{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-03-03T21:44:13.303048Z","iopub.status.busy":"2023-03-03T21:44:13.302317Z","iopub.status.idle":"2023-03-03T21:44:15.848420Z","shell.execute_reply":"2023-03-03T21:44:15.847507Z","shell.execute_reply.started":"2023-03-03T21:44:13.303010Z"},"trusted":true},"outputs":[{"ename":"","evalue":"","output_type":"error","traceback":["\u001b[1;31mFailed to start the Kernel. \n","\u001b[1;31mSyntaxError: invalid syntax. \n","\u001b[1;31mView Jupyter log for further details."]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n"," for filename in filenames:\n"," os.path.join(dirname, filename)\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:15.851609Z","iopub.status.busy":"2023-03-03T21:44:15.850335Z","iopub.status.idle":"2023-03-03T21:44:15.867155Z","shell.execute_reply":"2023-03-03T21:44:15.866069Z","shell.execute_reply.started":"2023-03-03T21:44:15.851570Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Modules loaded\n"]}],"source":["import pandas as pd\n","import numpy as np\n","import os\n","os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n","import time\n","import matplotlib.pyplot as plt\n","import cv2\n","import seaborn as sns\n","sns.set_style('darkgrid')\n","import shutil\n","from sklearn.metrics import confusion_matrix, classification_report\n","from sklearn.model_selection import train_test_split\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.layers import Dense, Activation,Dropout,Conv2D, MaxPooling2D,BatchNormalization\n","from tensorflow.keras.optimizers import Adam, Adamax\n","from tensorflow.keras.metrics import categorical_crossentropy\n","from tensorflow.keras import regularizers\n","from tensorflow.keras.models import Model\n","from tensorflow.keras import backend as K\n","import time\n","from tqdm import tqdm\n","from sklearn.metrics import f1_score\n","from IPython.display import YouTubeVideo\n","import sys\n","if not sys.warnoptions:\n"," import warnings\n"," warnings.simplefilter(\"ignore\")\n","pd.set_option('display.max_columns', None) # or 1000\n","pd.set_option('display.max_rows', None) # or 1000\n","pd.set_option('display.max_colwidth', None) # or 199\n","print ('Modules loaded')\n"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:15.869416Z","iopub.status.busy":"2023-03-03T21:44:15.868526Z","iopub.status.idle":"2023-03-03T21:44:15.885964Z","shell.execute_reply":"2023-03-03T21:44:15.884893Z","shell.execute_reply.started":"2023-03-03T21:44:15.869355Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100mtest of default colors\n","\u001b[0m\n"]}],"source":["def print_in_color(txt_msg,fore_tupple=(0,255,255),back_tupple=(100,100,100)):\n"," #prints the text_msg in the foreground color specified by fore_tupple with the background specified by back_tupple \n"," #text_msg is the text, fore_tupple is foregroud color tupple (r,g,b), back_tupple is background tupple (r,g,b)\n"," # default parameter print in cyan foreground and gray background\n"," rf,gf,bf=fore_tupple\n"," rb,gb,bb=back_tupple\n"," msg='{0}' + txt_msg\n"," mat='\\33[38;2;' + str(rf) +';' + str(gf) + ';' + str(bf) + ';48;2;' + str(rb) + ';' +str(gb) + ';' + str(bb) +'m' \n"," print(msg .format(mat), flush=True)\n"," print('\\33[0m', flush=True) # returns default print color to back to black\n"," return\n","\n","# example default print\n","msg='test of default colors'\n","print_in_color(msg)"]},{"cell_type":"markdown","metadata":{},"source":["**Read image paths and labels and create train, test and validation data frames\n","**"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:15.889974Z","iopub.status.busy":"2023-03-03T21:44:15.889147Z","iopub.status.idle":"2023-03-03T21:44:16.883668Z","shell.execute_reply":"2023-03-03T21:44:16.882656Z","shell.execute_reply.started":"2023-03-03T21:44:15.889935Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Cyst : 100%|\u001b[34m███████████████████████████████████████████████████████\u001b[0m| 3709/3709 [00:00<00:00, 484329.81files/s]\u001b[0m\n","Normal : 