{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":61446,"databundleVersionId":6962461,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## import dependencies:","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"code","source":"# detect and init the tpu\ntpu=tf.distribute.cluster_resolver.TPUClusterResolver()\n# istantiating a distribution strategy\ntf.tpu.experimental.initialize_tpu_system(tpu)\ntpu_strateg=tf.distribute.TPUStrategy(tpu)\n       ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\nimport sys\nif not sys.warnoptions:\n    import warnings\n    warnings.filterwarnings('ignore')\n    \nfrom __future__ import annotations\n    \nfrom tqdm import trange\nfrom time import time\n\nfrom typing import Union\nfrom typing import Optional\nfrom typing import List\nfrom typing import Tuple\nfrom typing import Callable\n\nfrom os.path import join\nfrom os import listdir\n\nfrom itertools import chain\n\nfrom numpy import array\nfrom numpy import ndarray\nimport numpy as np\n\nfrom pandas import DataFrame\nfrom pandas import read_csv\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\n\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\nimport tensorflow as tf\nfrom tensorflow import Tensor\nfrom tensorflow import constant\nfrom tensorflow import cast\nfrom tensorflow import float32\nfrom tensorflow import shape\nfrom tensorflow.debugging import assert_equal \nfrom tensorflow import convert_to_tensor\nfrom tensorflow import keras\nfrom tensorflow.data import Dataset\n\nfrom keras.layers import Input\nfrom keras.layers import Conv2D\nfrom keras.layers import BatchNormalization\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Conv2DTranspose\nfrom keras.layers import Concatenate\nfrom keras.layers import UpSampling2D\nfrom keras.activations import relu\nfrom keras.layers import Layer\nfrom keras.models import Model\nfrom keras.backend import flatten\nfrom keras.backend import sum\nfrom keras.utils import plot_model\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.callbacks import EarlyStopping \nfrom keras.callbacks import Callback\nfrom keras.optimizers import Adam\nfrom keras.optimizers import RMSprop\nfrom keras.optimizers import Adam\nfrom keras.losses import Loss\nfrom keras.metrics import Metric\nfrom keras.utils import normalize\n\n\n#from kerastuner.tuners import RandomSearch\n#from kerastuner.tuners import Hyperband\n#from kerastuner.tuners import BayesianOptimization\n\nimport tensorboard\n","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:58:37.098379Z","iopub.execute_input":"2024-01-22T09:58:37.098813Z","iopub.status.idle":"2024-01-22T09:58:37.109620Z","shell.execute_reply.started":"2024-01-22T09:58:37.098779Z","shell.execute_reply":"2024-01-22T09:58:37.108588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Configurations:","metadata":{"execution":{"iopub.status.busy":"2024-01-03T17:20:32.076793Z","iopub.execute_input":"2024-01-03T17:20:32.077155Z","iopub.status.idle":"2024-01-03T17:20:32.082357Z","shell.execute_reply.started":"2024-01-03T17:20:32.077128Z","shell.execute_reply":"2024-01-03T17:20:32.081389Z"}}},{"cell_type":"markdown","source":"check out this https://www.kaggle.com/docs/tpu#tpu1 for making use of TPU accelator with keras, tensorflow...etc","metadata":{}},{"cell_type":"code","source":"def configurations(accelerator:Optional[Union['GPU', 'TPU']]='GPU',\n                   tf_seed:int=1234,\n                   np_seed:int=42)->str:\n    \n    # we want to generate the same random tensors or arrays each time we raun the code(get the same outputs rhen same results)\n    # this come handy in data augmentation(image and label must have the same transformations)\n    tf.random.set_seed=tf_seed \n    np.random.seed=np_seed\n    \n    if tf.config.list_physical_devices('TPU') != []:\n        print('to enable \"TPU\" you must write down some more code')\n        # detect and init the TPU\n        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n\n        # instantiate a distribution strategy\n        tf.tpu.experimental.initialize_tpu_system(tpu)\n        tpu_strategy = tf.distribute.TPUStrategy(tpu)\n        \n        # you need to add 'with tpu_strategy.scope():' before define your model\n        accelerator='TPU VM v3-8'\n        return accelerator\n        \n    elif tf.config.list_physical_devices('GPU') != []:\n         print( 'the aviable \"GPU\" are [\"GPU P100\", \"GPU T4x2\"]')\n         print('you are enable \"GPU\", you do not need to write any more code ')\n         accelerator='GPU'\n         return accelerator\n    \n    else :\n        raise RuntimeError('you must to enable \"GPU\" or \"TPU\"',\n                           'On the right-hand side' ,\n                           'click on the dropdown menu next to Accelerator',\n                        'select \"GPU\" or \"TPU\" from the list [\"GPU P100\", \"GPU T4 x2\" \"TPU VM v3-8\"]')\n        \n\nconfigurations()    ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:58:43.403909Z","iopub.execute_input":"2024-01-22T09:58:43.404294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"if you are using 'TPU VM v3-8' as you choice accelerator  you need to add one more few lines of code before define your model as it shown in the below picture.","metadata":{"execution":{"iopub.status.busy":"2024-01-04T05:28:42.267478Z","iopub.execute_input":"2024-01-04T05:28:42.267947Z","iopub.status.idle":"2024-01-04T05:28:42.276068Z","shell.execute_reply.started":"2024-01-04T05:28:42.267912Z","shell.execute_reply":"2024-01-04T05:28:42.274952Z"}}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![TPUs.PNG](attachment:587eb603-3297-4fee-862f-dc99d39598b4.PNG)","metadata":{},"attachments":{"587eb603-3297-4fee-862f-dc99d39598b4.PNG":{"image/png":"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define a simple decorator(inner function) to mesure the execution of a function\ndef measure_time(func:Callable)->Callable:\n    '''\n    args: one argument, is the function that we want to decorated(e,g functions that:\n    build the model, train the model, optimize the model, ...etc)\n    '''\n    # define the wrapper function that executes the original function(e,g arg func)\n    def wrapper(*args, **kwargs)->float:\n        '''\n        the wrapper function have the same argument as the object(e,g arg)function\n        '''\n        start_time=time()\n        # call the object function to process and manipulate ...\n        result=func(*args, **kwargs)\n        end_time=time()\n        \n        processing_time=end_time-start_time\n        print(f'the processing time is {processing_time:.2f} secondes')\n        return result\n    \n    return wrapper\n\n# applying that decorator in a simple function\n@measure_time\ndef simple_function(n):\n  \"\"\"A simple function that calculates the sum of squares of numbers from 1 to n.