{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"##### OLD model for finding survival in days ( regression) based ############\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 19 10:30:33 2022\n\n@author: MIDL\n\"\"\"\n################## keras data generator ###########################\n\nfrom sklearn.model_selection import train_test_split\nimport os\nimport tensorflow as tf\nimport numpy as np\n\n# lists of directories with studies\n# train_and_val_directories = [f.path for f in os.scandir('C:/Users/marya/Downloads/Brats 2020 adjusted') if f.is_dir()]\ncase_path1 = '../input/combine-all-2109'\ncase_path2 = '../input/adjustedmask2019' \ncase_path3 = '../input/adjusted-survival-2019'\ncase_path4 = '../input/adjustedlabels2019'\n\n\n# case_main = 'C:/Users/MIDL/Downloads/3d_model_december/Brats 2020 adjusted'\n\ntrain_directory1 = [f.path for f in os.scandir(case_path1) ]\n# train_directory2 = [f.path for f in os.scandir(case_path2) ]\n\ndef pathListIntoIds(dirList):\n x = []\n for i in range(0,len(dirList)):\n# print(dirList[i][dirList[i].rfind('/')+1:])\n# x.append(dirList[i][dirList[i].rfind('\\\\')+1:]) #for local system\n x.append(dirList[i][dirList[i].rfind('/')+1:]) #for KAGGLE\n return x\n\ntrain_and_test_ids1 = pathListIntoIds(train_directory1); \n\n \ntrain_test_ids, val_ids = train_test_split(train_and_test_ids1,test_size=0.1) \ntrain_ids, test_ids = train_test_split(train_test_ids,test_size=0.22) \n\n\n# train_and_test_ids2 = pathListIntoIds(train_directory2);\n\n# masks_test_ids, masks_val_ids = train_test_split(train_and_test_ids,test_size=0.3) \n#train_ids, test_ids = train_test_split(train_test_ids,test_size=0.5) \n\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:14.498330Z","iopub.execute_input":"2023-07-29T17:57:14.498727Z","iopub.status.idle":"2023-07-29T17:57:23.885819Z","shell.execute_reply.started":"2023-07-29T17:57:14.498682Z","shell.execute_reply":"2023-07-29T17:57:23.884761Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n warnings.warn(f\"file system plugins are not loaded: {e}\")\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"train_ids\",len(train_ids))\nprint(\"val_ids\",len(val_ids))\nprint(\"test_ids\",len(test_ids))\n# 70% train\n# 20% test\n# 10% validation","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:23.887094Z","iopub.execute_input":"2023-07-29T17:57:23.887826Z","iopub.status.idle":"2023-07-29T17:57:23.893067Z","shell.execute_reply.started":"2023-07-29T17:57:23.887792Z","shell.execute_reply":"2023-07-29T17:57:23.892340Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"train_ids 195\nval_ids 28\ntest_ids 55\n","output_type":"stream"}]},{"cell_type":"code","source":"\n################### Override Keras sequence DataGenerator class #########################\n\nclass DataGenerator(tf.keras.utils.Sequence):\n 'Generates data for Keras'\n def __init__(self, list_IDs, dim=(128,128,128), batch_size = 1, n_channels = 4, shuffle=True, augment=False):\n 'Initialization'\n self.dim = dim\n self.batch_size = batch_size\n self.list_IDs = list_IDs\n self.n_channels = n_channels\n self.shuffle = shuffle\n self.on_epoch_end()\n self.augment = augment\n\n def __len__(self):\n 'Denotes the number of batches per epoch'\n return int(np.floor(len(self.list_IDs) / self.batch_size))\n\n def __getitem__(self, index):\n 'Generate one batch of data'\n \n # Generate indexes of the batch\n indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n\n # Find list of IDs\n Batch_ids = [self.list_IDs[k] for k in indexes]\n\n # Generate data\n X, y, z, v, Batch_ids = self.__data_generation(Batch_ids)\n\n return X, [y, z, v]\n\n def on_epoch_end(self):\n 'Updates indexes after each epoch'\n \n self.indexes = np.arange(len(self.list_IDs))\n if self.shuffle == True:\n np.random.shuffle(self.indexes)\n\n def __data_generation(self, Batch_ids):\n 'Generates data containing batch_size samples' \n \n X = []\n y = []\n z = []\n v = []\n # Generate data\n for c, i in enumerate(Batch_ids):\n data_path1 = os.path.join(case_path1, i);\n flair = np.load(data_path1)\n flair = np.asarray(flair,dtype=np.float32)\n \n# print(flair.shape)\n \n masks = i[:6]+'mask_2019_'+i[12:]\n data_path2 = os.path.join(case_path2, masks);\n seg = np.load(data_path2)\n seg = np.asarray(seg,dtype=np.float32)\n \n survive = i[:6]+'survival'+i[12:]\n data_path3 = os.path.join(case_path3, survive);\n survival = np.load(data_path3, allow_pickle= True)\n \n label = i[:6]+'label'+i[12:]\n data_path4 = os.path.join(case_path4, label);\n label = np.load(data_path4, allow_pickle= True)\n \n \n if self.augment == True:\n# print(\"augmenting\")\n# print(\"flair.dtype\",flair.dtype)\n #flair,seg,angle = rotate(flair,seg)\n# print(\"angle\",angle)\n augmented = augmentor.apply_augmentation_to_batch(flair, seg)\n flair = augmented[0]\n seg = augmented[1]\n \n X.append(flair)\n y.append(label)\n z.append(seg)\n v.append(survival)\n \n X = np.asarray(X,dtype=np.float32)\n y = np.asarray(y,dtype=np.float32)\n z = np.asarray(z,dtype=np.float32)\n v = np.asarray(v,dtype=np.float32)\n \n return X/np.max(X), y, z, v, Batch_ids\n \n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:23.894543Z","iopub.execute_input":"2023-07-29T17:57:23.895095Z","iopub.status.idle":"2023-07-29T17:57:23.916970Z","shell.execute_reply.started":"2023-07-29T17:57:23.895053Z","shell.execute_reply":"2023-07-29T17:57:23.915854Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"batch_size = 1\ntraining_generator = DataGenerator(train_ids,batch_size=batch_size,augment=False)\nvalid_generator = DataGenerator(val_ids,batch_size=batch_size, augment=False)\ntest_generator = DataGenerator(test_ids,batch_size=batch_size,shuffle=False,augment=False)\n\n# x,y = training_generator.__getitem__(6)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:23.920131Z","iopub.execute_input":"2023-07-29T17:57:23.920524Z","iopub.status.idle":"2023-07-29T17:57:23.935844Z","shell.execute_reply.started":"2023-07-29T17:57:23.920493Z","shell.execute_reply":"2023-07-29T17:57:23.934624Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# x,y = training_generator.__getitem__(6)\nind = np.random.randint(len(training_generator))\nx,y = training_generator.__getitem__(ind)\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:23.937351Z","iopub.execute_input":"2023-07-29T17:57:23.938132Z","iopub.status.idle":"2023-07-29T17:57:24.365038Z","shell.execute_reply.started":"2023-07-29T17:57:23.938099Z","shell.execute_reply":"2023-07-29T17:57:24.363841Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"x.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-29T18:23:40.495141Z","iopub.execute_input":"2023-07-29T18:23:40.495581Z","iopub.status.idle":"2023-07-29T18:23:40.502219Z","shell.execute_reply.started":"2023-07-29T18:23:40.495532Z","shell.execute_reply":"2023-07-29T18:23:40.501456Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(1, 128, 128, 128, 4)"},"metadata":{}}]},{"cell_type":"code","source":"\n#x=np.fliplr(x)\n#y[1]=np.fliplr(y[1])\nslice_index = 60\nplt.subplot(1,2,1)\nplt.imshow(x[0,:,:,slice_index,0])\nplt.subplot(1,2,2)\nplt.imshow(y[1][0,:,:,slice_index,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:24.366668Z","iopub.execute_input":"2023-07-29T17:57:24.367000Z","iopub.status.idle":"2023-07-29T17:57:24.796102Z","shell.execute_reply.started":"2023-07-29T17:57:24.366971Z","shell.execute_reply":"2023-07-29T17:57:24.794917Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nslice_index = 60\nplt.subplot(1,2,1)\nplt.imshow(x[0,:,:,slice_index,1])\nplt.subplot(1,2,2)\nplt.imshow(y[1][0,:,:,slice_index,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:24.797562Z","iopub.execute_input":"2023-07-29T17:57:24.797965Z","iopub.status.idle":"2023-07-29T17:57:25.417872Z","shell.execute_reply.started":"2023-07-29T17:57:24.797927Z","shell.execute_reply":"2023-07-29T17:57:25.416718Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nslice_index = 60\nplt.subplot(1,2,1)\nplt.imshow(x[0,:,:,slice_index,2])\nplt.subplot(1,2,2)\nplt.imshow(y[1][0,:,:,slice_index,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:25.419620Z","iopub.execute_input":"2023-07-29T17:57:25.420481Z","iopub.status.idle":"2023-07-29T17:57:25.807199Z","shell.execute_reply.started":"2023-07-29T17:57:25.420438Z","shell.execute_reply":"2023-07-29T17:57:25.806016Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nslice_index = 60\nplt.subplot(1,2,1)\nplt.imshow(x[0,:,:,slice_index,3])\nplt.subplot(1,2,2)\nplt.imshow(y[1][0,:,:,slice_index,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:25.808609Z","iopub.execute_input":"2023-07-29T17:57:25.809169Z","iopub.status.idle":"2023-07-29T17:57:26.196822Z","shell.execute_reply.started":"2023-07-29T17:57:25.809136Z","shell.execute_reply":"2023-07-29T17:57:26.195682Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"import os\nimport numpy as np\nfrom tensorflow.keras.models import *\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.optimizers import *\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\nfrom tensorflow.keras import backend as Keras\nfrom keras.layers import LeakyReLU\n\nfrom tensorflow.keras import backend as K\n\n\ndef dice_coef(y_true, y_pred, smooth=1):\n y_true_f = K.flatten(K.cast(y_true, 'float32'))\n y_pred_f = K.flatten(K.cast(y_pred, 'float32'))\n intersection = K.sum(y_true_f * y_pred_f)\n return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n\ndef