100%|\u001b[34m███████████████████████████████████████████████████████\u001b[0m| 5077/5077 [00:00<00:00, 520176.89files/s]\u001b[0m\n","Stone : 100%|\u001b[34m███████████████████████████████████████████████████████\u001b[0m| 1377/1377 [00:00<00:00, 344377.59files/s]\u001b[0m\n","Tumor : 100%|\u001b[34m███████████████████████████████████████████████████████\u001b[0m| 2283/2283 [00:00<00:00, 406193.10files/s]\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":["number of classes in processed dataset= 4\n","3554 \n","the maximum files in any class in train_df is 3554 the minimum files in any class in train_df is 964\n","train_df length: 8712 test_df length: 1867 valid_df length: 1867\n","average image height= 577 average image width= 638 aspect ratio h/w= 0.9043887147335423\n"]}],"source":["def make_dataframes(sdir): \n"," filepaths=[]\n"," labels=[]\n"," classlist=sorted(os.listdir(sdir) ) \n"," for klass in classlist:\n"," classpath=os.path.join(sdir, klass) \n"," if os.path.isdir(classpath):\n"," flist=sorted(os.listdir(classpath)) \n"," desc=f'{klass:25s}'\n"," for f in tqdm(flist, ncols=130,desc=desc, unit='files', colour='blue'):\n"," fpath=os.path.join(classpath,f)\n"," filepaths.append(fpath)\n"," labels.append(klass)\n"," Fseries=pd.Series(filepaths, name='filepaths')\n"," Lseries=pd.Series(labels, name='labels')\n"," df=pd.concat([Fseries, Lseries], axis=1) \n"," train_df, dummy_df=train_test_split(df, train_size=.7, shuffle=True, random_state=123, stratify=df['labels']) \n"," valid_df, test_df=train_test_split(dummy_df, train_size=.5, shuffle=True, random_state=123, stratify=dummy_df['labels']) \n"," classes=sorted(train_df['labels'].unique())\n"," class_count=len(classes)\n"," sample_df=train_df.sample(n=50, replace=False)\n"," # calculate the average image height and with\n"," ht=0\n"," wt=0\n"," count=0\n"," for i in range(len(sample_df)):\n"," fpath=sample_df['filepaths'].iloc[i]\n"," try:\n"," img=cv2.imread(fpath)\n"," h=img.shape[0]\n"," w=img.shape[1]\n"," wt +=w\n"," ht +=h\n"," count +=1\n"," except:\n"," pass\n"," have=int(ht/count)\n"," wave=int(wt/count)\n"," aspect_ratio=have/wave\n"," print('number of classes in processed dataset= ', class_count) \n"," counts=list(train_df['labels'].value_counts()) \n"," print(counts[0], type(counts[0]))\n"," print('the maximum files in any class in train_df is ', max(counts), ' the minimum files in any class in train_df is ', min(counts))\n"," print('train_df length: ', len(train_df), ' test_df length: ', len(test_df), ' valid_df length: ', len(valid_df)) \n"," print('average image height= ', have, ' average image width= ', wave, ' aspect ratio h/w= ', aspect_ratio) \n"," return train_df, test_df, valid_df, classes, class_count\n","\n","sdir=r'../input/ct-kidney-dataset-normal-cyst-tumor-and-stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone'\n","train_df, test_df, valid_df, classes, class_count=make_dataframes(sdir)\n"," "]},{"cell_type":"markdown","metadata":{},"source":["Since all classes have more than 500 images, the train_df dataset will be balanced and training time will be reduced¶\n"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:16.890308Z","iopub.status.busy":"2023-03-03T21:44:16.887946Z","iopub.status.idle":"2023-03-03T21:44:16.922423Z","shell.execute_reply":"2023-03-03T21:44:16.921480Z","shell.execute_reply.started":"2023-03-03T21:44:16.890264Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["dataframe initially is of length 8712 with 4 classes\n","after trimming, the maximum samples in any class is now 500 and the minimum samples in any class is 500\n","the trimmed dataframe now is of length 2000 with 4 classes\n"]}],"source":["def trim(df, max_samples, min_samples, column):\n"," df=df.copy()\n"," classes=df[column].unique()\n"," class_count=len(classes)\n"," length=len(df)\n"," print ('dataframe initially is of length ',length, ' with ', class_count, ' classes')\n"," groups=df.groupby(column) \n"," trimmed_df = pd.DataFrame(columns = df.columns)\n"," groups=df.groupby(column)\n"," for