{"cells":[{"cell_type":"markdown","id":"40-Y8eOzoEMW","metadata":{"id":"40-Y8eOzoEMW"},"source":["The NVIDIA System Management Interface (nvidia-smi) is a command line utility, based on top of the NVIDIA Management Library (NVML), intended to aid in the management and monitoring of NVIDIA GPU devices.\n","\n","### no name found???"]},{"cell_type":"code","execution_count":1,"id":"LsXvPziEmlRA","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1012,"status":"ok","timestamp":1666267350879,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"LsXvPziEmlRA","outputId":"b07d3385-a7e6-4e5e-9e18-b216d7265966"},"outputs":[{"name":"stdout","output_type":"stream","text":["Thu Oct 20 12:02:29 2022 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n","| N/A 34C P0 41W / 400W | 0MiB / 40536MiB | 0% Default |\n","| | | Disabled |\n","+-------------------------------+----------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=============================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"markdown","id":"q_h0gKriptXY","metadata":{"id":"q_h0gKriptXY"},"source":["### IMPORT LIBRARIES"]},{"cell_type":"code","execution_count":1,"id":"eeeb2d8b","metadata":{"executionInfo":{"elapsed":2333,"status":"ok","timestamp":1666267353677,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"eeeb2d8b"},"outputs":[],"source":["import os\n","import zipfile\n","import numpy as np\n","import tensorflow as tf\n","\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.utils import to_categorical"]},{"cell_type":"markdown","id":"zr0Fg8W7qrnO","metadata":{"id":"zr0Fg8W7qrnO"},"source":["### MOUNT TO DRIVE"]},{"cell_type":"code","execution_count":2,"id":"JAS-X5F3lw6n","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25591,"status":"ok","timestamp":1666267446789,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"JAS-X5F3lw6n","outputId":"51d7ea5e-e73d-401f-c17b-5eeb12584670"},"outputs":[],"source":["#from google.colab import drive\n","#drive.mount('/content/drive')"]},{"cell_type":"markdown","id":"UAwNnZyAq_cA","metadata":{"id":"UAwNnZyAq_cA"},"source":["### MAKE COPIES FROM DATA CLASSES"]},{"cell_type":"code","execution_count":4,"id":"1b6c9368","metadata":{},"outputs":[],"source":["rootPath=r\"/Users/maithaalarjani/Desktop/ct\""]},{"cell_type":"markdown","id":"oxpNeBSirRoR","metadata":{"id":"oxpNeBSirRoR"},"source":["### UNZIP DATA FILES"]},{"cell_type":"code","execution_count":32,"id":"K_QUEsY1t0-X","metadata":{"executionInfo":{"elapsed":20103,"status":"ok","timestamp":1666267601177,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"K_QUEsY1t0-X"},"outputs":[],"source":["import zipfile\n","# shutil.rmtree('/content/OASIS_Data')\n","with zipfile.ZipFile(\"OASIS3_AD.zip\", \"r\") as z_fp:\n"," z_fp.extractall(\"./OASIS_Data/AD\")\n","\n","with zipfile.ZipFile(\"OASIS3_CN.zip\", \"r\") as z_fp:\n"," z_fp.extractall(\"./OASIS_Data/CN\")"]},{"cell_type":"markdown","id":"_D_1XEAZraRL","metadata":{"id":"_D_1XEAZraRL"},"source":["### CALL NiBabel LIBRARY\n","Read / write access to some common neuroimaging file formats\n","\n"]},{"cell_type":"code","execution_count":6,"id":"cc247f84","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4300,"status":"ok","timestamp":1666267632686,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"cc247f84","outputId":"ca59594c-814d-44b5-cae1-6544237e20a7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: nibabel in /opt/anaconda3/lib/python3.9/site-packages (5.1.0)\n","Requirement already satisfied: numpy>=1.19 in /opt/anaconda3/lib/python3.9/site-packages (from nibabel) (1.23.5)\n","Requirement already satisfied: packaging>=17 in /opt/anaconda3/lib/python3.9/site-packages (from nibabel) (21.3)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/anaconda3/lib/python3.9/site-packages (from packaging>=17->nibabel) (3.0.4)\n"]}],"source":["! pip install nibabel"]},{"cell_type":"markdown","id":"nxbpZcJTsOoh","metadata":{"id":"nxbpZcJTsOoh"},"source":["### TAKE THE UNZIP FILES\n"]},{"cell_type":"code","execution_count":34,"id":"M4l96f05ictn","metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1666267635033,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"M4l96f05ictn"},"outputs":[],"source":["from glob import glob\n","abnormal_scan_paths = glob(rootPath + r'/OASIS_Data/AD/*/*/*/*T2*')\n","normal_scan_paths = glob(rootPath + r'/OASIS_Data/CN/*/*/*/*T2*')"]},{"cell_type":"code","execution_count":35,"id":"1828aa43","metadata":{},"outputs":[{"data":{"text/plain":["['/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30048_MR_d3375/anat7/NIFTI/sub-OAS30048_ses-d3375_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30048_MR_d3375/anat2/NIFTI/sub-OAS30048_ses-d3375_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30040_MR_d5108/anat1/NIFTI/sub-OAS30040_ses-d5108_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30040_MR_d5108/anat3/NIFTI/sub-OAS30040_ses-d5108_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30033_MR_d1267/anat1/NIFTI/sub-OAS30033_ses-d1267_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30033_MR_d1267/anat4/NIFTI/sub-OAS30033_ses-d1267_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30002_MR_d2340/anat7/NIFTI/sub-OAS30002_ses-d2340_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30002_MR_d2340/anat2/NIFTI/sub-OAS30002_ses-d2340_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30011_MR_d0055/anat1/NIFTI/sub-OAS30011_ses-d0055_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30011_MR_d0055/anat4/NIFTI/sub-OAS30011_ses-d0055_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30032_MR_d1661/anat1/NIFTI/sub-OAS30032_ses-d1661_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30032_MR_d1661/anat5/NIFTI/sub-OAS30032_ses-d1661_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30014_MR_d0196/anat1/NIFTI/sub-OAS30014_ses-d0196_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30014_MR_d0196/anat5/NIFTI/sub-OAS30014_ses-d0196_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30012_MR_d0124/anat1/NIFTI/sub-OAS30012_ses-d0124_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30012_MR_d0124/anat2/NIFTI/sub-OAS30012_ses-d0124_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30037_MR_d0154/anat5/NIFTI/sub-OAS30037_ses-d0154_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30037_MR_d0154/anat2/NIFTI/sub-OAS30037_ses-d0154_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30003_MR_d2669/anat1/NIFTI/sub-OAS30003_ses-d2669_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30003_MR_d2669/anat4/NIFTI/sub-OAS30003_ses-d2669_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30030_MR_d0170/anat1/NIFTI/sub-OAS30030_ses-d0170_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30030_MR_d0170/anat4/NIFTI/sub-OAS30030_ses-d0170_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30004_MR_d2229/anat1/NIFTI/sub-OAS30004_ses-d2229_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30004_MR_d2229/anat6/NIFTI/sub-OAS30004_ses-d2229_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30026_MR_d0696/anat2/NIFTI/sub-OAS30026_ses-d0696_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30044_MR_d0054/anat1/NIFTI/sub-OAS30044_ses-d0054_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30044_MR_d0054/anat4/NIFTI/sub-OAS30044_ses-d0054_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30049_MR_d0013/anat1/NIFTI/sub-OAS30049_ses-d0013_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30049_MR_d0013/anat5/NIFTI/sub-OAS30049_ses-d0013_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30009_MR_d0148/anat1/NIFTI/sub-OAS30009_ses-d0148_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30009_MR_d0148/anat5/NIFTI/sub-OAS30009_ses-d0148_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30028_MR_d1260/anat2/NIFTI/sub-OAS30028_ses-d1260_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30015_MR_d1374/anat1/NIFTI/sub-OAS30015_ses-d1374_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30015_MR_d1374/anat5/NIFTI/sub-OAS30015_ses-d1374_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30004_MR_d2232/anat1/NIFTI/sub-OAS30004_ses-d2232_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30004_MR_d2232/anat6/NIFTI/sub-OAS30004_ses-d2232_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30003_MR_d3731/anat2/NIFTI/sub-OAS30003_ses-d3731_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30025_MR_d2298/anat2/NIFTI/sub-OAS30025_ses-d2298_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30020_MR_d0092/anat1/NIFTI/sub-OAS30020_ses-d0092_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30020_MR_d0092/anat2/NIFTI/sub-OAS30020_ses-d0092_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30003_MR_d1631/anat1/NIFTI/sub-OAS30003_ses-d1631_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30003_MR_d1631/anat6/NIFTI/sub-OAS30003_ses-d1631_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30006_MR_d2342/anat2/NIFTI/sub-OAS30006_ses-d2342_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30014_MR_d1176/anat1/NIFTI/sub-OAS30014_ses-d1176_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30014_MR_d1176/anat5/NIFTI/sub-OAS30014_ses-d1176_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30039_MR_d0103/anat2/NIFTI/sub-OAS30039_ses-d0103_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30008_MR_d0061/anat1/NIFTI/sub-OAS30008_ses-d0061_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30008_MR_d0061/anat5/NIFTI/sub-OAS30008_ses-d0061_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30025_MR_d0210/anat1/NIFTI/sub-OAS30025_ses-d0210_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30025_MR_d0210/anat5/NIFTI/sub-OAS30025_ses-d0210_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30006_MR_d0166/anat1/NIFTI/sub-OAS30006_ses-d0166_acq-TSE_echo-1_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30006_MR_d0166/anat2/NIFTI/sub-OAS30006_ses-d0166_acq-TSE_echo-2_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30033_MR_d0133/anat1/NIFTI/sub-OAS30033_ses-d0133_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30033_MR_d0133/anat3/NIFTI/sub-OAS30033_ses-d0133_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30033_MR_d0133/anat5/NIFTI/sub-OAS30033_ses-d0133_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30001_MR_d0129/anat1/NIFTI/sub-OAS30001_ses-d0129_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30001_MR_d0129/anat4/NIFTI/sub-OAS30001_ses-d0129_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30005_MR_d3453/anat2/NIFTI/sub-OAS30005_ses-d3453_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30020_MR_d0911/anat1/NIFTI/sub-OAS30020_ses-d0911_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30020_MR_d0911/anat2/NIFTI/sub-OAS30020_ses-d0911_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30002_MR_d0653/anat1/NIFTI/sub-OAS30002_ses-d0653_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30002_MR_d0653/anat4/NIFTI/sub-OAS30002_ses-d0653_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30036_MR_d0059/anat1/NIFTI/sub-OAS30036_ses-d0059_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30036_MR_d0059/anat4/NIFTI/sub-OAS30036_ses-d0059_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30005_MR_d2384/anat2/NIFTI/sub-OAS30005_ses-d2384_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30048_MR_d0983/anat1/NIFTI/sub-OAS30048_ses-d0983_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30048_MR_d0983/anat2/NIFTI/sub-OAS30048_ses-d0983_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30046_MR_d0848/anat9/NIFTI/sub-OAS30046_ses-d0848_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30046_MR_d0848/anat6/NIFTI/sub-OAS30046_ses-d0848_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30005_MR_d2377/anat1/NIFTI/sub-OAS30005_ses-d2377_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30005_MR_d2377/anat4/NIFTI/sub-OAS30005_ses-d2377_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30022_MR_d1324/anat1/NIFTI/sub-OAS30022_ses-d1324_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/CN/OAS30022_MR_d1324/anat2/NIFTI/sub-OAS30022_ses-d1324_acq-TSE_run-02_T2w.nii.gz',\n"," 