dice_coef_loss(y_true, y_pred): \n return 1-dice_coef(y_true, y_pred)\n\ndef dice_loss(y_true, y_pred, numLabels=3):\n dice=0\n dice_list = []\n for index in range(numLabels):\n dice= dice_coef_loss(y_true[:,:,:,index], y_pred[:,:,:,index])\n dice_list.append(dice)\n return dice_list\n\ndef rmse(y_true, y_pred):\n mse= tf.sqrt(tf.metrics.mean_squared_error(y_true, y_pred))\n return (mse)\n\n\n# Computing Sensitivity \ndef sensitivity(y_true, y_pred):\n true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n return true_positives / (possible_positives + K.epsilon())\n\n\n# Computing Specificity\ndef specificity(y_true, y_pred):\n true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))\n possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))\n return true_negatives / (possible_negatives + K.epsilon())\n\npadding='valid'\npretrained_weights = None\ninput_size = (128,128,128, 4)\ninputTensor = Input(input_size,name='input')\n\n# conv01 = Conv3D(16, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(inputTensor)\n# conv01 = Conv3D(16, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv01)\n# bn01=BatchNormalization()(conv01) ######### batch normalization\n# pool01 = MaxPooling3D(pool_size=(2, 2, 2))(bn01)\n\n# conv00 = Conv3D(32, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(pool01)\n# conv00 = Conv3D(32, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv00)\n# bn00=BatchNormalization()(conv00) ######### batch normalization\n# pool00 = MaxPooling3D(pool_size=(2, 2, 2))(bn00)\n\nconv1 = Conv3D(64, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(inputTensor)\nconv1 = Conv3D(64, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv1)\nbn=BatchNormalization()(conv1) ######### batch normalization\npool1 = MaxPooling3D(pool_size=(2, 2, 2))(bn)\n\nconv2 = Conv3D(128, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(pool1)\nconv2 = Conv3D(128, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv2)\nbn1=BatchNormalization()(conv2) ######### batch normalization\npool2 = MaxPooling3D(pool_size=(2, 2, 2))(bn1)\n\nconv3 = Conv3D(256, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(pool2)\nconv3 = Conv3D(256, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv3)\nbn2=BatchNormalization()(conv3) ######### batch normalization\npool3 = MaxPooling3D(pool_size=(2, 2, 2))(bn2)\n\nconv4 = Conv3D(512, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(pool3)\nconv4 = Conv3D(512, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv4)\nbn3=BatchNormalization()(conv4) ######### batch normalization\ndrop4 = Dropout(0.5)(bn3)\npool4 = MaxPooling3D(pool_size=(2, 2, 2))(drop4)\n\n########################## bottle neck #################################################################\n\nconv5 = Conv3D(512, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\nconv5 = Conv3D(512, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\nbn4=BatchNormalization()(conv5) ######### batch normalization\ndrop5 = Dropout(0.2)(bn4)\n\n\n#############################3 Decoder path ##############################################################\n\nup6 = Conv3D(256, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(drop5))\nmerge6 = concatenate([conv4,up6], axis = 4)\ndrop6 = Dropout(0.5)(merge6)\nconv6 = Conv3D(256, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop6)\nconv6 = Conv3D(256, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv6)\nbn5=BatchNormalization()(conv6) ######### batch normalization\n\nup7 = Conv3D(128, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(bn5))\nmerge7 = concatenate([conv3,up7], axis = 4)\ndrop7 = Dropout(0.5)(merge7)\nconv7 = Conv3D(128, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop7)\nconv7 = Conv3D(128, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv7)\nbn6=BatchNormalization()(conv7) ######### batch normalization\n\nup8 = Conv3D(64, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(bn6))\nmerge8 = concatenate([conv2,up8], axis = 4)\ndrop8 = Dropout(0.5)(merge8)\nconv8 = Conv3D(64, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop8)\nconv8 = Conv3D(64, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv8)\nbn7=BatchNormalization()(conv8) ######### batch normalization\n\nup9 = Conv3D(32, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(bn7))\nmerge9 = concatenate([conv1,up9], axis = 4)\ndrop9 = Dropout(0.5)(merge9)\nconv9 = Conv3D(32, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop9)\nconv9 = Conv3D(32, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv9)\nbn8=BatchNormalization()(conv9)\n\n# up10 = Conv3D(16, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(bn8))\n# merge10 = concatenate([conv00,up10], axis = 4)\n# drop10 = Dropout(0.5)(merge10)\n# conv10 = Conv3D(16, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop10)\n# conv10 = Conv3D(16, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv10)\n# bn9=BatchNormalization()(conv10)\n# print(bn9.shape)\n\n# up11 = Conv3D(8, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (2,2,2))(bn9))\n# merge11 = concatenate([conv01,up11], axis = 4)\n# drop11 = Dropout(0.5)(merge11)\n# conv11 = Conv3D(8, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(drop11)\n# conv11 = Conv3D(8, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(conv11)\n# bn10=BatchNormalization()(conv11)\n# print(bn10.shape)\n\n######################## Classification output ##############\n\npool15 = MaxPooling3D(pool_size=(2, 2, 2))(bn3)\npool16 = MaxPooling3D(pool_size=(2, 2, 2))(pool15)\nclassify =Flatten()(pool16)\nclassify = Dense(128, activation = 'relu')(classify)\nclassify = Dense(64, activation = 'relu')(classify )\nclassify = Dense(32, activation = 'relu')(classify )\nclassify = Dense(2, activation='softmax', kernel_regularizer='l2',name='classify')(classify)\n\n\n########################### attention block ##########\n\n# print(bn5.shape)\n# seg300 = MaxPooling3D(pool_size=(2, 2, 2))(bn6)\n# print(seg300.shape)\n# seg400 = MaxPooling3D(pool_size=(4, 4, 4))(bn7)\n# print(seg400.shape)\n# seg500 = MaxPooling3D(pool_size=(8, 8, 8))(bn8)\n# print(seg500.shape)\n# seg600 = MaxPooling3D(pool_size=(16, 16, 16))(bn9)\n# print(seg600.shape)\n# # seg700 = MaxPooling3D(pool_size=(32, 32, 32))(bn10)\n# # print(seg700.shape)\n\n\n# mergeall = concatenate([bn5,seg300,seg400,seg500, seg600], axis = 4)\n# print(mergeall.shape)\n\n# conv900 = Conv3D(32, (3,3,3), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(mergeall)\n\n# up900 = Conv3D(32, (2,2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2')(UpSampling3D(size = (32,32,32))(conv900))\n# print(up900.shape)\n\n# mergelast = concatenate([bn10, up900], axis = 4)\n# print(mergelast.shape)\n\n\n######################## segmentation output ##############\n\n# segmentation = Conv3D(3, (1,1,1), activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2',name='segmentation')(mergelast)\nsegmentation = Conv3D(3, (1,1,1), activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer='l2',name='segmentation')(bn8)\n\n\n####################### survival prediction as regression #############\n\n\n\npool17 = MaxPooling3D(pool_size=(2, 2, 2))(bn1)\npool18 = MaxPooling3D(pool_size=(2, 2, 2))(pool17)\n# pool18 = MaxPooling3D(pool_size=(2, 2, 2))(pool17)\n\n# bn11=BatchNormalization()(pool17)\nsurvival=Flatten()(pool18)\n\nsurvival = Dropout(0.2)(survival)\nsurvival = Dense(128,activation=\"relu\")(survival)\nsurvival = Dense(64,activation=\"relu\")(survival)\nsurvival = Dense(32,activation=\"relu\")(survival)\nsurvival = Dense(32,activation=\"relu\")(survival)\n# survival = Dense(16,activation=\"relu\")(survival)\n# survival = Dense(8,activation=\"relu\")(survival)\nsurvival= Dense(1, activation='linear', kernel_regularizer='l2',name='survival')(survival)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:26.198398Z","iopub.execute_input":"2023-07-29T17:57:26.198757Z","iopub.status.idle":"2023-07-29T17:57:28.220537Z","shell.execute_reply.started":"2023-07-29T17:57:26.198727Z","shell.execute_reply":"2023-07-29T17:57:28.219573Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"model = Model(inputs = inputTensor,\n outputs = [classify, segmentation, survival])\n #outputs = [segmentation])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:28.221714Z","iopub.execute_input":"2023-07-29T17:57:28.221997Z","iopub.status.idle":"2023-07-29T17:57:28.453386Z","shell.execute_reply.started":"2023-07-29T17:57:28.221971Z","shell.execute_reply":"2023-07-29T17:57:28.452307Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\n Layer (type) Output Shape Param # Connected to \n==================================================================================================\n input (InputLayer) [(None, 128, 128, 1 0 [] \n 28, 4)] \n \n conv3d (Conv3D) (None, 128, 128, 12 6976 ['input[0][0]'] \n 8, 64) \n \n conv3d_1 (Conv3D) (None, 128, 128, 12 110656 ['conv3d[0][0]'] \n 8, 64) \n \n batch_normalization (BatchNorm (None, 128, 128, 12 256 ['conv3d_1[0][0]'] \n alization) 8, 64) \n \n max_pooling3d (MaxPooling3D) (None, 64, 64, 64, 0 ['batch_normalization[0][0]'] \n 64) \n \n conv3d_2 (Conv3D) (None, 64, 64, 64, 221312 ['max_pooling3d[0][0]'] \n 128) \n \n conv3d_3 (Conv3D) (None, 64, 64, 64, 442496 ['conv3d_2[0][0]'] \n 128) \n \n batch_normalization_1 (BatchNo (None, 64, 64, 64, 512 ['conv3d_3[0][0]'] \n rmalization) 128) \n \n max_pooling3d_1 (MaxPooling3D) (None, 32, 32, 32, 0 ['batch_normalization_1[0][0]'] \n 128) \n \n conv3d_4 (Conv3D) (None, 32, 32, 32, 884992 ['max_pooling3d_1[0][0]'] \n 256) \n \n conv3d_5 (Conv3D) (None, 32, 32, 32, 1769728 ['conv3d_4[0][0]'] \n 256) \n \n batch_normalization_2 (BatchNo (None, 32, 32, 32, 1024 ['conv3d_5[0][0]'] \n rmalization) 256) \n \n max_pooling3d_2 (MaxPooling3D) (None, 16, 16, 16, 0 ['batch_normalization_2[0][0]'] \n 256) \n \n conv3d_6 (Conv3D) (None, 16, 16, 16, 3539456 ['max_pooling3d_2[0][0]'] \n 512) \n \n conv3d_7 (Conv3D) (None, 16, 16, 16, 7078400 ['conv3d_6[0][0]'] \n 512) \n \n batch_normalization_3 (BatchNo (None, 16, 16, 16, 2048 ['conv3d_7[0][0]'] \n rmalization) 512) \n \n dropout (Dropout) (None, 16, 16, 16, 0 ['batch_normalization_3[0][0]'] \n 512) \n \n max_pooling3d_3 (MaxPooling3D) (None, 8, 8, 8, 512 0 ['dropout[0][0]'] \n ) \n \n conv3d_8 (Conv3D) (None, 8, 8, 8, 512 7078400 ['max_pooling3d_3[0][0]'] \n ) \n \n conv3d_9 (Conv3D) (None, 8, 8, 8, 512 7078400 ['conv3d_8[0][0]'] \n ) \n \n batch_normalization_4 (BatchNo (None, 8, 8, 8, 512 2048 ['conv3d_9[0][0]'] \n rmalization) ) \n \n dropout_1 (Dropout) (None, 8, 8, 8, 512 0 ['batch_normalization_4[0][0]'] \n ) \n \n up_sampling3d (UpSampling3D) (None, 16, 16, 16, 0 ['dropout_1[0][0]'] \n 512) \n \n conv3d_10 (Conv3D) (None, 16, 16, 16, 1048832 ['up_sampling3d[0][0]'] \n 256) \n \n concatenate (Concatenate) (None, 16, 16, 16, 0 ['conv3d_7[0][0]', \n 768) 'conv3d_10[0][0]'] \n \n dropout_2 (Dropout) (None, 16, 16, 16, 0 ['concatenate[0][0]'] \n 768) \n \n conv3d_11 (Conv3D) (None, 16, 16, 16, 5308672 ['dropout_2[0][0]'] \n 256) \n \n conv3d_12 (Conv3D) (None, 16, 16, 16, 1769728 ['conv3d_11[0][0]'] \n 256) \n \n batch_normalization_5 (BatchNo (None, 16, 16, 16, 1024 ['conv3d_12[0][0]'] \n rmalization) 256) \n \n up_sampling3d_1 (UpSampling3D) (None, 32, 32, 32, 0 ['batch_normalization_5[0][0]'] \n 256) \n \n conv3d_13 (Conv3D) (None, 32, 32, 32, 262272 ['up_sampling3d_1[0][0]'] \n 128) \n \n concatenate_1 (Concatenate) (None, 32, 32, 32, 0 ['conv3d_5[0][0]', \n 384) 'conv3d_13[0][0]'] \n \n dropout_3 (Dropout) (None, 32, 32, 32, 0 ['concatenate_1[0][0]'] \n 384) \n \n conv3d_14 (Conv3D) (None, 32, 32, 32, 1327232 ['dropout_3[0][0]'] \n 128) \n \n conv3d_15 (Conv3D) (None, 32, 32, 32, 442496 ['conv3d_14[0][0]'] \n 128) \n \n batch_normalization_6 (BatchNo (None, 32, 32, 32, 512 ['conv3d_15[0][0]'] \n rmalization) 128) \n \n up_sampling3d_2 (UpSampling3D) (None, 64, 64, 64, 0 ['batch_normalization_6[0][0]'] \n 128) \n \n conv3d_16 (Conv3D) (None, 64, 64, 64, 65600 ['up_sampling3d_2[0][0]'] \n 64) \n \n concatenate_2 (Concatenate) (None, 64, 64, 64, 0 ['conv3d_3[0][0]', \n 192) 'conv3d_16[0][0]'] \n \n dropout_4 (Dropout) (None, 64, 64, 64, 0 ['concatenate_2[0][0]'] \n 192) \n \n conv3d_17 (Conv3D) (None, 64, 64, 64, 331840 ['dropout_4[0][0]'] \n 64) \n \n conv3d_18 (Conv3D) (None, 64, 64, 64, 110656 ['conv3d_17[0][0]'] \n 64) \n \n batch_normalization_7 (BatchNo (None, 64, 64, 64, 256 ['conv3d_18[0][0]'] \n rmalization) 64) \n \n max_pooling3d_6 (MaxPooling3D) (None, 32, 32, 32, 0 ['batch_normalization_1[0][0]'] \n 128) \n \n up_sampling3d_3 (UpSampling3D) (None, 128, 128, 12 0 ['batch_normalization_7[0][0]'] \n 8, 64) \n \n max_pooling3d_7 (MaxPooling3D) (None, 16, 16, 16, 0 ['max_pooling3d_6[0][0]'] \n 128) \n \n max_pooling3d_4 (MaxPooling3D) (None, 8, 8, 8, 512 0 ['batch_normalization_3[0][0]'] \n ) \n \n conv3d_19 (Conv3D) (None, 128, 128, 12 16416 ['up_sampling3d_3[0][0]'] \n 8, 32) \n \n flatten_1 (Flatten) (None, 524288) 0 ['max_pooling3d_7[0][0]'] \n \n max_pooling3d_5 (MaxPooling3D) (None, 4, 4, 4, 512 0 ['max_pooling3d_4[0][0]'] \n ) \n \n concatenate_3 (Concatenate) (None, 128, 128, 12 0 ['conv3d_1[0][0]', \n 8, 96) 'conv3d_19[0][0]'] \n \n dropout_6 (Dropout) (None, 524288) 0 ['flatten_1[0][0]'] \n \n flatten (Flatten) (None, 32768) 0 ['max_pooling3d_5[0][0]'] \n \n dropout_5 (Dropout) (None, 128, 128, 12 0 ['concatenate_3[0][0]'] \n 8, 96) \n \n dense_3 (Dense) (None, 128) 67108992 ['dropout_6[0][0]'] \n \n dense (Dense) (None, 128) 4194432 ['flatten[0][0]'] \n \n conv3d_20 (Conv3D) (None, 128, 128, 12 82976 ['dropout_5[0][0]'] \n 8, 32) \n \n dense_4 (Dense) (None, 64) 8256 ['dense_3[0][0]'] \n \n dense_1 (Dense) (None, 64) 8256 ['dense[0][0]'] \n \n conv3d_21 (Conv3D) (None, 128, 128, 12 27680 ['conv3d_20[0][0]'] \n 8, 32) \n \n dense_5 (Dense) (None, 32) 2080 ['dense_4[0][0]'] \n \n dense_2 (Dense) (None, 32) 2080 ['dense_1[0][0]'] \n \n batch_normalization_8 (BatchNo (None, 128, 128, 12 128 ['conv3d_21[0][0]'] \n rmalization) 8, 32) \n \n dense_6 (Dense) (None, 32) 1056 ['dense_5[0][0]'] \n \n classify (Dense) (None, 2) 66 ['dense_2[0][0]'] \n \n segmentation (Conv3D) (None, 128, 128, 12 99 ['batch_normalization_8[0][0]'] \n 8, 3) \n \n survival (Dense) (None, 1) 33 ['dense_6[0][0]'] \n \n==================================================================================================\nTotal params: 110,338,374\nTrainable params: 110,334,470\nNon-trainable params: 3,904\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\n\n# Load the .npy file into a NumPy array\ndata = np.load('/kaggle/input/adjustedlabels2019/FLAIR_label0.npy')\n\n# Now you can check the unique labels and their counts\nunique_labels, label_counts = np.unique(data, return_counts=True)\n\n# Print the unique labels and their counts\nfor label, count in zip(unique_labels, label_counts):\n print(f\"Label: {label}, Count: {count}\")\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:28.454980Z","iopub.execute_input":"2023-07-29T17:57:28.455336Z","iopub.status.idle":"2023-07-29T17:57:28.465232Z","shell.execute_reply.started":"2023-07-29T17:57:28.455296Z","shell.execute_reply":"2023-07-29T17:57:28.464149Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Label: 0.0, Count: 1\nLabel: 1.0, Count: 1\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset\n\n# Function to load loss weights from .npy files in a directory\ndef load_loss_weights_from_directory(directory_path):\n weight_files = [filename for filename in os.listdir(directory_path) if filename.endswith(\".npy\")]\n weights = [np.load(os.path.join(directory_path, filename)) for filename in weight_files]\n return np.concatenate(weights)\n\n# Directory path for classification loss weights\nclassification_weights_directory = '/kaggle/input/adjustedlabels2019'\n\n# Load initial classification loss weights from the directory\nclassification_weight = load_loss_weights_from_directory(classification_weights_directory)\n\n# Define your classification model\nclass ClassificationModel(nn.Module):\n def __init__(self, input_size, num_classes):\n super(ClassificationModel, self).__init__()\n self.fc = nn.Linear(input_size, num_classes)\n\n def forward(self, x):\n x = self.fc(x)\n return x\n\n# Custom dataset class for the classification task\nclass ClassificationDataset(Dataset):\n def __init__(self, data, targets):\n self.data = data\n self.targets = targets\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n x = self.data[idx]\n y = self.targets[idx]\n\n return x, y\n\n# Example: Creating dummy data for the classification task\n# Replace the following lines with your actual data and targets for the classification task\nnum_samples = 1000\ninput_size = 784 # Replace with the actual input size of your model\nnum_classes = 10 # Replace with the actual number of classes for classification\n\ntrain_data_classification = torch.rand(num_samples, input_size)\ntrain_targets_classification = torch.randint(0, num_classes, (num_samples,))\n\n# Assuming you have your data for the classification task as 'train_data_classification', 'train_targets_classification'\ntrain_dataset_classification = ClassificationDataset(data=train_data_classification, targets=train_targets_classification)\ntrain_loader_classification = DataLoader(train_dataset_classification, batch_size=64, shuffle=True)\n\n# Initialize the classification model\nmodel = ClassificationModel(input_size, num_classes)\n\n# Define your optimizer and loss function\noptimizer = optim.Adam(model.parameters(), lr=0.001)\ncriterion_classification = nn.CrossEntropyLoss()\n\n# Set the device to 'cuda' if available, else set it to 'cpu'\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\n\n# Training loop\nnum_epochs_update = 5 # Number of epochs to update the weights\nfor epoch in range(num_epochs_update):\n for data, targets_classification in train_loader_classification:\n # Transfer data and labels to the device (e.g., GPU)\n data = data.to(device)\n targets_classification = targets_classification.to(device)\n\n # Zero the gradients\n optimizer.zero_grad()\n\n # Forward pass\n outputs_classification = model(data)\n\n # Calculate classification loss\n loss_classification = criterion_classification(outputs_classification, targets_classification)\n\n # Backpropagation and update model parameters\n loss_classification.backward()\n optimizer.step()\n\n # Calculate mean and standard deviation for the classification loss\n mean_loss_classification = torch.mean(loss_classification).item()\n std_loss_classification = torch.std(loss_classification).item()\n\n # Define reference value (you can use mean or maximum normalized loss, or any other desired reference value)\n reference_value_classification = 1.0\n\n # Calculate loss-weight update factor for the classification task\n update_factor_classification = np.exp((mean_loss_classification - reference_value_classification) / std_loss_classification)\n\n # Normalize the update factor to ensure it is between 0 and 1\n update_factor_classification = max(0.0, min(1.0, update_factor_classification))\n\n # Update the classification weight based on the calculated update factor\n classification_weight *= update_factor_classification\n\n # After updating the classification_weight, save it back to a writable directory (e.g., for printing purposes)\n np.save('/kaggle/working/classification_weights_updated.npy', classification_weight)\n\n # Print the updated classification weight after each epoch\n print(f\"Classification Weight (Epoch {epoch + 1}): {classification_weight}\")\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:28.472222Z","iopub.execute_input":"2023-07-29T17:57:28.472609Z","iopub.status.idle":"2023-07-29T17:57:32.921057Z","shell.execute_reply.started":"2023-07-29T17:57:28.472580Z","shell.execute_reply":"2023-07-29T17:57:32.919842Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Classification Weight (Epoch 1): [0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.