label in df[column].unique(): \n"," group=groups.get_group(label)\n"," count=len(group) \n"," if count > max_samples:\n"," sampled_group=group.sample(n=max_samples, random_state=123,axis=0)\n"," trimmed_df=pd.concat([trimmed_df, sampled_group], axis=0)\n"," else:\n"," if count>=min_samples:\n"," sampled_group=group \n"," trimmed_df=pd.concat([trimmed_df, sampled_group], axis=0)\n"," print('after trimming, the maximum samples in any class is now ',max_samples, ' and the minimum samples in any class is ', min_samples)\n"," classes=trimmed_df[column].unique()# return this in case some classes have less than min_samples\n"," class_count=len(classes) # return this in case some classes have less than min_samples\n"," length=len(trimmed_df)\n"," print ('the trimmed dataframe now is of length ',length, ' with ', class_count, ' classes')\n"," return trimmed_df, classes, class_count\n","\n","max_samples=500\n","min_samples=500\n","column='labels'\n","train_df, classes, class_count=trim(train_df, max_samples, min_samples, column)\n"]},{"cell_type":"markdown","metadata":{},"source":["Balancing data with data Augumentation "]},{"cell_type":"markdown","metadata":{},"source":["Since the trim function balanced the dataset and we have an ample number of image samples in each class this function is not used in this notebook¶\n"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:16.928808Z","iopub.status.busy":"2023-03-03T21:44:16.926555Z","iopub.status.idle":"2023-03-03T21:44:16.951778Z","shell.execute_reply":"2023-03-03T21:44:16.950774Z","shell.execute_reply.started":"2023-03-03T21:44:16.928770Z"},"trusted":true},"outputs":[],"source":["def balance(df, n, working_dir, img_size):\n"," df=df.copy()\n"," print('Initial length of dataframe is ', len(df))\n"," aug_dir=os.path.join(working_dir, 'aug')# directory to store augmented images\n"," if os.path.isdir(aug_dir):# start with an empty directory\n"," shutil.rmtree(aug_dir)\n"," os.mkdir(aug_dir) \n"," for label in df['labels'].unique(): \n"," dir_path=os.path.join(aug_dir,label) \n"," os.mkdir(dir_path) # make class directories within aug directory\n"," # create and store the augmented images \n"," total=0\n"," gen=ImageDataGenerator(horizontal_flip=True, rotation_range=20, width_shift_range=.2,\n"," height_shift_range=.2, zoom_range=.2)\n"," groups=df.groupby('labels') # group by class\n"," for label in df['labels'].unique(): # for every class \n"," group=groups.get_group(label) # a dataframe holding only rows with the specified label \n"," sample_count=len(group) # determine how many samples there are in this class \n"," if sample_count< n: # if the class has less than target number of images\n"," aug_img_count=0\n"," delta=n - sample_count # number of augmented images to create\n"," target_dir=os.path.join(aug_dir, label) # define where to write the images\n"," msg='{0:40s} for class {1:^30s} creating {2:^5s} augmented images'.format(' ', label, str(delta))\n"," print(msg, '\\r', end='') # prints over on the same line\n"," aug_gen=gen.flow_from_dataframe( group, x_col='filepaths', y_col=None, target_size=img_size,\n"," class_mode=None, batch_size=1, shuffle=False, \n"," save_to_dir=target_dir, save_prefix='aug-', color_mode='rgb',\n"," save_format='jpg')\n"," while aug_img_count"]},"metadata":{},"output_type":"display_data"}],"source":["def show_image_samples(gen ):\n"," t_dict=gen.class_indices\n"," classes=list(t_dict.keys()) \n"," images,labels=next(gen) # get a sample batch from the generator \n"," plt.figure(figsize=(25, 25))\n"," length=len(labels)\n"," if length<25: #show maximum of 25 images\n"," r=length\n"," else:\n"," r=25\n"," for i in range(r): \n"," plt.subplot(5, 5, i + 1)\n"," image=images[i] /255 \n"," plt.imshow(image)\n"," index=np.argmax(labels[i])\n"," class_name=classes[index]\n"," plt.title(class_name, color='blue', fontsize=18)\n"," plt.axis('off')\n"," plt.show()\n"," \n","show_image_samples(train_gen )\n"]},{"cell_type":"markdown","metadata":{},"source":["Function To