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'/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30342_MR_d0001/anat5/NIFTI/sub-OAS30342_ses-d0001_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30265_MR_d0192/anat1/NIFTI/sub-OAS30265_ses-d0192_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30265_MR_d0192/anat5/NIFTI/sub-OAS30265_ses-d0192_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30100_MR_d0158/anat2/NIFTI/sub-OAS30100_ses-d0158_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30035_MR_d3850/anat1/NIFTI/sub-OAS30035_ses-d3850_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30035_MR_d3850/anat4/NIFTI/sub-OAS30035_ses-d3850_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30334_MR_d0000/anat1/NIFTI/sub-OAS30334_ses-d0000_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30334_MR_d0000/anat5/NIFTI/sub-OAS30334_ses-d0000_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d1209/anat1/NIFTI/sub-OAS30119_ses-d1209_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d1209/anat2/NIFTI/sub-OAS30119_ses-d1209_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30267_MR_d0043/anat1/NIFTI/sub-OAS30267_ses-d0043_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30267_MR_d0043/anat5/NIFTI/sub-OAS30267_ses-d0043_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30095_MR_d0033/anat1/NIFTI/sub-OAS30095_ses-d0033_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30095_MR_d0033/anat4/NIFTI/sub-OAS30095_ses-d0033_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30259_MR_d0679/anat1/NIFTI/sub-OAS30259_ses-d0679_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30259_MR_d0679/anat2/NIFTI/sub-OAS30259_ses-d0679_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30150_MR_d0100/anat1/NIFTI/sub-OAS30150_ses-d0100_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30150_MR_d0100/anat5/NIFTI/sub-OAS30150_ses-d0100_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30064_MR_d0687/anat1/NIFTI/sub-OAS30064_ses-d0687_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30064_MR_d0687/anat5/NIFTI/sub-OAS30064_ses-d0687_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30087_MR_d0260/anat1/NIFTI/sub-OAS30087_ses-d0260_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30087_MR_d0260/anat5/NIFTI/sub-OAS30087_ses-d0260_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d3722/anat5/NIFTI/sub-OAS30119_ses-d3722_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d3722/anat2/NIFTI/sub-OAS30119_ses-d3722_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30255_MR_d0019/anat1/NIFTI/sub-OAS30255_ses-d0019_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30255_MR_d0019/anat5/NIFTI/sub-OAS30255_ses-d0019_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30172_MR_d0028/anat1/NIFTI/sub-OAS30172_ses-d0028_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30172_MR_d0028/anat5/NIFTI/sub-OAS30172_ses-d0028_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30237_MR_d1353/anat1/NIFTI/sub-OAS30237_ses-d1353_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30237_MR_d1353/anat2/NIFTI/sub-OAS30237_ses-d1353_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30334_MR_d0889/anat1/NIFTI/sub-OAS30334_ses-d0889_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30334_MR_d0889/anat4/NIFTI/sub-OAS30334_ses-d0889_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30027_MR_d1300/anat7/NIFTI/sub-OAS30027_ses-d1300_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30027_MR_d1300/anat1/NIFTI/sub-OAS30027_ses-d1300_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30027_MR_d1300/anat2/NIFTI/sub-OAS30027_ses-d1300_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30314_MR_d6379/anat1/NIFTI/sub-OAS30314_ses-d6379_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30314_MR_d6379/anat2/NIFTI/sub-OAS30314_ses-d6379_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30094_MR_d0679/anat1/NIFTI/sub-OAS30094_ses-d0679_acq-TSE_run-01_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30094_MR_d0679/anat2/NIFTI/sub-OAS30094_ses-d0679_acq-TSE_run-02_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30124_MR_d0046/anat1/NIFTI/sub-OAS30124_ses-d0046_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30124_MR_d0046/anat5/NIFTI/sub-OAS30124_ses-d0046_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30133_MR_d0505/anat1/NIFTI/sub-OAS30133_ses-d0505_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30133_MR_d0505/anat4/NIFTI/sub-OAS30133_ses-d0505_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30332_MR_d0091/anat1/NIFTI/sub-OAS30332_ses-d0091_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30332_MR_d0091/anat4/NIFTI/sub-OAS30332_ses-d0091_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30165_MR_d0563/anat1/NIFTI/sub-OAS30165_ses-d0563_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30165_MR_d0563/anat5/NIFTI/sub-OAS30165_ses-d0563_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30394_MR_d0039/anat5/NIFTI/sub-OAS30394_ses-d0039_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30394_MR_d0039/anat2/NIFTI/sub-OAS30394_ses-d0039_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30212_MR_d3043/anat2/NIFTI/sub-OAS30212_ses-d3043_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30271_MR_d0004/anat3/NIFTI/sub-OAS30271_ses-d0004_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30286_MR_d0047/anat1/NIFTI/sub-OAS30286_ses-d0047_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30286_MR_d0047/anat4/NIFTI/sub-OAS30286_ses-d0047_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30344_MR_d1052/anat1/NIFTI/sub-OAS30344_ses-d1052_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30344_MR_d1052/anat5/NIFTI/sub-OAS30344_ses-d1052_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30078_MR_d0210/anat1/NIFTI/sub-OAS30078_ses-d0210_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30078_MR_d0210/anat4/NIFTI/sub-OAS30078_ses-d0210_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30158_MR_d2467/anat1/NIFTI/sub-OAS30158_ses-d2467_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30158_MR_d2467/anat5/NIFTI/sub-OAS30158_ses-d2467_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d3260/anat1/NIFTI/sub-OAS30119_ses-d3260_acq-TSE_T2w.nii.gz',\n"," '/Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30119_MR_d3260/anat4/NIFTI/sub-OAS30119_ses-d3260_T2w.nii.gz']"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["abnormal_scan_paths\n"]},{"cell_type":"markdown","id":"bHg2Y3VRsaxV","metadata":{"id":"bHg2Y3VRsaxV"},"source":["\n","###Library for 3D augmentations"]},{"cell_type":"code","execution_count":8,"id":"syuk3WPXicxT","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3924,"status":"ok","timestamp":1666267641443,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"syuk3WPXicxT","outputId":"de5c5b13-b908-47b0-8ab3-3a5902e0eec0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting volumentations-3D\n"," Downloading volumentations_3D-1.0.4-py3-none-any.whl (31 kB)\n","Requirement already satisfied: scikit-image in /opt/anaconda3/lib/python3.9/site-packages (from volumentations-3D) (0.19.3)\n","Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.9/site-packages (from volumentations-3D) (1.23.5)\n","Requirement already satisfied: scipy in /opt/anaconda3/lib/python3.9/site-packages (from volumentations-3D) (1.10.1)\n","Collecting opencv-python\n"," Downloading opencv_python-4.8.0.74-cp37-abi3-macosx_10_16_x86_64.whl (54.6 MB)\n","\u001b[K |████████████████████████████████| 54.6 MB 258 kB/s ta 0:00:016 |███▌ | 6.0 MB 6.5 MB/s eta 0:00:08\n","\u001b[?25hRequirement already satisfied: PyWavelets>=1.1.1 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (1.3.0)\n","Requirement already satisfied: tifffile>=2019.7.26 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (2021.7.2)\n","Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (21.3)\n","Requirement already satisfied: networkx>=2.2 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (2.7.1)\n","Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (9.5.0)\n","Requirement already satisfied: imageio>=2.4.1 in /opt/anaconda3/lib/python3.9/site-packages (from scikit-image->volumentations-3D) (2.9.0)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/anaconda3/lib/python3.9/site-packages (from packaging>=20.0->scikit-image->volumentations-3D) (3.0.4)\n","Installing collected packages: opencv-python, volumentations-3D\n","Successfully installed opencv-python-4.8.0.74 volumentations-3D-1.0.4\n"]}],"source":["! pip install volumentations-3D"]},{"cell_type":"code","execution_count":9,"id":"QFkkwtFBic2G","metadata":{"executionInfo":{"elapsed":408,"status":"ok","timestamp":1666267648757,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"QFkkwtFBic2G"},"outputs":[],"source":["# !rm -r '/content/drive/MyDrive/3D Neural Images'"]},{"cell_type":"markdown","id":"j9icLXDatYmE","metadata":{"id":"j9icLXDatYmE"},"source":["### mkdir() method creates a blank directory on your file system."]},{"cell_type":"markdown","id":"d9a91c83","metadata":{},"source":["create 3- folders "]},{"cell_type":"code","execution_count":37,"id":"f740ebf7","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["mkdir: /Users/maithaalarjani/Desktop/ct/3D Neural Images: File exists\n"]}],"source":["!mkdir '{rootPath}/3D Neural Images'"]},{"cell_type":"code","execution_count":38,"id":"_rauCcrbic5e","metadata":{"executionInfo":{"elapsed":384,"status":"ok","timestamp":1666267652303,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"_rauCcrbic5e"},"outputs":[{"name":"stdout","output_type":"stream","text":["mkdir: /Users/maithaalarjani/Desktop/ct/3D Neural Images: File exists\n"]},{"name":"stdout","output_type":"stream","text":["mkdir: /Users/maithaalarjani/Desktop/ct/3D Neural Images/Abnormal Images: File exists\n","mkdir: /Users/maithaalarjani/Desktop/ct/3D Neural Images/Normal Images: File exists\n"]}],"source":["!mkdir '{rootPath}/3D Neural Images'\n","!mkdir '{rootPath}/3D Neural Images/Abnormal Images'\n","!mkdir '{rootPath}/3D Neural Images/Normal Images'"]},{"cell_type":"code","execution_count":18,"id":"9GUb3rDWic9N","metadata":{"executionInfo":{"elapsed":1088,"status":"ok","timestamp":1666267657661,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"9GUb3rDWic9N"},"outputs":[],"source":["from volumentations import *\n","import nibabel as nib\n","import tqdm"]},{"cell_type":"markdown","id":"gwQhpMrY396W","metadata":{"id":"gwQhpMrY396W"},"source":["###IMAGES AUGMENTATION"]},{"cell_type":"markdown","id":"Wj6b5xvbMraW","metadata":{"id":"Wj6b5xvbMraW"},"source":["WHAT TYPE OF AUGMENTATION DONE????