\n 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0.\n 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 1. 0.]\nClassification Weight (Epoch 2): [0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.\n 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0.\n 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 1. 0.]\nClassification Weight (Epoch 3): [0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.\n 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0.\n 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 1. 0.]\nClassification Weight (Epoch 4): [0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.\n 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0.\n 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 1. 0.]\nClassification Weight (Epoch 5): [0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0.\n 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0.\n 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0.\n 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1.\n 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1.\n 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n 0. 1. 1. 0.]\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport numpy as np\nimport torch\n\n# Function to load loss weights from .npy files in a directory\ndef load_loss_weights_from_directory(directory_path):\n weight_files = [filename for filename in os.listdir(directory_path) if filename.endswith(\".npy\")]\n weights = [np.load(os.path.join(directory_path, filename)) for filename in weight_files]\n return np.concatenate(weights)\n\n# Function to save the updated weights to a directory\ndef save_weights_to_directory(directory_path, weights):\n os.makedirs(directory_path, exist_ok=True) # Create the directory if it doesn't exist\n np.save(os.path.join(directory_path, \"updated_regression_weights.npy\"), weights)\n\n# Directory path for initial regression loss weights\nregression_weights_directory = '/kaggle/input/adjusted-survival-2019'\n\n# Load initial regression loss weights from the directory\nregression_weight = load_loss_weights_from_directory(regression_weights_directory)\n\n# Training loop for regression task\nnum_epochs_update_regression = 5 # Number of epochs to update the regression weight\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# Create a new directory to save the updated regression weights\noutput_directory = '/path/to/updated_regression_weights'\nos.makedirs(output_directory, exist_ok=True)\n\nfor epoch in range(num_epochs_update_regression):\n # Calculate mean and standard deviation for the regression loss (replace this with your actual loss calculation)\n mean_loss_regression = 0.5\n std_loss_regression = 0.1\n\n # Define reference value for regression\n reference_value_regression = 0.5\n\n # Calculate loss-weight update factor for regression\n update_factor_regression = np.exp((mean_loss_regression - reference_value_regression) / std_loss_regression)\n\n # Normalize the update factor to ensure it is between 0 and 1\n update_factor_regression = max(0.0, min(1.0, update_factor_regression))\n\n # Convert regression_weight to torch tensor before performing multiplication\n regression_weight = torch.tensor(regression_weight, dtype=torch.float32, device=device)\n\n # Check if the update factor is close to 0 and the initial regression weights are all zeros\n if update_factor_regression < 1e-6 and torch.all(regression_weight == 0):\n # In this case, set the update factor to a small value to ensure some weight update occurs\n update_factor_regression = 1e-3\n\n # Update the regression weight based on the calculated update factor\n regression_weight *= update_factor_regression\n\n # Save the updated regression weight back to the separate directory\n save_weights_to_directory(output_directory, regression_weight.cpu().numpy())\n\n # Print the updated regression weight after each epoch\n # Print the updated regression weight after each epoch\nwith np.printoptions(threshold=np.inf):\n print(f\"Regression Weight (Epoch {epoch + 1}): \\n{regression_weight}\")\n\n\n# End of the training loop for regression task\n\n# ... (rest of your code for other tasks such as classification, segmentation, and survival)\n\n# Now, you can copy and paste the code for updating weights and the training loop for other tasks as needed.\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:32.924992Z","iopub.execute_input":"2023-07-29T17:57:32.925654Z","iopub.status.idle":"2023-07-29T17:57:34.856983Z","shell.execute_reply.started":"2023-07-29T17:57:32.925618Z","shell.execute_reply":"2023-07-29T17:57:34.850526Z"},"trusted":true},"execution_count":14,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[14], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m regression_weights_directory \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input/adjusted-survival-2019\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Load initial regression loss weights from the directory\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m regression_weight \u001b[38;5;241m=\u001b[39m \u001b[43mload_loss_weights_from_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mregression_weights_directory\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Training loop for regression task\u001b[39;00m\n\u001b[1;32m 23\u001b[0m num_epochs_update_regression \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;66;03m# Number of epochs to update the regression weight\u001b[39;00m\n","Cell \u001b[0;32mIn[14], line 9\u001b[0m, in \u001b[0;36mload_loss_weights_from_directory\u001b[0;34m(directory_path)\u001b[0m\n\u001b[1;32m 7\u001b[0m weight_files \u001b[38;5;241m=\u001b[39m [filename \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mlistdir(directory_path) \u001b[38;5;28;01mif\u001b[39;00m filename\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.npy\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n\u001b[1;32m 8\u001b[0m weights \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(directory_path, filename)) \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m weight_files]\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated"],"ename":"ValueError","evalue":"zero-dimensional arrays cannot be concatenated","output_type":"error"}]},{"cell_type":"code","source":"import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.cuda.amp import autocast, GradScaler\n\n# Function to load loss weights from .npy files in a directory\ndef load_loss_weights_from_directory(directory_path):\n weight_files = [filename for filename in os.listdir(directory_path) if filename.endswith(\".npy\")]\n weights = [np.load(os.path.join(directory_path, filename)) for filename in weight_files]\n return np.concatenate(weights)\n\n# Directory path for segmentation loss weights\nsegmentation_weights_directory = '/kaggle/input/adjustedmask2019'\n\n# Load initial segmentation loss weights from the directory\nsegmentation_weight = load_loss_weights_from_directory(segmentation_weights_directory)\n\n# Define your segmentation model (Use a smaller and more memory-efficient model)\nclass SegmentationModel(nn.Module):\n def __init__(self, input_channels, num_classes):\n super(SegmentationModel, self).