calculate F1 Matrix"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:22.941478Z","iopub.status.busy":"2023-03-03T21:44:22.940870Z","iopub.status.idle":"2023-03-03T21:44:22.949607Z","shell.execute_reply":"2023-03-03T21:44:22.948417Z","shell.execute_reply.started":"2023-03-03T21:44:22.941434Z"},"trusted":true},"outputs":[],"source":["def F1_score(y_true, y_pred): #taken from old keras source code\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"," predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n"," precision = true_positives / (predicted_positives + K.epsilon())\n"," recall = true_positives / (possible_positives + K.epsilon())\n"," f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())\n"," return f1_val\n","\n"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:22.954966Z","iopub.status.busy":"2023-03-03T21:44:22.953821Z","iopub.status.idle":"2023-03-03T21:44:27.680838Z","shell.execute_reply":"2023-03-03T21:44:27.679854Z","shell.execute_reply.started":"2023-03-03T21:44:22.954931Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100mCreated EfficientNet B3 model with initial learning rate set to 0.001\n","\u001b[0m\n"]}],"source":["## Model Using Transfer Learnign Effecient net \n","def make_model(img_size, lr, mod_num=3): \n"," img_shape=(img_size[0], img_size[1], 3)\n"," if mod_num == 0:\n"," base_model=tf.keras.applications.efficientnet.EfficientNetB0(include_top=False, weights=\"imagenet\",input_shape=img_shape, pooling='max')\n"," msg='Created EfficientNet B0 model'\n"," elif mod_num == 3:\n"," base_model=tf.keras.applications.efficientnet.EfficientNetB3(include_top=False, weights=\"imagenet\",input_shape=img_shape, pooling='max') \n"," msg='Created EfficientNet B3 model'\n"," elif mod_num == 5:\n"," base_model=tf.keras.applications.efficientnet.EfficientNetB5(include_top=False, weights=\"imagenet\",input_shape=img_shape, pooling='max') \n"," msg='Created EfficientNet B5 model'\n"," \n"," else:\n"," base_model=tf.keras.applications.efficientnet.EfficientNetB7(include_top=False, weights=\"imagenet\",input_shape=img_shape, pooling='max')\n"," msg='Created EfficientNet B7 model' \n"," \n"," base_model.trainable=True\n"," x=base_model.output\n"," x=BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)\n"," x = Dense(256, kernel_regularizer = regularizers.l2(l = 0.016),activity_regularizer=regularizers.l1(0.006),\n"," bias_regularizer=regularizers.l1(0.006) ,activation='relu')(x)\n"," x=Dropout(rate=.4, seed=123)(x) \n"," output=Dense(class_count, activation='softmax')(x)\n"," model=Model(inputs=base_model.input, outputs=output)\n"," model.compile(Adamax(learning_rate=lr), loss='categorical_crossentropy', metrics=['accuracy', F1_score]) \n"," msg=msg + f' with initial learning rate set to {lr}'\n"," print_in_color(msg)\n"," return model\n","\n","lr=.001\n","model=make_model(img_size, lr) # using B3 model by default\n"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:27.683281Z","iopub.status.busy":"2023-03-03T21:44:27.682670Z","iopub.status.idle":"2023-03-03T21:44:27.707231Z","shell.execute_reply":"2023-03-03T21:44:27.706268Z","shell.execute_reply.started":"2023-03-03T21:44:27.683238Z"},"trusted":true},"outputs":[],"source":["class LR_ASK(keras.callbacks.Callback):\n"," def __init__ (self, model, epochs, ask_epoch, dwell=True, factor=.4): # initialization of the callback\n"," super(LR_ASK, self).__init__()\n"," self.model=model \n"," self.ask_epoch=ask_epoch\n"," self.epochs=epochs\n"," self.ask=True # if True query the user on a specified epoch\n"," self.lowest_vloss=np.inf\n"," self.lowest_aloss=np.inf\n"," self.best_weights=self.model.get_weights() # set best weights to model's initial weights\n"," self.best_epoch=1\n"," self.plist=[]\n"," self.alist=[]\n"," self.dwell= dwell\n"," self.factor=factor\n"," \n"," def get_list(self): # define a function to return the list of % validation change\n"," return self.plist, self.alist\n"," def on_train_begin(self, logs=None): # this runs on the beginning of training\n"," if