\n","#### ERROR?? "]},{"cell_type":"code","execution_count":40,"id":"_JADm5fYidA5","metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1666267660744,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"_JADm5fYidA5"},"outputs":[],"source":["def get_augmentation(patch_size):\n"," return Compose([\n"," RandomResizedCrop(patch_size, p = 0.5),\n"," Resize(patch_size, interpolation=1, resize_type=0, always_apply=True, p=1.0),\n"," \n"," ], p=1.0)\n","\n","aug = get_augmentation((128, 128, 32))\n","\n","def get_resize_3D_images(folder_path , folder_name):\n"," for i , x in tqdm.tqdm(enumerate(folder_path)):\n"," print (i,x)\n"," print (\"++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n"," img = nib.load(x)\n"," img = img.get_fdata()\n"," data = {'image': img}\n"," aug_data = aug(**data)\n"," img = aug_data['image']\n"," np.save(rootPath+ r'/3D Neural Images/' + folder_name + '/' + 'random_res_' + str(i) + '.npy' , img)\n","\n"," #SAVE IMAGES AS NUMPY ARRAY FOR LESS TIME UPLOADING"]},{"cell_type":"markdown","id":"ZebneByV3l7h","metadata":{"id":"ZebneByV3l7h"},"source":["### ERROR\n"]},{"cell_type":"code","execution_count":41,"id":"FgsKFfk5idE2","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"executionInfo":{"elapsed":1032,"status":"error","timestamp":1666267668012,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"FgsKFfk5idE2","outputId":"c33881eb-62fe-4f57-bcd6-711845429830"},"outputs":[{"name":"stderr","output_type":"stream","text":["0it [00:00, ?it/s]"]},{"name":"stdout","output_type":"stream","text":["0 /Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30153_MR_d2220/anat1/NIFTI/sub-OAS30153_ses-d2220_acq-TSE_run-01_T2w.nii.gz\n","++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"]},{"name":"stderr","output_type":"stream","text":["1it [00:00, 3.52it/s]"]},{"name":"stdout","output_type":"stream","text":["1 /Users/maithaalarjani/Desktop/ct/OASIS_Data/AD/OAS30153_MR_d2220/anat2/NIFTI/sub-OAS30153_ses-d2220_acq-TSE_run-02_T2w.nii.gz\n","++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"ename":"RuntimeError","evalue":"sequence argument must have length equal to input rank","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)","\u001b[1;32m/Users/maithaalarjani/Desktop/ct/training.ipynb Cell 29\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get_resize_3D_images(abnormal_scan_paths , \u001b[39m'\u001b[39;49m\u001b[39mAbnormal Images\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 2\u001b[0m get_resize_3D_images(normal_scan_paths , \u001b[39m'\u001b[39m\u001b[39mNormal Images\u001b[39m\u001b[39m'\u001b[39m)\n","\u001b[1;32m/Users/maithaalarjani/Desktop/ct/training.ipynb Cell 29\u001b[0m in \u001b[0;36mget_resize_3D_images\u001b[0;34m(folder_path, folder_name)\u001b[0m\n\u001b[1;32m 15\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mget_fdata()\n\u001b[1;32m 16\u001b[0m data \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mimage\u001b[39m\u001b[39m'\u001b[39m: img}\n\u001b[0;32m---> 17\u001b[0m aug_data \u001b[39m=\u001b[39m aug(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdata)\n\u001b[1;32m 18\u001b[0m img \u001b[39m=\u001b[39m aug_data[\u001b[39m'\u001b[39m\u001b[39mimage\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 19\u001b[0m np\u001b[39m.\u001b[39msave(rootPath\u001b[39m+\u001b[39m \u001b[39mr\u001b[39m\u001b[39m'\u001b[39m\u001b[39m/3D Neural Images/\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m folder_name \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39mrandom_res_\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(i) \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m.npy\u001b[39m\u001b[39m'\u001b[39m , img)\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/volumentations/core/composition.py:60\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, force_apply, **data)\u001b[0m\n\u001b[1;32m 57\u001b[0m transforms \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms \u001b[39mif\u001b[39;00m need_to_run \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_always_apply_transforms()\n\u001b[1;32m 59\u001b[0m \u001b[39mfor\u001b[39;00m tr \u001b[39min\u001b[39;00m transforms:\n\u001b[0;32m---> 60\u001b[0m data \u001b[39m=\u001b[39m tr(force_apply, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtargets, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdata)\n\u001b[1;32m 62\u001b[0m \u001b[39mreturn\u001b[39;00m data\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/volumentations/core/transforms_interface.py:117\u001b[0m, in \u001b[0;36mDualTransform.__call__\u001b[0;34m(self, force_apply, targets, **data)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 116\u001b[0m \u001b[39mif\u001b[39;00m k \u001b[39min\u001b[39;00m targets[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 117\u001b[0m data[k] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply(v, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mparams)\n\u001b[1;32m 118\u001b[0m \u001b[39melif\u001b[39;00m k \u001b[39min\u001b[39;00m targets[\u001b[39m1\u001b[39m]:\n\u001b[1;32m 119\u001b[0m data[k] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_to_mask(v, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mparams)\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/volumentations/augmentations/transforms.py:232\u001b[0m, in \u001b[0;36mRandomResizedCrop.apply\u001b[0;34m(self, img, scale, scaled_shape, h_start, w_start, d_start)\u001b[0m\n\u001b[1;32m 230\u001b[0m scaled_shape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshape\n\u001b[1;32m 231\u001b[0m img \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrandom_crop(img, scaled_shape[\u001b[39m0\u001b[39m], scaled_shape[\u001b[39m1\u001b[39m], scaled_shape[\u001b[39m2\u001b[39m], h_start, w_start, d_start)\n\u001b[0;32m--> 232\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mresize(img, new_shape\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshape, interpolation\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minterpolation, resize_type\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mresize_type)\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/volumentations/augmentations/functional.py:217\u001b[0m, in \u001b[0;36mresize\u001b[0;34m(img, new_shape, interpolation, resize_type)\u001b[0m\n\u001b[1;32m 215\u001b[0m data \u001b[39m=\u001b[39m []\n\u001b[1;32m 216\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(img\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]):\n\u001b[0;32m--> 217\u001b[0m d0 \u001b[39m=\u001b[39m zoom(img[\u001b[39m.\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m.\u001b[39;49m, i]\u001b[39m.\u001b[39;49mastype(np\u001b[39m.\u001b[39;49muint8)\u001b[39m.\u001b[39;49mcopy(), shp, order\u001b[39m=\u001b[39;49minterpolation)\n\u001b[1;32m 218\u001b[0m data\u001b[39m.\u001b[39mappend(d0\u001b[39m.\u001b[39mcopy())\n\u001b[1;32m 219\u001b[0m new_img \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mstack(data, axis\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m)\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/ndimage/_interpolation.py:770\u001b[0m, in \u001b[0;36mzoom\u001b[0;34m(input, zoom, output, order, mode, cval, prefilter, grid_mode)\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39mndim \u001b[39m<\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 769\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39minput and output rank must be > 0\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m--> 770\u001b[0m zoom \u001b[39m=\u001b[39m _ni_support\u001b[39m.\u001b[39;49m_normalize_sequence(zoom, \u001b[39minput\u001b[39;49m\u001b[39m.\u001b[39;49mndim)\n\u001b[1;32m 771\u001b[0m output_shape \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(\n\u001b[1;32m 772\u001b[0m [\u001b[39mint\u001b[39m(\u001b[39mround\u001b[39m(ii \u001b[39m*\u001b[39m jj)) \u001b[39mfor\u001b[39;00m ii, jj \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39minput\u001b[39m\u001b[39m.\u001b[39mshape, zoom)])\n\u001b[1;32m 773\u001b[0m complex_output \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39miscomplexobj(\u001b[39minput\u001b[39m)\n","File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/ndimage/_ni_support.py:67\u001b[0m, in \u001b[0;36m_normalize_sequence\u001b[0;34m(input, rank)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(normalized) \u001b[39m!=\u001b[39m rank:\n\u001b[1;32m 66\u001b[0m err \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msequence argument must have length equal to input rank\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m---> 67\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(err)\n\u001b[1;32m 68\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 69\u001b[0m normalized \u001b[39m=\u001b[39m [\u001b[39minput\u001b[39m] \u001b[39m*\u001b[39m rank\n","\u001b[0;31mRuntimeError\u001b[0m: sequence argument must have length equal to input rank"]}],"source":["get_resize_3D_images(abnormal_scan_paths , 'Abnormal Images')\n","get_resize_3D_images(normal_scan_paths , 'Normal Images')"]},{"cell_type":"code","execution_count":14,"id":"Bv81qy9qidJ1","metadata":{"executionInfo":{"elapsed":413,"status":"ok","timestamp":1666267767747,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"Bv81qy9qidJ1"},"outputs":[],"source":["ROOT = '/content/drive/MyDrive/3D Neural Images/Normal Images'"]},{"cell_type":"markdown","id":"4ua7MttM67gT","metadata":{"id":"4ua7MttM67gT"},"source":["WHAT DOES NUMBER MEAN????????"]},{"cell_type":"code","execution_count":15,"id":"NVC9nTIwi0kw","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":165},"executionInfo":{"elapsed":866,"status":"error","timestamp":1666267772882,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"NVC9nTIwi0kw","outputId":"00a72f60-dc44-4288-fd92-4cc6afda5c66"},"outputs":[{"ename":"IndexError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/3D Neural Images/Normal Images'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m455\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mIndexError\u001b[0m: list index out of range"]}],"source":["img_path = os.listdir('/content/drive/MyDrive/3D Neural Images/Normal Images')[455]"]},{"cell_type":"markdown","id":"de5kOLWLPVQq","metadata":{"id":"de5kOLWLPVQq"},"source":["### LOAD AN IMAGE"]},{"cell_type":"code","execution_count":null,"id":"ZbcujNKyi1Is","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1666160825168,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"ZbcujNKyi1Is","outputId":"b5f9da46-513a-4616-880a-2ac62ff754f8"},"outputs":[{"data":{"text/plain":["(128, 128, 32)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["np.load(ROOT + '/' + img_path).shape"]},{"cell_type":"markdown","id":"4wl_UlLVRK-W","metadata":{"id":"4wl_UlLVRK-W"},"source":["### SHOW IMAGE SAMPLE"]},{"cell_type":"markdown","id":"hnOkj6d47OaY","metadata":{"id":"hnOkj6d47OaY"},"source":["23 MEANING????"]},{"cell_type":"code","execution_count":null,"id":"b2L11YEUi1SP","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1666160827546,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"b2L11YEUi1SP","outputId":"6abc8052-d2c6-4bf7-885c-bed4ed773f07"},"outputs":[{"data":{"text/plain":[""]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","plt.imshow(np.load(ROOT + '/' + img_path)[:,:,23])"]},{"cell_type":"markdown","id":"QxTxyVc67WCo","metadata":{"id":"QxTxyVc67WCo"},"source":["MEANING????????????