__init__()\n self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, padding=1)\n self.conv2 = nn.Conv2d(32, num_classes, kernel_size=1)\n\n def forward(self, x):\n x = nn.functional.relu(self.conv1(x))\n x = self.conv2(x)\n return x\n\n# Custom dataset class for the segmentation task\nclass SegmentationDataset(Dataset):\n def __init__(self, data, targets):\n self.data = data\n self.targets = targets\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n x = self.data[idx]\n y = self.targets[idx]\n return x, y\n\n# Example: Creating dummy data for the segmentation task\n# Replace the following lines with your actual data and targets for the segmentation task\nnum_samples = 278\ninput_channels = 4 # Replace with the actual number of input channels for your model\nnum_classes = 3 # Replace with the actual number of segmentation classes\n\ntrain_data_segmentation = torch.rand(num_samples, input_channels, 128, 128)\ntrain_targets_segmentation = torch.randint(0, num_classes, (num_samples, 128, 128))\n\n# Assuming you have your data for the segmentation task as 'train_data_segmentation', 'train_targets_segmentation'\ntrain_dataset_segmentation = SegmentationDataset(data=train_data_segmentation, targets=train_targets_segmentation)\ntrain_loader_segmentation = DataLoader(train_dataset_segmentation, batch_size=16, shuffle=True)\n\n# Initialize the segmentation model (Use the smaller segmentation model)\nsegmentation_model = SegmentationModel(input_channels, num_classes)\n\n# Define your optimizer and loss function\noptimizer = optim.Adam(segmentation_model.parameters(), lr=0.001)\ncriterion_segmentation = nn.CrossEntropyLoss()\n\n# Set the device to 'cuda' if available, else set it to 'cpu'\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nsegmentation_model.to(device)\n\n# Training loop\nnum_epochs_update = 5 # Number of epochs to update the weights\ngrad_scaler = GradScaler() # Mixed precision training scaler\n\nfor epoch in range(num_epochs_update):\n for data, targets_segmentation in train_loader_segmentation:\n # Transfer data and labels to the device (e.g., GPU)\n data = data.to(device)\n targets_segmentation = targets_segmentation.to(device)\n\n # Zero the gradients\n optimizer.zero_grad()\n\n # Forward pass (Use autocast to enable mixed precision training)\n with autocast():\n outputs_segmentation = segmentation_model(data)\n loss_segmentation = criterion_segmentation(outputs_segmentation, targets_segmentation)\n\n # Backpropagation and update model parameters (Use GradScaler for mixed precision training)\n grad_scaler.scale(loss_segmentation).backward()\n grad_scaler.step(optimizer)\n grad_scaler.update()\n\n # Calculate mean and standard deviation for the segmentation loss\n mean_loss_segmentation = torch.mean(loss_segmentation).item()\n std_loss_segmentation = torch.std(loss_segmentation).item()\n\n # Define reference value (you can use mean or maximum normalized loss, or any other desired reference value)\n reference_value_segmentation = 1.0\n\n # Calculate loss-weight update factor for the segmentation task\n update_factor_segmentation = np.exp((mean_loss_segmentation - reference_value_segmentation) / std_loss_segmentation)\n\n # Normalize the update factor to ensure it is between 0 and 1\n update_factor_segmentation = max(0.0, min(1.0, update_factor_segmentation))\n\n # Update the segmentation weight based on the calculated update factor\n segmentation_weight *= update_factor_segmentation\n\n # After updating the segmentation_weight, save it back to a writable directory (e.g., for printing purposes)\n np.save('/kaggle/working/segmentation_weights_updated.npy', segmentation_weight)\n\n # Print the updated segmentation weight after each epoch\n print(f\"Segmentation Weight (Epoch {epoch + 1}): {segmentation_weight}\")\n\n# End of the training loop for segmentation task\n\n# (Rest of your code for other tasks such as classification and survival)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T18:13:48.087932Z","iopub.execute_input":"2023-07-29T18:13:48.088431Z","iopub.status.idle":"2023-07-29T18:15:51.720266Z","shell.execute_reply.started":"2023-07-29T18:13:48.088397Z","shell.execute_reply":"2023-07-29T18:15:51.718335Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py:120: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n warnings.warn(\"torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\")\n/opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:204: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mUFuncTypeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[15], line 108\u001b[0m\n\u001b[1;32m 105\u001b[0m update_factor_segmentation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m0.0\u001b[39m, \u001b[38;5;28mmin\u001b[39m(\u001b[38;5;241m1.0\u001b[39m, update_factor_segmentation))\n\u001b[1;32m 107\u001b[0m \u001b[38;5;66;03m# Update the segmentation weight based on the calculated update factor\u001b[39;00m\n\u001b[0;32m--> 108\u001b[0m segmentation_weight \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m update_factor_segmentation\n\u001b[1;32m 110\u001b[0m \u001b[38;5;66;03m# After updating the segmentation_weight, save it back to a writable directory (e.g., for printing purposes)\u001b[39;00m\n\u001b[1;32m 111\u001b[0m np\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/working/segmentation_weights_updated.npy\u001b[39m\u001b[38;5;124m'\u001b[39m, segmentation_weight)\n","\u001b[0;31mUFuncTypeError\u001b[0m: Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int32') with casting rule 'same_kind'"],"ename":"UFuncTypeError","evalue":"Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int32') with casting rule 'same_kind'","output_type":"error"}]},{"cell_type":"markdown","source":"## Keras Functional API\n- This is the main branch\n- These layers are common to both the tasks\n","metadata":{"id":"ID-EAnETs_gg"}},{"cell_type":"markdown","source":"- This is where the network branches for multiple outputs/tasks\n- gender is n x 2 output where as age is n x 1 output","metadata":{"id":"g0c2VGQUs_gh"}},{"cell_type":"code","source":"model = Model(inputs = inputTensor,\n outputs = [classify, segmentation, survival])\n #outputs = [segmentation])\nmodel.summary()","metadata":{"id":"DLIYbd5bs_gh","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:34.861315Z","iopub.status.idle":"2023-07-29T17:57:34.862223Z","shell.execute_reply.started":"2023-07-29T17:57:34.861943Z","shell.execute_reply":"2023-07-29T17:57:34.861970Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.output","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.864470Z","iopub.status.idle":"2023-07-29T17:57:34.865724Z","shell.execute_reply.started":"2023-07-29T17:57:34.865509Z","shell.execute_reply":"2023-07-29T17:57:34.865532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Multi loss optimizations\n\n- Dice loss for segmnetation and crossentropy loss for survival task\n- we can weight these individual losses\n- Loss = weight1 * loss1 + weight2 * loss2","metadata":{"id":"j_nPXez0s_gi"}},{"cell_type":"code","source":"# os.mkdir('output')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.866670Z","iopub.status.idle":"2023-07-29T17:57:34.867637Z","shell.execute_reply.started":"2023-07-29T17:57:34.867428Z","shell.execute_reply":"2023-07-29T17:57:34.867449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow.keras\nimport tensorflow as tf\n#from tensorflow.keras import ModelCheckpoint\nfrom tensorflow.keras.callbacks import ModelCheckpoint\n\n\n# checkpoint\n# filepath=\"D:/maria/weights-improvement-{epoch:02d}-{val_segmentation_dice_coef:.2f}.hdf5\"\n# filepath=\"C:/Users/MIDL/Downloads/3d_model_december/best_model/weights-improvement-{epoch:02d}-{val_segmentation_dice_coef:.2f}.hdf5\"\n# filepath=\"./output/weights-best-{epoch:02d}-{val_segmentation_dice_coef:.2f}.hdf5\" # \nfilepath=\"./output/weights-best.hdf5\" \n\n\ncheckpoint_cp = ModelCheckpoint(filepath, monitor='val_segmentation_dice_coef', verbose=1, save_best_only=True, mode='max')\n\n\n\ninitial_learning_rate = 0.0001\nlr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n initial_learning_rate,\n decay_steps=100000,\n decay_rate=0.96,\n staircase=True)\n\nearly_stop = tf.keras.callbacks.EarlyStopping(\n monitor=\"val_segmentation_dice_coef\",\n min_delta=0,\n patience=20,\n verbose=1,\n mode=\"max\",\n baseline=None,\n restore_best_weights=False,\n)\n\nopt = tensorflow.keras.optimizers.RMSprop(lr_schedule)\n\nclassification_weight = load_loss_weights_from_directory(classification_weights_directory)\nsegmentation_weight = load_loss_weights_from_directory(segmentation_weights_directory)\nregression_weight = load_loss_weights_from_directory(regression_weights_directory)\n\n# Define the optimizer and learning rate schedule (replace this with your actual optimizer and lr_schedule)\nopt = tf.keras.optimizers.RMSprop(lr_schedule)\n\n# Define batch size for each task\nbatch_size_classification = 64\nbatch_size_segmentation = 16\nbatch_size_regression = 32\n\n","metadata":{"id":"gqmgJXWMc0I0","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:34.868556Z","iopub.status.idle":"2023-07-29T17:57:34.869109Z","shell.execute_reply.started":"2023-07-29T17:57:34.868898Z","shell.execute_reply":"2023-07-29T17:57:34.868918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nhistory = model.fit(training_generator,\n epochs=100, batch_size = 64, verbose=1, validation_split=None,validation_data = valid_generator,\n callbacks=[checkpoint_cp])#,early_stop]) ","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.870698Z","iopub.status.idle":"2023-07-29T17:57:34.871501Z","shell.execute_reply.started":"2023-07-29T17:57:34.871190Z","shell.execute_reply":"2023-07-29T17:57:34.871218Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.save('./output/2019-4m-regression-19feb.npy', history.history)\n# np.save('./output/2019-32-04-Dec-attention.npy', history.history)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.873119Z","iopub.status.idle":"2023-07-29T17:57:34.874331Z","shell.execute_reply.started":"2023-07-29T17:57:34.874007Z","shell.execute_reply":"2023-07-29T17:57:34.874037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.models import load_model\n\n# # model=tensorflow.keras.models.load_model('3d_full_06_jan_0.0001.h5',custom_objects={'dice_coef_loss':dice_coef_loss,'dice_coef':dice_coef})\n# model=tensorflow.keras.models.load_model('./output/weights-best.hdf5',custom_objects={'dice_coef_loss':dice_coef_loss,'dice_coef':dice_coef,'sensitivity': sensitivity, 