\"\"\"\n  S= 0\n  for i in trange(1, n + 1):\n    S+=i**2\n  return S\n\n# Example usage\nn = 10000000\nresult = simple_function(n)\n\n        ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:35:55.117008Z","iopub.execute_input":"2024-01-22T09:35:55.118016Z","iopub.status.idle":"2024-01-22T09:36:01.061318Z","shell.execute_reply.started":"2024-01-22T09:35:55.117978Z","shell.execute_reply":"2024-01-22T09:36:01.060243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"if you don't love build things from scratch you can  use the %%timeit magic command to measure the execution time of a code cell","metadata":{}},{"cell_type":"code","source":"%%timeit\ndef simple_function(n):\n  \"\"\"A simple function that calculates the sum of squares of numbers from 1 to n.\"\"\"\n  S= 0\n  for i in trange(1, n + 1):\n    S+=i**2\n  return S\n\n\n# Example usage\nn = 10000000\nsimple_function(n)","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:36:05.366591Z","iopub.execute_input":"2024-01-22T09:36:05.366984Z","iopub.status.idle":"2024-01-22T09:36:52.902278Z","shell.execute_reply.started":"2024-01-22T09:36:05.366955Z","shell.execute_reply":"2024-01-22T09:36:52.901458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# the submission file\nsubmission_path='/kaggle/input/blood-vessel-segmentation/sample_submission.csv'\nexample_submission=read_csv(submission_path)\nexample_submission","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:28.655132Z","iopub.execute_input":"2024-01-22T09:37:28.655531Z","iopub.status.idle":"2024-01-22T09:37:28.675865Z","shell.execute_reply.started":"2024-01-22T09:37:28.655501Z","shell.execute_reply":"2024-01-22T09:37:28.675082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# the sample from ttrain_rles csv file\ntrain_rles_path='/kaggle/input/blood-vessel-segmentation/train_rles.csv'\nsample_train_rles=read_csv(train_rles_path)\nsample_train_rles.head()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:31.377011Z","iopub.execute_input":"2024-01-22T09:37:31.377403Z","iopub.status.idle":"2024-01-22T09:37:32.425321Z","shell.execute_reply.started":"2024-01-22T09:37:31.377371Z","shell.execute_reply":"2024-01-22T09:37:32.424433Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Surface Dice Metric for evaluation:","metadata":{"execution":{"iopub.status.busy":"2023-12-28T04:48:23.020768Z","iopub.execute_input":"2023-12-28T04:48:23.021168Z","iopub.status.idle":"2023-12-28T04:48:23.025667Z","shell.execute_reply.started":"2023-12-28T04:48:23.021138Z","shell.execute_reply":"2023-12-28T04:48:23.024592Z"}}},{"cell_type":"markdown","source":"the mathematic formula of the $surface$ $~$ $dice$ as a metric for segmentation is giveen by:\n\nSurface_Dice = $2 * |A ∩ B| / (|A| + |B|)$ $(\\star)$\n\nwhere:\n\n$A$ represent $labels$ .\n\n$B$ represent the predictions.\n\nwe add some real positive called $tolerance$ (to prevent divded by zero), the defaut value is set to 0.0.\n\nwe rewrite the $(\\star)$ , we obtain:\n\nSurface_Dice_with_tolerance = $2 * (|A ∩ B|+tolerance)/ (|A| + |B|+tolerance)$\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"![dice_surface.png](attachment:b4947b74-8a26-4cf7-b748-87fee7c5f850.png)","metadata":{},"attachments":{"b4947b74-8a26-4cf7-b748-87fee7c5f850.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"\nclass Dice(y_true:Union[Tensor, array, DataFrame],\n                 y_pred:Union[Tensor, array, DataFrame],\n                 tolerance:Optional[Union[float, int]]=0.0)->float:\n    \n          '''\n          :param y_true:labels\n          :param y_pred:predictions\n          :param tolerance:float\n          '''\n          dtype=Union[ type(Tensor), type(ndarray), type(DataFrame)]\n            \n          if isinstance(tolerance, Union[float, int]):\n             assert tolerance>=0.0\n          else:\n              raise TypeError(f'the dtype {type(tolerance)} does not supported')\n\n         \n          if isinstance(y_true, dtype):\n              y_pred=convert_to_tensor(y_pred)\n            \n          else:\n                raise TypeError(f\"{type(y_true)} is Unsupported type.\"\n                                     f\"Expected TensorFlow tensor or NumPy array or pandas DataFrame\")\n\n\n          if isinstance(y_pred, dtype):\n              y_pred=convert_to_tensor(y_pred)\n        \n          else:\n                raise TypeError(f\"{type(y_pred)} is Unsupported type.\"\n                                     f\"Expected TensorFlow tensor or NumPy array or pandas DataFrame\")\n\n          assert shape(y_true)==shape(y_pred)\n\n          # flatten the labels and predictions\n          y_true=flatten(y_true)\n          y_pred=flatten(y_pred)\n\n          intersection=sum(y_pred*y_true, axis=0)\n          union=sum(y_true, axis=0)+sum(y_pred, axis=0)\n\n          if union+tolerance==0:\n              raise ZeroDivisionError(f'we can not divide by zero, '\n                                      f'change the value{tolerance} of tolerance ')\n\n          dice_coefficient=(2.0*intersection+tolerance)/(union+tolerance)\n\n          return dice_coefficient\n\n# Fake numerical data for testing surface dice loss function\n\n# Ground truth values\n'''\nground_truth=<Tensor: shape=(5, 5), dtype=uint16, numpy=([\n    array[0, 1, 1, 0, 0],\n    [0, 1, 1, 1, 0],\n    [0, 0, 1, 0, 0],\n    [0, 1, 1, 1, 0],\n    [0, 0, 1, 0, 0]\n], dtype=uint16>)\n\n# Predicted values\npredicted=<Tensor: shape=(5, 5), dtype=uint16, numpy=([\n    array[0, 1, 0, 0, 0],\n    [0, 1, 1, 1, 0],\n    [0, 0, 1, 1, 0],\n    [0, 1, 1, 1, 0],\n    [0, 0, 1, 0, 0]\n], dtype=uint16>)\ntype(predicted)\n'''","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.684467Z","iopub.execute_input":"2024-01-17T03:52:01.684732Z","iopub.status.idle":"2024-01-17T03:52:01.694126Z","shell.execute_reply.started":"2024-01-17T03:52:01.684705Z","shell.execute_reply":"2024-01-17T03:52:01.691688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class DiceLoss(Loss):\n    \n    def __init__(self,\n                 smooth=1)->None:\n    \n              # Define the inializer of the parent class\n              super(Dice, self).__init__(**kwargs)\n              self.smooth=smooth\n        \n    def call(self, y_true, y_pred):\n        \n        # flatten the true labels and predicted labels\n        self.y_true=K.flatten(y_true)\n        self.y_pred=K.flatten(y_pred)\n        \n        # computing the score\n        self.score=(K.sum(y_true*y_pred)+self.smooth)/(K.sum(y_true)+K.sum(y_pred)+self.smooth)\n        \n        return 1-self.score    \n        \n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:39.887889Z","iopub.execute_input":"2024-01-22T09:37:39.888288Z","iopub.status.idle":"2024-01-22T09:37:39.894448Z","shell.execute_reply.started":"2024-01-22T09:37:39.888243Z","shell.execute_reply":"2024-01-22T09:37:39.893568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class DiceMetric(Metric):\n    \n      def __init__(self, \n                  with_iou_metric=False,\n                  with_coef_metric=True,\n                  smooth:float=1.)->None:\n            \n            super(DiceMetric, self).__init__()\n            assert with_iou_metric is not  with_coef_metric # to make use of one metric not 2 or no one\n            self.iou_metric=with_iou_metric\n            self.coef_metric=with_coef_metric\n            self.smooth=smooth\n            \n      def call(self, y_true, y_pred)->float:\n          \n            # First we flatten the labels\n            y_true=K.flatten(y_true)\n            y_pred=K.flatten(y_pred)\n            \n            if self.coef_metric:\n                    # computing the intesection\n                    intersection=K.sum(y_true*y_pred)\n                    return (2.