell.execute_reply.started":"2025-11-04T17:50:05.127954Z","shell.execute_reply":"2025-11-04T17:50:05.877964Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x500 with 5 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os\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\n\n# Path to your dataset\nTRAIN_DATASET_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"\n\n# --- List of all patient directories ---\ntrain_and_val_directories = [f.path for f in os.scandir(TRAIN_DATASET_PATH) if f.is_dir()]\n\n# --- Helper: Extract patient IDs from folder paths ---\ndef pathListIntoIds(dirList):\n    return [os.path.basename(path) for path in dirList]\n\ntrain_and_test_ids = pathListIntoIds(train_and_val_directories)\n\n# --- Split into Train / Validation / Test ---\ntrain_test_ids, val_ids = train_test_split(train_and_test_ids, test_size=0.2, random_state=42)\ntrain_ids, test_ids = train_test_split(train_test_ids, test_size=0.15, random_state=42)\n\n# --- Print summary ---\nprint(f\"Train length: {len(train_ids)}\")\nprint(f\"Validation length: {len(val_ids)}\")\nprint(f\"Test length: {len(test_ids)}\")\n\n# --- Visualize data distribution ---\nplt.figure(figsize=(6, 4))\nplt.bar([\"Train\", \"Valid\", \"Test\"],\n        [len(train_ids), len(val_ids), len(test_ids)],\n        color=['green', 'red', 'blue'])\nplt.ylabel('Number of Patients')\nplt.title('Data Distribution across Sets')\nplt.grid(axis='y', linestyle='--', alpha=0.6)\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:08.030482Z","iopub.execute_input":"2025-11-04T17:50:08.031193Z","iopub.status.idle":"2025-11-04T17:50:08.76883Z","shell.execute_reply.started":"2025-11-04T17:50:08.031168Z","shell.execute_reply":"2025-11-04T17:50:08.768239Z"}},"outputs":[{"name":"stdout","text":"Train length: 850\nValidation length: 251\nTest length: 150\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"import os\nimport numpy as np\nimport nibabel as nib\nimport tensorflow as tf\nimport cv2\nfrom tensorflow import keras\nfrom scipy.ndimage import zoom\n\n# --- Define segmentation classes ---\nSEGMENT_CLASSES = {\n    0: 'NOT tumor',\n    1: 'NECROTIC/CORE',    # Non-enhancing core\n    2: 'EDEMA',\n    3: 'ENHANCING'         # Original label 4 -> converted to 3\n}\n\n# --- Config ---\nVOLUME_SLICES = 100\nVOLUME_START_AT = 22\nIMG_SIZE = 128\nPATCH_SIZE = (128, 128, 128)  # For 3D generator\nTRAIN_DATASET_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"\nRADIOGENOMIC_LABELS_PATH = \"/kaggle/input/brats2021-radiogenomic-classification/train_labels.csv\"  # optional, for radiogenomics\n\n# --- Data Generator ---\nclass DataGenerator(keras.utils.Sequence):\n    'Generates data for Keras (2-channel: FLAIR + T1CE)'\n    def __init__(self, list_IDs, dim=(IMG_SIZE, IMG_SIZE), batch_size=1, n_channels=2, shuffle=True):\n        self.dim = dim\n        self.batch_size = batch_size\n        self.list_IDs = list_IDs\n        self.n_channels = n_channels\n        self.shuffle = shuffle\n        self.on_epoch_end()\n\n    def __len__(self):\n        'Denotes the number of batches per epoch'\n        return int(np.floor(len(self.list_IDs) / self.batch_size))\n\n    def __getitem__(self, index):\n        'Generate one batch of data'\n        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n        Batch_ids = [self.list_IDs[k] for k in indexes]\n        X, Y = self.__data_generation(Batch_ids)\n        return X, Y\n\n    def on_epoch_end(self):\n        'Shuffle at the end of each epoch'\n        self.indexes = np.arange(len(self.list_IDs))\n        if self.shuffle:\n            np.random.shuffle(self.indexes)\n\n    def __data_generation(self, Batch_ids):\n        'Generates data containing batch_size samples'\n        X = np.zeros((self.batch_size * VOLUME_SLICES, *self.dim, self.n_channels), dtype=np.float32)\n        y = np.zeros((self.batch_size * VOLUME_SLICES, 240, 240), dtype=np.uint8)\n\n        for c, pid in enumerate(Batch_ids):\n            case_path = os.path.join(TRAIN_DATASET_PATH, pid)\n\n            # BraTS 2023 modality file names\n            flair_path = os.path.join(case_path, f\"{pid}-t2f.nii\")\n            t1ce_path  = os.path.join(case_path, f\"{pid}-t1c.nii\")\n            seg_path   = os.path.join(case_path, f\"{pid}-seg.nii\")\n\n            # Load modalities\n            flair = nib.load(flair_path).get_fdata()\n            t1ce  = nib.load(t1ce_path).get_fdata()\n            seg   = nib.load(seg_path).get_fdata()\n\n            for j in range(VOLUME_SLICES):\n                slice_idx = j + VOLUME_START_AT\n                if slice_idx >= flair.shape[2]:\n                    break\n                X[j + VOLUME_SLICES*c, :, :, 0] = cv2.resize(flair[:, :, slice_idx], self.dim)\n                X[j + VOLUME_SLICES*c, :, :, 1] = cv2.resize(t1ce[:, :, slice_idx], self.dim)\n                y[j + VOLUME_SLICES*c] = seg[:, :, slice_idx]\n\n        # Normalize X\n        X /= np.max(X)\n\n        # Fix label 4 -> 3 and one-hot encode masks\n        y[y == 4] = 3\n        mask = tf.one_hot(y, depth=4)\n        Y = tf.image.resize(mask, self.dim)\n        return X, Y\n\n\n# ======================================================\n# 3D Radiogenomic Data Generator\n# ======================================================\nclass DataGenerator3D(keras.utils.Sequence):\n    \"\"\"Generates 3D MRI volumes (FLAIR + T1CE) + segmentation + optional IDH labels.\"\"\"\n\n    def __init__(self, list_IDs, patch_size=PATCH_SIZE, batch_size=1, shuffle=True, use_radiogenomics=False):\n        self.patch_size = patch_size\n        self.batch_size = batch_size\n        self.list_IDs = list_IDs\n        self.shuffle = shuffle\n        self.use_radiogenomics = use_radiogenomics\n        self.on_epoch_end()\n\n    def __len__(self):\n        \"\"\"Number of batches per epoch.\"\"\"\n        return int(np.floor(len(self.list_IDs) / self.batch_size))\n\n    def on_epoch_end(self):\n        \"\"\"Shuffle indexes after each epoch.\"\"\"\n        self.indexes = np.arange(len(self.list_IDs))\n        if self.shuffle:\n            np.random.shuffle(self.indexes)\n\n    def __normalize(self, vol):\n        vol = np.nan_to_num(vol)\n        vmin, vmax = np.min(vol), np.max(vol)\n        return (vol - vmin) / (vmax - vmin + 1e-6)\n\n    def __resize_volume(self, img):\n        \"\"\"Resize 3D volume to PATCH_SIZE.\"\"\"\n        zoom_factors = (\n            self.patch_size[0] / img.shape[0],\n            self.patch_size[1] / img.shape[1],\n            self.patch_size[2] / img.shape[2],\n        )\n        return zoom(img, zoom_factors, order=1)  # linear interpolation\n\n    def __getitem__(self, index):\n        \"\"\"Generate one batch.\"\"\"\n        indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n        batch_ids = [self.list_IDs[k] for k in indexes]\n        return self.__data_generation(batch_ids)\n\n    def __data_generation(self, batch_ids):\n        \"\"\"Load and preprocess MRI volumes.