self.ask_epoch == 0: \n"," print('you set ask_epoch = 0, ask_epoch will be set to 1', flush=True)\n"," self.ask_epoch=1\n"," if self.ask_epoch >= self.epochs: # you are running for epochs but ask_epoch>epochs\n"," print('ask_epoch >= epochs, will train for ', epochs, ' epochs', flush=True)\n"," self.ask=False # do not query the user\n"," if self.epochs == 1:\n"," self.ask=False # running only for 1 epoch so do not query user\n"," else:\n"," msg =f'Training will proceed until epoch {ask_epoch} then you will be asked to' \n"," print_in_color(msg )\n"," msg='enter H to halt training or enter an integer for how many more epochs to run then be asked again'\n"," print_in_color(msg)\n"," if self.dwell:\n"," msg='learning rate will be automatically adjusted during training'\n"," print_in_color(msg, (0,255,0))\n"," self.start_time= time.time() # set the time at which training started\n"," \n"," def on_train_end(self, logs=None): # runs at the end of training \n"," msg=f'loading model with weights from epoch {self.best_epoch}'\n"," print_in_color(msg, (0,255,255))\n"," self.model.set_weights(self.best_weights) # set the weights of the model to the best weights\n"," tr_duration=time.time() - self.start_time # determine how long the training cycle lasted \n"," hours = tr_duration // 3600\n"," minutes = (tr_duration - (hours * 3600)) // 60\n"," seconds = tr_duration - ((hours * 3600) + (minutes * 60))\n"," msg = f'training elapsed time was {str(hours)} hours, {minutes:4.1f} minutes, {seconds:4.2f} seconds)'\n"," print_in_color (msg) # print out training duration time\n"," \n"," def on_epoch_end(self, epoch, logs=None): # method runs on the end of each epoch\n"," vloss=logs.get('val_loss') # get the validation loss for this epoch\n"," aloss=logs.get('loss')\n"," if epoch >0:\n"," deltav = self.lowest_vloss- vloss \n"," pimprov=(deltav/self.lowest_vloss) * 100 \n"," self.plist.append(pimprov)\n"," deltaa=self.lowest_aloss-aloss\n"," aimprov=(deltaa/self.lowest_aloss) * 100\n"," self.alist.append(aimprov)\n"," else:\n"," pimprov=0.0 \n"," aimprov=0.0\n"," if vloss< self.lowest_vloss:\n"," self.lowest_vloss=vloss\n"," self.best_weights=self.model.get_weights() # set best weights to model's initial weights\n"," self.best_epoch=epoch + 1 \n"," msg=f'\\n validation loss of {vloss:7.4f} is {pimprov:7.4f} % below lowest loss, saving weights from epoch {str(epoch + 1):3s} as best weights'\n"," print_in_color(msg, (0,255,0)) # green foreground\n"," else: # validation loss increased\n"," pimprov=abs(pimprov)\n"," msg=f'\\n validation loss of {vloss:7.4f} is {pimprov:7.4f} % above lowest loss of {self.lowest_vloss:7.4f} keeping weights from epoch {str(self.best_epoch)} as best weights'\n"," print_in_color(msg, (255,255,0)) # yellow foreground\n"," if self.dwell: # if dwell is True when the validation loss increases the learning rate is automatically reduced and model weights are set to best weights\n"," lr=float(tf.keras.backend.get_value(self.model.optimizer.lr)) # get the current learning rate\n"," new_lr=lr * self.factor\n"," msg=f'learning rate was automatically adjusted from {lr:8.6f} to {new_lr:8.6f}, model weights set to best weights'\n"," print_in_color(msg) # cyan foreground\n"," tf.keras.backend.set_value(self.model.optimizer.lr, new_lr) # set the learning rate in the optimizer\n"," self.model.set_weights(self.best_weights) # set the weights of the model to the best weights \n"," \n"," if aloss< self.lowest_aloss:\n"," self.lowest_aloss=aloss \n"," if self.ask: # are the conditions right to query the user?\n"," if epoch + 1 ==self.ask_epoch: # is this epoch the one for quering the user?\n"," msg='press enter to continue or enter a comment below '\n"," print_in_color(msg)\n"," comment=input(' ')\n"," if comment !