+intersection+self.smooth)/(K.sum(y_true)+K.sum(y_pred)+self.smooth)\n            \n            else:\n                    # computing  the intersection of the the value absolue over axes 1, 2, 3\n                    intersection=K.sum(K.abs(y_true*y_pred), [1, 2, 3])\n                    \n                    # computing  the union of the the value absolue over axes 1, 2, 3\n                    union=K.sum(y_true, [1, 2, 3])+K.sum(y_pred, [1, 2, 3])-intersection  # A U B = A + B -intersection(A, B)\n                    \n                    # computing the mean of the intersection versus union over axis=0\n                    iou=K.mean((intersection+self.smooth)/(union+self.smooth), axis=0)\n                    \n                    return iou\n                    \n                \n            ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:41.760890Z","iopub.execute_input":"2024-01-22T09:37:41.761319Z","iopub.status.idle":"2024-01-22T09:37:41.769012Z","shell.execute_reply.started":"2024-01-22T09:37:41.761282Z","shell.execute_reply":"2024-01-22T09:37:41.768311Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Preparation and Preprocessing:","metadata":{"execution":{"iopub.status.busy":"2023-12-28T05:07:41.823584Z","iopub.execute_input":"2023-12-28T05:07:41.824608Z","iopub.status.idle":"2023-12-28T05:07:41.828481Z","shell.execute_reply.started":"2023-12-28T05:07:41.824571Z","shell.execute_reply":"2023-12-28T05:07:41.827563Z"}}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n# setting the paths of the train data\ntrain_datasets=['/kaggle/input/blood-vessel-segmentation/train/kidney_1_dense',\n             '/kaggle/input/blood-vessel-segmentation/train/kidney_1_voi', \n             '/kaggle/input/blood-vessel-segmentation/train/kidney_2',\n             '/kaggle/input/blood-vessel-segmentation/train/kidney_3_dense',\n             '/kaggle/input/blood-vessel-segmentation/train/kidney_3_sparse']\n\n# setting the path of test data\ntest_datasets=['/kaggle/input/blood-vessel-segmentation/test/kidney_5', \n          '/kaggle/input/blood-vessel-segmentation/test/kidney_6']\n\n\ndef sorting_images(dataset):\n    \n      # the train_path 4 doesn't contains images so we must skip it\n      if dataset is not train_datasets[3]:\n            images_path = os.path.join(dataset, 'images')\n            image_files = sorted([os.path.join(images_path, f) for f in os.listdir(images_path) if f.endswith('.tif')], \n                                reverse=False)\n            return image_files\n            \ndef sorting_labels(dataset):\n    \n    labels_path = os.path.join(dataset, 'labels')\n    labels_files = sorted([os.path.join(labels_path, f) for f in os.listdir(labels_path) if f.endswith('.tif')], \n                                reverse=False)\n    return labels_files\n        \ntrai_images=[]\ntrai_labels=[]\ntes_images=[]\ntrain_images_paths=map(sorting_images, train_datasets)  \ntrain_labels_paths=map(sorting_labels, train_datasets) \ntest_images_paths=map(sorting_images, test_datasets)\n\n\nfor train_pa in train_images_paths:\n   trai_images.append(train_pa)\n\nfor train_la in train_labels_paths:\n   trai_labels.append(train_la)\n\nfor test_pa in test_images_paths:\n   tes_images.append(test_pa)\n\n\n# the trai_images contains None path , we must clean it \nfor paths in trai_images:\n    if paths is None:\n       trai_images.remove(paths)\n    else:\n       for path in paths:\n          if path is None:\n            paths.remove(path)\n            \n# as we said before the fourth dataset (indice 3 due we start from 0) d\n# doesn't contains images so we must remove their labels\n\nlabels3=trai_labels[3]\ntrai_labels.remove(labels3)\n\n# check out that we have the same number of images and labels\nfor i in range(4):\n    assert len(trai_images[i])==len(trai_labels[i])\n    print('images', len(trai_images[i]), 'labels', len(trai_labels[i]))\n\n# list of lists to a list\n\ndef list_of_lists_to_list(list_of_lists:List[list])->list:\n    #assert  list_of_lists is list\n    flat_list=list(chain.from_iterable(list_of_lists))\n    return flat_list\n\n# train_images_paths=list_of_lists_to_list(trai_images)  ==> the train images are not the same shapes , \n                                                          #so do not flatten the lists of tarin  images\n# train_labels_paths=list_of_lists_to_list(trai_labels)  ==> the train labels are not the same shapes \ntest_images_paths=list_of_lists_to_list(tes_images)\n\nprint('#######------path of the test images######-----')\ntest_images_paths\n","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:48.922767Z","iopub.execute_input":"2024-01-22T09:37:48.923144Z","iopub.status.idle":"2024-01-22T09:37:50.639380Z","shell.execute_reply.started":"2024-01-22T09:37:48.923115Z","shell.execute_reply":"2024-01-22T09:37:50.638619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# images_paths, label_paths to tensors\nstart_time=time()\ndef path_to_tensor(path):\n    img=Image.open(path)\n    tensor=convert_to_tensor(img)\n    return tensor\n\nX_train=[]\ny_train=[]\nX_test=[]\n\nfor i in trange(4):\n    X=map(path_to_tensor, trai_images[i])\n    X_train.append(X)\n    y=map(path_to_tensor, trai_labels[i])\n    y_train.append(y)\n\nX_test_map=map(path_to_tensor,  test_images_paths )\n\n# convert a map object into a list\nfor i in trange(4):\n  X_train[i]=list(X_train[i])\n  y_train[i]=list(y_train[i])\nX_test=list(X_test_map)\n\n# convert a list into tensor of 3D\nfor i in trange(4):\n    X_train[i]=convert_to_tensor(X_train[i])\n    y_train[i]=convert_to_tensor(y_train[i])\nX_test=convert_to_tensor(X_test)\n\n\nend_time=time()\nt=end_time-start_time\nprint(f'the temps for executionis: {t:.4f}secondes ')","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:37:58.560604Z","iopub.execute_input":"2024-01-22T09:37:58.561002Z","iopub.status.idle":"2024-01-22T09:46:25.524766Z","shell.execute_reply.started":"2024-01-22T09:37:58.560969Z","shell.execute_reply":"2024-01-22T09:46:25.523693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(4):\n    print(f'elements of X_images{i} has the shape {X_train[i][0].shape}  <====>   elements of labels{i} has the shape {y_train[i][0].shape}')\nX_train[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:48:41.370586Z","iopub.execute_input":"2024-01-22T09:48:41.370960Z","iopub.status.idle":"2024-01-22T09:48:41.388787Z","shell.execute_reply.started":"2024-01-22T09:48:41.370930Z","shell.execute_reply":"2024-01-22T09:48:41.387920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"so we can not $ concatenate$ the train data in one large data","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"@measure_time\ndef disply_samples( index:int=450,figsize:tuple[int]=(14, 20))->None:\n    \n    for num_dataset in range(4):\n        assert index in range(len(X_train[num_dataset]))\n        # extracting the images and labels \n        image=X_train[num_dataset][index]\n        label=y_train[num_dataset][index]\n    \n        # Create subplots for displaying images and segmentations\n        fig, axs = plt.subplots(1, 2, figsize=figsize)\n        \n        axs[0].imshow(image, cmap='gray')\n        axs[0].axis('off')\n        axs[0].set_title('Image')\n\n        # Display labels\n        axs[1].imshow(label, cmap='gray')\n        axs[1].axis('off')\n        axs[1].set_title('Label')\n\n        # Adjust the layout and display the plot\n        plt.tight_layout()\n        plt.show()\ndisply_samples()\n      ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:48:47.753006Z","iopub.execute_input":"2024-01-22T09:48:47.753331Z","iopub.status.idle":"2024-01-22T09:48:50.178415Z","shell.execute_reply.started":"2024-01-22T09:48:47.753305Z","shell.execute_reply":"2024-01-22T09:48:50.177473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# normalizing data\nimport numpy as np\n@measure_time\ndef scaling_data(dataset, normalization=True, standardization=False)->Tuple[Tensor, Tensor]:\n    \n    assert normalization is not standardization\n    \n    if normalization:\n           dataset=np.asarray(dataset)\n           dataset=Dataset.from_tensor_slices(dataset)\n           normalized_data=dataset.map(lambda x:(x-tf.reduce_min(x, axis=0))/(tf.reduce_max(x, axis=0)-x))\n           return normalized_data\n    \n    else:  \n           dataset=np.asarray(dataset, dtype=np.float32)\n           dataset=Dataset.from_tensor_slices(dataset)\n           mean=tf.reduce_mean(dataset, axis=0)\n           std_dev=tf.math.reduce_std(dataset, axis=0)\n           normalized_data=dataset.map(lambda x:(x-tf.reduce_mean(x, axis=0))/tf.math.reduce_std(x, axis=0))\n           return normalized_data\n\nnormalized_X_train0=scaling_data(X_train[0])\nnormalized_X_train0","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:48:55.734039Z","iopub.execute_input":"2024-01-22T09:48:55.734970Z","iopub.status.idle":"2024-01-22T09:49:02.428662Z","shell.execute_reply.started":"2024-01-22T09:48:55.734934Z","shell.execute_reply":"2024-01-22T09:49:02.427893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#display the first 5 elements of the dataset\nnum_elements=5\ndataset_subset=normalized_X_train0.take(num_elements)# take the first five element\nfor element in dataset_subset:\n    print(element)\n    \n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:49:14.438684Z","iopub.execute_input":"2024-01-22T09:49:14.439015Z","iopub.status.idle":"2024-01-22T09:49:19.176375Z","shell.execute_reply.started":"2024-01-22T09:49:14.438988Z","shell.execute_reply":"2024-01-22T09:49:19.175444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# we check the shapes the images and labels\nprint('##########---- Train Data shape -----######')\nfor i in trange(4):\n   for image , label in zip(scaling_data(X_train[i]).take(1), scaling_data(y_train[i]).take(1)):\n       print(f'shape of images in dataset {i+1} est {image.shape}<====>shape of labels in dataset {i+1} est {label.shape}')\n             \nprint('##########---- Test Data shape(no labels) -----##########')\nfor image in scaling_data(X_test).take(5):# take the whole data\n    print(f'shape of images  {i} est {image.shape}')\n             \n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-20T09:32:05.103738Z","iopub.execute_input":"2024-01-20T09:32:05.104152Z","iopub.status.idle":"2024-01-20T09:33:23.608379Z","shell.execute_reply.started":"2024-01-20T09:32:05.104117Z","shell.execute_reply":"2024-01-20T09:33:23.607153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"as we can see in the training set ,every dataset's element has their own size and shape , and the test set that contains only images has the same shape as the shape of the training dataset(e,g(1303, 912)).","metadata":{}},{"cell_type":"code","source":"# assertion of dimensions for the training inputs data(e,g images shapes)\nimage1=X_train[0][0]    # take the first image of the first dataset\nassert  image1.shape.is_compatible_with(tf.TensorShape([1303, 912]))\n\nimage2=X_train[1][0]    #take the first image of the second dataset\nassert  image2.shape.is_compatible_with(tf.TensorShape([1928, 1928]))\n\nimage3=X_train[2][0]    #take the first image of the third dataset\nassert  image3.shape.is_compatible_with(tf.TensorShape([1041, 1511]))\n\nimage4=X_train[3][0]    #take the first image of the fourth dataset\nassert  image4.shape.is_compatible_with(tf.TensorShape([1706, 1510]))\n\nimage_test=X_test[0]    #take the first image of the test dataset\nassert  image_test.shape.is_compatible_with(image1.shape)\n\nprint('assertion is clearning out')\n","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:52:24.039465Z","iopub.execute_input":"2024-01-22T09:52:24.039792Z","iopub.status.idle":"2024-01-22T09:52:24.049890Z","shell.execute_reply.started":"2024-01-22T09:52:24.039766Z","shell.execute_reply":"2024-01-22T09:52:24.049097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"the images are Black-White so we add 1 channel for grayscale intensity (e,hg expanding dimensions selon first axis)as the model will be feeded by images so the input dismention is :$(1, 1303, 912)$","metadata":{}},{"cell_type":"code","source":"input_dims1=tf.expand_dims(image1, axis=0).shape\ninput_dims1","metadata":{"execution":{"iopub.status.busy":"2024-01-20T09:33:50.394216Z","iopub.execute_input":"2024-01-20T09:33:50.394609Z","iopub.status.idle":"2024-01-20T09:33:50.400974Z","shell.execute_reply.started":"2024-01-20T09:33:50.394576Z","shell.execute_reply":"2024-01-20T09:33:50.400140Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Augmentation:","metadata":{}},{"cell_type":"markdown","source":"$data$ $~$ $augumentation$ is the technique that commonly used to increase the size of data.\n\nfor segmentation tasks, we can applied $augumentation$ through :\n\n$1$-Flipping and Rotating images.(images means images and their labels).\n\n$2$-Cropping images.\n\n$3$-Resizing and Scaling images (as we did )\n\n$4$-Shiffting images and labels.\n\n$5$-add nnoise(random noise, normal noise...)\n\nand more....\n\nyou can implement this taking advantage of $tf.image$ , check out this https://www.tensorflow.org/api_docs/python/tf/image for more details, \n\nor you can take advantage of  $ImageDataGenerator$ from $keras.preprocessing.image$ (but has less features than  $tf.image$) check out this https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image.","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport numpy as np\n\n@measure_time\ndef display_tranformed_images(data, num_dataset:int=0, index:int=980, figsize=(12, 12))->None:\n    \n                  assert num_dataset  in range(4)\n                  assert index in range(len(data[num_dataset]))\n                  \n                  # getting an sole image\n                  original=data[num_dataset][index]\n    \n                  # Create subplots for displaying image or label and their transformations\n                  fig, axs = plt.subplots(1, 4, figsize=figsize)\n                 \n                  # Display image\n                  axs[0].imshow(original, cmap='gray')\n                  axs[0].axis('off')\n                  axs[0].set_title('original')\n\n                  # Display