\"\"\"\n        X = np.zeros((self.batch_size, *self.patch_size, 2), dtype=np.float32)\n        Y = np.zeros((self.batch_size, *self.patch_size), dtype=np.uint8)\n        idh_labels = np.zeros((self.batch_size, 1), dtype=np.float32)\n\n        for i, pid in enumerate(batch_ids):\n            case_path = os.path.join(TRAIN_DATASET_PATH, pid)\n\n            # --- Load volumes ---\n            flair = nib.load(os.path.join(case_path, f\"{pid}-t2f.nii\")).get_fdata()\n            t1ce  = nib.load(os.path.join(case_path, f\"{pid}-t1c.nii\")).get_fdata()\n            seg   = nib.load(os.path.join(case_path, f\"{pid}-seg.nii\")).get_fdata()\n\n            # --- Normalize ---\n            flair = self.__normalize(flair)\n            t1ce  = self.__normalize(t1ce)\n\n            # --- Resize ---\n            flair = self.__resize_volume(flair)\n            t1ce  = self.__resize_volume(t1ce)\n            seg   = self.__resize_volume(seg)\n\n            # --- Stack & assign ---\n            X[i, ...] = np.stack([flair, t1ce], axis=-1)\n            seg[seg == 4] = 3\n            Y[i, ...] = seg.astype(np.uint8)\n\n            # --- Optional IDH label ---\n            if self.use_radiogenomics:\n                idh_labels[i, 0] = self.idh_dict.get(pid, 0)\n\n        # --- One-hot encode mask ---\n        Y = tf.one_hot(Y, depth=4, dtype=tf.float32)\n\n        # --- Return depending on setup ---\n        if self.use_radiogenomics:\n            return X, {\"seg_output\": Y, \"idh_status\": idh_labels}\n        else:\n            return X, Y\n\n\n# Note: You'll need to define train_ids, val_ids, and test_ids before creating generators\n# Example:\n# training_generator = DataGenerator(train_ids)\n# valid_generator = DataGenerator(val_ids)\n# test_generator = DataGenerator(test_ids)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:11.543937Z","iopub.execute_input":"2025-11-04T17:50:11.544496Z","iopub.status.idle":"2025-11-04T17:50:23.392675Z","shell.execute_reply.started":"2025-11-04T17:50:11.544473Z","shell.execute_reply":"2025-11-04T17:50:23.392106Z"}},"outputs":[{"name":"stderr","text":"2025-11-04 17:50:12.858563: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1762278613.024607      37 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1762278613.092869      37 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"# ======================================================\n# 🎨 Visualization Utility\n# ======================================================\ndef display_slice_and_segmentation(flair, t1ce, segmentation):\n    \"\"\"Displays a sample FLAIR, T1CE, and segmentation slice.\"\"\"\n    fig, axes = plt.subplots(1, 3, figsize=(12, 5))\n\n    axes[0].imshow(flair, cmap='gray')\n    axes[0].set_title('FLAIR')\n    axes[0].axis('off')\n\n    axes[1].imshow(t1ce, cmap='gray')\n    axes[1].set_title('T1CE')\n    axes[1].axis('off')\n\n    axes[2].imshow(segmentation, cmap='nipy_spectral', vmin=0, vmax=3)\n    axes[2].set_title('Segmentation')\n    axes[2].axis('off')\n\n    plt.tight_layout()\n    plt.show()\n\n\n# ======================================================\n# 🧪 Test the Generator on Sample Folders\n# ======================================================\nall_folders = sorted(os.listdir(TRAIN_DATASET_PATH))\nsample_ids = all_folders[:2]  # Test with first 2 patients\n\ntraining_generator = DataGenerator(sample_ids)\n\n# Fetch one batch\nX_batch, Y_batch = training_generator[0]\nprint(\"✅ X_batch shape:\", X_batch.shape)\nprint(\"✅ Y_batch shape:\", Y_batch.shape)\n\n# Extract modalities and segmentation\nflair_batch = X_batch[:, :, :, 0]\nt1ce_batch = X_batch[:, :, :, 1]\nsegmentation_batch = np.argmax(Y_batch, axis=-1)\n\n# Pick random slice to visualize\nslice_index = np.random.randint(0, flair_batch.shape[0])\nslice_flair = flair_batch[slice_index]\nslice_t1ce = t1ce_batch[slice_index]\nslice_segmentation = segmentation_batch[slice_index]\n\n# Display the random slice\ndisplay_slice_and_segmentation(slice_flair, slice_t1ce, slice_segmentation)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:42.97141Z","iopub.execute_input":"2025-11-04T17:50:42.971679Z","iopub.status.idle":"2025-11-04T17:50:43.504938Z","shell.execute_reply.started":"2025-11-04T17:50:42.97166Z","shell.execute_reply":"2025-11-04T17:50:43.504282Z"}},"outputs":[{"name":"stdout","text":"✅ X_batch shape: (100, 128, 128, 2)\n✅ Y_batch shape: (100, 128, 128, 4)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x500 with 3 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras import layers, models, optimizers\nimport numpy as np\n\ndef dice_coef(y_true, y_pred, smooth=1.0):\n    class_num = 4\n    dice_total = 0\n    for i in range(class_num):\n        y_true_f = K.flatten(y_true[:,:,:,i])\n        y_pred_f = K.flatten(y_pred[:,:,:,i])\n        intersection = K.sum(y_true_f * y_pred_f)\n        dice = (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n        dice_total += dice\n    return dice_total / class_num\n\ndef dice_loss(y_true, y_pred):\n    return 1 - dice_coef(y_true, y_pred)\n\ndef focal_loss(gamma=2., alpha=0.25):\n    def focal_loss_fixed(y_true, y_pred):\n        epsilon = K.epsilon()\n        y_pred = K.clip(y_pred, epsilon, 1. - epsilon)\n        cross_entropy = -y_true * K.log(y_pred)\n        weight = alpha * K.pow(1 - y_pred, gamma)\n        loss = weight * cross_entropy\n        return K.sum(loss, axis=-1)\n    return focal_loss_fixed\n\ndef tversky_loss(y_true, y_pred, alpha=0.7, beta=0.3, smooth=1e-6):\n    y_true_pos = K.flatten(y_true)\n    y_pred_pos = K.flatten(y_pred)\n    true_pos = K.sum(y_true_pos * y_pred_pos)\n    false_neg = K.sum(y_true_pos * (1 - y_pred_pos))\n    false_pos = K.sum((1 - y_true_pos) * y_pred_pos)\n    return 1 - ((true_pos + smooth) / (true_pos + alpha * false_neg + beta * false_pos + smooth))\n\ndef combined_loss(y_true, y_pred):\n    dl = dice_loss(y_true, y_pred)\n    fl = focal_loss()(y_true, y_pred)\n    tl = tversky_loss(y_true, y_pred)\n    return 0.4 * dl + 0.3 * fl + 0.3 * tl\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:46.257049Z","iopub.execute_input":"2025-11-04T17:50:46.257351Z","iopub.status.idle":"2025-11-04T17:50:46.26674Z","shell.execute_reply.started":"2025-11-04T17:50:46.257332Z","shell.execute_reply":"2025-11-04T17:50:46.266117Z"}},"outputs":[],"execution_count":29},{"cell_type":"code","source":"def dice_coef_necrotic(y_true, y_pred, epsilon=1e-6):\n    intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))\n    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,1])) + K.sum(K.square(y_pred[:,:,:,1])) + epsilon)\n\ndef dice_coef_edema(y_true, y_pred, epsilon=1e-6):\n    intersection = K.sum(K.abs(y_true[:,:,:,2] * y_pred[:,:,:,2]))\n    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,2])) + K.sum(K.square(y_pred[:,:,:,2])) + epsilon)\n\ndef dice_coef_enhancing(y_true, y_pred, epsilon=1e-6):\n    intersection = K.sum(K.abs(y_true[:,:,:,3] * y_pred[:,:,:,3]))\n    