/"]},{"cell_type":"code","execution_count":null,"id":"jU0fXWITi9Os","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":608,"status":"ok","timestamp":1666160887799,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"jU0fXWITi9Os","outputId":"74a7d2a4-b974-49dc-962a-8fa5900dd735"},"outputs":[{"name":"stdout","output_type":"stream","text":["0\t//content/drive/MyDrive/3D Neural Images\n"]}],"source":["!du -sh /'/content/drive/MyDrive/3D Neural Images'"]},{"cell_type":"markdown","id":"LAfb7OAZPtxC","metadata":{"id":"LAfb7OAZPtxC"},"source":["### TO UNIFY IMAGES SHAPE TO GET READY TO THE CNN (FILTER)"]},{"cell_type":"code","execution_count":null,"id":"UzrWIojBcm8o","metadata":{"id":"UzrWIojBcm8o"},"outputs":[],"source":["def make_dataset_into_batches(folder_path):\n"," files = []\n"," for x in os.listdir(folder_path):\n"," img = np.load(os.path.join(folder_path, x)) \n"," if(img.shape == (128 , 128 , 32)):\n"," files.append(img)\n"," return files"]},{"cell_type":"code","execution_count":null,"id":"CuVI9PiecnGI","metadata":{"id":"CuVI9PiecnGI"},"outputs":[],"source":["###AS LIST\n","abnormal_images = make_dataset_into_batches('/content/drive/MyDrive/3D Neural Images/Abnormal Images')\n","normal_images = make_dataset_into_batches('/content/drive/MyDrive/3D Neural Images/Normal Images')"]},{"cell_type":"code","execution_count":null,"id":"9Ez-Ve68GYN9","metadata":{"id":"9Ez-Ve68GYN9"},"outputs":[],"source":["### CONVERT TO NP ARRAY\n","abnormal_images = np.array(abnormal_images)\n","normal_images = np.array(normal_images)"]},{"cell_type":"code","execution_count":null,"id":"R0JA2-VgcnOZ","metadata":{"id":"R0JA2-VgcnOZ"},"outputs":[],"source":["### GIVE EACH CLASS A LABEL\n","\n","abnormal_labels = [0 for x in range(len(abnormal_images))]\n","normal_labels = [1 for x in range(len(normal_images))]"]},{"cell_type":"markdown","id":"PEkIsujU6jCc","metadata":{"id":"PEkIsujU6jCc"},"source":["### GET LENGTH\n","DATA IS BALANCED\n","\n"]},{"cell_type":"code","execution_count":null,"id":"RFtNBe1MWTMv","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":838,"status":"ok","timestamp":1666161006868,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"RFtNBe1MWTMv","outputId":"e14a05bb-de0f-46be-9337-3f6e45ea0cd8"},"outputs":[{"data":{"text/plain":["(945, 925)"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["len(abnormal_labels) , len(normal_labels)"]},{"cell_type":"code","execution_count":null,"id":"RsaIdLFCnOH-","metadata":{"id":"RsaIdLFCnOH-"},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":null,"id":"C6GeK12U8gUR","metadata":{"id":"C6GeK12U8gUR"},"outputs":[],"source":["### DIVIDE DATA INTO TRAIN AND TEST \n","#EACH R IS 85% OF A WHOLE CLASS AS TRAINING\n","## VALIDATION TESTING ?????????"]},{"cell_type":"code","execution_count":null,"id":"LHwb4_ApcnVA","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1448,"status":"ok","timestamp":1666161509775,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"LHwb4_ApcnVA","outputId":"69ad1fbd-569e-4f1e-dfb0-306a6d7e9965"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of samples in train and validation are 1589 and 281.\n"]}],"source":["r1=int(0.85*abnormal_images.shape[0])\n","r2=int(0.85*normal_images.shape[0])\n","x_train = np.concatenate((abnormal_images[:r1], normal_images[:r2]), axis=0)\n","### IS THE CONCTENATE OF BOTH CLASS TRAINING PART\n","y_train = np.concatenate((abnormal_labels[:r1], normal_labels[:r2]), axis=0)\n","### THIER LABELS \n","x_val = np.concatenate((abnormal_images[r1:], normal_images[r2:]), axis=0)\n","### IS THE CONCTENATE OF BOTH CLASS VALIDATION PART\n","y_val = np.concatenate((abnormal_labels[r1:], normal_labels[r2:]), axis=0)\n","### THIER LABELS \n","\n","print(\n"," \"Number of samples in train and validation are %d and %d.\"\n"," % (x_train.shape[0], x_val.shape[0])\n",")"]},{"cell_type":"markdown","id":"xY9aOIQA91Rc","metadata":{"id":"xY9aOIQA91Rc"},"source":["CONVERT LABELS 0,1 INTO CATEGORICAL\n"]},{"cell_type":"code","execution_count":null,"id":"DODT2U0lXpFv","metadata":{"id":"DODT2U0lXpFv"},"outputs":[],"source":["y_train = to_categorical(y_train)\n","y_val = to_categorical(y_val)"]},{"cell_type":"markdown","id":"hoie7_Mw-zK5","metadata":{"id":"hoie7_Mw-zK5"},"source":["CONVERT FROM NP ARRAY INTO TENSORS????\n"]},{"cell_type":"code","execution_count":null,"id":"52d92a8e","metadata":{"id":"52d92a8e"},"outputs":[],"source":["# Define data loaders.\n","train_loader = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n","validation_loader = tf.data.Dataset.from_tensor_slices((x_val, y_val))\n","\n","batch_size = 32\n","### DIVIDE DATA INTO PACHES FOR EACH EPOCH\n","# Augment the on the fly during training.\n","train_dataset = (\n"," train_loader.shuffle(len(x_train))\n"," ### Shuffling data serves the purpose of reducing variance and making sure that models remain general and overfit less.\n"," ### TO CHOOSE DATA RANDOMLY\n","\n","\n"," .batch(batch_size)\n"," .prefetch(32)\n",")\n","# Only rescale.\n","validation_dataset = (\n"," validation_loader.shuffle(len(x_val))\n"," .batch(batch_size)\n"," .prefetch(32)\n",")"]},{"cell_type":"code","execution_count":null,"id":"4ac62aea","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":303},"executionInfo":{"elapsed":2688,"status":"ok","timestamp":1666162307590,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"4ac62aea","outputId":"e6d39fbb-07a3-48f9-bc6f-0b26e8450c1f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Dimension of the MRI scan is: (128, 128, 32)\n"]},{"data":{"text/plain":[""]},"execution_count":30,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","data = train_dataset.take(1)\n","images, labels = list(data)[0]\n","images = images.numpy()\n","image = images[1]\n","print(\"Dimension of the MRI scan is:\", image.shape)\n","plt.imshow(np.squeeze(image[:, :, 5]), cmap=\"gray\")"]},{"cell_type":"markdown","id":"V7B72V1hBIE0","metadata":{"id":"V7B72V1hBIE0"},"source":["### SET UP KERAS, CLASSIFICATION MODEL 3D , TO BUILD THE NETWORK (PRETRAINED NET)"]},{"cell_type":"code","execution_count":null,"id":"0Vj_VqaCBtKR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4232,"status":"ok","timestamp":1666162317507,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"0Vj_VqaCBtKR","outputId":"0f60db38-1601-4e61-c1c5-fd2efc8a5f34"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting Keras-Applications\n"," Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n","\u001b[K |████████████████████████████████| 50 kB 3.1 MB/s \n","\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from Keras-Applications) (3.1.0)\n","Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from Keras-Applications) (1.21.6)\n","Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->Keras-Applications) (1.5.2)\n","Installing collected packages: Keras-Applications\n","Successfully installed Keras-Applications-1.0.8\n"]}],"source":["!pip install Keras-Applications"]},{"cell_type":"code","execution_count":null,"id":"9OHPjmxrBtUE","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3980,"status":"ok","timestamp":1666162382972,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"9OHPjmxrBtUE","outputId":"868239e0-1b78-49ca-dc72-6919fe8d3047"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting classification-models-3D\n"," Downloading classification_models_3D-1.0.6-py3-none-any.whl (62 kB)\n","\u001b[K |████████████████████████████████| 62 kB 1.2 MB/s \n","\u001b[?25hInstalling collected packages: classification-models-3D\n","Successfully installed classification-models-3D-1.0.6\n"]}],"source":["!pip install classification-models-3D"]},{"cell_type":"code","execution_count":null,"id":"mGSJ2fg5y5jH","metadata":{"id":"mGSJ2fg5y5jH"},"outputs":[],"source":["from classification_models_3D.tfkeras import Classifiers\n","\n","base_model, preprocess_input = Classifiers.get('seresnet18')\n","# GET SERESNET 28 MODEL \n","model = base_model(input_shape=(128, 128, 32, 1 ), weights=None , include_top = False , classes=2 )\n","# INPUT 128,128,32 AND 1 FOR LABELS\n","x = layers.GlobalAveragePooling3D()(model.output)\n","x = layers.Dropout(0.5)(x)\n","# The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting\n","outputs = layers.Dense(units=2, activation=\"sigmoid\")(x)\n","# 2 OUTPUTS AND ACTIVATION FUNCTION\n","model = keras.Model(model.inputs , outputs)\n"]},{"cell_type":"code","execution_count":null,"id":"1YjXS_gBBuBr","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1832,"status":"ok","timestamp":1666162839137,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"1YjXS_gBBuBr","outputId":"46bdb529-aa79-439a-ba00-152709098f8d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_5\"\n","__________________________________________________________________________________________________\n"," Layer (type) Output Shape Param # Connected to \n","==================================================================================================\n"," data (InputLayer) [(None, 128, 128, 3 0 [] \n"," 2, 1)] \n"," \n"," bn_data (BatchNormalization) (None, 128, 128, 32 3 ['data[0][0]'] \n"," , 1) \n"," \n"," zero_padding3d_36 (ZeroPadding (None, 134, 134, 38 0 ['bn_data[0][0]'] \n"," 3D) , 1) \n"," \n"," conv0 (Conv3D) (None, 64, 64, 16, 21952 ['zero_padding3d_36[0][0]'] \n"," 64) \n"," \n"," bn0 (BatchNormalization) (None, 64, 64, 16, 256 ['conv0[0][0]'] \n"," 64) \n"," \n"," relu0 (Activation) (None, 64, 64, 16, 0 ['bn0[0][0]'] \n"," 64) \n"," \n"," zero_padding3d_37 (ZeroPadding (None, 66, 66, 18, 0 ['relu0[0][0]'] \n"," 3D) 64) \n"," \n"," pooling0 (MaxPooling3D) (None, 32, 32, 8, 6 0 ['zero_padding3d_37[0][0]'] \n"," 4) \n"," \n"," stage1_unit1_bn1 (BatchNormali (None, 32, 32, 8, 6 256 ['pooling0[0][0]'] \n"," zation) 4) \n"," \n"," stage1_unit1_relu1 (Activation (None, 32, 32, 8, 6 0 ['stage1_unit1_bn1[0][0]'] \n"," ) 4) \n"," \n"," zero_padding3d_38 (ZeroPadding (None, 34, 34, 10, 0 ['stage1_unit1_relu1[0][0]'] \n"," 3D) 64) \n"," \n"," stage1_unit1_conv1 (Conv3D) (None, 32, 32, 8, 6 110592 ['zero_padding3d_38[0][0]'] \n"," 4) \n"," \n"," stage1_unit1_bn2 (BatchNormali (None, 32, 32, 8, 6 256 ['stage1_unit1_conv1[0][0]'] \n"," zation) 4) \n"," \n"," stage1_unit1_relu2 (Activation (None, 32, 32, 8, 6 0 ['stage1_unit1_bn2[0][0]'] \n"," ) 4) \n"," \n"," zero_padding3d_39 (ZeroPadding (None, 34, 34, 10, 0 ['stage1_unit1_relu2[0][0]'] \n"," 3D) 64) \n"," \n"," stage1_unit1_conv2 (Conv3D) (None, 32, 32, 8, 6 110592 ['zero_padding3d_39[0][0]'] \n"," 4) \n"," \n"," global_average_pooling3d_18 (G (None, 64) 0 ['stage1_unit1_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_16 (Lambda) (None, 1, 1, 1, 64) 0 ['global_average_pooling3d_18[0][\n"," 0]'] \n"," \n"," conv3d_32 (Conv3D) (None, 1, 1, 1, 4) 260 ['lambda_16[0][0]'] \n"," \n"," activation_32 (Activation) (None, 1, 1, 1, 4) 0 ['conv3d_32[0][0]'] \n"," \n"," conv3d_33 (Conv3D) (None, 1, 1, 1, 64) 320 ['activation_32[0][0]'] \n"," \n"," activation_33 (Activation) (None, 1, 1, 1, 64) 0 ['conv3d_33[0][0]'] \n"," \n"," multiply_16 (Multiply) (None, 32, 32, 8, 6 0 ['stage1_unit1_conv2[0][0]', \n"," 4) 'activation_33[0][0]'] \n"," \n"," stage1_unit1_sc (Conv3D) (None, 32, 32, 8, 6 4096 ['stage1_unit1_relu1[0][0]'] \n"," 4) \n"," \n"," add_16 (Add) (None, 32, 32, 8, 6 0 ['multiply_16[0][0]', \n"," 4) 'stage1_unit1_sc[0][0]'] \n"," \n"," stage1_unit2_bn1 (BatchNormali (None, 32, 32, 8, 6 256 ['add_16[0][0]'] \n"," zation) 4) \n"," \n"," stage1_unit2_relu1 (Activation (None, 32, 32, 8, 6 0 ['stage1_unit2_bn1[0][0]'] \n"," ) 4) \n"," \n"," zero_padding3d_40 (ZeroPadding (None, 34, 34, 10, 0 ['stage1_unit2_relu1[0][0]'] \n"," 3D) 64) \n"," \n"," stage1_unit2_conv1 (Conv3D) (None, 32, 32, 8, 