flipped_image or flipped_label\n                  # Flipping and Add channel dimension for grayscaleimages[i])\n                  flipped=tf.image.flip_left_right(np.expand_dims(original, axis=-1))\n                  axs[1].imshow(flipped, cmap='gray')\n                  axs[1].axis('off')\n                  axs[1].set_title('flipped')\n                    \n                  # Display  rotated_image \n                  # Rotation and Add channel dimension for grayscaleimages[i])\n                  angle=tf.random.uniform([], minval=1, maxval=360, dtype=tf.int32)  # Random rotation angle between 1 and 360 degrees\n                  rotated=tf.image.rot90(np.expand_dims(original, axis=-1), k=angle // 90)\n                  axs[2].imshow(rotated, cmap='gray')\n                  axs[2].axis('off')\n                  axs[2].set_title('rotated')\n                  \n                  # Display a sheared image using tf.keras.preprocessing.image.random_shear\n                  sheared=tf.keras.preprocessing.image.random_shear(np.expand_dims(original, axis=-1), intensity=90)  # Shear up to 45 degrees\n                  # if you want to shear image selon X-axis or y-axis you nedd to importtf tensorflow_addons\n                  #sheared_x=tfa.image.shear_x(original, shear=0.2)  # Shear 20% along the x-axis\n                  #sheared_y=tfa.image.shear_y(original, shear=-0.15)  # Shear -15% along the y-axis\n                  axs[3].imshow(sheared, cmap='gray')\n                  axs[3].axis('off')\n                  axs[3].set_title(' sheared')\n\n\n\n                  # Adjust the layout and display the plot\n                  plt.tight_layout()\n                  plt.show() \n                    \n\ndisplay_tranformed_images(data=X_train)\ndisplay_tranformed_images(data=y_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:52:31.123040Z","iopub.execute_input":"2024-01-22T09:52:31.123385Z","iopub.status.idle":"2024-01-22T09:52:32.756407Z","shell.execute_reply.started":"2024-01-22T09:52:31.123357Z","shell.execute_reply":"2024-01-22T09:52:32.755620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"we define a $Augument$ class as subclass of $keras.layers.Layer$ to augument the images using $tf.image$:","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"class Augment(Layer):\n    \"\"\"\n    Data augmentation layer for images.\n\n    Args:\n        horizontal_flip (bool): Whether to randomly flip images horizontally.\n        vertical_flip (bool): Whether to randomly flip images vertically.\n        rotation_range (float): Range for random rotation in degrees.\n        zoom_range (tuple): Range for random zooming.\n        shear_range (float): Range for random shearing in degrees.\n        brightness_range (tuple): Range for random brightness adjustment.\n        contrast_range (tuple): Range for random contrast adjustment.\n    \"\"\"\n\n    def __init__(self,\n                 horizontal_flip=False,\n                 vertical_flip=False,\n                 rotation_range=0.0,\n                 zoom_range=(1.0, 1.0),\n                 shear_range=0.0,\n                 brightness_range=(1.0, 1.0),\n                 contrast_range=(1.0, 1.0),\n                 **kwargs):\n\n        # first we define the inilizer of the parent class (Layer)\n        super(Augment, self).__init__(**kwargs)\n        self.horizontal_flip = horizontal_flip\n        self.vertical_flip = vertical_flip\n        self.rotation_range = rotation_range\n        self.zoom_range = zoom_range\n        self.shear_range = shear_range\n        self.brightness_range = brightness_range\n        self.contrast_range = contrast_range\n\n    def call(self, inputs, label, training=None):\n        if training is not None and not training:\n            return inputs\n        \n        dtype=Union[tf.Tensor, np.ndarray, pd.DataFrame]\n        assert isinstance(inputs, classinfo=dtype)\n        assert isinstance(labels, classinfo=dtype)\n\n        image = inputs\n        segmentation_mask=label\n\n        # Random horizontal flip\n        if self.horizontal_flip:\n            image = tf.image.random_flip_left_right(image)\n            segmentation_mask= tf.image.random_flip_left_right(segmentation_mask)\n\n        # Random vertical flip\n        if self.vertical_flip:\n            image = tf.image.random_flip_up_down(image)\n            segmentation_mask = tf.image.random_flip_up_down(segmentation_mask)\n\n        # Random rotation\n        if self.rotation_range > 0.0:\n            angle = tf.random.uniform([], -self.rotation_range, self.rotation_range)\n            image = tf.image.rot90(image, k=tf.cast(angle, tf.int32))\n            segmentation_mask = tf.image.rot90(segmentation_mask, k=tf.cast(angle, tf.int32))\n\n        # Random zoom\n        if self.zoom_range[0] != 1.0 or self.zoom_range[1] != 1.0:\n            zoom = tf.random.uniform([], self.zoom_range[0], self.zoom_range[1], dtype=tf.float32)\n            image = tf.image.resize(image, tf.cast(tf.shape(image)[:2] * zoom, tf.int32))\n            segmentation_mask = tf.image.resize(segmentation_mask, tf.cast(tf.shape(segmentation_mask)[:2] * zoom, tf.int32))\n\n        # Random shear\n        if self.shear_range > 0.0:\n            shear = tf.random.uniform([], -self.shear_range, self.shear_range)\n            image = tf.image.shear_x(image, shear)\n            segmentation_mask = tf.image.shear_x(segmentation_mask , shear)\n\n        # Random brightness\n        if self.brightness_range[0] != 1.0 or self.brightness_range[1] != 1.0:\n            brightness = tf.random.uniform([], self.brightness_range[0], self.brightness_range[1])\n            image = tf.image.adjust_brightness(image, brightness)\n            segmentation_mask = tf.image.adjust_brightness(segmentation_mask, brightness)\n\n        # Random contrast\n        if self.contrast_range[0] != 1.0 or self.contrast_range[1] != 1.0:\n            contrast = tf.random.uniform([], self.contrast_range[0], self.contrast_range[1])\n            image = tf.image.adjust_contrast(image, contrast)\n            segmentation_mask=tf.image.adjust_contrast(segmentation_mask, contrast)\n\n        return image, segmentation_mask","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:52:41.296938Z","iopub.execute_input":"2024-01-22T09:52:41.297277Z","iopub.status.idle":"2024-01-22T09:52:41.311410Z","shell.execute_reply.started":"2024-01-22T09:52:41.297237Z","shell.execute_reply":"2024-01-22T09:52:41.310626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"before feeded training data into model we needed to add more crucial pipline steps such :\n\n$batching$ by dividing data into smaller batches for more efficient training.\n\n$caching$ copy the data in order to speed up the training.\n\n$shuffling$ to decrease rate of $overfitting$ ...etc\n\n$repeating$ enables continuous training over multiple epochs.\n\nand more...