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,3])) + K.sum(K.square(y_pred[:,:,:,3])) + epsilon)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:49.331992Z","iopub.execute_input":"2025-11-04T17:50:49.332711Z","iopub.status.idle":"2025-11-04T17:50:49.33891Z","shell.execute_reply.started":"2025-11-04T17:50:49.332686Z","shell.execute_reply":"2025-11-04T17:50:49.33811Z"}},"outputs":[],"execution_count":30},{"cell_type":"code","source":"def precision(y_true, y_pred):\n    true_pos = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n    pred_pos = K.sum(K.round(K.clip(y_pred, 0, 1)))\n    return true_pos / (pred_pos + K.epsilon())\n\ndef sensitivity(y_true, y_pred):\n    true_pos = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n    possible_pos = K.sum(K.round(K.clip(y_true, 0, 1)))\n    return true_pos / (possible_pos + K.epsilon())\n\ndef specificity(y_true, y_pred):\n    true_neg = K.sum(K.round(K.clip((1 - y_true) * (1 - y_pred), 0, 1)))\n    possible_neg = K.sum(K.round(K.clip(1 - y_true, 0, 1)))\n    return true_neg / (possible_neg + K.epsilon())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:50:52.57042Z","iopub.execute_input":"2025-11-04T17:50:52.570686Z","iopub.status.idle":"2025-11-04T17:50:52.576416Z","shell.execute_reply.started":"2025-11-04T17:50:52.570665Z","shell.execute_reply":"2025-11-04T17:50:52.575547Z"}},"outputs":[],"execution_count":31},{"cell_type":"code","source":"def build_unet(input_shape=(128,128,2)):\n    inputs = layers.Input(shape=input_shape)\n    \n    # Encoder\n    c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(inputs)\n    c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(c1)\n    p1 = layers.MaxPooling2D((2, 2))(c1)\n\n    c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(p1)\n    c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(c2)\n    p2 = layers.MaxPooling2D((2, 2))(c2)\n\n    c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(p2)\n    c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(c3)\n    p3 = layers.MaxPooling2D((2, 2))(c3)\n\n    # Bottleneck\n    bn = layers.Conv2D(256, 3, activation='relu', padding='same')(p3)\n    bn = layers.Conv2D(256, 3, activation='relu', padding='same')(bn)\n\n    # Decoder\n    u3 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(bn)\n    u3 = layers.concatenate([u3, c3])\n    c4 = layers.Conv2D(128, 3, activation='relu', padding='same')(u3)\n    c4 = layers.Conv2D(128, 3, activation='relu', padding='same')(c4)\n\n    u2 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(c4)\n    u2 = layers.concatenate([u2, c2])\n    c5 = layers.Conv2D(64, 3, activation='relu', padding='same')(u2)\n    c5 = layers.Conv2D(64, 3, activation='relu', padding='same')(c5)\n\n    u1 = layers.Conv2DTranspose(32, 2, strides=(2, 2), padding='same')(c5)\n    u1 = layers.concatenate([u1, c1])\n    c6 = layers.Conv2D(32, 3, activation='relu', padding='same')(u1)\n    c6 = layers.Conv2D(32, 3, activation='relu', padding='same')(c6)\n\n    outputs = layers.Conv2D(4, (1, 1), activation='softmax')(c6)\n    model = models.Model(inputs=[inputs], outputs=[outputs])\n    return model\n\nmodel = build_unet((128, 128, 2))\nmodel.compile(optimizer='adam',\n              loss=combined_loss,\n              metrics=[dice_coef, dice_coef_necrotic, dice_coef_edema, dice_coef_enhancing,\n                       precision, sensitivity, specificity])\nmodel.summary()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:51:29.044628Z","iopub.execute_input":"2025-11-04T17:51:29.045386Z","iopub.status.idle":"2025-11-04T17:51:29.193116Z","shell.execute_reply.started":"2025-11-04T17:51:29.04536Z","shell.execute_reply":"2025-11-04T17:51:29.192559Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_1\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_3       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m2\u001b[0m)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m608\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m9,248\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m18,496\u001b[0m │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d_4[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_5[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m590,080\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_3  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m131,200\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_3       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m256\u001b[0m)              │            │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m295,040\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_4  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m32,832\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m128\u001b[0m)              │            │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m73,792\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_5  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m8,224\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m64\u001b[0m)               │            │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m18,464\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m9,248\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m132\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m4\u001b[0m)                │            │                   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_3       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">608</span> │ input_layer_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ conv2d_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │ max_pooling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ conv2d_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ conv2d_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ max_pooling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ conv2d_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_3  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │ conv2d_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_3       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ conv2d_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ conv2d_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_4  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │ conv2d_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │ conv2d_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ conv2d_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_5  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,224</span> │ conv2d_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │ conv2d_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ conv2d_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span> │ conv2d_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                │            │                   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,925,412\u001b[0m (7.34 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,925,412</span> (7.34 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,925,412\u001b[0m (7.34 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,925,412</span> (7.34 