='':\n"," print_in_color(comment, (155,245,66))\n"," msg='\\n Enter H to end training or an integer for the number of additional epochs to run then ask again'\n"," print_in_color(msg) # cyan foreground\n"," ans=input()\n"," \n"," if ans == 'H' or ans =='h' or ans == '0': # quit training for these conditions\n"," msg=f'you entered {ans}, Training halted on epoch {epoch+1} due to user input\\n'\n"," print_in_color(msg)\n"," self.model.stop_training = True # halt training\n"," else: # user wants to continue training\n"," self.ask_epoch += int(ans)\n"," if self.ask_epoch > self.epochs:\n"," print('\\nYou specified maximum epochs of as ', self.epochs, ' cannot train for ', self.ask_epoch, flush =True)\n"," else:\n"," msg=f'you entered {ans} Training will continue to epoch {self.ask_epoch}'\n"," print_in_color(msg) # cyan foreground\n"," if self.dwell==False:\n"," lr=float(tf.keras.backend.get_value(self.model.optimizer.lr)) # get the current learning rate\n"," msg=f'current LR is {lr:8.6f} hit enter to keep this LR or enter a new LR'\n"," print_in_color(msg) # cyan foreground\n"," ans=input(' ')\n"," if ans =='':\n"," msg=f'keeping current LR of {lr:7.5f}'\n"," print_in_color(msg) # cyan foreground\n"," else:\n"," new_lr=float(ans)\n"," tf.keras.backend.set_value(self.model.optimizer.lr, new_lr) # set the learning rate in the optimizer\n"," msg=f' changing LR to {ans}'\n"," print_in_color(msg) # cyan foreground\n","\n"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:27.710875Z","iopub.status.busy":"2023-03-03T21:44:27.709909Z","iopub.status.idle":"2023-03-03T21:44:27.855083Z","shell.execute_reply":"2023-03-03T21:44:27.853998Z","shell.execute_reply.started":"2023-03-03T21:44:27.710845Z"},"trusted":true},"outputs":[],"source":["epochs=40\n","ask_epoch=5\n","ask=LR_ASK(model, epochs, ask_epoch)\n","callbacks=[ask]"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:27.858100Z","iopub.status.busy":"2023-03-03T21:44:27.857328Z","iopub.status.idle":"2023-03-03T22:01:15.044730Z","shell.execute_reply":"2023-03-03T22:01:15.043548Z","shell.execute_reply.started":"2023-03-03T21:44:27.858059Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100mTraining will proceed until epoch 5 then you will be asked to\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100menter H to halt training or enter an integer for how many more epochs to run then be asked again\n","\u001b[0m\n","\u001b[38;2;0;255;0;48;2;100;100;100mlearning rate will be automatically adjusted during training\n","\u001b[0m\n","Epoch 1/40\n","67/67 [==============================] - ETA: 0s - loss: 7.7724 - accuracy: 0.7780 - F1_score: 0.7746\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 7.2377 is 0.0000 % below lowest loss, saving weights from epoch 1 as best weights\n","\u001b[0m\n","67/67 [==============================] - 76s 875ms/step - loss: 7.7724 - accuracy: 0.7780 - F1_score: 0.7746 - val_loss: 7.2377 - val_accuracy: 0.7836 - val_F1_score: 0.7803\n","Epoch 2/40\n","67/67 [==============================] - ETA: 0s - loss: 5.9533 - accuracy: 0.9470 - F1_score: 0.9469\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 5.7819 is 20.1143 % below lowest loss, saving weights from epoch 2 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 675ms/step - loss: 5.9533 - accuracy: 0.9470 - F1_score: 0.9469 - val_loss: 5.7819 - val_accuracy: 0.7890 - val_F1_score: 0.7854\n","Epoch 3/40\n","67/67 [==============================] - ETA: 0s - loss: 4.7990 - accuracy: 0.9770 - F1_score: 0.9761\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 4.6071 is 20.3181 % below lowest loss, saving weights from epoch 3 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 676ms/step - loss: 4.7990 - accuracy: 0.9770 - F1_score: 0.9761 - val_loss: 4.6071 - val_accuracy: 0.9020 - val_F1_score: 0.9010\n","Epoch 4/40\n","67/67 [==============================] - ETA: 0s - loss: 3.9851 - accuracy: 0.9840 - F1_score: 0.9825\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 3.7455 is 18.7008 % below lowest loss, saving weights from epoch 4 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 678ms/step - loss: 3.9851 - accuracy: 0.9840 - F1_score: 0.9825 - val_loss: 3.7455 - val_accuracy: 0.9550 - val_F1_score: 0.9518\n","Epoch 5/40\n","67/67 [==============================] - ETA: 