\n\n\n\n","metadata":{}},{"cell_type":"code","source":"# Setting values for pipline of preprocessing data\nbatch_size=32   # as rule of thumb\nEPOCHS=20\ntf_buffer_size=tf.data.AUTOTUNE # Don't bother yourself, \n                                # TensorFlow will dynamically determinal the optimal buffer_size in function for your accelerator, memory...etc\nbuffer_size=1000\n\n\ndef process_pipline(dataset):\n    \n     # Create a TensorFlow dataset from the input data\n     dataset=tf.data.Dataset.from_tensor_slices(data)\n    \n     # Cache  dataset \n     dataset=dataset.cache()\n     \n     #scaling data\n     # dataset=scaling_data(dataset)( we don't apply it, try it)\n        \n     # Shuffle dataset\n     dataset=dataset.shuffle(batch_size=batch_size)\n        \n     # enable continuous training through several epochs\n     dataset=dataset.repeat()\n        \n     # augmenting data \n     dataset=dataset.map(augment())\n    \n     # perfetching data\n     dataset=dataset.prefetch(buffer_size=tf_buffer_size)\n    \n     return dataset\n        \n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:52:48.482233Z","iopub.execute_input":"2024-01-22T09:52:48.483092Z","iopub.status.idle":"2024-01-22T09:52:48.490852Z","shell.execute_reply.started":"2024-01-22T09:52:48.483040Z","shell.execute_reply":"2024-01-22T09:52:48.489830Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"usually in large model of deep learning we use $callbacks$ as method to performance several method such save best parameters of the model or even best model, stop the training where is no progress through earlystopping then provent overfitting , tracking the progress, visualizing results, er can evn use $callbacks$ for fine-tune of the hyperparameters(e,g $tensorbord$ as callback) , add functionalities that indicates the training behaviors ...etc\nwe implement our Custom Callbacks (earlystopping, modelcheckpoint, tensorboard) as a subclass of $keras.callbacks.Callback$.\n\nif you don't need more flexibility you can call the predefine onses through $ keras.callbacks.EarlyStopping$ or $ keras.callbacks.ModelCheckPoint$ or$keras.callbacks.tensorboard$ , $keras.callbacks.ReduceLROnPlateau$ for reducing and optimizing the learning rate for good performance...etc","metadata":{}},{"cell_type":"code","source":"class CustomEarlyStopping(Callback):\n    \n      def __init__(self, monitor:str='val_loss', patience:int=5):\n            # Define the inializer of the parent class\n            super(CustomEarlyStopping).__init__()\n            \n            self.monitor=monitor                    # the objectif\n            self.patience=patience                  # if there is no improvement after 5 successive training is stopped\n            self.best_weights=None                  # such there is no model defined yet or the training are performed\n            self.best_metric=float('inf')             # as start point:this for minimization set it -float(inf) for maximization \n            \n       \n      def on_epoch_end(self, epoch, logs=None):\n         '''\n          on end of each epoch we update the weights, metric \n          if the metric on the following epoch is better that the precedent epoch otherwise\n          preserve the current parameters. if there no improvement after 5 epochs drop out \n          the training process\n          \n         '''\n         assert self.patience in range(epochs)     # if this condition not affirmed this callback will be a waste of time\n                                                 # (don't set it very high or very low)\n        \n         # getting the current metric\n         assert logs is not None\n         current_metric=logs.get(self.monitor)\n         \n         if current_metrick<self.best_metric:\n            # getting and updating the parameters of the models(e,g weights)\n            self.best_weights=self.model.get_weights()\n            # updating the metric\n            self.best_metric=current_metric\n            \n         else:\n             # if there is no progress in the current epoch decrease patience by 1\n             self.patience-=1\n             \n             # when the patience is run out(e,g patience=0)\n             while self.patience==0:\n                    # make model stop his training\n                    self.model.stop_training=True\n                    # updating the parameters of the models\n                    self.model.set_weights(self.best_weights)\n                    print('Restoring the weights')\n                    \nclass CustomModelCheckpoint(Callback):\n      \n      def __init__(self, file_path:str=None, monitor='val_accuracy', save_best_only:bool=True):\n                   # define the initializer of the parent calss(e,g Callback)\n                   super(CustomModelCheckPoint).__init__()\n                   self.file_path=file_path\n                   self.monitor=monitor\n                   self.save_best_only=save_best_only # set it to False if you to save all models\n                   self.best_metric=float('inf')\n                    \n      def on_epoch_end(self, epoch, logs=None):\n                    assert logs is not None\n                    current_metric=logs.get(sel.monitor)\n                    if (current_metrtic>self.best_metric) or (not save_best_only):\n                        self.model.save(self.file_path.format(epoch=epoch+1))\n                        self.best_metric=current_metric\n\nclass CustomTensorBoard(Callback):\n      \n      def __init__(self, log_dir):\n            super(CustomTensorBoeard).__init__()\n            self.log_dir=log_dir\n            self.file_writer=tf.summary.create_file_writer(log_dir)\n            \n      def on_epoch_end(self, epoch, logs=None):\n                 with self.file_writer.as_defaults():\n                       for name, value in logs.items():\n                             tf.summary.scalar(name, value, step=epoch+1)\n                             self.file_writer.flush()\n            \n\n# Create an instances for our custom callbacks\nearly_stopping=CustomEarlyStopping(patience=4)\nmodel_checkpoint=CustomModelCheckpoint('best_model.h5')\ntensorboard_callback=CustomTensorBoard('logs')\n\n# stacking custom callbacks in a list\ncustom_callbacks=[early_stopping, model_checkpoint, tensorboard_callback]\n      ","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:52:55.688494Z","iopub.execute_input":"2024-01-22T09:52:55.689372Z","iopub.status.idle":"2024-01-22T09:52:56.299584Z","shell.execute_reply.started":"2024-01-22T09:52:55.689336Z","shell.execute_reply":"2024-01-22T09:52:56.298514Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Simple Model Unet:\n","metadata":{}},{"cell_type":"markdown","source":"the $Unet$ model is a popular architecture of convolutional neural network (CNN) that has a gret impact in the field segmentation tasks(especialy in segmentation medical images), it was firstly introdued in the paper titled \"U-Net: Convolutional Networks for Biomedical Image Segmentation\" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox , chaek out this link https://arxiv.org/abs/1505.04597.\n$Unet$ has two major composant :\n$ Encoder $ and $Decoder$\n\n$Encoder$: the inputs goes through a series of convulation layer(2D) with the kernel size $(3, 3)$ and 'relu' as function activation.\n\n$Decoder$: it takes the outputs of $Encoder$ as inputs that goes in turn by multiples series UpSampling layers (2D), and  the we use Concatenate layer to link The upsampled feature maps with feature maps from the contracting path (skip connections) following by series of convolutinal layer with 'relu' as activation function.