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"!apt-get install graphviz -y\n!pip install --upgrade pydot graphviz\n!pip install --upgrade tensorflow pydotplus\n!pip install pydot\n\n","metadata":{"trusted":true,"collapsed":true,"jupyter":{"source_hidden":true,"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tensorflow.keras.utils import plot_model\n!pip install pydot\n\nimport pydot\nimport graphviz\n\n\nplot_model(\n    model,\n    to_file='unet_architecture.png',\n    show_shapes=True,\n    show_layer_names=True,\n    rankdir='TB',\n    dpi=70\n)\n","metadata":{"trusted":true,"collapsed":true,"jupyter":{"source_hidden":true,"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install -q pydot graphviz pydotplus\nimport pydot\nimport graphviz\nimport tensorflow as tf\nfrom tensorflow.keras.utils import plot_model\nimport os, shutil\n\n# --- Ensure Graphviz 'dot' is in PATH ---\nif not shutil.which(\"dot\"):\n    os.environ[\"PATH\"] += os.pathsep + '/usr/bin'\nprint(\"✅ Graphviz path:\", shutil.which(\"dot\"))\n\n# --- Verify pydot & graphviz ---\nprint(\"✅ pydot version:\", pydot.__version__)\nprint(\"✅ TensorFlow version:\", tf.__version__)\n","metadata":{"trusted":true,"collapsed":true,"jupyter":{"source_hidden":true,"outputs_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import KFold\n\nkf = KFold(n_splits=5, shuffle=True, random_state=42)\nall_ids = np.array(all_patient_ids)\n\ntrain_ids_folds, val_ids_folds = [], []\nfor train_idx, val_idx in kf.split(all_ids):\n    train_ids_folds.append(all_ids[train_idx])\n    val_ids_folds.append(all_ids[val_idx])\n","metadata":{"trusted":true,"jupyter":{"source_hidden":true}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# --- Custom MeanIoU that works with one-hot encoded outputs ---\nclass MeanIoUOneHot(tf.keras.metrics.MeanIoU):\n    def __init__(self, num_classes=4, name=\"mean_iou_onehot\", **kwargs):\n        super().__init__(num_classes=num_classes, name=name, **kwargs)\n\n    def update_state(self, y_true, y_pred, sample_weight=None):\n        y_true = tf.argmax(y_true, axis=-1)\n        y_pred = tf.argmax(y_pred, axis=-1)\n        return super().update_state(y_true, y_pred, sample_weight)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:51:34.017128Z","iopub.execute_input":"2025-11-04T17:51:34.017796Z","iopub.status.idle":"2025-11-04T17:51:34.022279Z","shell.execute_reply.started":"2025-11-04T17:51:34.017771Z","shell.execute_reply":"2025-11-04T17:51:34.021624Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"# ======================================================\n# MODEL: 3D U-Net with IDH Mutation Classification Head\n# ======================================================\ndef build_radiogenomic_unet3d(input_shape=(128,128,64,2)):\n    inputs = keras.layers.Input(shape=input_shape)\n\n    def conv_block(x, f):\n        x = keras.layers.Conv3D(f, 3, activation='relu', padding='same')(x)\n        x = keras.layers.Conv3D(f, 3, activation='relu', padding='same')(x)\n        return x\n\n    # --- Encoder ---\n    c1 = conv_block(inputs, 32); p1 = keras.layers.MaxPooling3D(2)(c1)\n    c2 = conv_block(p1, 64); p2 = keras.layers.MaxPooling3D(2)(c2)\n    c3 = conv_block(p2, 128); p3 = keras.layers.MaxPooling3D(2)(c3)\n    c4 = conv_block(p3, 256); p4 = keras.layers.MaxPooling3D(2)(c4)\n    bn = conv_block(p4, 512)\n\n    # --- IDH classification branch ---\n    x = keras.layers.GlobalAveragePooling3D()(bn)\n    x = keras.layers.Dense(256, activation='relu')(x)\n    x = keras.layers.Dropout(0.4)(x)\n    idh_out = keras.layers.Dense(1, activation='sigmoid', name=\"idh_status\")(x)\n\n    # --- Decoder ---\n    def up_block(x, skip, f):\n        x = keras.layers.Conv3DTranspose(f, 2, strides=2, padding='same')(x)\n        x = keras.layers.concatenate([x, skip])\n        x = conv_block(x, f)\n        return x\n\n    u6 = up_block(bn, c4, 256)\n    u7 = up_block(u6, c3, 128)\n    u8 = up_block(u7, c2, 64)\n    u9 = up_block(u8, c1, 32)\n    seg_out = keras.layers.Conv3D(4, 1, activation='softmax', name=\"seg_output\")(u9)\n\n    model = keras.models.Model(inputs, [seg_out, idh_out])\n    return model\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:51:36.051225Z","iopub.execute_input":"2025-11-04T17:51:36.051502Z","iopub.status.idle":"2025-11-04T17:51:36.058776Z","shell.execute_reply.started":"2025-11-04T17:51:36.051483Z","shell.execute_reply":"2025-11-04T17:51:36.058113Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"test_results = model.evaluate(test_generator)\nprint(dict(zip(model.metrics_names, test_results)))\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers, models, backend as K\nfrom sklearn.model_selection import KFold\nfrom tensorflow.keras.callbacks import CSVLogger\nfrom tensorflow.keras.losses import categorical_focal_crossentropy\n\n# ======================================================\n# Custom Metrics\n# ======================================================\ndef dice_coef(y_true, y_pred, smooth=1e-6):\n    y_true_f = K.flatten(y_true)\n    y_pred_f = K.flatten(y_pred)\n    intersection = K.sum(y_true_f * y_pred_f)\n    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n\nclass MeanIoUOneHot(tf.keras.metrics.MeanIoU):\n    def __init__(self, num_classes=4, name=\"mean_iou_onehot\", **kwargs):\n        super().__init__(num_classes=num_classes, name=name, **kwargs)\n\n    def update_state(self, y_true, y_pred, sample_weight=None):\n        y_true = tf.argmax(y_true, axis=-1)\n        y_pred = tf.argmax(y_pred, axis=-1)\n        return super().update_state(y_true, y_pred, sample_weight)\n\n# ======================================================\n# U-Net Model Definition\n# ======================================================\ndef build_unet(input_shape=(128, 128, 2)):\n    inputs = layers.Input(shape=input_shape)\n\n    # Encoder\n    c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(inputs)\n    c1 = layers.Conv2D(32, 3, activation='relu', padding='same')(c1)\n    p1 = layers.MaxPooling2D((2, 2))(c1)\n\n    c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(p1)\n    c2 = layers.Conv2D(64, 3, activation='relu', padding='same')(c2)\n    p2 = layers.MaxPooling2D((2, 2))(c2)\n\n    c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(p2)\n    c3 = layers.Conv2D(128, 3, activation='relu', padding='same')(c3)\n    p3 = layers.MaxPooling2D((2, 2))(c3)\n\n    # Bottleneck\n    bn = layers.Conv2D(256, 3, activation='relu', padding='same')(p3)\n    bn = layers.Conv2D(256, 3, activation='relu', padding='same')(bn)\n\n    # Decoder\n    u3 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(bn)\n    u3 = layers.concatenate([u3, c3])\n    c4 = layers.Conv2D(128, 3, activation='relu', padding='same')(u3)\n    c4 = layers.Conv2D(128, 3, activation='relu', padding='same')(c4)\n\n    u2 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(c4)\n    u2 = layers.concatenate([u2, c2])\n    c5 = layers.Conv2D(64, 3, activation='relu', padding='same')(u2)\n    c5 = layers.Conv2D(64, 