'specificity': specificity, 'rmse':rmse})\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.875992Z","iopub.status.idle":"2023-07-29T17:57:34.876533Z","shell.execute_reply.started":"2023-07-29T17:57:34.876241Z","shell.execute_reply":"2023-07-29T17:57:34.876265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test data prediction","metadata":{"id":"ewu2WLHic0I7"}},{"cell_type":"code","source":"\n# print(images_test.shape)\n# print(masks_test.shape)\n \n\n# plt.imshow(masks[2000,:,:,:])","metadata":{"id":"h6Cffex-c0I7","execution":{"iopub.status.busy":"2023-07-29T17:57:34.877832Z","iopub.status.idle":"2023-07-29T17:57:34.878683Z","shell.execute_reply.started":"2023-07-29T17:57:34.878397Z","shell.execute_reply":"2023-07-29T17:57:34.878424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# images_test.shape","metadata":{"id":"WszwnSYEc0I7","execution":{"iopub.status.busy":"2023-07-29T17:57:34.880336Z","iopub.status.idle":"2023-07-29T17:57:34.880851Z","shell.execute_reply.started":"2023-07-29T17:57:34.880581Z","shell.execute_reply":"2023-07-29T17:57:34.880607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Evaluate test set","metadata":{}},{"cell_type":"code","source":"evaluation_results = model.evaluate(test_generator, verbose=1) ","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.882547Z","iopub.status.idle":"2023-07-29T17:57:34.883082Z","shell.execute_reply.started":"2023-07-29T17:57:34.882802Z","shell.execute_reply":"2023-07-29T17:57:34.882827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# evalutae_array = evalutae_array.astype(np.uint8)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.884971Z","iopub.status.idle":"2023-07-29T17:57:34.885487Z","shell.execute_reply.started":"2023-07-29T17:57:34.885218Z","shell.execute_reply":"2023-07-29T17:57:34.885243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# evalutae_array = evalutae_array.squeeze()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.886617Z","iopub.status.idle":"2023-07-29T17:57:34.887558Z","shell.execute_reply.started":"2023-07-29T17:57:34.887329Z","shell.execute_reply":"2023-07-29T17:57:34.887353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(evalutae_array[2,:,:,60]*256, 'gray')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.889236Z","iopub.status.idle":"2023-07-29T17:57:34.889622Z","shell.execute_reply.started":"2023-07-29T17:57:34.889438Z","shell.execute_reply":"2023-07-29T17:57:34.889456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# evalutae_array = np.expand_dims(evalutae_array, -1)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.891027Z","iopub.status.idle":"2023-07-29T17:57:34.891413Z","shell.execute_reply.started":"2023-07-29T17:57:34.891210Z","shell.execute_reply":"2023-07-29T17:57:34.891227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_results = model.predict(test_generator, batch_size = 1, verbose=1) ","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.892774Z","iopub.status.idle":"2023-07-29T17:57:34.893350Z","shell.execute_reply.started":"2023-07-29T17:57:34.893066Z","shell.execute_reply":"2023-07-29T17:57:34.893094Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# len(test_results[1])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.894485Z","iopub.status.idle":"2023-07-29T17:57:34.895252Z","shell.execute_reply.started":"2023-07-29T17:57:34.895046Z","shell.execute_reply":"2023-07-29T17:57:34.895066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# two_new = np.array(test_results[1])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.896690Z","iopub.status.idle":"2023-07-29T17:57:34.897067Z","shell.execute_reply.started":"2023-07-29T17:57:34.896884Z","shell.execute_reply":"2023-07-29T17:57:34.896901Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# two_new.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.898501Z","iopub.status.idle":"2023-07-29T17:57:34.898882Z","shell.execute_reply.started":"2023-07-29T17:57:34.898696Z","shell.execute_reply":"2023-07-29T17:57:34.898714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(two_new[2,:,:,40,:]*256)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.900358Z","iopub.status.idle":"2023-07-29T17:57:34.900743Z","shell.execute_reply.started":"2023-07-29T17:57:34.900549Z","shell.execute_reply":"2023-07-29T17:57:34.900567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# predicted = np.array(test_results[1])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.901984Z","iopub.status.idle":"2023-07-29T17:57:34.902552Z","shell.execute_reply.started":"2023-07-29T17:57:34.902362Z","shell.execute_reply":"2023-07-29T17:57:34.902382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(predicted[2,:,:,40,:]*256)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.903540Z","iopub.status.idle":"2023-07-29T17:57:34.904099Z","shell.execute_reply.started":"2023-07-29T17:57:34.903910Z","shell.execute_reply":"2023-07-29T17:57:34.903929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# predicted = predicted.astype(np.uint8)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.905250Z","iopub.status.idle":"2023-07-29T17:57:34.905681Z","shell.execute_reply.started":"2023-07-29T17:57:34.905488Z","shell.execute_reply":"2023-07-29T17:57:34.905508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from tensorflow.keras import backend as Keras\n# new_2=Keras.max(predicted,axis=-1)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.907476Z","iopub.status.idle":"2023-07-29T17:57:34.908340Z","shell.execute_reply.started":"2023-07-29T17:57:34.908081Z","shell.execute_reply":"2023-07-29T17:57:34.908102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new_2 = np.array(new_2)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.909953Z","iopub.status.idle":"2023-07-29T17:57:34.910418Z","shell.execute_reply.started":"2023-07-29T17:57:34.910161Z","shell.execute_reply":"2023-07-29T17:57:34.910180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new_2.dtype","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.912639Z","iopub.status.idle":"2023-07-29T17:57:34.913365Z","shell.execute_reply.started":"2023-07-29T17:57:34.913132Z","shell.execute_reply":"2023-07-29T17:57:34.913153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import matplotlib.pyplot as plt\n# plt.imshow(new_2[2,:,:,40])\n\n# plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.914458Z","iopub.status.idle":"2023-07-29T17:57:34.915348Z","shell.execute_reply.started":"2023-07-29T17:57:34.915112Z","shell.execute_reply":"2023-07-29T17:57:34.915132Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# prediction= np.load('prediction.npy')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.916742Z","iopub.status.idle":"2023-07-29T17:57:34.917140Z","shell.execute_reply.started":"2023-07-29T17:57:34.916949Z","shell.execute_reply":"2023-07-29T17:57:34.916968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# np.save('prediction.npy', predicted)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.918583Z","iopub.status.idle":"2023-07-29T17:57:34.918972Z","shell.execute_reply.started":"2023-07-29T17:57:34.918781Z","shell.execute_reply":"2023-07-29T17:57:34.918800Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#new_squeeze= predicted.squeeze(-1)\n# images = predicted.squeeze()\n# images = np.delete(images, obj=0, axis=3)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.921083Z","iopub.status.idle":"2023-07-29T17:57:34.921520Z","shell.execute_reply.started":"2023-07-29T17:57:34.921321Z","shell.execute_reply":"2023-07-29T17:57:34.921341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# images.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.923110Z","iopub.status.idle":"2023-07-29T17:57:34.923531Z","shell.execute_reply.started":"2023-07-29T17:57:34.923334Z","shell.execute_reply":"2023-07-29T17:57:34.923354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import os\n# from PIL import Image\n# import cv2\n# from tqdm import tqdm\n# import numpy as np\n\n# output_shape = (240,240,155)\n# resized_array=[]\n# for i in range(125):\n# image = new_2[i]\n# image = resize(image, shape=output_shape, mode='constant')\n# #image = cv2.resize(image, dsize=(240, 240), interpolation=cv2.INTER_NEAREST)\n# resized_array.append(image)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.925150Z","iopub.status.idle":"2023-07-29T17:57:34.925626Z","shell.execute_reply.started":"2023-07-29T17:57:34.925427Z","shell.execute_reply":"2023-07-29T17:57:34.925447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# #resized_array.shape\n# resized_array = np.array (resized_array)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.927113Z","iopub.status.idle":"2023-07-29T17:57:34.927523Z","shell.execute_reply.started":"2023-07-29T17:57:34.927329Z","shell.execute_reply":"2023-07-29T17:57:34.927348Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import matplotlib.pyplot as plt\n\n\n# plt.imshow(resized_array[70][:,:,73])\n\n# plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.928846Z","iopub.status.idle":"2023-07-29T17:57:34.929209Z","shell.execute_reply.started":"2023-07-29T17:57:34.929028Z","shell.execute_reply":"2023-07-29T17:57:34.929045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new = scipy.ndimage.zoom(predicted[0], 2, order=3)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.930724Z","iopub.status.idle":"2023-07-29T17:57:34.932358Z","shell.execute_reply.started":"2023-07-29T17:57:34.932039Z","shell.execute_reply":"2023-07-29T17:57:34.932066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import numpy as np\n# from scipy.ndimage.interpolation import zoom\n\n# def resize(img, shape, mode='constant', orig_shape=(128,128,128)):\n# \"\"\"\n# Wrapper for scipy.ndimage.zoom suited for MRI images.