6 110592 ['zero_padding3d_40[0][0]'] \n"," 4) \n"," \n"," stage1_unit2_bn2 (BatchNormali (None, 32, 32, 8, 6 256 ['stage1_unit2_conv1[0][0]'] \n"," zation) 4) \n"," \n"," stage1_unit2_relu2 (Activation (None, 32, 32, 8, 6 0 ['stage1_unit2_bn2[0][0]'] \n"," ) 4) \n"," \n"," zero_padding3d_41 (ZeroPadding (None, 34, 34, 10, 0 ['stage1_unit2_relu2[0][0]'] \n"," 3D) 64) \n"," \n"," stage1_unit2_conv2 (Conv3D) (None, 32, 32, 8, 6 110592 ['zero_padding3d_41[0][0]'] \n"," 4) \n"," \n"," global_average_pooling3d_19 (G (None, 64) 0 ['stage1_unit2_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_17 (Lambda) (None, 1, 1, 1, 64) 0 ['global_average_pooling3d_19[0][\n"," 0]'] \n"," \n"," conv3d_34 (Conv3D) (None, 1, 1, 1, 4) 260 ['lambda_17[0][0]'] \n"," \n"," activation_34 (Activation) (None, 1, 1, 1, 4) 0 ['conv3d_34[0][0]'] \n"," \n"," conv3d_35 (Conv3D) (None, 1, 1, 1, 64) 320 ['activation_34[0][0]'] \n"," \n"," activation_35 (Activation) (None, 1, 1, 1, 64) 0 ['conv3d_35[0][0]'] \n"," \n"," multiply_17 (Multiply) (None, 32, 32, 8, 6 0 ['stage1_unit2_conv2[0][0]', \n"," 4) 'activation_35[0][0]'] \n"," \n"," add_17 (Add) (None, 32, 32, 8, 6 0 ['multiply_17[0][0]', \n"," 4) 'add_16[0][0]'] \n"," \n"," stage2_unit1_bn1 (BatchNormali (None, 32, 32, 8, 6 256 ['add_17[0][0]'] \n"," zation) 4) \n"," \n"," stage2_unit1_relu1 (Activation (None, 32, 32, 8, 6 0 ['stage2_unit1_bn1[0][0]'] \n"," ) 4) \n"," \n"," zero_padding3d_42 (ZeroPadding (None, 34, 34, 10, 0 ['stage2_unit1_relu1[0][0]'] \n"," 3D) 64) \n"," \n"," stage2_unit1_conv1 (Conv3D) (None, 16, 16, 4, 1 221184 ['zero_padding3d_42[0][0]'] \n"," 28) \n"," \n"," stage2_unit1_bn2 (BatchNormali (None, 16, 16, 4, 1 512 ['stage2_unit1_conv1[0][0]'] \n"," zation) 28) \n"," \n"," stage2_unit1_relu2 (Activation (None, 16, 16, 4, 1 0 ['stage2_unit1_bn2[0][0]'] \n"," ) 28) \n"," \n"," zero_padding3d_43 (ZeroPadding (None, 18, 18, 6, 1 0 ['stage2_unit1_relu2[0][0]'] \n"," 3D) 28) \n"," \n"," stage2_unit1_conv2 (Conv3D) (None, 16, 16, 4, 1 442368 ['zero_padding3d_43[0][0]'] \n"," 28) \n"," \n"," global_average_pooling3d_20 (G (None, 128) 0 ['stage2_unit1_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_18 (Lambda) (None, 1, 1, 1, 128 0 ['global_average_pooling3d_20[0][\n"," ) 0]'] \n"," \n"," conv3d_36 (Conv3D) (None, 1, 1, 1, 8) 1032 ['lambda_18[0][0]'] \n"," \n"," activation_36 (Activation) (None, 1, 1, 1, 8) 0 ['conv3d_36[0][0]'] \n"," \n"," conv3d_37 (Conv3D) (None, 1, 1, 1, 128 1152 ['activation_36[0][0]'] \n"," ) \n"," \n"," activation_37 (Activation) (None, 1, 1, 1, 128 0 ['conv3d_37[0][0]'] \n"," ) \n"," \n"," multiply_18 (Multiply) (None, 16, 16, 4, 1 0 ['stage2_unit1_conv2[0][0]', \n"," 28) 'activation_37[0][0]'] \n"," \n"," stage2_unit1_sc (Conv3D) (None, 16, 16, 4, 1 8192 ['stage2_unit1_relu1[0][0]'] \n"," 28) \n"," \n"," add_18 (Add) (None, 16, 16, 4, 1 0 ['multiply_18[0][0]', \n"," 28) 'stage2_unit1_sc[0][0]'] \n"," \n"," stage2_unit2_bn1 (BatchNormali (None, 16, 16, 4, 1 512 ['add_18[0][0]'] \n"," zation) 28) \n"," \n"," stage2_unit2_relu1 (Activation (None, 16, 16, 4, 1 0 ['stage2_unit2_bn1[0][0]'] \n"," ) 28) \n"," \n"," zero_padding3d_44 (ZeroPadding (None, 18, 18, 6, 1 0 ['stage2_unit2_relu1[0][0]'] \n"," 3D) 28) \n"," \n"," stage2_unit2_conv1 (Conv3D) (None, 16, 16, 4, 1 442368 ['zero_padding3d_44[0][0]'] \n"," 28) \n"," \n"," stage2_unit2_bn2 (BatchNormali (None, 16, 16, 4, 1 512 ['stage2_unit2_conv1[0][0]'] \n"," zation) 28) \n"," \n"," stage2_unit2_relu2 (Activation (None, 16, 16, 4, 1 0 ['stage2_unit2_bn2[0][0]'] \n"," ) 28) \n"," \n"," zero_padding3d_45 (ZeroPadding (None, 18, 18, 6, 1 0 ['stage2_unit2_relu2[0][0]'] \n"," 3D) 28) \n"," \n"," stage2_unit2_conv2 (Conv3D) (None, 16, 16, 4, 1 442368 ['zero_padding3d_45[0][0]'] \n"," 28) \n"," \n"," global_average_pooling3d_21 (G (None, 128) 0 ['stage2_unit2_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_19 (Lambda) (None, 1, 1, 1, 128 0 ['global_average_pooling3d_21[0][\n"," ) 0]'] \n"," \n"," conv3d_38 (Conv3D) (None, 1, 1, 1, 8) 1032 ['lambda_19[0][0]'] \n"," \n"," activation_38 (Activation) (None, 1, 1, 1, 8) 0 ['conv3d_38[0][0]'] \n"," \n"," conv3d_39 (Conv3D) (None, 1, 1, 1, 128 1152 ['activation_38[0][0]'] \n"," ) \n"," \n"," activation_39 (Activation) (None, 1, 1, 1, 128 0 ['conv3d_39[0][0]'] \n"," ) \n"," \n"," multiply_19 (Multiply) (None, 16, 16, 4, 1 0 ['stage2_unit2_conv2[0][0]', \n"," 28) 'activation_39[0][0]'] \n"," \n"," add_19 (Add) (None, 16, 16, 4, 1 0 ['multiply_19[0][0]', \n"," 28) 'add_18[0][0]'] \n"," \n"," stage3_unit1_bn1 (BatchNormali (None, 16, 16, 4, 1 512 ['add_19[0][0]'] \n"," zation) 28) \n"," \n"," stage3_unit1_relu1 (Activation (None, 16, 16, 4, 1 0 ['stage3_unit1_bn1[0][0]'] \n"," ) 28) \n"," \n"," zero_padding3d_46 (ZeroPadding (None, 18, 18, 6, 1 0 ['stage3_unit1_relu1[0][0]'] \n"," 3D) 28) \n"," \n"," stage3_unit1_conv1 (Conv3D) (None, 8, 8, 2, 256 884736 ['zero_padding3d_46[0][0]'] \n"," ) \n"," \n"," stage3_unit1_bn2 (BatchNormali (None, 8, 8, 2, 256 1024 ['stage3_unit1_conv1[0][0]'] \n"," zation) ) \n"," \n"," stage3_unit1_relu2 (Activation (None, 8, 8, 2, 256 0 ['stage3_unit1_bn2[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_47 (ZeroPadding (None, 10, 10, 4, 2 0 ['stage3_unit1_relu2[0][0]'] \n"," 3D) 56) \n"," \n"," stage3_unit1_conv2 (Conv3D) (None, 8, 8, 2, 256 1769472 ['zero_padding3d_47[0][0]'] \n"," ) \n"," \n"," global_average_pooling3d_22 (G (None, 256) 0 ['stage3_unit1_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_20 (Lambda) (None, 1, 1, 1, 256 0 ['global_average_pooling3d_22[0][\n"," ) 0]'] \n"," \n"," conv3d_40 (Conv3D) (None, 1, 1, 1, 16) 4112 ['lambda_20[0][0]'] \n"," \n"," activation_40 (Activation) (None, 1, 1, 1, 16) 0 ['conv3d_40[0][0]'] \n"," \n"," conv3d_41 (Conv3D) (None, 1, 1, 1, 256 4352 ['activation_40[0][0]'] \n"," ) \n"," \n"," activation_41 (Activation) (None, 1, 1, 1, 256 0 ['conv3d_41[0][0]'] \n"," ) \n"," \n"," multiply_20 (Multiply) (None, 8, 8, 2, 256 0 ['stage3_unit1_conv2[0][0]', \n"," ) 'activation_41[0][0]'] \n"," \n"," stage3_unit1_sc (Conv3D) (None, 8, 8, 2, 256 32768 ['stage3_unit1_relu1[0][0]'] \n"," ) \n"," \n"," add_20 (Add) (None, 8, 8, 2, 256 0 ['multiply_20[0][0]', \n"," ) 'stage3_unit1_sc[0][0]'] \n"," \n"," stage3_unit2_bn1 (BatchNormali (None, 8, 8, 2, 256 1024 ['add_20[0][0]'] \n"," zation) ) \n"," \n"," stage3_unit2_relu1 (Activation (None, 8, 8, 2, 256 0 ['stage3_unit2_bn1[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_48 (ZeroPadding (None, 10, 10, 4, 2 0 ['stage3_unit2_relu1[0][0]'] \n"," 3D) 56) \n"," \n"," stage3_unit2_conv1 (Conv3D) (None, 8, 8, 2, 256 1769472 ['zero_padding3d_48[0][0]'] \n"," ) \n"," \n"," stage3_unit2_bn2 (BatchNormali (None, 8, 8, 2, 256 1024 ['stage3_unit2_conv1[0][0]'] \n"," zation) ) \n"," \n"," stage3_unit2_relu2 (Activation (None, 8, 8, 2, 256 0 ['stage3_unit2_bn2[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_49 (ZeroPadding (None, 10, 10, 4, 2 0 ['stage3_unit2_relu2[0][0]'] \n"," 3D) 56) \n"," \n"," stage3_unit2_conv2 (Conv3D) (None, 8, 8, 2, 256 1769472 ['zero_padding3d_49[0][0]'] \n"," ) \n"," \n"," global_average_pooling3d_23 (G (None, 256) 0 ['stage3_unit2_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_21 (Lambda) (None, 1, 1, 1, 256 0 ['global_average_pooling3d_23[0][\n"," ) 0]'] \n"," \n"," conv3d_42 (Conv3D) (None, 1, 1, 1, 16) 4112 ['lambda_21[0][0]'] \n"," \n"," activation_42 (Activation) (None, 1, 1, 1, 16) 0 ['conv3d_42[0][0]'] \n"," \n"," conv3d_43 (Conv3D) (None, 1, 1, 1, 256 4352 ['activation_42[0][0]'] \n"," ) \n"," \n"," activation_43 (Activation) (None, 1, 1, 1, 256 0 ['conv3d_43[0][0]'] \n"," ) \n"," \n"," multiply_21 (Multiply) (None, 8, 8, 2, 256 0 ['stage3_unit2_conv2[0][0]', \n"," ) 'activation_43[0][0]'] \n"," \n"," add_21 (Add) (None, 8, 8, 2, 256 0 ['multiply_21[0][0]', \n"," ) 'add_20[0][0]'] \n"," \n"," stage4_unit1_bn1 (BatchNormali (None, 8, 8, 2, 256 1024 ['add_21[0][0]'] \n"," zation) ) \n"," \n"," stage4_unit1_relu1 (Activation (None, 8, 8, 2, 256 0 ['stage4_unit1_bn1[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_50 (ZeroPadding (None, 10, 10, 4, 2 0 ['stage4_unit1_relu1[0][0]'] \n"," 3D) 56) \n"," \n"," stage4_unit1_conv1 (Conv3D) (None, 4, 4, 1, 512 3538944 ['zero_padding3d_50[0][0]'] \n"," ) \n"," \n"," stage4_unit1_bn2 (BatchNormali (None, 4, 4, 1, 512 2048 ['stage4_unit1_conv1[0][0]'] \n"," zation) ) \n"," \n"," stage4_unit1_relu2 (Activation (None, 4, 4, 1, 512 0 ['stage4_unit1_bn2[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_51 (ZeroPadding (None, 6, 6, 3, 512 0 ['stage4_unit1_relu2[0][0]'] \n"," 3D) ) \n"," \n"," stage4_unit1_conv2 (Conv3D) (None, 4, 4, 1, 512 7077888 ['zero_padding3d_51[0][0]'] \n"," ) \n"," \n"," global_average_pooling3d_24 (G (None, 512) 0 ['stage4_unit1_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_22 (Lambda) (None, 1, 1, 1, 512 0 ['global_average_pooling3d_24[0][\n"," ) 0]'] \n"," \n"," conv3d_44 (Conv3D) (None, 1, 1, 1, 32) 16416 ['lambda_22[0][0]'] \n"," \n"," activation_44 (Activation) (None, 1, 1, 1, 32) 0 ['conv3d_44[0][0]'] \n"," \n"," conv3d_45 (Conv3D) (None, 1, 1, 1, 512 16896 ['activation_44[0][0]'] \n"," ) \n"," \n"," activation_45 (Activation) (None, 1, 1, 1, 512 0 ['conv3d_45[0][0]'] \n"," ) \n"," \n"," multiply_22 (Multiply) (None, 4, 4, 1, 512 0 ['stage4_unit1_conv2[0][0]', \n"," ) 'activation_45[0][0]'] \n"," \n"," stage4_unit1_sc (Conv3D) (None, 4, 4, 1, 512 131072 ['stage4_unit1_relu1[0][0]'] \n"," ) \n"," \n"," add_22 (Add) (None, 4, 4, 1, 512 0 ['multiply_22[0][0]', \n"," ) 'stage4_unit1_sc[0][0]'] \n"," \n"," stage4_unit2_bn1 (BatchNormali (None, 4, 4, 1, 512 2048 ['add_22[0][0]'] \n"," zation) ) \n"," \n"," stage4_unit2_relu1 (Activation (None, 4, 4, 1, 512 0 ['stage4_unit2_bn1[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_52 (ZeroPadding (None, 6, 6, 3, 512 0 ['stage4_unit2_relu1[0][0]'] \n"," 3D) ) \n"," \n"," stage4_unit2_conv1 (Conv3D) (None, 4, 4, 1, 512 7077888 ['zero_padding3d_52[0][0]'] \n"," ) \n"," \n"," stage4_unit2_bn2 (BatchNormali (None, 4, 4, 1, 512 2048 ['stage4_unit2_conv1[0][0]'] \n"," zation) ) \n"," \n"," stage4_unit2_relu2 (Activation (None, 4, 4, 1, 512 0 ['stage4_unit2_bn2[0][0]'] \n"," ) ) \n"," \n"," zero_padding3d_53 (ZeroPadding (None, 6, 6, 3, 512 0 ['stage4_unit2_relu2[0][0]'] \n"," 3D) ) \n"," \n"," stage4_unit2_conv2 (Conv3D) (None, 4, 4, 1, 512 7077888 ['zero_padding3d_53[0][0]'] \n"," ) \n"," \n"," global_average_pooling3d_25 (G (None, 512) 0 ['stage4_unit2_conv2[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," lambda_23 (Lambda) (None, 1, 1, 1, 512 0 ['global_average_pooling3d_25[0][\n"," ) 0]'] \n"," \n"," conv3d_46 (Conv3D) (None, 1, 1, 1, 32) 16416 ['lambda_23[0][0]'] \n"," \n"," activation_46 (Activation) (None, 1, 1, 1, 32) 0 ['conv3d_46[0][0]'] \n"," \n"," conv3d_47 (Conv3D) (None, 1, 1, 1, 512 16896 ['activation_46[0][0]'] \n"," ) \n"," \n"," activation_47 (Activation) (None, 1, 1, 1, 512 0 ['conv3d_47[0][0]'] \n"," ) \n"," \n"," multiply_23 (Multiply) (None, 4, 4, 1, 512 0 ['stage4_unit2_conv2[0][0]', \n"," ) 'activation_47[0][0]'] \n"," \n"," add_23 (Add) (None, 4, 4, 1, 512 0 ['multiply_23[0][0]', \n"," ) 'add_22[0][0]'] \n"," \n"," bn1 (BatchNormalization) (None, 4, 4, 1, 512 2048 ['add_23[0][0]'] \n"," ) \n"," \n"," relu1 (Activation) (None, 4, 4, 1, 512 0 ['bn1[0][0]'] \n"," ) \n"," \n"," global_average_pooling3d_26 (G (None, 512) 0 ['relu1[0][0]'] \n"," lobalAveragePooling3D) \n"," \n"," dropout_2 (Dropout) (None, 512) 0 ['global_average_pooling3d_26[0][\n"," 0]'] \n"," \n"," dense_2 (Dense) (None, 2) 1026 ['dropout_2[0][0]'] \n"," \n","==================================================================================================\n","Total params: 33,260,477\n","Trainable params: 33,252,539\n","Non-trainable params: 7,938\n","__________________________________________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"id":"2l3-30YOAHq5","metadata":{"id":"2l3-30YOAHq5"},"outputs":[],"source":["model.compile(\n"," loss=\"binary_crossentropy\",\n"," optimizer=keras.optimizers.Adadelta(learning_rate=1.0),\n"," metrics=[\"acc\"],\n",")\n","# LOSS FOR BINARY\n","# OTHER HYPERPARAMETER ARE ADHOC"]},{"cell_type":"markdown","id":"zsk-mfgwHrzs","metadata":{"id":"zsk-mfgwHrzs"},"source":["SAVE BEST WIEGHTS IN .h5 TO NOT DUPLICATE"]},{"cell_type":"code","execution_count":null,"id":"32Vd_7iyAuTT","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":779},"executionInfo":{"elapsed":4257,"status":"error","timestamp":1666164042630,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"32Vd_7iyAuTT","outputId":"3bf02aa1-eeac-482d-f28e-50e9c56e4752"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/50\n"]},{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheckpoint_cb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m )\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m 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\u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: in user code:\n\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1051, in train_function *\n return step_function(self, iterator)\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1040, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1030, in run_step **\n outputs = model.train_step(data)\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 890, in train_step\n loss = self.compute_loss(x, y, y_pred, sample_weight)\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 949, in compute_loss\n y, y_pred, sample_weight, regularization_losses=self.losses)\n File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 201, in __call__\n loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 139, in __call__\n losses = call_fn(y_true, y_pred)\n File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 243, in call **\n return ag_fn(y_true, y_pred, **self._fn_kwargs)\n File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 1930, in binary_crossentropy\n backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),\n File \"/usr/local/lib/python3.7/dist-packages/keras/backend.py\", line 5283, in binary_crossentropy\n return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)\n\n ValueError: `logits` and `labels` must have the same shape, received ((None, 2) vs (None, 2, 2)).\n"]}],"source":["with tf.device('/GPU:0'):\n"," checkpoint_cb = keras.callbacks.ModelCheckpoint(\n"," \"3d_image_classification.h5\", save_best_only=True\n"," ) \n"," # Train the model, doing validation at the end of each epoch\n"," epochs = 50\n"," model.fit(\n"," train_dataset,\n"," validation_data=validation_dataset,\n"," epochs=epochs,\n"," shuffle=True,\n"," verbose=2,\n"," callbacks = checkpoint_cb\n"," )\n"," # SHUFFLE TO CHOOSE DATA RANDOMLY\n"]},{"cell_type":"code","execution_count":null,"id":"bded481c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":433},"executionInfo":{"elapsed":685,"status":"error","timestamp":1666164278014,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"bded481c","outputId":"67651639-e300-47ae-c9ec-6874f07a8526"},"outputs":[{"ename":"KeyError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in 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\u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"val_\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'acc'"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(1, 2, figsize=(20, 3))\n","ax = ax.ravel()\n","\n","for i, metric in enumerate([\"acc\", \"loss\"]):\n"," ax[i].plot(model.history.history[metric])\n"," ax[i].plot(model.history.history[\"val_\" + metric])\n"," ax[i].set_title(\"Model {}\".format(metric))\n"," ax[i].set_xlabel(\"epochs\")\n"," ax[i].set_ylabel(metric)\n"," ax[i].legend([\"train\", \"val\"])"]},{"cell_type":"code","execution_count":null,"id":"uVhQpi45MlSV","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uVhQpi45MlSV","outputId":"0ae244c0-258c-46c0-e991-d9ca4d2bd0db"},"outputs":[{"name":"stdout","output_type":"stream","text":["9/9 [==============================] - 4s 248ms/step - loss: 0.2968 - acc: 0.9110\n"]}],"source":["model2 = tf.keras.models.load_model(\"/content/3d_image_classification.h5\")\n","loss, val_accuracy = model2.evaluate(validation_dataset)"]},{"cell_type":"code","execution_count":null,"id":"IYhdNYNejriP","metadata":{"id":"IYhdNYNejriP"},"outputs":[],"source":["#COPY CONTENT PATH INTO\n","!cp '/content/3d_image_classification.h5' '/content/drive/MyDrive/3D Neural Images'"]},{"cell_type":"markdown","id":"HGQO873BKoXn","metadata":{"id":"HGQO873BKoXn"},"source":["### CONFUSION MATRICS"]},{"cell_type":"code","execution_count":null,"id":"8cd6b6cb","metadata":{"id":"8cd6b6cb"},"outputs":[],"source":["from sklearn.metrics import confusion_matrix\n","test_labels = []\n","rounded_predictions2 = []\n","rounded_predictions3 = []\n","class_names2 = ['Normal', 'Abnormal']\n","for image, labels in validation_dataset:\n","\n"," #FOR EACH IMAGE IN VAL SET PREDICT \n","# for j in labels:\n"," predictions= model2.predict(image)\n"," # print(predictions)\n"," rounded_predictions3.append(predictions)\n"," rounded_predictions = np.array(predictions>0.5)\n"," # print(rounded_predictions)\n"," rounded_predictions2.append(rounded_predictions)\n"," test_labels.append(labels)\n","\n","rounded_predictions2 = np.concatenate(rounded_predictions2)\n","test_labels = np.concatenate(test_labels)\n","rounded_predictions3 = np.concatenate(rounded_predictions3)\n","\n","\n","### PROBABILITY FUNCTION FOR EACH IMAGE MORE THAN 0.5 BELONGS TO CLASS 1 OTHERWISE CLASS 2"]},{"cell_type":"code","execution_count":null,"id":"4GnV7fw0hQT8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4GnV7fw0hQT8","outputId":"acc74e50-35e5-404c-83d5-9264646b7bed"},"outputs":[{"data":{"text/plain":["array([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,\n"," 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n"," 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1,\n"," 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n"," 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n"," 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n"," 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,\n"," 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n"," 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,\n"," 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,\n"," 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n"," 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1])"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["##LABELS FOR EACH INSTANCE IN TEST GROUP\n","\n","test_labels.argmax(axis=1)"]},{"cell_type":"code","execution_count":null,"id":"fyMkq446WaTR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fyMkq446WaTR","outputId":"b85c9a6c-310d-4492-da01-7f7490d4aba3"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[135 7]\n"," [ 18 121]]\n"]}],"source":["from sklearn.metrics import confusion_matrix\n","import matplotlib.pyplot as plt\n","import cv2\n","\n","cm = confusion_matrix(y_true=test_labels.argmax(axis=1), y_pred=rounded_predictions2.argmax(axis=1))\n","print(cm)\n","### TOTAL MUST BE AS TEST GROUP 281\n","# TRUE "]},{"cell_type":"code","execution_count":null,"id":"e1764885","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e1764885","outputId":"f453c30d-fd97-4d58-aa5f-8cafad2b006b"},"outputs":[{"data":{"text/plain":["array([[0.95070423, 0.8705036 ],\n"," [0.8705036 , 0.95070423],\n"," [0.88235294, 0.9453125 ],\n"," [0.91525424, 0.90636704],\n"," [0.91103203, 0.91103203],\n"," [0.82120782, 0.82120782],\n"," [0.1294964 , 0.04929577]])"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["confusion_matrix = cm\n","FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix) \n","FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n","TP = np.diag(confusion_matrix)\n","TN = confusion_matrix.sum() - (FP + FN + TP)\n","\n","# Sensitivity\n","TPR = TP/(TP+FN)\n","# Specificity\n","TNR = TN/(TN+FP) \n","# Precision\n","PPV = TP/(TP+FP)\n","# Negative predictive value\n","NPV = TN/(TN+FN)\n","# f1 score\n","FSC = 2*TPR*PPV/(TPR+PPV)\n","# False negative rate\n","FNR = FN/(TP+FN)\n","# False discovery rate\n","FDR = FP/(TP+FP)\n","# J score\n","JSC = TPR+TNR-1\n","# false positive rate\n","FPR = FP/(FP+TN) # FPR = 1-specificity\n","# Overall accuracy\n","ACC = (TP+TN)/(TP+FP+FN+TN)\n","TPR, TNR, PPV, NPV, FPR, FNR, ACC, TN, TP, FP, FN\n","resultss = np.zeros([7,2])\n","import pandas as pd\n","TPR\n","resultss[0,:] =TPR\n","resultss[1,:] =TNR\n","resultss[2,:] =PPV\n","resultss[3,:] =FSC\n","resultss[4,:] =ACC\n","resultss[5,:] = JSC\n","resultss[6,:] = FPR\n","resultss"]},{"cell_type":"code","execution_count":null,"id":"c56dcf7b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"c56dcf7b","outputId":"a8398438-8881-4cd2-83e0-e5005b733e35"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:37: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hUZfbA8e+BUBUBAV0FFKQoIVIDIgpEEESQIkgRlZViQ0BsP911dVnrWtaCIoKAiArSFkVRQJqg9CqEDosSQUJXxAhJzu+PexMmIZlMIJM7mTmf55lnZm49d8o99773vu8rqooxxhiTnUJeB2CMMSa0WaIwxhjjlyUKY4wxflmiMMYY45clCmOMMX5ZojDGGOOXJQoPiUi8iMR5HYfXROQ9EXk6n9c5TkSez891BouI3CEic85y3oj+DYpInIgkeB1HqLNE4RKR3SLyh4gcF5Ff3B3J+cFcp6rWVtWFwVxHqBGRu0XkO99hqnq/qj7nVUxeEpGhIvLxuSxDVT9R1TYBrOuM5Hg2v0ERqSIiKiJRuQw1bf44EUl1/2u/ichWEemTaRoVkd/daQ6KyEQRKeMzXkRksIhsdKdLEJEpInL12cRk/LNEkVEHVT0fqAfUB/7mcTy5drZ/3oK+bi/ZZ35W9rr/tQuAh4H3ReTKTNPUdae5AigLDPUZ9xbwEDAYuBCoCXwGtA9y3JFJVe3h1E7fDdzo8/4VYKbP+ybAEuAosB6I8xl3IfABsBc4AnzmM+4WYJ073xKgTuZ1ApcCfwAX+oyrDxwEirjv+wKb3eXPBi73mVaBB4HtwP+y2b6OQLwbx0KgVqY4/gZscpf/AVA8F9vwBPAD8CcQBTwJ7AR+c5d5qzttLSAJSAGOA0fd4eOA593XcUAC8CiQCOwD+visrxzwBfArsBJ4HvjOz/d6vc/3tge422edw4GZbpzLgWo+873lTv8rsBpo5jNuKDAV+Ngd3x9oDCx117MPeAco6jNPbeAb4DCwH/g70BY4CZxyP4/17rSlgTHucn52t7GwO+5u4HvgDeCQO+7utM8AEHdcohvbBiAGuNddz0l3XV9k/t0Dhd240r671UDlLD7Tn3B+c8fdx7U4B53/AH501z0eKJ3NdxIHJGQalgh0y/Sbru7zfgAwx31dA+c31DgX/+8s/6OZYyGb3647rjrwLXAM5785yd9n7o4rBrzmfmb7gfeAEu648sCX7m/mMLAYKOT1vjDLz8/rAELlkekPU8n9st9y31d0/5Tt3D9Ea/d9BXf8TGASzlFPEaCFO7y+++O5xv0T/tVdT7Es1jkfuMcnnleB99zXnYAdODvaKPcPucRnWsXZCV2Y9iPMtG01gd/duIsA/+cur6hPHBuByu4yvuf0jjuQbVjnzpv2B+iGk/wKAT3cdV/ijrubTDt2zkwUycCzbqztgBNAWXf8p+6jJBCNszPPMlEAl+P84W93l1UOqOezzkM4O/go4BPgU59573Snj8JJWr/gJk+cRHEK6OxuYwmgIc7BRBRQBSepD3GnL4Wz038UKO6+v8ZnWR9nins6MBI4D7gIWAHc5/P5JQOD3HWVIGOiuAlnB18GZwdWy+ezT/+cs/ndP47zu7/SnbcuUC6Lz7UKzm8uymdYX5zf1BXA+cB/gY+y+V7icHfO7ufXEUgF6mf6TVd3X5cF5gDPuu/vB37M5f87u/9oeiwB/HYnAk+544oD1wfwmb8BzMD5X5XCOch5yR33Ek7iKOI+mgHi9b4wy8/P6wBC5eH+YY7j7FgUmAeUccc9kflHj3NU/1fgEvdHXjaLZY4Anss0bKvPj9T3T9ofmO++FpwdYHP3/ddAP59lFMLZeV7uvlegpZ9texqYnGn+n3HPitw47vcZ3w7YmYtt6JvDZ7sO6OS+vpucE8UfZNwJJeLshAvj7KCv9BmX7RkFzlnS9GzGjQNGZ9rmLX624QhOUQg4O/dFOWzzkLR14ySqtdlMNxSfRAFcjHNmVsJn2O3AAp/P76dMy0j/TIGWwDb38yqUabr0zznT7z7tN7g17XvKYduqcGaimAcM8Hl/pftdRWUxfxzOf+aou60puEnVZxrFOTo/6o7fAlR0xz0FLMspTp9l+fuPxpHp7MbPb3c8MAqolGmaLD9znP/x72Q8U70W96wf52Doc3zOnEL1YdcoMuqsqqVwfjxX4ZwagnNk2k1EjqY9cIo0LsE5kj6sqkeyWN7lwKOZ5quMc8SS2TTgWhG5BGiO88Ne7LOct3yWcRjnR1jRZ/49frbrUpwiAQBUNdWdPrv5f/SJMZBtyLBuEektIut8po/h9GcZiEOqmuzz/gTOUWoFnKNo3/X52+7KOMUI2fkli3UAICKPichmETnmbkNpMm5D5m2uKSJfujdC/Aq86DN9TnH4uhzn6HKfz+c3EufMIst1+1LV+TjFXsOBRBEZJSIXBLju3MSZWYbfmPs6CifxZWWvqpbBuUYxDGdnm1kDd5riOAcsi0WkOM6Z4CW5iM3ffzSDHH67/4fzv1vh3i3WF/x+5hVwznxX+yxvljscnFKDHcAcEdklIk/mYpvylSWKLKjqtzhHX6+5g/bgnFGU8Xmcp6r/dsdd6HtHho89wAuZ5iupqhOzWOcRnNPrHkAvnGIQ9VnOfZmWU0JVl/guws8m7cXZAQHOHSM4f56ffaap7PP6MneeQLchfd0icjnwPjAQp9iiDE6xlgQQZ04O4BS7VMom7sz2ANVyuxIRaYazU+iOcxRaBqdcWnwmy7wdI3COemuo6gU4Zf1p0+/BKZLJSubl7ME5yi7v83lfoKq1/cyTcYGqw1S1IU7RXE2cIqUc5yPwzyur5WT4jeH8hpJxyuX9xfonzhn71SLSOZtpTgGjgao4O+55QCURiQ0gVvD/H02X029XVX9R1XtU9VLgPuBdEanujsvqMz+Ic3Zc2+e7LK3OBXpU9TdVfVRVr8ApfntERFoFuE35yhJF9t4EWotIXZyLlh1E5CYRKSwixd1b/Cqp6j6coqF3RaSsiBQRkebuMt4H7heRa9zb+c4TkfYiUiqbdU4AegO3ua/TvAf8TURqA4hIaRHplottmQy0F5FWIlIEp6z8T5yLvGkeFJFKInIhzqn9pLPchvNwdiQH3Fj74Py50+zH+ZMXzUX8AKhqCk7Z91ARKSkiV+F8Xtn5BLhRRLqLSJSIlBORegGsqhTOTu4AECUiz+Ac+eY0z6/AcTeuB3zGfQlcIiJDRKSYiJQSkWvccfuBKiJSyN3GfTgHDP8RkQtEpJCIVBORFgHEjYg0cr+rIjjFHkk4Z6dp68ouYYGzM35ORGq433UdESmXxXQH3GX6Lmsi8LCIVBXntvIXcS72JmcxfwaqehL4D/BMNttUGOiDs9PdparbgXeBie7/sKj7n+yZ1VF5Dv9RX35/uyLSTUTSDlKOuNOmZveZu2fu7wNviMhF7jIqishN7utbRKS6e+B2DKeILe27CimWKLKhqgdwyiSfUdU9OBeU/47zI9qDc8SQ9vndhVMeuwWnPH2Iu4xVwD04p6VHcE4z7/az2hk4d3T8oqrrfWKZDrwMfOoWa2wEbs7FtmzFuTj7Ns5RTgecW4FP+kw2AWcHtQun+OH5s9kGVd2E86dfirNjuhrn4nia+Th3X/0iIgcD3QYfA3GKgX4BPsLZQf2ZTSw/4Vx7eBSnuG4dzgXanMzGKSLYhlOEkoT/Ii6Ax3DOBH/D2TmkJVpU9TecGwk6uHFvB25wR09xnw+JyBr3dW+gKKfvQptK4EUtF7jrP+LGfginiAOcO6mi3WKQz7KY93Wcg4o5OElvDM7F8gxU9QTwAvC9u6wmwFic72MR8D+cz2xQgDHjzn+ZiHTwGbZeRI672/JXnDuQDrvjBnO6uOcozm/2VpyLxVnJ8j+aabty+u02Apa7Mc0AHlLVXfj/zJ/A+c8sc/+7c3Gu34DzX5+Lc210KfCuqi7I/iPyjpwu3TCRSkR2A/1Vda7XseSWiLwM/EVV/+p1LMaEKzujMAWKiFzlFomIiDQG+uHcTmqMCZKCWqvTRK5SOMVNl+IUD/wH5xZDY0yQWNGTMcYYv6zoyRhjjF8FruipfPnyWqVKFa/DMMaYAmX16tUHVbVCzlOeqcAliipVqrBq1SqvwzDGmAJFRH7MeaqsWdGTMcYYvyxRGGOM8csShTHGGL8sURhjjPHLEoUxxhi/LFEYY4zxK2iJQkTGikiiiGzMZryIyDAR2SEiP4hIg2DFYowx5uwF84xiHE7n8dm5GaeZ3Ro4Hb+PCGIsxhgTsXLsFCQHQatwp6qLRKSKn0k6AePdXtyWiUgZEbnE7WTEGGOMH3/gtIq5H6eDjexe7922jaM1a57TurysmV2RjJ3BJLjDzkgUInIvzlkHl112Wb4EZ4wx+Ulxurnzt/P3HXY8m+VcgNPB+oUHD/LbkCEcmziRe1etYtQ5xFYgmvBQ1VHgbGdsbKw1d2uMKRBScLqUzOmoP+35ZBbLEKA8zs7/YqCxz+u057TXFwHFVZk8eTKDBg3i6NGjPPP00/y9du0Cmyh+Bir7vK/kDjPGmJCVRNZH+Fm9PohzppBZETLu5GPIuMP3fV2ewHfUqkrPnj2ZPHkyjRo1YsyYMVx99dVns5kZeJkoZgADReRT4BrgmF2fMMbkN8Xp6DyQHf9+nM7Es3I+p3fy1YGmZL/zL4NzppBn26CKiCAiNGrUiMaNG/PQQw8RFZU3u/igJQoRmQjEAeVFJAH4J04iRVXfA77C6fh+B3AC6BOsWIwxkSUVOETgRT5J2SynHKd38g04c4fv+1wyOJuSo507d3LPPffw0EMP0alTJx577LE8X0cw73q6PYfxCjwYrPUbY8LLSfwX+fgOO4CTLDIrTMad/VVkf9RfAffINkSlpKTw1ltv8Y9//IMiRYpw4sSJoK2rQFzMNsaEp+MEXuRzNJtllOD0Dr4KzsXe7Hb+ZQmP5ig2btxIv379WLFiBR06dGDEiBFUrFgxaOuzRGGMyTOpwBECL/LJ7hi4LKd38nXI/i6fi3GuDUSatWvXsmvXLiZOnEiPHj0QycsrHmcSpwSo4IiNjVXr4c6Y/HMKpygnu2KezEU+WdUCLoxTlONvh+97i2fRoG1NwbVixQp27dpFz549UVWOHj1K2bJlA55fRFarauzZrNvOKIyJQCcIvMjncDbLKMbpHXwlnIu92e38yxEeRT5eOHHiBM888wxvvPEGNWrU4LbbbiMqKipXSeJcWaIwJgwoThl+oEU+2dXqLc3pnXw0zm2L2d3lU4q8vcXTnGnBggX079+fXbt2cd999/Hyyy/n2S2vuWGJwpgQlYxTYSuQo/5EnCKizISMRT7X4L/Ip3jQtsbk1rZt22jVqhVXXHEFCxYsIC4uzrNYLFEYk4+SCLzI5xBZ1+otyumd/F9wLvb6q9VbOGhbY4Jh8+bN1KpVi5o1azJlyhRuvvlmSpb0qpaGwxKFMedAcWrqBnJvfyLZ1+otxemdfE3gerLf+ZfGinzC0YEDBxg8eDCTJ09m1apV1K9fn65du3odFmCJwpgzpOAczQda5PNnFssQMtbqjSX7O34uwrtavcZ7qsrEiRMZPHgwv/76K0OHDqV27dpeh5WBJQoTEf7E2akHcqE3u1q9aQ25+V7s9Ver1/5cJieqSrdu3Zg2bRpNmjRh9OjRIZckwH7LpoBSnDt3Aj3qz65W73mc3slfATTBf61eK/IxecG3Eb+mTZty/fXXM2jQIAoXDs0rSpYoTMhIxblnP9Cd/x/ZLOdCTu/k65F15a605/OCsynGZGv79u3cc889DBkyhM6dO/PII494HVKOLFGYoDpF9kU+mYcl4lwfyCytIbe0nfuVZF/eXwGr1WtCU3JyMm+++SZPP/00xYoVIykpuzZrQ48lCpNrvxP4UX92tXqLc3oHfxnOxd7sinwuxGr1moLthx9+oF+/fqxatYpOnTrx7rvvcumll3odVsAsURgUpyG3QHf+v2eznDKc3snHkHWRT9rr87HyfhM51q9fz08//cTkyZO57bbbgt6IX16zRgHDVDLO3TuBdNmYXa3eQuSuIbdiQdsaYwqepUuX8r///Y9evXqhqhw7dowyZcp4Fo81Chgh/iDwo35/tXrTdvCX4lzs9deQW2jeg2FM6Pr999956qmnGDZsGFdddRXdu3cnKirK0yRxrixReEiBYwR2oXc/Tr++WbmA0zv5q4DmZH+XzwVYkY8xwTJ37lzuuecedu/ezYMPPshLL73kSSN+ea3gb0GISavVG8hRv79aveU5vXNvRPbFPxfh9PBljPHWtm3baNOmDdWrV2fRokU0a9bM65DyjCWKXPgfsAn/O/+D+K/Vm7aTjyH7Ip/y2BdjTEGxadMmoqOjqVmzJtOmTaNt27aUKBFeh2+2PwrQKZydu2/XjedzeidfHWhK9jv/MliRjzHhZP/+/QwaNIhp06alN+J36623eh1WUFiiCFBa/75PAf1wEoA15GZM5FFVPv74Y4YMGcLx48d57rnniImJ8TqsoLJEEaBE97khUNXLQIwxnlFVunbtyvTp07n22msZM2YMtWrV8jqsoLNEEaC0RHGRp1EYY7zg24hf8+bNiYuL48EHHwzZRvzymrWMEKD97rMlCmMiy9atW2nevDnTp08HYMiQIQwePDhikgRYoghY2hnFxZ5GYYzJL8nJyfz73/+mbt26xMfHk5yc7HVInrGipwAl4jRRUcrrQIwxQbdu3Tr69evHmjVr6Nq1K++88w5/+ctfvA7LM5YoArQfp9jJbnE1JvzFx8fz888/M3Xq1JDpt9pLVvQUoESs2MmYcLZkyRI++eQTAHr16sW2bdssSbgsUQQoEbuQbUw4On78OIMHD+b666/nxRdfJDk5GRHhggsu8Dq0kGGJIkBpRU/GmPAxZ84cYmJieOeddxg4cCDLli0Li0b88pp9IgFQrOjJmHCzdetW2rZtS82aNVm8eDHXXXed1yGFrKCeUYhIWxHZKiI7ROTJLMZfJiILRGStiPwgIu2CGc/ZOobT1pOdURhT8G3YsAGAK6+8kunTp7Nu3TpLEjkIWqIQkcLAcOBmIBq4XUSiM032D2CyqtYHegLvBiuec2GV7Ywp+Pbt20fXrl2pW7cua9asAaBTp04UL17c48hCXzDPKBoDO1R1l6qeBD4FOmWaRnH60gEoDewNYjxnzSrbGVNwqSrjxo0jOjqamTNn8tJLL1GnTh2vwypQgnmNoiKwx+d9AnBNpmmGAnNEZBBwHnBjVgsSkXuBewEuu+yyPA80J9bOkzEFk6rSuXNnZsyYwfXXX8/o0aO58sorvQ6rwPH6rqfbgXGqWgloB3wkImfEpKqjVDVWVWMrVKiQ70Fa0ZMxBUtqqtN9mIjQsmVLhg8fzrfffmtJ4iwFM1H8DFT2eV/JHearHzAZQFWXAsVxOngLKWlnFPmfoowxubV582aaNWvGtGnTAHjooYcYMGAAhQp5fVxccAXzk1sJ1BCRqiJSFOdi9YxM0/wEtAIQkVo4ieJAEGM6K4lAOexeYmNC2alTp3jxxRepV68eW7ZsQVW9DilsBG3fp6rJIjIQmA0UBsaqaryIPAusUtUZwKPA+yLyMM6F7bs1BL9dq2xnTGhbu3Ytffv2Zd26dXTv3p1hw4Zx8cV2+0leCepBsqp+BXyVadgzPq83ASF/A7NVtjMmtG3evJlffvmF6dOn07lzZ6/DCTtWaBcAa+fJmNCzePFiPvroIwBuv/12tm3bZkkiSCxRBMCKnowJHb/99hsPPvggzZs355VXXiElJQURoVQp6y0mWCxR5OBPnCY8rOjJGO99/fXX1K5dmxEjRjBkyBCWLVsWUV2SesVu5MlB2i1YdkZhjLe2bt1K+/btqVWrFkuWLKFJkyZehxQx7IwiB1bZzhjvqCrr168HnEb8ZsyYwZo1ayxJ5DNLFDmwdp6M8cbevXvp0qUL9evXT2/E75ZbbqFYsWIeRxZ5LFHkwNp5MiZ/qSpjx44lOjqaWbNm8fLLL1sjfh6zaxQ5sERhTP5RVTp16sQXX3xB8+bNGT16NDVq1PA6rIgXcKIQkZKqeiKYwYSi/UAJ4HyvAzEmjKWmplKoUCFEhNatW9OuXTvuvfdea58pROT4LYhIUxHZBGxx39cVkZDsYCgY0irbideBGBOm4uPjadq0KVOnTgVg0KBB3H///ZYkQkgg38QbwE3AIQBVXQ80D2ZQocRqZRsTHCdPnuS5556jfv367NixwxJDCAuo6ElV94hkOKZOCU44oWc/Tvvoxpi8s2rVKvr27cuGDRvo2bMnw4YNw4u+ZkxgAkkUe0SkKaAiUgR4CNgc3LBCRyLQwOsgjAkzO3bs4NChQ3z++ed07NjR63BMDgI517sfeBCna9OfgXrAgGAGFSoUK3oyJq98++23jB8/HoAePXqwdetWSxIFRCCJ4kpVvUNVL1bVi1T1TqBWsAMLBUeAZCxRGHMufv31Vx544AHi4uJ47bXX0hvxO/98u5ewoAgkUbwd4LCwY7WyjTk3M2fOpHbt2owaNYpHHnnEGvEroLK9RiEi1wJNgQoi8ojPqAtweqwLe1bZzpizt3XrVjp06EB0dDRTp07lmmuu8Tokc5b8nVEUxalnFgWU8nn8CtwW/NC8Zw0CGpM7qsratWsBpxG/L774gjVr1liSKOCyPaNQ1W+Bb0VknKr+mI8xhQwrejImcAkJCQwYMIAvv/ySVatW0aBBA9q3b+91WCYPBHJ77AkReRWoDRRPG6iqLYMWVYhIxKmRXc7rQIwJYampqYwePZrHH3+cU6dO8Z///Ie6det6HZbJQ4Ekik+AScAtOLfK/pXT/fmEtf04ScJaTjQma6pKhw4d+Oqrr7jhhht4//33qVatmtdhmTwWyD6wnKqOEZGHfIqjVgY7sFCQiBU7GZOVlJSU9Eb8br75Zm699Vb69etHphYcTJgI5PbYU+7zPhFpLyL1gQuDGFPIsMp2xpxp48aNNG3alGnTpgEwcOBA+vfvb0kijAWSKJ4XkdLAo8BjwGhgSFCjChH7sURhTJqTJ08ydOhQGjRowK5du4iKskLZSJHjN62qX7ovjwE3AIjIdcEMKlRY0ZMxjpUrV9KnTx/i4+O54447ePPNNylfvrzXYZl84q/CXWGgO04bT7NUdaOI3AL8Hacvn/r5E6I3knAqjNgZhTGwc+dOjh07xpdffmm3vEYgf2cUY4DKwApgmIjsBWKBJ1X1s/wIzktWK9tEuvnz5/PTTz9x991306NHDzp06MB5553ndVjGA/4SRSxQR1VTRaQ48AtQTVUP5U9o3rLKdiZSHT16lMcff5zRo0dTp04d7rrrLgoXLmxJIoL5u5h9UlVTAVQ1CdgVKUkC7IzCRKYZM2ZQu3Ztxo4dy//93/9ZI34G8H9GcZWI/OC+FqCa+14AVdU6QY/OQ9bOk4k0W7dupXPnzsTExPD5558TGxvrdUgmRPhLFBHR50R2rOjJRAJVZc2aNTRs2JArr7ySr776ipYtW1K0aFGvQzMhJNuiJ1X90d8jP4P0QiJQErBSWROu9uzZQ4cOHWjUqBFr1qwBoG3btpYkzBkCqXB31kSkrYhsFZEdIvJkNtN0F5FNIhIvIhOCGU9uWGU7E65SU1N57733qF27NgsWLOCNN96wRvyMX0GrWunWwxgOtAYSgJUiMkNVN/lMUwP4G3Cdqh4RkZDZN1tlOxOOVJX27dsza9YsbrzxRkaNGkXVqlW9DsuEuIDOKESkhIhcmctlNwZ2qOouVT0JfAp0yjTNPcBwVT0CoKqJhAhr58mEk5SUFFQVEaFDhw6MGTOGOXPmWJIwAckxUYhIB2AdMMt9X09EZgSw7IrAHp/3Ce4wXzWBmiLyvYgsE5G2gYUdfFb0ZMLF+vXrady4MVOnTgVgwIAB9O3b1xrxMwEL5IxiKM7ZwVEAVV0H5NVhSBRQA4gDbgfeF5EymScSkXtFZJWIrDpwIPhdYaTidLhhRU+mIPvzzz95+umniY2NZc+ePXaR2py1gJoZV9VjmYZpAPP9jNMESJpK7jBfCcAMVT2lqv8DtuEkjowrUx2lqrGqGluhQoUAVn1ujgAp2BmFKbiWL19O/fr1ef7557n99tvZvHkznTplLvk1JjCBJIp4EekFFBaRGiLyNrAkgPlWAjVEpKqIFAV6ApmLrD7DOZtARMrjFEXtCjT4YLFa2aag+/HHH/n999/5+uuvGT9+POXKWYe+5uwFkigG4fSX/ScwAae58Rz7o1DVZGAgMBvYDExW1XgReVZEOrqTzQYOicgmYAHweCg0E5JWK9uKnkxBMnfuXMaOHQtAt27d2Lx5M23bhsxlP1OAiar/UiQRaaCqa/IpnhzFxsbqqlWrgrqOyUAPYAMQE9Q1GXPujhw5wmOPPcbYsWOpW7cuq1evtvaZzBlEZLWqnlW7LIGcUfxHRDaLyHMiEhH7TSt6MgXF9OnTiY6O5sMPP+TJJ5+0RvxMUATSw90NIvIXnE6MRorIBcAkVX0+6NF5ZD9OBrVSXRPKtmzZQteuXalbty4zZ86kQYMGXodkwlRAFe5U9RdVHQbcj1On4pmgRuWxRKA8YMdlJtSoKitXrgTgqquuYtasWaxYscKShAmqQCrc1RKRoSKyAUi746lS0CPzkNXKNqHop59+ol27dlxzzTXpjfi1adOGIkWKeByZCXeBtPU0FpgE3KSqe4McT0iwWtkmlKSmpjJixAiefPJJVJVhw4ZRr149r8MyESSQaxTX5kcgoSQRpyq6MV5TVdq2bcs333xDmzZtGDlyJFWqVPE6LBNhsk0UIjJZVbu7RU6+99CGfQ93VvRkvJacnEzhwoUREW699VbuuOMOevfube0zGU/4O6N4yH2+JT8CCRV/AL9hicJ4Z+3atfTr148nn3yS7t2788ADD3gdkolw/nq42+e+HJBF73YD8ie8/GddoBqvJCUl8dRTT9GoUSP27t1LyZIlvQ7JGCCw22NbZzHs5rwOJFRYZTvjhaVLl1KvXj1efPFF7rrrLjZt2sQtt0TUybwJYf6uUTyAc+ZwhYj84DOqFPB9sAPzSlo7T5YoTH7as2cPSUlJzJ49mxyIR0EAAB/kSURBVDZt2ngdjjEZ+LtGMQH4GngJ8O3v+jdVPRzUqDxkRU8mv8yePZuEhAT69etHt27d6NChAyVKlPA6LGPO4K/oSVV1N/AgzvXdtAcicmHwQ/NGWqIIfq8XJlIdPnyYu+++m7Zt2zJ8+HBSUlIQEUsSJmT5SxQT3OfVwCr3ebXP+7C0HzjPfRiT16ZNm0Z0dDQff/wxTz31FEuWLLFG/EzIy7boSVVvcZ8jqvf1RKzYyQTHli1b6NatG/Xr12fWrFlWu9oUGIG09XSdiJznvr5TRF4XkcuCH5o3rLKdyUuqyrJlywCnEb9vvvmG5cuXW5IwBUogt8eOAE6ISF3gUWAn8FFQo/KQtfNk8sru3bu56aabuPbaa9Mb8WvVqhVRUYE0sWZM6AgkUSSr0w1eJ+AdVR2Oc4tsWLKiJ3OuUlJSGDZsGDExMSxdupThw4fbGYQp0AI5tPlNRP4G3AU0E5FCQFi2a5wKHMDOKMzZS2vEb+7cubRt25aRI0dy2WVhW1JrIkQgZxQ9gD+Bvqr6C05fFK8GNSqPHMJJFpYoTG4lJyejqogIt912G+PHj+err76yJGHCQo6Jwk0OnwClReQWIElVxwc9Mg9YZTtzNtasWUNsbCyTJ08G4L777uOuu+6yll5N2AjkrqfuwAqgG06/2ctF5LZgB+YFa+fJ5MYff/zBk08+SePGjdm/fz/nn3++1yEZExSBXKN4CmikqokAIlIBmAtMDWZgXrB2nkygvv/+e/r06cP27dvp168fr776KmXLlvU6LGOCIpBEUSgtSbgOEdi1jQLHip5MoPbt20dycjJz586lVatWXodjTFAFkihmichsYKL7vgfwVfBC8k4iTgYM24aszDn5+uuvSUhI4J577qFr167ccsstFC9e3OuwjAm6QC5mPw6MBOq4j1Gq+kSwA/PCfpzGAMPydMmctUOHDtG7d2/atWvHyJEj0xvxsyRhIoW//ihqAK8B1YANwGOq+nN+BeYFq2xnfKkqU6ZMYeDAgRw5coRnnnmGv//979aIn4k4/oqexgLjgUVAB+BtoEt+BOUVa+fJ+NqyZQs9e/akYcOGzJ07lzp16ngdkjGe8JcoSqnq++7rrSKyJj8C8lIicIXXQRhPpTXid+2111KrVi3mzp1L8+bNrX0mE9H8FccXF5H6ItJARBoAJTK9Dzv7saKnSLZr1y5at25N06ZN0xvxa9mypSUJE/H8/QP2Aa/7vP/F570CLYMVlBd+dx9W9BR50hrx+8c//kHhwoUZMWKENeJnjA9/HRfdkJ+BeO2A+2yJIrKoKm3atGH+/Pm0a9eO9957j8qVK3sdljEhxc6pXVYrO7KcOnWKqKgoRISePXvSt29fevXqZe0zGZOFoFYZEJG2IrJVRHaIyJN+pusqIioiscGMxx+rlR05Vq5cScOGDZk0aRIA99xzD3fccYclCWOyEbREISKFgeHAzUA0cLuIRGcxXSngIWB5sGIJhDUIGP5OnDjB448/TpMmTTh8+DBlypTxOiRjCoRAWo8Vt6/sZ9z3l4lI4wCW3RjYoaq7VPUk8ClOL3mZPQe8DCTlIu48Z0VP4e27776jbt26vPbaa/Tv35/4+Hjatm3rdVjGFAiBnFG8C1wL3O6+/w3nTCEnFYE9Pu8T3GHp3NtsK6vqTH8LEpF7RWSViKw6cOCAv0nPWiJO/64lgrJ047X9+/ejqsyfP5+RI0dSunRpr0MypsAIJFFco6oP4h7xq+oRoOi5rtjtUvV14NGcplXVUaoaq6qxFSpUONdVZ8lqZYefmTNnMmrUKAC6du1KfHw8N9wQUTfzGZMnAkkUp9zrDQrp/VGkBjDfz4DvfYaV3GFpSgExwEIR2Q00AWZ4dUF7P5YowsWBAwe44447uOWWWxg9ejQpKSkAFCtWzOPIjCmYAkkUw4DpwEUi8gLwHfBiAPOtBGqISFURKQr0BGakjVTVY6paXlWrqGoVYBnQUVVX5XYj8oI1CFjwqSqffvop0dHRTJkyhaFDh/Ldd99ZI37GnKMc61Go6icishpoBQjQWVU3BzBfsogMBGYDhYGxqhovIs8Cq1R1hv8l5K9EoKnXQZhzsmXLFnr16kWjRo0YM2YMMTExXodkTFjIMVGIyGXACeAL32Gq+lNO86rqV2Tq5EhVn8lm2riclhcsKcBBrOipIEpNTWXJkiVcf/311KpVi/nz59OsWTM7izAmDwVS9DQT+NJ9ngfsAr4OZlD57RDORRcreipYduzYQatWrWjWrFl6I35xcXGWJIzJY4H0cHe1qtZxn2vg1I9YGvzQ8o9VtitYUlJSeO2117j66qtZs2YN77//PvXr1/c6LGPCVq7belLVNSJyTTCC8YpVtis4VJXWrVuzYMECOnbsyLvvvkvFihVzntEYc9YCuUbxiM/bQkADYG/QIvKAtfMU+nwb8evVqxf33Xcf3bt3t/aZjMkHgVyjKOXzKIZzrSKrpjgKLCt6Cm0rVqygfv36TJw4EYD+/fvTo0cPSxLG5BO/ZxRuRbtSqvpYPsXjif049++W9ToQk8GJEyd4+umnefPNN7n00kspV66c1yEZE5GyTRQiEuXWhbguPwPyQlrzHUFtc93kyqJFi+jTpw+7du3i/vvv5+WXX+aCCy7wOixjIpK/M4oVONcj1onIDGAKTm+hAKjqf4McW76xdp5Cz8GDBylUqBALFy6kRYsWXodjTEQL5K6n4jhVDVritPck7nPYJApr5yk0zJgxg71793L//ffTpUsX2rdvb+0zGRMC/JW2XOTe8bQR2OA+x7vPG/Mhtnxj7Tx5KzExkZ49e9KpUyc++OADa8TPmBDjL1EUBs53H6V8Xqc9woYVPXlDVfn444+pVasW06dP57nnnrNG/IwJQf6Knvap6rP5FolHjuM0ZGWJIv9t2bKF3r1706RJE0aPHk109Bk95RpjQoC/M4qIuEndKtvlr9TUVBYtWgRArVq1WLhwIYsXL7YkYUwI85coWuVbFB6yynb5Z/v27dxwww20aNEivRG/5s2bW1GTMSEu20ShqofzMxCvWDtPwZecnMwrr7xCnTp1WL9+PWPGjLFG/IwpQHLdKGC4saKn4FJVbrzxRr799ls6d+7M8OHDufTSS70OyxiTCxFfGTktUVTwNIrwc/LkSVQVEaF3795MnjyZ//73v5YkjCmALFEAF+DUKjR5Y+nSpdSrV48JEyYA0LdvX7p162aN+BlTQEV8orBa2Xnn+PHjDBkyhOuuu47ff/+diy+2Aj1jwoFdo8CuT+SFhQsX0qdPH3bv3s3AgQN58cUXKVWqlNdhGWPygCUKoKbXQYSBo0ePUqxYMRYvXsz111/vdTjGmDxkRU9Y0dPZmj59OiNGjACgc+fO/PDDD5YkjAlDEZ0oknGaxbWip9zZv38/3bp1o0uXLowfPz69Eb+iRYt6HJkxJhgiOlEcwmkv3c4oAqOqjB8/nlq1ajFjxgxeeOEFFi1aZDWrjQlzEX2Nwmpl586WLVvo06cPTZo0YcyYMVx11VVeh2SMyQcRfUZhtbJzlpqayoIFCwCnEb9vv/2WxYsXW5IwJoJYosDOKLKzdetWWrRoQcuWLdMb8bv++uspVCiifzbGRJyI/sdb0VPWTp06xUsvvUTdunWJj49n3Lhx1oifMREsoq9RJOJ8AGW9DiSEqCqtWrVi8eLF3Hbbbbz99tv85S9/8TosY4yHIvqMIq0LVGuBCP7888/0Rvz69OnDtGnTmDJliiUJY0xkJwqrbOf4/vvvqVu3Lp988gkAffr0oUuXLh5HZYwJFRGdKCK9nafffvuNQYMG0axZM5KSkrjkkku8DskYE4KCmihEpK2IbBWRHSLyZBbjHxGRTSLyg4jME5HLgxlPZmlFT5Fo/vz5xMTEMHz4cAYNGsTGjRtp1Soier81xuRS0C5mi0hhYDjQGkgAVorIDFXd5DPZWiBWVU+IyAPAK0CPYMXkS4nsoqdff/2VkiVL8t1339G0aVOvwzHGhLBgnlE0Bnao6i5VPQl8CnTynUBVF6jqCfftMqBSEOPJ4DiQRGQVPU2dOpXhw4cDpxvxsyRhjMlJMBNFRWCPz/sEd1h2+gFfZzVCRO4VkVUisurAgQN5ElwkVbbbt28fXbp0oVu3bkyYMCG9Eb8iRYp4HJkxpiAIiYvZInInEAu8mtV4VR2lqrGqGluhQt70bh0Jle1UlQ8++IDo6Gi+/vprXn75Zb799ltrxM8YkyvBrHD3M1DZ530ld1gGInIj8BTQQlX/DGI8GURCO0+bN2+mf//+XHfddYwePZqaNa2LJmNM7gXzjGIlUENEqopIUaAnMMN3AhGpD4wEOqpqYhbLCJpwLXpKSUlh/vz5AERHR/Pdd9+xcOFCSxLGmLMWtEShqsnAQGA2sBmYrKrxIvKsiHR0J3sVOB+YIiLrRGRGNovLc2lFT3lTkBUaNm/eTPPmzWnVqhVr164F4Nprr7VG/Iwx5ySobT2p6lfAV5mGPePz+sZgrt+fRKAMUMyrAPLQqVOneOWVV3j22Wc5//zz+eijj6hXr57XYRljwkTENgoYLpXtVJWWLVvy3Xff0b17d95++20uuigctswYEyoitkyioFe2S0pKSm/Er3///kyfPp1JkyZZkjDG5LmITRQFuZ2nRYsWUadOHT7++GMA/vrXv9K5c2ePozLGhKuIThQF7dj7119/ZcCAAbRo0YLk5GQqV66c80zGGHOOIjJRnAIOUbASxdy5c4mJieG9997j4YcfZsOGDcTFxXkdljEmAkTkxeyD7nNBKno6ceIEpUqVYsmSJTRp0sTrcIwxESQiE0VBqGynqkyZMoX9+/czaNAgOnbsSLt27YiKisivzBjjoYgsegr1RLF3715uvfVWevTowaRJk9Ib8bMkYYzxQkQmilBtEFBVGTNmDNHR0cyePZtXX32VhQsXWiN+xhhPReQhaqg2CLh582buvfdemjVrxujRo6levbrXIRljTGSeUSQCRYDSXgeC04jfN998AziN+H3//ffMnz/fkoQxJmREZKJIq5UtHscRHx/PddddR5s2bdIb8WvSpIk14meMCSkRuUfyulb2yZMnefbZZ6lfvz47d+5kwoQJ1oifMSZkRew1Cq8uZKsqcXFxLF26lF69evHmm2+SV732GXOuTp06RUJCAklJSV6HYs5S8eLFqVSpUp52dRyRiWI/EJ3P60xKSqJYsWKICPfffz9/+9vf6NChQz5HYYx/CQkJlCpViipVqiDideGsyS1V5dChQyQkJFC1atU8W27EFT0p+V/0tHDhQmJiYvjoo48A6N27tyUJE5KSkpIoV66cJYkCSkQoV65cnp8RRlyi+A34k/wpejp27Bj33XcfN9xwAwCXX355PqzVmHNjSaJgC8b3F3GJIr8q282ZM4fatWszevRoHnvsMX744QdatGgR5LUaY0zei7hEkV+V7ZKSkihbtixLly7l1VdfpWTJkkFeozHh4Y8//qBFixbpTdcAvPnmmxQvXpxjx46lDxs3bhwDBw7MMG9cXByrVq0C4Pjx49x3331Uq1aNhg0bEhcXx/Lly88pNlVl8ODBVK9enTp16rBmzZosp5s0aRJ16tShdu3aPPHEE+nDFy1aRIMGDYiKimLq1KkZ5vnwww+pUaMGNWrU4MMPP0wffvLkSe69915q1qzJVVddxbRp0wB45513GDt27DltT6AiNlHk9RmFqjJx4kTeeustADp27MjatWtp3LhxHq/JmPA2duxYunTpkqHpmokTJ9KoUSP++9//Bryc/v37c+GFF7J9+3ZWr17NBx98wMGDB3Oe0Y+vv/6a7du3s337dkaNGsUDDzxwxjSHDh3i8ccfZ968ecTHx/PLL78wb948AC677DLGjRtHr169Msxz+PBh/vWvf7F8+XJWrFjBv/71L44cOQLACy+8wEUXXcS2bdvYtGlTeslE3759efvtt89pewIVcXc9BaPoKSEhgQceeIAvv/yS5s2bM2jQIAoVKmSN+JkCbQiwLo+XWQ94M4dpPvnkEyZMmJD+fufOnRw/fpx3332XF154gT59+uS4np07d7J8+XI++eST9AqsVatWPec7gT7//HN69+6NiNCkSROOHj3Kvn37uOSSS9Kn2bVrFzVq1Ei/7f3GG29k2rRptGrViipVqgCcUal29uzZtG7dmgsvvBCA1q1bM2vWLG6//XbGjh3Lli1b0ucrX748ACVLlqRKlSqsWLEi6AekEXtGkRc1F1JTUxk5ciTR0dHMmzeP119/nfnz51vNamPO0smTJ9m1a1f6DhXg008/pWfPnjRr1oytW7eyf//+7Bfgio+Pp169egE1qNmjRw/q1at3xmP8+PFnTPvzzz9n6FmyUqVK/PzzzxmmqV69Olu3bmX37t0kJyfz2WefsWfPHr8xZLfco0ePAvD000/ToEEDunXrlmH7Y2NjWbx4cY7beK4i7pA3ESgLFM2DZW3ZsoUBAwYQFxfH+++/zxVXXJEHSzUmNOR05B8MBw8epEyZMhmGTZw4kenTp1OoUCG6du3KlClTGDhwYLZ39+T2rp9JkyaddbxZKVu2LCNGjKBHjx4UKlSIpk2bsnPnzrNaVnJyMgkJCTRt2pTXX3+d119/ncceeyz9VvuLLroo/WwjmCIuUaS183S2kpOTmTdvHjfddBPR0dEsW7aM2NhYu6XQmDxQokSJDHUANmzYwPbt22ndujXgnHFUrVqVgQMHUq5cufRy/DSHDx+mfPnylClThvXr15OSkpLjWUWPHj3YunXrGcMfeeQRevfunWFYxYoVM5wdJCQkULFixTPm7dChQ3pdqVGjRuUYQ8WKFVm4cGGG5cbFxVGuXDlKlixJly5dAOjWrRtjxoxJny4pKYkSJUr4XXZeiLgyknOpbLdhwwaaNm1K27Zt0xvxa9SokSUJY/JI2bJlSUlJSU8WEydOZOjQoezevZvdu3ezd+9e9u7dy48//kijRo34/vvv+eWXXwBYtWoVf/75J5UrV6ZatWrExsbyz3/+E1UFYPfu3cycOfOMdU6aNIl169ad8cicJMC5SWX8+PGoKsuWLaN06dIZrk+kSUx0CrmPHDnCu+++S//+/f1u90033cScOXM4cuQIR44cYc6cOdx0002ICB06dEhPIvPmzSM6+nS7Etu2bSMmJiaAT/YcqWqBejRs2FDPxVWqelsu50lKStJnnnlGo6KitEKFCvrpp59qamrqOcVhTCjatGmT1yFo37599ZtvvlFV1apVq+rmzZszjH/44Yf13//+t6qqfvbZZ1q/fn2tW7euXnfddbp69er06Y4dO6b9+/fXK664QmvXrq0tWrTQFStWnFNsqampOmDAAL3iiis0JiZGV65cmT6ubt266a979uyptWrV0lq1aunEiRPTh69YsUIrVqyoJUuW1AsvvFCjo6PTx40ZM0arVaum1apV07Fjx6YP3717tzZr1kyvvvpqbdmypf7444/p4+rXr68HDx48I86svkdglZ7lftfzHX9uH+eaKMqq6oBcTJ+amqpNmjRRQO+88049cODAOa3fmFAWColi9erVeuedd3odRshbs2ZNtp9TXieKiCp6OgkcIbCipxMnTqCqiAgPPvggX375JR999FH6rWnGmOBo0KABN9xwQ4YKd+ZMBw8e5LnnnsuXdUVUokirapPTxex58+YRExOTXjvyzjvvpH379kGNzRhzWt++fa2v+By0bt06w23EwRRRiSKnynZHjx6lf//+3HjjjURFRVGtWrX8Cs2YkKHuxV9TMAXj+4uoROGvnadZs2YRHR3NuHHjeOKJJ1i/fj3NmjXLz/CM8Vzx4sU5dOiQJYsCSt3+KIoXL56ny42oehT+2nk6deoUF110EV988QUNGzbMz7CMCRmVKlUiISGBAwcOeB2KOUtpPdzlpYhKFL5FT6rKhAkTSExM5OGHH6ZDhw60a9fOykVNRCtSpEie9oxmwkNQi55EpK2IbBWRHSLyZBbji4nIJHf8chGpEsx4EoFiwLE9e7jlllu48847+fzzz0lNTQWwJGGMMVkIWqIQkcLAcOBmnC6qbxeRzF1V9wOOqGp14A3g5WDFA7A/NZWSI0YQU7s2Cxcu5K233mLevHnWiJ8xxvgRzD1kY2CHqu5S1ZPAp0CnTNN0AtJ66JgKtJIgtoexa8sWjgwaxDXXXMPGjRsZPHiwnUUYY0wOgnmNoiLg27ZuAnBNdtOoarKIHAPKcbrKAwAici9wr/v2uIic2YJX4MrPnTv3YAS39FqeTJ9vhInk7Y/kbQfb/ivPdsYCcTFbVUcBo/JiWSKySlVj82JZBZFtf+RufyRvO9j2i8iqs503mEVPPwOVfd5XcodlOY2IRAGlgUNBjMkYY0wuBTNRrARqiEhVESkK9ARmZJpmBvBX9/VtwHy1mj7GGBNSglb05F5zGAjMBgoDY1U1XkSexWnFcAYwBvhIRHYAh3GSSbDlSRFWAWbbH7kiedvBtv+st1/sAN4YY4w/VoHAGGOMX5YojDHG+BW2iSLUmg/JTwFs+yMisklEfhCReSJyuRdxBktO2+8zXVcRUREJq1smA9l+Eenu/gbiRWRCfscYTAH8/i8TkQUistb9D7TzIs5gEJGxIpIoIhuzGS8iMsz9bH4QkQYBLfhsu8YL5QfOxfOdwBVAUWA9EJ1pmgHAe+7rnsAkr+POx22/ASjpvn4gXLY90O13pysFLAKWAbFex53P338NYC1Q1n1/kddx5/P2jwIecF9HA7u9jjsPt7850ADYmM34dsDXgABNgOWBLDdczyhCrvmQfJTjtqvqAlU94b5dhlPHJVwE8t0DPIfTtlhSfgaXDwLZ/nuA4ap6BEBVEwkfgWy/Ahe4r0sDe/MxvqBS1UU4d5BmpxMwXh3LgDIicklOyw3XRJFV8yEVs5tGVZOBtOZDCrpAtt1XP5wjjHCR4/a7p9uVVXVmfgaWTwL5/msCNUXkexFZJiJt8y264Atk+4cCd4pIAvAVMCh/QgsJud0/AAWkCQ8THCJyJxALtPA6lvwiIoWA14G7PQ7FS1E4xU9xOGeTi0TkalU96mlU+ed2YJyq/kdErsWpyxWjqqleBxaqwvWMIpKbDwlk2xGRG4GngI6q+mc+xZYfctr+UkAMsFBEduOU084IowvagXz/CcAMVT2lqv8DtuEkjnAQyPb3AyYDqOpSoDhOg4GRIKD9Q2bhmigiufmQHLddROoDI3GSRDiVT0MO26+qx1S1vKpWUdUqONdoOqrqWTeYFmIC+e1/hnM2gYiUxymK2pWfQQZRINv/E9AKQERq4SSKSOn7dQbQ2737qQlwTFX35TRTWBY9aeg2HxJ0AW77q8D5wBT3+v1PqtrRs6DzUIDbH7YC3P7ZQBsR2QSkAI+rajicTQe6/Y8C74vIwzgXtu8Ok4NERGQizkFAefcazD+BIgCq+h7ONZl2wA7gBNAnoOWGyedjjDEmSMK16MkYY0wesURhjDHGL0sUxhhj/LJEYYwxxi9LFMYYY/yyRGFCkoikiMg6n0cVP9Mez4P1jROR/7nrWuPW2M3tMkaLSLT7+u+Zxi051xjd5aR9LhtF5AsRKZPD9PXCqXVU4w27PdaEJBE5rqrn5/W0fpYxDvhSVaeKSBvgNVWtcw7LO+eYclquiHwIbFPVF/xMfzdO67gD8zoWEznsjMIUCCJyvtt3xhoR2SAiZ7QIKyKXiMginyPuZu7wNiKy1J13iojktANfBFR3533EXdZGERniDjtPRGaKyHp3eA93+EIRiRWRfwMl3Dg+cccdd58/FZH2PjGPE5HbRKSwiLwqIivdfgLuC+BjWYrboJuINHa3ca2ILBGRK92ayc8CPdxYerixjxWRFe60WbWsa0xGXrefbg97ZPXAqTG8zn1Mx2lF4AJ3XHmcmqVpZ8TH3edHgafc14Vx2nUqj7PjP88d/gTwTBbrGwfc5r7uBiwHGgIbgPNwarLHA/WBrsD7PvOWdp8X4vZtkRaTzzRpMd4KfOi+LorTkmcJ4F7gH+7wYsAqoGoWcR732b4pQFv3/QVAlPv6RmCa+/pu4B2f+V8E7nRfl8Fp5+k8r79ve4T2Iyyb8DBh4Q9VrZf2RkSKAC+KSHMgFedI+mLgF595VgJj3Wk/U9V1ItICp3Oa793mSoriHIln5VUR+QdOuz/9cNoDmq6qv7sx/BdoBswC/iMiL+MUVy3OxXZ9DbwlIsWAtsAiVf3DLe6qIyK3udOVxmmo73+Z5i8hIuvc7d8MfOMz/YciUgOnWYoi2ay/DdBRRB5z3xcHLnOXZUyWLFGYguIOoALQUFVPidPya3HfCVR1kZtI2gPjROR14AjwjareHsA6HlfVqWlvRKRVVhOp6jZx+rRoBzwvIvNU9dlANkJVk0RkIXAT0AOnYx1wehwbpKqzc1jEH6paT0RK4rRn9CAwDKcjpgWqeqt74X9hNvML0FVVtwYSrzFg1yhMwVEaSHSTxA3AGf18i9P3935VfR8YjdMl5DLgOhFJu+ZwnojUDHCdi4HOIlJSRM7DKTZaLCKXAidU9WOcBhaz6nf4lHtmk5VJOI2xpZ2dgLPTfyBtHhGp6a4zS+r0UDgYeFRON5Of1lz03T6T/oZTBJdmNjBI3NMrcVoSNsYvSxSmoPgEiBWRDUBvYEsW08QB60VkLc7R+luqegBnxzlRRH7AKXa6KpAVquoanGsXK3CuWYxW1bXA1cAKtwjon8DzWcw+Cvgh7WJ2JnNwOouaq053neAktk3AGhHZiNMMvN8zfjeWH3A64nkFeMnddt/5FgDRaRezcc48irixxbvvjfHLbo81xhjjl51RGGOM8csShTHGGL8sURhjjPHLEoUxxhi/LFEYY4zxyxKFMcYYvyxRGGOM8ev/AVdMwVcPhCGxAAAAAElFTkSuQmCC","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import numpy as np\n","from scipy import interp\n","import matplotlib.pyplot as plt\n","from itertools import cycle\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.preprocessing import label_binarize\n","\n","# Plot linewidth.\n","lw = 1.5\n","\n","# Compute ROC curve and ROC area for each class\n","fpr = dict()\n","tpr = dict()\n","roc_auc = dict()\n","num_classes = 1\n","y_score = np.array(rounded_predictions3)\n","y_score = y_score.argmax(axis=1)\n","y_test = label_binarize(test_labels, classes=[0, 1])\n","y_test = y_test.argmax(axis=1)\n","# for i in range(num_classes):\n","# fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n","# roc_auc[i] = auc(fpr[i], tpr[i])\n","\n","fpr[0], tpr[0], _ = roc_curve(y_test, y_score)\n","roc_auc[0] = auc(fpr[0], tpr[0])\n","\n"," # Compute micro-average ROC curve and ROC area\n","fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n","roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n"," \n","# First aggregate all false positive rates\n","all_fpr = np.unique(np.concatenate([fpr[i] for i in range(num_classes)]))\n","\n","# Then interpolate all ROC curves at this points\n","mean_tpr = np.zeros_like(all_fpr)\n","for i in range(num_classes):\n"," mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n","\n","# Finally average it and compute AUC\n","mean_tpr /= num_classes\n","\n","fpr[\"macro\"] = all_fpr\n","tpr[\"macro\"] = mean_tpr\n","roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n","\n","# Plot all ROC curves\n","plt.figure()\n","colors = cycle(['aqua', 'darkorange', 'cornflowerblue', 'red', 'green'])\n","for i, color in zip(range(num_classes), colors):\n"," plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n"," label='(AUC = {0:0.4f})'\n"," ''.format(roc_auc[i]))\n","\n","plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n","plt.xlim([-0.05, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver operating characteristic to RBC classes')\n","plt.legend(loc=\"lower right\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"SgQUQe4WhkrO","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"elapsed":307,"status":"error","timestamp":1666168342587,"user":{"displayName":"ADProject Deep learning","userId":"16077525050569453708"},"user_tz":-180},"id":"SgQUQe4WhkrO","outputId":"f826761a-77a5-4423-901f-baa344d4051b"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclassification_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my_true\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrounded_predictions2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'test_labels' is not defined"]}],"source":["from sklearn.metrics import classification_report\n","y_true=test_labels.argmax(axis=1)\n","y_pred=rounded_predictions2.argmax(axis=1)\n","print(classification_report(y_true, y_pred))\n"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","provenance":[]},"gpuClass":"premium","kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"}},"nbformat":4,"nbformat_minor":5}