3, activation='relu', padding='same')(c5)\n\n    u1 = layers.Conv2DTranspose(32, 2, strides=(2, 2), padding='same')(c5)\n    u1 = layers.concatenate([u1, c1])\n    c6 = layers.Conv2D(32, 3, activation='relu', padding='same')(u1)\n    c6 = layers.Conv2D(32, 3, activation='relu', padding='same')(c6)\n\n    outputs = layers.Conv2D(4, (1, 1), activation='softmax')(c6)\n    model = models.Model(inputs=[inputs], outputs=[outputs])\n    return model\n\n# ======================================================\n# Configuration\n# ======================================================\nBASE_DIR = \"/kaggle/working/Radiogenomics_BraTS2023\"\nTRAIN_DATASET_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"\nos.makedirs(BASE_DIR, exist_ok=True)\n\nEPOCHS = 10\nIMG_SIZE = 128\nBATCH_SIZE = 1\nSUBSET_SIZE = 100\nNUM_FOLDS = 5\n\n# Replace this with actual list of folder IDs\ntrain_and_test_ids = sorted(os.listdir(TRAIN_DATASET_PATH))\nall_ids = np.array(train_and_test_ids[:SUBSET_SIZE])\n\nkf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=42)\ntrain_idx, val_idx = list(kf.split(all_ids))[0]\ntrain_ids_fold1 = all_ids[train_idx]\nval_ids_fold1 = all_ids[val_idx]\n\nprint(f\"📊 Fold 1 -> Train: {len(train_ids_fold1)}, Val: {len(val_ids_fold1)}\")\n\n# ======================================================\n# Data Generators (Assuming you have a DataGenerator class)\n# ======================================================\ntraining_generator = DataGenerator(train_ids_fold1)\nvalid_generator = DataGenerator(val_ids_fold1)\n\n# ======================================================\n# Build and Compile Model\n# ======================================================\nmodel = build_unet(input_shape=(IMG_SIZE, IMG_SIZE, 2))\nmodel.compile(\n    optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n    loss=categorical_focal_crossentropy,\n    metrics=['accuracy', dice_coef, MeanIoUOneHot(num_classes=4)]\n)\n\n# ======================================================\n# Setup Callbacks\n# ======================================================\nfold_dir = os.path.join(BASE_DIR, \"fold_1_fasttrain\")\nos.makedirs(fold_dir, exist_ok=True)\n\ncallbacks = [\n    keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=2, min_lr=1e-6, verbose=1),\n    keras.callbacks.ModelCheckpoint(filepath=os.path.join(fold_dir, 'unet_fold1_best.weights.h5'),\n                                    monitor='val_loss', save_best_only=True, save_weights_only=True, verbose=1),\n    CSVLogger(os.path.join(fold_dir, 'fold1_training_log.csv'))\n]\n\n# ======================================================\n# Train Model\n# ======================================================\nK.clear_session()\nprint(\"\\n🚀 Starting Fold 1 training...\\n\")\n\nhistory = model.fit(\n    training_generator,\n    validation_data=valid_generator,\n    epochs=EPOCHS,\n    steps_per_epoch=len(training_generator),\n    callbacks=callbacks,\n    verbose=1\n)\n\n# ======================================================\n# Save Final Model\n# ======================================================\nfinal_model_path = os.path.join(fold_dir, \"unet_fold1_final.keras\")\nmodel.save(final_model_path)\n\nprint(f\"\\n✅ Training complete!\")\nprint(f\"📁 Model saved to: {final_model_path}\")\nprint(f\"🧾 Logs saved to: {os.path.join(fold_dir, 'fold1_training_log.csv')}\")\n\n# ======================================================\n# Print Final Metrics Summary\n# ======================================================\nprint(\"\\n\" + \"=\"*50)\nprint(\"📊 FINAL TRAINING METRICS\")\nprint(\"=\"*50)\nprint(f\"Final Training Accuracy:   {history.history['accuracy'][-1]:.4f}\")\nprint(f\"Final Training Dice:       {history.history['dice_coef'][-1]:.4f}\")\nprint(f\"Final Training MeanIoU:    {history.history['mean_iou_onehot'][-1]:.4f}\")\nprint(f\"\\nFinal Validation Accuracy: {history.history['val_accuracy'][-1]:.4f}\")\nprint(f\"Final Validation Dice:     {history.history['val_dice_coef'][-1]:.4f}\")\nprint(f\"Final Validation MeanIoU:  {history.history['val_mean_iou_onehot'][-1]:.4f}\")\nprint(\"=\"*50)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T17:56:19.715617Z","iopub.execute_input":"2025-11-04T17:56:19.716212Z","iopub.status.idle":"2025-11-04T18:01:53.664378Z","shell.execute_reply.started":"2025-11-04T17:56:19.716188Z","shell.execute_reply":"2025-11-04T18:01:53.663546Z"}},"outputs":[{"name":"stdout","text":"📊 Fold 1 -> Train: 80, Val: 20\n\n🚀 Starting Fold 1 training...\n\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n  self._warn_if_super_not_called()\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/10\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.\nExpected: ['keras_tensor_55']\nReceived: inputs=Tensor(shape=(None, 128, 128, 2))\n  warnings.warn(msg)\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1762278989.623295     144 service.cc:148] XLA service 0x7aedb801e0f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1762278989.624136     144 service.cc:156]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\nI0000 00:00:1762278990.536447     144 cuda_dnn.cc:529] Loaded cuDNN version 90300\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 1/80\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m36:03\u001b[0m 27s/step - accuracy: 0.2037 - dice_coef: 0.2503 - loss: 0.1946 - mean_iou_onehot: 0.0520","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1762279009.318886     144 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 878ms/step - accuracy: 0.8998 - dice_coef: 0.3952 - loss: 0.1488 - mean_iou_onehot: 0.2292\nEpoch 1: val_loss improved from inf to 0.01651, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 1s/step - accuracy: 0.9006 - dice_coef: 0.3976 - loss: 0.1481 - mean_iou_onehot: 0.2294 - val_accuracy: 0.9811 - val_dice_coef: 0.8085 - val_loss: 0.0165 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 2/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - accuracy: 0.9846 - dice_coef: 0.8417 - loss: 0.0110 - mean_iou_onehot: 0.2461\nEpoch 2: val_loss improved from 0.01651 to 0.00925, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 295ms/step - accuracy: 0.9846 - dice_coef: 0.8421 - loss: 0.0109 - mean_iou_onehot: 0.2461 - val_accuracy: 0.9811 - val_dice_coef: 0.8748 - val_loss: 0.0092 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 3/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - accuracy: 0.9831 - dice_coef: 0.9019 - loss: 0.0081 - mean_iou_onehot: 0.2458\nEpoch 3: val_loss improved from 0.00925 to 0.00875, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 290ms/step - accuracy: 0.9831 - dice_coef: 0.9020 - loss: 0.0081 - mean_iou_onehot: 0.2458 - val_accuracy: 0.9811 - val_dice_coef: 0.8860 - val_loss: 0.0087 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 4/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - accuracy: 0.9836 - dice_coef: 0.9130 - loss: 0.0072 - mean_iou_onehot: 0.2459\nEpoch 4: val_loss improved from 0.00875 to 0.00821, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 