\n\nthere is lot modifications and enhancement  of $Unet$ archicture,such as $SegNet$, $U-Net++$, $Attention U-Ne$, $R2U-Net$, $TernausNet$...etc\n\n","metadata":{}},{"cell_type":"markdown","source":"![Unet_architecture.png](attachment:6c3c470e-46a4-45b2-8b07-28a6f20bfdc8.png)","metadata":{},"attachments":{"6c3c470e-46a4-45b2-8b07-28a6f20bfdc8.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"as we can see, the functional keras API is the best option(such the architecture has a concatenate layers) to create $Unet$ model.\nas you can relize the blocks of the $Encoder$ and $Decoder$ is the same with some differiations in $filters$ argument , so we can define the $block$ layer as a subclass of $keras.layers.Layer$ and $Unet$ model as subclass of $keras.models.Model$.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class ConvBlock(Layer):\n    def __init__(self, filters):\n        super(ConvBlock, self).__init__()\n        self.conv1 = Conv2D(filters, 3, activation='relu', padding='same')\n        self.conv2 = Conv2D(filters, 3, activation='relu', padding='same')\n        self.maxpool = MaxPooling2D()\n\n    def call(self, inputs):\n        x = self.conv1(inputs)\n        x = self.conv2(x)\n        pool = self.maxpool(x)\n        return x, pool\n\n\nclass UNet(Model):\n    def __init__(self, num_classes=3):\n        super(UNet, self).__init__()\n        self.conv_block1 = ConvBlock(64)\n        self.conv_block2 = ConvBlock(128)\n        self.conv_block3 = ConvBlock(256)\n        self.conv_block4 = ConvBlock(512)\n        self.conv_block5 = ConvBlock(1024)\n        self.upconv4 = Conv2D(512, 2, activation='relu', padding='same')\n        self.upconv3 = Conv2D(256, 2, activation='relu', padding='same')\n        self.upconv2 = Conv2D(128, 2, activation='relu', padding='same')\n        self.upconv1 = Conv2D(64, 2, activation='relu', padding='same')\n        self.up1 = UpSampling2D()\n        self.up2 = UpSampling2D()\n        self.up3 = UpSampling2D()\n        self.concat = Concatenate()\n        self.final_conv = Conv2D(num_classes, 1, activation='softmax')\n\n    def call(self, inputs):\n        inputs=tf.cast(inputs, tf.float32)\n        x, pool1 = self.conv_block1(inputs)\n        x, pool2 = self.conv_block2(pool1)\n        x, pool3 = self.conv_block3(pool2)\n        x, pool4 = self.conv_block4(pool3)\n\n        x = self.conv_block5(pool4)\n\n        x = self.upconv4(x)\n        x = self.up1(x)\n        x = self.concat([x, pool4])\n\n        x = self.upconv3(x)\n        x = self.up2(x)\n        x = self.concat([x, pool3])\n\n        x = self.upconv2(x)\n        x = self.up3(x)\n        x = self.concat([x, pool2])\n\n        x = self.upconv1(x)\n        x = self.concat([x, pool1])\n\n        x = self.final_conv(x)\n\n        return x","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:53:04.054966Z","iopub.execute_input":"2024-01-22T09:53:04.055303Z","iopub.status.idle":"2024-01-22T09:53:04.065995Z","shell.execute_reply.started":"2024-01-22T09:53:04.055257Z","shell.execute_reply":"2024-01-22T09:53:04.065206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ntf.experimental.numpy.experimental_enable_numpy_behavior()     \nX_train[0]=X_train[0].reshape((2279, 1303, 912, 1))\ny_train[0]=y_train[0].reshape((2279, 1303, 912, 1))\n\n# detect and init the tpu\ntpu=tf.distribute.cluster_resolver.TPUClusterResolver()\n\n# istantiating a distribution strategy\ntf.tpu.experimental.initialize_tpu_system(tpu)\ntpu_strategy=tf.distribute.TPUStrategy(tpu)\n\n# instantating thhe model in the strategy scope creates the model on the TPU\nwith tpu_strategy.scope():\n\n   ### create an Unet object\n   unet=UNet()\n   # compile the model\n   unet.compile(loss=DiceLoss , optimizer='adam', metrics=[DiceLoss])\n\n# train the model\n#unet.fit(X_train[0], y_train[0], epochs=5) \nunet.fit(X_train[0], y_train[0], epochs=5)\n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-22T09:53:10.442879Z","iopub.execute_input":"2024-01-22T09:53:10.443208Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train_model(model: Model, X_train, y_train, \n                EPOCHS:int,Val_data, BATCH_SIZE=32,\n                CALLBACKS=[CALLBACKS], trainable=True)->None:\n    \n    # model compile assertion\n    if model.optimizer is None:\n        raise ValueError('you must call model.compile() before using the model')\n    \n         if not trainable:\n             print('the model is not training')\n    \n         else:\n             print('the model is  training')\n             model.fit(X_train=X_train, y_train=y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n                   validation_data=Val_data, callbacks=CALLBACKS)\n        \n        \n        \n    ","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.718331Z","iopub.status.idle":"2024-01-17T03:52:01.718651Z","shell.execute_reply.started":"2024-01-17T03:52:01.718491Z","shell.execute_reply":"2024-01-17T03:52:01.718507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndef make_preditions(model:Model=Unet, image:Tensor)->Tensor:\n    '''\n    this is a helper function to tranform prediction made by the model \n    into approprite form for visualization\n    the process is a little ambigous ..., i don't understand it well,\n    but(as you said in english) let's give it a try\n    '''\n    expected_shape=image.shape\n    actual_shape=model.input_shape[1:]  # Exclude the batch dimension\n\n    if not assert_shapes(expected_shape, actual_shape) \n        raise ValueError(f\"Model input shape does not match the image shape: {expected_shape} != {actual_shape}\")\n        \n    pred_label=model(image)\n    # find the index of the max value in last axis(axis=-1)\n    \n    pred=argmax( pred_label, axis=-1)\n    # add new axe to the  pred_label tensor\n    \n    pred_label= pred_label[..., newaxis]\n    # take the first element of the  pred_label tensor\n    \n    pred_label=pred_label[0]\n    return  pred_label\n\n\n# helper function to show results\ndef show_segmentation_results(image, label, predicted_label, before_training=False, colormap='gray'):\n             titles=['image', 'label', 'predicted_label']\n             # Create a figure with three subplots\n             fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n\n             # Display the original image\n             axs[0].imshow(image)\n             axs[0].set_title(titles[0])\n\n             # Display the ground truth label\n             axs[1].imshow(label, cmap='gray')\n             axs[1].set_title(titles[1])\n\n             # Display the predicted label\n             axs[2].imshow(predicted_label, cmap='gray')\n             axs[2].set_title(titles[2])\n\n             # Remove the axis labels\n             for ax in axs:\n                ax.axis('off')\n\n             # Adjust the spacing between subplots\n             plt.tight_layout()\n\n             # Show the plot\n             plt.show()\n\n    \ndef display_predictions(model=Unet:Model, num_samples:int=1, index:int=550, before_trainig:bool=True)->None:\n    \n      # we diplay one prediction of the unet for each dataset\n      for i in trange(4):\n            assert index in range(len(X_train[i]))\n            image=X_train[i][index]\n            label=y_train[i][index]\n        \n            if before_training:\n                    # we make sure that the model is not trainable\n                    if model.trainable:\n                         raise ModelIsTrainableError('the model is trainable')\n                    \n                    else:\n                          # we make predictions\n                          pred_label=make_preditions(model, image)\n                          print('the results before training')\n                          show_segmentation_results(image, label, predicted_label, before_training=before_training)\n                          print(f'set the bool argument before_trainig to True to show the predictions made by the {Unet} after feeding the model by the data')\n        \n            else:\n        \n                    # we make sure that the model is trainable\n                    if not model.trainable:\n                         raise ModelNotTrainableError('the model is not trainable set model.trainable to True')\n         \n                    # otherwise we make predictions\n                    else:\n                         pred_label=make_preditions(model=model, image=imge)\n                         print('the results after training the model')\n                         show_segmentation_results(image, label, before_training=before_training)\n                         print(f'set the bool argument before_trainig to False to show the predictions made by the {Unet} beforer feeding the model by the data')\n                  ","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.720043Z","iopub.status.idle":"2024-01-17T03:52:01.720400Z","shell.execute_reply.started":"2024-01-17T03:52:01.720219Z","shell.execute_reply":"2024-01-17T03:52:01.720238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Cross Validation:","metadata":{"execution":{"iopub.status.busy":"2024-01-03T08:05:46.787342Z","iopub.execute_input":"2024-01-03T08:05:46.787737Z","iopub.status.idle":"2024-01-03T08:05:46.791967Z","shell.execute_reply.started":"2024-01-03T08:05:46.787708Z","shell.execute_reply":"2024-01-03T08:05:46.790909Z"}}},{"cell_type":"markdown","source":"if you want to know about kfold (from scratch), check out this https://github.com/said-ml/pattern-recognition-and-machine-learning/blob/main/Chapter1%3AIntroduction/cross-validation.ipynb","metadata":{}},{"cell_type":"code","source":"# if you want to know about kfold (from scratch )\nkfold=KFold(n_splits=5, shuffle=True, random_stae=42)\n\n# we want to store accuracies and losses of the model\nlosses=[]\naccuracies=[]\n\nfor (train, test), fold  in enumerate(zip(kfold(X, y))):\n    # we train the model\n    model.fit(X[train], y[train], epochs=25, batch_size=32)\n    # model's evaluation and testing\n    loss, accuracy=model.evaluate(X[test], y[test])\n    losses.append(loss)\n    accuracies.append(accuracy)\n    print(f\"Fold: {fold + 1}, Accuracy: {accuracy:.4f}, Loss: {loss:.4f}\")\n    \nprint(f\"Average Accuracy: {np.mean(accuracies):.4f}\")\nprint(f\"Average Loss: {np.mean(losses):.4f}\")\n\n# some plots to track losses and accuracies in each fold\n\nplt.figure(figsize=(8, 5))\nplt.plot(range(1, len(kfold.split(X, y)) + 1), accuracies, label='Accuracy')\nplt.plot(range(1, len(kfold.split(X, y)) + 1), losses, label='Loss')\nplt.xlabel('Fold Number')\nplt.ylabel('Score')\nplt.title('KFold Cross-Validation Results')\nplt.legend()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.721331Z","iopub.status.idle":"2024-01-17T03:52:01.721682Z","shell.execute_reply.started":"2024-01-17T03:52:01.721506Z","shell.execute_reply":"2024-01-17T03:52:01.721524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fine-Tune with kerastuner:","metadata":{}},{"cell_type":"markdown","source":"we can use sklearn.model_selection to select our algorithm of optimization(e, g $GridSearchCV$, $BayesianSearch$...) to finfd good hypeparameters ($optimizer$, learning_rate, $activations$, $droput$, $regularizations$, weights_initialization , $layers$(eg,$CNNs$, $RNNs$, $DNNs$...etc) to build a robust model for our task.\n\nbut instaed we use $kerastuner$ to performe this .\n\nbut before that we must define a function that all possible of sets of combinaisons of the hyparameters that we can choose from, and use $kerastuner$ the choose the best combinaison","metadata":{}},{"cell_type":"code","source":"def build_model()","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.722492Z","iopub.status.idle":"2024-01-17T03:52:01.722786Z","shell.execute_reply.started":"2024-01-17T03:52:01.722640Z","shell.execute_reply":"2024-01-17T03:52:01.722656Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# We create a RandomSerach object as the first tuner\ntuner=Randomsearch(hypermodel:Callable=build_model(), \n                   # this function provided a set of values of each parameters that we can choose from it\n                   objective:Callable=accuracy        \n                   # this is metric of the model tha we want to potimze it\n                   max_trials:int=10,                 \n                   # number of configurations \n                   directory=None,\n                   # directory is optinal to save the model\n                   projection_name:str='Human Vasculature in 3D with keras/tensorflow'\n                  )\n\n# Search the best hyperparameters\ntuner.search(X_train, y_train,\n             validation_data=(x_val, y_val),\n             epochs=5,\n             callbacks=EarlyStopping(patience=3)\n            )\n\n# Get The Best Hyperparameters:\nbest_hypers=tuner.get_best_hyperparameters(num_trials=1)[0]\nbest_models=tuner.hypermodel.build(best_hyperparameters)\n\n\n                   \n                   ","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.723862Z","iopub.status.idle":"2024-01-17T03:52:01.724248Z","shell.execute_reply.started":"2024-01-17T03:52:01.724065Z","shell.execute_reply":"2024-01-17T03:52:01.724084Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Results Analysis:","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from numbers import Integral, Real\na=1\nif isinstance(a, Real):\n    print('the code is work', type(Integral), type(a))","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.725329Z","iopub.status.idle":"2024-01-17T03:52:01.725793Z","shell.execute_reply.started":"2024-01-17T03:52:01.725488Z","shell.execute_reply":"2024-01-17T03:52:01.725504Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import random\ndef upvote(you_like_it:bool=True)->str:\n    \n    if you_like_it:\n         return random.choice(['upvote it', 'leave a comment'])\n\n    else:\n         return 'self descipline is the key of success'\nupvote()","metadata":{"execution":{"iopub.status.busy":"2024-01-17T03:52:01.727913Z","iopub.status.idle":"2024-01-17T03:52:01.728812Z","shell.execute_reply.started":"2024-01-17T03:52:01.728315Z","shell.execute_reply":"2024-01-17T03:52:01.728371Z"},"trusted":true},"execution_count":null,"outputs":[]}]}