\n# \"\"\"\n# assert len(shape) == 3, \"Can not have more than 3 dimensions\"\n# factors = (\n# shape[0]/orig_shape[0],\n# shape[1]/orig_shape[1], \n# shape[2]/orig_shape[2]\n# )\n \n# # Resize to the given shape\n# return zoom(img, factors, mode=mode)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.933592Z","iopub.status.idle":"2023-07-29T17:57:34.934357Z","shell.execute_reply.started":"2023-07-29T17:57:34.934028Z","shell.execute_reply":"2023-07-29T17:57:34.934058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(test_results[1][50,:,:,10,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.935959Z","iopub.status.idle":"2023-07-29T17:57:34.936959Z","shell.execute_reply.started":"2023-07-29T17:57:34.936750Z","shell.execute_reply":"2023-07-29T17:57:34.936771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_classify = test_results[0]\npred_seg = test_results[1]\npred_surv = test_results[2]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.938387Z","iopub.status.idle":"2023-07-29T17:57:34.938802Z","shell.execute_reply.started":"2023-07-29T17:57:34.938601Z","shell.execute_reply":"2023-07-29T17:57:34.938620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# pred_seg.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.939910Z","iopub.status.idle":"2023-07-29T17:57:34.940354Z","shell.execute_reply.started":"2023-07-29T17:57:34.940110Z","shell.execute_reply":"2023-07-29T17:57:34.940129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nvol_index = np.random.randint(50)\ngt_x,gt_y = test_generator.__getitem__(vol_index)\n\nprint(\"actual classification: \", gt_y[0])\nprint(\"predicted classification: \",pred_classify[vol_index])\n\nprint(\"predicted survival: \", pred_surv[vol_index])\nprint(\"actual survival: \", gt_y[2])\n\nslice_index = 60\nplt.subplot(1,2,1)\nplt.imshow(pred_seg[vol_index,:,:,slice_index,:])\nplt.subplot(1,2,2)\nplt.imshow(gt_y[1][0,:,:,slice_index,:])\nplt.show()\n\n\n# print(\"actual classification: \", gt_y[0])\n# print(\"predicted classification: \",pred_classify[vol_index])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.942470Z","iopub.status.idle":"2023-07-29T17:57:34.943438Z","shell.execute_reply.started":"2023-07-29T17:57:34.943192Z","shell.execute_reply":"2023-07-29T17:57:34.943212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# for uncertainty test","metadata":{}},{"cell_type":"code","source":"# x,y = test_generator.__getitem__(3)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.944324Z","iopub.status.idle":"2023-07-29T17:57:34.945005Z","shell.execute_reply.started":"2023-07-29T17:57:34.944805Z","shell.execute_reply":"2023-07-29T17:57:34.944832Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# yhat_arr = []\n\n# for t in range(100):\n# yhat = model(x, training=True)\n# yhat_arr.append(yhat[1])\n\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.946048Z","iopub.status.idle":"2023-07-29T17:57:34.946648Z","shell.execute_reply.started":"2023-07-29T17:57:34.946448Z","shell.execute_reply":"2023-07-29T17:57:34.946467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# yhat_arr = np.stack(yhat_arr, -1)\n# probs = np.mean(yhat_arr, axis=-1)\n# entropy = - 1.0 * np.sum(probs * np.log(probs + 1e-16), axis=-1)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.947966Z","iopub.status.idle":"2023-07-29T17:57:34.948669Z","shell.execute_reply.started":"2023-07-29T17:57:34.948477Z","shell.execute_reply":"2023-07-29T17:57:34.948496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(entropy[0,:,:,65])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.949769Z","iopub.status.idle":"2023-07-29T17:57:34.950422Z","shell.execute_reply.started":"2023-07-29T17:57:34.950211Z","shell.execute_reply":"2023-07-29T17:57:34.950231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.savefig('uncertinity1.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.951463Z","iopub.status.idle":"2023-07-29T17:57:34.952157Z","shell.execute_reply.started":"2023-07-29T17:57:34.951954Z","shell.execute_reply":"2023-07-29T17:57:34.951974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# test_results1 = model(x, training=True) \n# test_results2 = model(x, training=True) ","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.953399Z","iopub.status.idle":"2023-07-29T17:57:34.954136Z","shell.execute_reply.started":"2023-07-29T17:57:34.953899Z","shell.execute_reply":"2023-07-29T17:57:34.953925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# difference_img = test_results1[0]-test_results2[0]\n# np.sum(difference_img)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.955411Z","iopub.status.idle":"2023-07-29T17:57:34.956115Z","shell.execute_reply.started":"2023-07-29T17:57:34.955916Z","shell.execute_reply":"2023-07-29T17:57:34.955935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# test_results2[0].shape\n# difference_img.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.957411Z","iopub.status.idle":"2023-07-29T17:57:34.958100Z","shell.execute_reply.started":"2023-07-29T17:57:34.957909Z","shell.execute_reply":"2023-07-29T17:57:34.957929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# slice_index = 70\n# plt.subplot(1,3,1)\n# plt.imshow(test_results1[0][0,:,:,slice_index,:])\n# plt.subplot(1,3,2)\n# plt.imshow(test_results2[0][0,:,:,slice_index,:])\n# plt.show()\n# plt.subplot(1,3,3)\n# plt.imshow(difference_img[0,:,:,slice_index,:])\n# plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.959303Z","iopub.status.idle":"2023-07-29T17:57:34.960096Z","shell.execute_reply.started":"2023-07-29T17:57:34.959901Z","shell.execute_reply":"2023-07-29T17:57:34.959921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new = np.array(test_results)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.961136Z","iopub.status.idle":"2023-07-29T17:57:34.961869Z","shell.execute_reply.started":"2023-07-29T17:57:34.961672Z","shell.execute_reply":"2023-07-29T17:57:34.961692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new= model.predict(x)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.963046Z","iopub.status.idle":"2023-07-29T17:57:34.963604Z","shell.execute_reply.started":"2023-07-29T17:57:34.963399Z","shell.execute_reply":"2023-07-29T17:57:34.963421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# #new1= new[1].dtype(int8)\n# new1=new[1].astype(np.int32)\n# #new1= new[0].squeeze()","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.965260Z","iopub.status.idle":"2023-07-29T17:57:34.965864Z","shell.execute_reply.started":"2023-07-29T17:57:34.965531Z","shell.execute_reply":"2023-07-29T17:57:34.965556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# new[1]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.967425Z","iopub.status.idle":"2023-07-29T17:57:34.968089Z","shell.execute_reply.started":"2023-07-29T17:57:34.967674Z","shell.execute_reply":"2023-07-29T17:57:34.967699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plt.imshow(new1[:,:,80,1]*255)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.970001Z","iopub.status.idle":"2023-07-29T17:57:34.970548Z","shell.execute_reply.started":"2023-07-29T17:57:34.970257Z","shell.execute_reply":"2023-07-29T17:57:34.970301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# test_data = test_generator[0] \n# #gt = labels[20]\n# #p=test_data.reshape(1,128,128,1)\n# prediction=model.predict(test_data)\n# print(prediction[1].shape)\n# #print(gt)\n# seg_predicted=prediction[1]\n# seg_predicted.dtype\n# print(np.unique(seg_predicted[0,:,:,:]))\n# plt.imshow(seg_predicted[0,:,:,:])","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.972015Z","iopub.status.idle":"2023-07-29T17:57:34.972550Z","shell.execute_reply.started":"2023-07-29T17:57:34.972262Z","shell.execute_reply":"2023-07-29T17:57:34.972305Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### dice loss","metadata":{}},{"cell_type":"code","source":"# test_generator[2]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.974007Z","iopub.status.idle":"2023-07-29T17:57:34.974542Z","shell.execute_reply.started":"2023-07-29T17:57:34.974253Z","shell.execute_reply":"2023-07-29T17:57:34.974296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndice_loss_list= []\nfor i in range(55):\n test_dice_loss = dice_loss(test_generator[i][1][1][0], test_results[1][i].astype('float32'))\n dice_loss_list.append(test_dice_loss)\n\n# # print('dsc',dice_coef(gt.reshape([128,128,3]).astype('float32'),seg_predicted.reshape([128,128,3]).astype('float32')))\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.976714Z","iopub.status.idle":"2023-07-29T17:57:34.977528Z","shell.execute_reply.started":"2023-07-29T17:57:34.977207Z","shell.execute_reply":"2023-07-29T17:57:34.977235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# test_generator[59][2]","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.979656Z","iopub.status.idle":"2023-07-29T17:57:34.980429Z","shell.execute_reply.started":"2023-07-29T17:57:34.980108Z","shell.execute_reply":"2023-07-29T17:57:34.980139Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dice_loss_list = np.asarray(dice_loss_list)","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.981964Z","iopub.status.idle":"2023-07-29T17:57:34.982558Z","shell.execute_reply.started":"2023-07-29T17:57:34.982244Z","shell.execute_reply":"2023-07-29T17:57:34.982301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"dice coef class 0: \\t\", np.mean(1-dice_loss_list[:,0]))\nprint(\"dice coef class 1: \\t\", np.mean(1-dice_loss_list[:,1]))\nprint(\"dice coef class 2: \\t\", np.mean(1-dice_loss_list[:,2]))","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.984225Z","iopub.status.idle":"2023-07-29T17:57:34.984786Z","shell.execute_reply.started":"2023-07-29T17:57:34.984521Z","shell.execute_reply":"2023-07-29T17:57:34.984547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import matplotlib.pyplot as plt\n\n# inst=images_test.shape[0]\n# dice=[]\n# pred_labels=np.zeros([inst,128,128,3])\n# actual_labels=np.zeros([inst,128,128,3])\n\n# list_0 =[]\n# list_1 = []\n# list_2 = []\n\n# for img in range(inst):\n# print('------------------Image:',img)\n \n# data=images_test[img,:,:,:]\n# print(data.shape)\n# gt=masks_test[img,:,:,:]\n# print('gt',gt.shape)\n# actual_labels[img,:,:,:]=gt\n# plt.imshow(data[:,:,:])\n# plt.title(\"Image\")\n# plt.show()\n \n# plt.imshow(gt[:,:,:])\n# plt.title(\"Ground Truth\")\n# plt.show()\n \n# reshaped_img=data.reshape(1,128,128,1)\n# prediction=model.predict(reshaped_img)\n# seg_predicted=prediction[1]\n \n# converted = seg_predicted.astype(np.float64)\n# converted = converted[0,:,:,:]*255\n# print('converted',converted.shape)\n \n# pred_labels[img,:,:,:]=seg_predicted.reshape(128,128,3)\n# plt.imshow(converted.astype(np.uint8))\n# plt.title(\"Predicted\")\n# plt.show()\n# dsc_list = dice_loss(gt,converted)\n# print(len(dsc_list))\n# print('dice coef for class 0: ',1-dsc_list[0])\n# print('dice coef for class 1: ',1-dsc_list[1])\n# print('dice coef for class 2: ',1-dsc_list[2])\n \n# list_0.append(dsc_list[0])\n# list_1.append(dsc_list[1])\n# list_2.append(dsc_list[2])\n\n\n \n# print('dsc',dice_coef(gt.reshape([128,128,3]).astype('float32'),seg_predicted.reshape([128,128,3]).astype('float32')))\n# dice.append(dice_coef(gt.reshape([128,128,3]).astype('float32'),seg_predicted.reshape([128,128,3]).astype('float32')))","metadata":{"id":"kysWz9jYc0I7","execution":{"iopub.status.busy":"2023-07-29T17:57:34.986514Z","iopub.status.idle":"2023-07-29T17:57:34.987027Z","shell.execute_reply.started":"2023-07-29T17:57:34.986757Z","shell.execute_reply":"2023-07-29T17:57:34.986783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dice_coef","metadata":{"id":"Y0tCpC5kc0I7","execution":{"iopub.status.busy":"2023-07-29T17:57:34.988537Z","iopub.status.idle":"2023-07-29T17:57:34.989922Z","shell.execute_reply.started":"2023-07-29T17:57:34.989721Z","shell.execute_reply":"2023-07-29T17:57:34.989742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Single Imge Prediction","metadata":{"id":"jadLBSaWc0I8"}},{"cell_type":"code","source":"# #!pip install tensorflow-estimator=2.1.0\n# test_data = images_test[10000] \n# gt = labels[10000]\n# p=test_data.reshape(1,128,128,1)\n# prediction=model.predict(p)\n# print(prediction[1].shape)\n# print(gt)\n# seg_predicted=prediction[1]\n# seg_predicted.dtype\n# print(np.unique(seg_predicted[0,:,:,:]))\n# plt.imshow(seg_predicted[0,:,:,:])\n\n# #plt.imshow(seg_predicted[0,:,:,0]*255,'gray')","metadata":{"id":"S1C-3PDYc0I8","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:34.991001Z","iopub.status.idle":"2023-07-29T17:57:34.991827Z","shell.execute_reply.started":"2023-07-29T17:57:34.991624Z","shell.execute_reply":"2023-07-29T17:57:34.991644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# converted = seg_predicted.astype(np.uint8)\n# converted = converted[0,:,:,0]*255\n# plt.imshow(converted)","metadata":{"id":"S_-EBsI3c0I9","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:34.992876Z","iopub.status.idle":"2023-07-29T17:57:34.993686Z","shell.execute_reply.started":"2023-07-29T17:57:34.993488Z","shell.execute_reply":"2023-07-29T17:57:34.993508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# list all data in history\nprint(history.history.keys())\n# summarize history for accuracy\n# plt.plot(history.history['classification_accuracy'])\n# plt.plot(history.history['val_classification_accuracy'])\n# plt.title('Classification accuracy')\n# plt.ylabel('accuracy')\n# plt.xlabel('epoch')\n# plt.legend(['train', 'test'], loc='upper left')\n# plt.show()\n# summarize history for loss\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('training loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('overall loss.jpg')","metadata":{"id":"aALTmH2-5nXK","execution":{"iopub.status.busy":"2023-07-29T17:57:34.994879Z","iopub.status.idle":"2023-07-29T17:57:34.995569Z","shell.execute_reply.started":"2023-07-29T17:57:34.995365Z","shell.execute_reply":"2023-07-29T17:57:34.995385Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.savefig('overall loss.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.996738Z","iopub.status.idle":"2023-07-29T17:57:34.997416Z","shell.execute_reply.started":"2023-07-29T17:57:34.997189Z","shell.execute_reply":"2023-07-29T17:57:34.997209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# list all data in history\nprint(history.history.keys())\n# summarize history for accuracy\nplt.plot(history.history['classify_accuracy'])\nplt.plot(history.history['val_classify_accuracy'])\nplt.title('Classification Accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('classify.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:34.998612Z","iopub.status.idle":"2023-07-29T17:57:34.999247Z","shell.execute_reply.started":"2023-07-29T17:57:34.999049Z","shell.execute_reply":"2023-07-29T17:57:34.999068Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.savefig('classify.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:35.000430Z","iopub.status.idle":"2023-07-29T17:57:35.001060Z","shell.execute_reply.started":"2023-07-29T17:57:35.000862Z","shell.execute_reply":"2023-07-29T17:57:35.000883Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# summarize history for Dice_loss\nplt.plot(history.history['segmentation_loss'])\nplt.plot(history.history['val_segmentation_loss'])\nplt.title('Dice Loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('seg.jpg')","metadata":{"id":"apt9HXzec0I-","execution":{"iopub.status.busy":"2023-07-29T17:57:35.002255Z","iopub.status.idle":"2023-07-29T17:57:35.002968Z","shell.execute_reply.started":"2023-07-29T17:57:35.002754Z","shell.execute_reply":"2023-07-29T17:57:35.002775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.savefig('seg.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:35.004115Z","iopub.status.idle":"2023-07-29T17:57:35.004792Z","shell.execute_reply.started":"2023-07-29T17:57:35.004587Z","shell.execute_reply":"2023-07-29T17:57:35.004607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# list all data in history\nprint(history.history.keys())\n# summarize history for accuracy\nplt.plot(history.history['survival_mean_squared_error'])\nplt.plot(history.history['val_survival_mean_squared_error'])\nplt.title('Survival MSE')\nplt.ylabel('Error')\nplt.xlabel('Epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('Survival.jpg')\n","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:35.005958Z","iopub.status.idle":"2023-07-29T17:57:35.006650Z","shell.execute_reply.started":"2023-07-29T17:57:35.006433Z","shell.execute_reply":"2023-07-29T17:57:35.006459Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.savefig('survival.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-07-29T17:57:35.007911Z","iopub.status.idle":"2023-07-29T17:57:35.008336Z","shell.execute_reply.started":"2023-07-29T17:57:35.008115Z","shell.execute_reply":"2023-07-29T17:57:35.008133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print(history.history['val_segmentation_loss'])","metadata":{"id":"j3FPY_0gJM7j","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:35.010153Z","iopub.status.idle":"2023-07-29T17:57:35.010672Z","shell.execute_reply.started":"2023-07-29T17:57:35.010475Z","shell.execute_reply":"2023-07-29T17:57:35.010495Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# masks_images.shape","metadata":{"id":"vAFB3WWls_gk","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:35.012075Z","iopub.status.idle":"2023-07-29T17:57:35.012495Z","shell.execute_reply.started":"2023-07-29T17:57:35.012289Z","shell.execute_reply":"2023-07-29T17:57:35.012315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# np.save('./output/2019-32-04-Dec-attention.npy', history.history)\n\n\n#history=np.load('./output/my_history.npy',allow_pickle='TRUE').item()","metadata":{"id":"BsKpxJ-4Jsib","scrolled":true,"execution":{"iopub.status.busy":"2023-07-29T17:57:35.013858Z","iopub.status.idle":"2023-07-29T17:57:35.014763Z","shell.execute_reply.started":"2023-07-29T17:57:35.014558Z","shell.execute_reply":"2023-07-29T17:57:35.014579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# model.save('./new_full_2_may.h5')","metadata":{"id":"wtk3DOYKc0I_","execution":{"iopub.status.busy":"2023-07-29T17:57:35.016233Z","iopub.status.idle":"2023-07-29T17:57:35.017074Z","shell.execute_reply.started":"2023-07-29T17:57:35.016869Z","shell.execute_reply":"2023-07-29T17:57:35.016890Z"},"trusted":true},"execution_count":null,"outputs":[]}]}