290ms/step - accuracy: 0.9836 - dice_coef: 0.9130 - loss: 0.0072 - mean_iou_onehot: 0.2459 - val_accuracy: 0.9811 - val_dice_coef: 0.9303 - val_loss: 0.0082 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 5/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - accuracy: 0.9859 - dice_coef: 0.9278 - loss: 0.0066 - mean_iou_onehot: 0.2465\nEpoch 5: val_loss improved from 0.00821 to 0.00810, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 278ms/step - accuracy: 0.9858 - dice_coef: 0.9278 - loss: 0.0066 - mean_iou_onehot: 0.2465 - val_accuracy: 0.9811 - val_dice_coef: 0.9196 - val_loss: 0.0081 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 6/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - accuracy: 0.9846 - dice_coef: 0.9301 - loss: 0.0063 - mean_iou_onehot: 0.2461\nEpoch 6: val_loss improved from 0.00810 to 0.00770, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 282ms/step - accuracy: 0.9846 - dice_coef: 0.9300 - loss: 0.0063 - mean_iou_onehot: 0.2461 - val_accuracy: 0.9811 - val_dice_coef: 0.9251 - val_loss: 0.0077 - val_mean_iou_onehot: 0.2453 - learning_rate: 1.0000e-04\nEpoch 7/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - accuracy: 0.9832 - dice_coef: 0.9211 - loss: 0.0069 - mean_iou_onehot: 0.2458\nEpoch 7: val_loss did not improve from 0.00770\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 276ms/step - accuracy: 0.9832 - dice_coef: 0.9212 - loss: 0.0069 - mean_iou_onehot: 0.2458 - val_accuracy: 0.9807 - val_dice_coef: 0.9234 - val_loss: 0.0078 - val_mean_iou_onehot: 0.2481 - learning_rate: 1.0000e-04\nEpoch 8/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - accuracy: 0.9839 - dice_coef: 0.9297 - loss: 0.0063 - mean_iou_onehot: 0.2512\nEpoch 8: val_loss improved from 0.00770 to 0.00731, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 282ms/step - accuracy: 0.9839 - dice_coef: 0.9297 - loss: 0.0063 - mean_iou_onehot: 0.2512 - val_accuracy: 0.9806 - val_dice_coef: 0.9006 - val_loss: 0.0073 - val_mean_iou_onehot: 0.2463 - learning_rate: 1.0000e-04\nEpoch 9/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - accuracy: 0.9850 - dice_coef: 0.9254 - loss: 0.0060 - mean_iou_onehot: 0.2473\nEpoch 9: val_loss improved from 0.00731 to 0.00708, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 286ms/step - accuracy: 0.9850 - dice_coef: 0.9255 - loss: 0.0060 - mean_iou_onehot: 0.2473 - val_accuracy: 0.9789 - val_dice_coef: 0.9202 - val_loss: 0.0071 - val_mean_iou_onehot: 0.2477 - learning_rate: 1.0000e-04\nEpoch 10/10\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - accuracy: 0.9841 - dice_coef: 0.9301 - loss: 0.0059 - mean_iou_onehot: 0.2537\nEpoch 10: val_loss improved from 0.00708 to 0.00686, saving model to /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_best.weights.h5\n\u001b[1m80/80\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 283ms/step - accuracy: 0.9841 - dice_coef: 0.9302 - loss: 0.0059 - mean_iou_onehot: 0.2537 - val_accuracy: 0.9790 - val_dice_coef: 0.9110 - val_loss: 0.0069 - val_mean_iou_onehot: 0.2486 - learning_rate: 1.0000e-04\n\n✅ Training complete!\n📁 Model saved to: /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/unet_fold1_final.keras\n🧾 Logs saved to: /kaggle/working/Radiogenomics_BraTS2023/fold_1_fasttrain/fold1_training_log.csv\n\n==================================================\n📊 FINAL TRAINING METRICS\n==================================================\nFinal Training Accuracy:   0.9844\nFinal Training Dice:       0.9338\nFinal Training MeanIoU:    0.2536\n\nFinal Validation Accuracy: 0.9790\nFinal Validation Dice:     0.9110\nFinal Validation MeanIoU:  0.2486\n==================================================\n","output_type":"stream"}],"execution_count":43},{"cell_type":"code","source":"\n# --- 1) Map RSNA labels (MGMT) to BraTS2023 subjects and create radiogenomics.csv ---\nimport os, glob, pandas as pd, numpy as np\n\nRSNA_PATH = \"/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification\"\nBRATS_PATH = \"/kaggle/input/brats2023-full/BraTS2023\"\n\n# Load RSNA labels\nrsna_csv = os.path.join(RSNA_PATH, \"train_labels.csv\")\nrsna_df = pd.read_csv(rsna_csv)\n# RSNA uses BraTS21ID as numeric string like \"00000\"\nrsna_df[\"numeric_id\"] = rsna_df[\"BraTS21ID\"].astype(str).str.zfill(5)\n\n# Collect BraTS folders (may be named differently; take basenames)\nbrats_folders = sorted([os.path.basename(p) for p in glob.glob(os.path.join(BRATS_PATH, \"*\")) if os.path.isdir(p)])\ndef extract_numeric_from_brats(name):\n    nums = \"\".join([c for c in name if c.isdigit()])\n    return nums[-5:] if len(nums)>=5 else nums\n\nbrats_df = pd.DataFrame({\"brats_folder\": brats_folders})\nbrats_df[\"numeric_id\"] = brats_df[\"brats_folder\"].apply(extract_numeric_from_brats)\n\n# Merge on numeric_id\nmerged = pd.merge(brats_df, rsna_df, on=\"numeric_id\", how=\"inner\")\nprint(f\"Found {len(merged)} matching subjects between BRATS and RSNA datasets.\")\n\n# Create radiogenomics.csv with columns: patient_id, MGMT_value\nout_df = merged[[\"brats_folder\", \"MGMT_value\"]].rename(columns={\"brats_folder\":\"patient_id\"})\nout_csv = \"/kaggle/working/radiogenomics_mapped.csv\"\nout_df.to_csv(out_csv, index=False)\nprint(f\"Saved mapped radiogenomics CSV to: {out_csv}\")\nout_df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T18:05:13.638395Z","iopub.execute_input":"2025-11-04T18:05:13.638793Z","iopub.status.idle":"2025-11-04T18:05:14.390571Z","shell.execute_reply.started":"2025-11-04T18:05:13.63877Z","shell.execute_reply":"2025-11-04T18:05:14.389791Z"}},"outputs":[{"name":"stdout","text":"Found 25 matching subjects between BRATS and RSNA datasets.\nSaved mapped radiogenomics CSV to: /kaggle/working/radiogenomics_mapped.csv\n","output_type":"stream"},{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"            patient_id  MGMT_value\n0  BraTS-GLI-00000-000           1\n1  BraTS-GLI-00100-000           1\n2  BraTS-GLI-00101-000           1\n3  BraTS-GLI-00201-000           1\n4  BraTS-GLI-00300-000           1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>patient_id</th>\n      <th>MGMT_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BraTS-GLI-00000-000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BraTS-GLI-00100-000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BraTS-GLI-00101-000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BraTS-GLI-00201-000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BraTS-GLI-00300-000</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":45},{"cell_type":"code","source":"# --- 2) Helpers: load NIfTI volumes from BraTS and extract slices with labels ---\nimport nibabel as nib\nimport numpy as np\nimport tensorflow as tf\nfrom glob import glob\nimport os\n\ndef load_nifti(path):\n    img = nib.load(path)\n    return img.get_fdata().astype(np.float32)\n\ndef get_case_paths(brats_root, patient_folder):\n    folder = os.path.join(brats_root, patient_folder)\n    files = glob(os.path.join(folder, \"*\"))\n    # find modalities and seg by common keywords\n    case = {}\n    for f in files:\n        fn = os.path.basename(f).lower()\n        if \"flair\" in fn:\n            case[\"flair\"] = f\n        elif (\"t1ce\" in fn) or (\"t1gd\" in fn) or (\"t1ce\" in os.path.basename(f).lower()):\n            case[\"t1ce\"] = f\n        elif (\"t1\" in fn) and (\"t1ce\" not in fn):\n            case[\"t1\"] = f\n        elif \"t2\" in fn:\n            case[\"t2\"] = f\n        elif \"seg\" in fn or \"segmentation\" in fn:\n            case[\"seg\"] = f\n    return case\n\ndef stack_modalities_brats(case_paths):\n    flair = load_nifti(case_paths[\"flair\"])\n    t1   = load_nifti(case_paths[\"t1\"])\n    t1ce = load_nifti(case_paths[\"t1ce\"])\n    t2   = load_nifti(case_paths[\"t2\"])\n    imgs = np.stack([flair, t1, t1ce, t2], axis=-1)  # H,W,D,4\n    # normalize per-modality\n    for c in range(imgs.shape[-1]):\n        vol = imgs[..., c]\n        imgs[..., c] = (vol - vol.mean()) / (vol.std()+1e-6)\n    seg = load_nifti(case_paths[\"seg\"]).astype(np.uint8)\n    return imgs, seg\n\ndef extract_slices_with_mask(imgs, seg, min_nonzero=100):\n    H,W,D,_ = imgs.shape\n    out = []\n    for z in range(D):\n        mask = seg[..., z]\n        if np.sum(mask>0) < min_nonzero:\n            continue\n        im = imgs[..., z, :]\n        m = mask[..., None]\n        out.append((im, m))\n    return out\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T18:06:18.409244Z","iopub.execute_input":"2025-11-04T18:06:18.409757Z","iopub.status.idle":"2025-11-04T18:06:18.418822Z","shell.execute_reply.started":"2025-11-04T18:06:18.409733Z","shell.execute_reply":"2025-11-04T18:06:18.41811Z"}},"outputs":[],"execution_count":46},{"cell_type":"code","source":"# --- 3) Create tf.data.Dataset yielding (image_slice, mask), label ---\nimport pandas as pd, os, random, tensorflow as tf\nfrom functools import partial\n\nradiogenomics_csv = \"/kaggle/working/radiogenomics_mapped.csv\"\nrg_df = pd.read_csv(radiogenomics_csv).set_index(\"patient_id\")\nprint(\"Radiogenomics mapping loaded:\", rg_df.shape)\n\n# Build a list of samples (patient_id, slice_idx, image, mask) preloaded for prototyping\nsamples = []\nMAX_SUBJECTS = 50  # limit for quick runs, adjust as needed\ncnt = 0\nfor pid in rg_df.index.tolist():\n    case = get_case_paths(\"/kaggle/input/brats2023-full\", pid)\n    if len(case) < 5:\n        continue\n    try:\n        imgs, seg = stack_modalities_brats(case)\n    except Exception as e:\n        print(\"skip\", pid, \"error\", e)\n        continue\n    slices = extract_slices_with_mask(imgs, seg, min_nonzero=200)\n    for im, m in slices:\n        samples.append((pid, im.astype(np.float32), m.astype(np.uint8)))\n    cnt += 1\n    if cnt >= MAX_SUBJECTS:\n        break\n\nprint(\"Total loaded slices:\", len(samples))\n\n# Create tf.data.Dataset from samples\nIMG_SIZE = 128\ndef generator():\n    for pid, im, m in samples:\n        # resize\n        im_r = tf.image.resize(im, (IMG_SIZE, IMG_SIZE)).numpy()\n        m_r  = tf.image.resize(m, (IMG_SIZE, IMG_SIZE), method='nearest').numpy()\n        label = rg_df.loc[pid, \"MGMT_value\"]\n        yield (im_r, label.astype(np.float32)), m_r\n\noutput_types = ((tf.float32, tf.float32), tf.uint8)\noutput_shapes = (((IMG_SIZE, IMG_SIZE, 4), ()), (IMG_SIZE, IMG_SIZE, 1))\nds = tf.data.Dataset.from_generator(generator, output_types=output_types, output_shapes=output_shapes)\nds = ds.shuffle(512).batch(4).prefetch(tf.data.AUTOTUNE)\n\n# ds yields ((image_batch, label_batch), mask_batch)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T18:06:39.390666Z","iopub.execute_input":"2025-11-04T18:06:39.390933Z","iopub.status.idle":"2025-11-04T18:06:39.43656Z","shell.execute_reply.started":"2025-11-04T18:06:39.390914Z","shell.execute_reply":"2025-11-04T18:06:39.435771Z"}},"outputs":[{"name":"stdout","text":"Radiogenomics mapping loaded: (25, 1)\nTotal loaded slices: 0\n","output_type":"stream"}],"execution_count":48},{"cell_type":"code","source":"# --- 4) Build multi-task U-Net: segmentation + genomic prediction head ---\nimport tensorflow as tf\nfrom tensorflow.keras import layers, Model, backend as K\n\ndef conv_block(x, filters):\n    x = layers.Conv2D(filters, 3, padding='same', activation='relu')(x)\n    x = layers.Conv2D(filters, 3, padding='same', activation='relu')(x)\n    return x\n\ndef encoder_block(x, filters):\n    c = conv_block(x, filters)\n    p = layers.MaxPooling2D(2)(c)\n    return c, p\n\ndef decoder_block(x, skip, filters):\n    x = layers.UpSampling2D(2)(x)\n    x = layers.Concatenate()([x, skip])\n    x = conv_block(x, filters)\n    return x\n\ndef build_multitask_unet(input_shape=(128,128,4), base_filters=32):\n    img_in = layers.Input(shape=input_shape, name='image_input')\n    # encoder\n    c1, p1 = encoder_block(img_in, base_filters)\n    c2, p2 = encoder_block(p1, base_filters*2)\n    c3, p3 = encoder_block(p2, base_filters*4)\n    c4, p4 = encoder_block(c3 if False else p3, base_filters*8)  # safety if shapes vary\n    bn = conv_block(p4, base_filters*16)\n    # decoder for segmentation\n    d4 = decoder_block(bn, c4, base_filters*8)\n    d3 = decoder_block(d4, c3, base_filters*4)\n    d2 = decoder_block(d3, c2, base_filters*2)\n    d1 = decoder_block(d2, c1, base_filters)\n    seg_out = layers.Conv2D(1, 1, activation='sigmoid', name='seg_output')(d1)\n    # genomic prediction head: global pooling + dense\n    gp = layers.GlobalAveragePooling2D()(bn)\n    g = layers.Dense(128, activation='relu')(gp)\n    g = layers.Dense(64, activation='relu')(g)\n    geno_out = layers.Dense(1, activation='sigmoid', name='geno_output')(g)\n    model = Model(inputs=img_in, outputs=[seg_out, geno_out])\n    return model\n\nmodel = build_multitask_unet(input_shape=(128,128,4))\nmodel.summary()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T18:07:06.77756Z","iopub.execute_input":"2025-11-04T18:07:06.778051Z","iopub.status.idle":"2025-11-04T18:07:06.984947Z","shell.execute_reply.started":"2025-11-04T18:07:06.778027Z","shell.execute_reply":"2025-11-04T18:07:06.984413Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ image_input         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m4\u001b[0m)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m1,184\u001b[0m │ image_input[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m9,248\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m18,496\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m590,080\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m) │  \u001b[38;5;34m1,180,160\u001b[0m │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m) │  \u001b[38;5;34m2,359,808\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ up_sampling2d[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m768\u001b[0m)              │            │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │  \u001b[38;5;34m1,769,728\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m590,080\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ up_sampling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m384\u001b[0m)              │            │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m442,496\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_2       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ up_sampling2d_2[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m192\u001b[0m)              │            │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │    \u001b[38;5;34m110,656\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_3       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ up_sampling2d_3[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m96\u001b[0m)               │            │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)       │          \u001b[38;5;34m0\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m27,680\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │     \u001b[38;5;34m65,664\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m9,248\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │      \u001b[38;5;34m8,256\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ seg_output (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m33\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m1\u001b[0m)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ geno_output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │         \u001b[38;5;34m65\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ image_input         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,184</span> │ image_input[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ conv2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ conv2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ max_pooling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │ max_pooling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>) │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ conv2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)              │            │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,769,728</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ conv2d_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)              │            │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ conv2d_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_2       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)              │            │ conv2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ conv2d_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ up_sampling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_3       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ up_sampling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)               │            │ conv2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │     <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ conv2d_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ seg_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ conv2d_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ geno_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,920,930\u001b[0m (30.22 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,920,930</span> (30.22 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,920,930\u001b[0m (30.22 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,920,930</span> (30.22 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}}],"execution_count":49},{"cell_type":"code","source":"# --- 5) Compile and train multi-task model (TF 2.15–compatible) ---\nimport tensorflow as tf\nfrom tensorflow.keras.losses import BinaryCrossentropy\nfrom tensorflow.keras.optimizers import Adam\n\n# ----- Custom loss functions -----\ndef dice_loss(y_true, y_pred, smooth=1e-6):\n    y_true_f = tf.reshape(y_true, (-1,))\n    y_pred_f = tf.reshape(y_pred, (-1,))\n    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n    return 1 - (2. * intersection + smooth) / (\n        tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth\n    )\n\ndef seg_loss(y_true, y_pred):\n    bce = BinaryCrossentropy()(y_true, y_pred)\n    return 0.5 * bce + 0.5 * dice_loss(y_true, y_pred)\n\n# ----- Compile model -----\nmodel.compile(\n    optimizer=Adam(1e-4),\n    loss={\n        'seg_output': seg_loss,\n        'geno_output': BinaryCrossentropy(),\n    },\n    loss_weights={'seg_output': 1.0, 'geno_output': 0.5},\n    metrics={'seg_output': [], 'geno_output': ['accuracy']},\n)\n\n# ----- Dataset mapping -----\ndef map_fn(data, mask):\n    img, label = data\n    img = tf.cast(img, tf.float32)\n    mask = tf.cast(mask, tf.float32)\n    label = tf.expand_dims(tf.cast(label, tf.float32), axis=-1)\n    return img, {'seg_output': mask, 'geno_output': label}\n\nds_mapped = ds.map(map_fn)\n\n# ----- Train (quick test run) -----\nmodel.fit(ds_mapped, epochs=3, steps_per_epoch=10)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-04T18:07:38.076729Z","iopub.execute_input":"2025-11-04T18:07:38.077172Z","iopub.status.idle":"2025-11-04T18:07:47.389437Z","shell.execute_reply.started":"2025-11-04T18:07:38.077147Z","shell.execute_reply":"2025-11-04T18:07:47.388915Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/3\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 917ms/step\nEpoch 2/3\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\nEpoch 3/3\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n  self._interrupted_warning()\n","output_type":"stream"},{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7aee5b2ee3d0>"},"metadata":{}}],"execution_count":50},{"cell_type":"code","source":"import os\nimport shutil\nfrom tqdm import tqdm\n\n# Source directory containing folders like 00001, 00163, etc.\nsource_root = \"/kaggle/input/rsna-dataset\"  # Adjust if needed\ntarget_folder = \"/kaggle/working/test\"\n\nos.makedirs(target_folder, exist_ok=True)\n\n# Counter to avoid filename collisions\nfile_counter = 0\n\n# Walk through all subfolders and move .nii or .nii.gz files\nfor root, dirs, files in os.walk(source_root):\n    for file in files:\n        if file.endswith(\".nii\") or file.endswith(\".nii.gz\"):\n            source_path = os.path.join(root, file)\n            # Create a unique filename to avoid duplicates\n            new_filename = f\"file_{file_counter}_{file}\"\n            target_path = os.path.join(target_folder, new_filename)\n            shutil.copy2(source_path, target_path)\n            file_counter += 1\n\nprint(f\"\\n✅ All NIfTI files moved to: {target_folder}\")\nprint(f\"📦 Total files copied: {file_counter}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-07T19:48:39.361382Z","iopub.execute_input":"2025-11-07T19:48:39.361649Z","iopub.status.idle":"2025-11-07T19:48:45.730139Z","shell.execute_reply.started":"2025-11-07T19:48:39.361628Z","shell.execute_reply":"2025-11-07T19:48:45.727686Z"}},"outputs":[{"name":"stdout","text":"\n✅ All NIfTI files moved to: /kaggle/working/test\n📦 Total files copied: 112\n","output_type":"stream"}],"execution_count":9}]}