0s - loss: 3.3343 - accuracy: 0.9905 - F1_score: 0.9899\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 3.1946 is 14.7086 % below lowest loss, saving weights from epoch 5 as best weights\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100mpress enter to continue or enter a comment below \n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" 5\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;155;245;66;48;2;100;100;100m5\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100m\n"," Enter H to end training or an integer for the number of additional epochs to run then ask again\n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" 5\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100myou entered 5 Training will continue to epoch 10\n","\u001b[0m\n","67/67 [==============================] - 100s 2s/step - loss: 3.3343 - accuracy: 0.9905 - F1_score: 0.9899 - val_loss: 3.1946 - val_accuracy: 0.9566 - val_F1_score: 0.9503\n","Epoch 6/40\n","67/67 [==============================] - ETA: 0s - loss: 2.8076 - accuracy: 0.9935 - F1_score: 0.9933\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 2.6461 is 17.1704 % below lowest loss, saving weights from epoch 6 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 674ms/step - loss: 2.8076 - accuracy: 0.9935 - F1_score: 0.9933 - val_loss: 2.6461 - val_accuracy: 0.9711 - val_F1_score: 0.9701\n","Epoch 7/40\n","67/67 [==============================] - ETA: 0s - loss: 2.3669 - accuracy: 0.9955 - F1_score: 0.9954\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 2.1949 is 17.0503 % below lowest loss, saving weights from epoch 7 as best weights\n","\u001b[0m\n","67/67 [==============================] - 46s 684ms/step - loss: 2.3669 - accuracy: 0.9955 - F1_score: 0.9954 - val_loss: 2.1949 - val_accuracy: 0.9791 - val_F1_score: 0.9788\n","Epoch 8/40\n","67/67 [==============================] - ETA: 0s - loss: 1.9908 - accuracy: 0.9970 - F1_score: 0.9952\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 1.8379 is 16.2663 % below lowest loss, saving weights from epoch 8 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 668ms/step - loss: 1.9908 - accuracy: 0.9970 - F1_score: 0.9952 - val_loss: 1.8379 - val_accuracy: 0.9850 - val_F1_score: 0.9826\n","Epoch 9/40\n","67/67 [==============================] - ETA: 0s - loss: 1.6690 - accuracy: 0.9990 - F1_score: 0.9987\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 1.5169 is 17.4672 % below lowest loss, saving weights from epoch 9 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 676ms/step - loss: 1.6690 - accuracy: 0.9990 - F1_score: 0.9987 - val_loss: 1.5169 - val_accuracy: 0.9909 - val_F1_score: 0.9914\n","Epoch 10/40\n","67/67 [==============================] - ETA: 0s - loss: 1.4070 - accuracy: 0.9945 - F1_score: 0.9942\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 1.3045 is 14.0009 % below lowest loss, saving weights from epoch 10 as best weights\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100mpress enter to continue or enter a comment below \n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" 5\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;155;245;66;48;2;100;100;100m5\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100m\n"," Enter H to end training or an integer for the number of additional epochs to run then ask again\n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" 5\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100myou entered 5 Training will continue to epoch 15\n","\u001b[0m\n","67/67 [==============================] - 115s 2s/step - loss: 1.4070 - accuracy: 0.9945 - F1_score: 0.9942 - val_loss: 1.3045 - val_accuracy: 0.9888 - val_F1_score: 0.9874\n","Epoch 11/40\n","67/67 [==============================] - ETA: 0s - loss: 1.1923 - accuracy: 0.9965 - F1_score: 0.9967\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 1.0756 is 17.5438 % below lowest loss, saving weights from epoch 11 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 674ms/step - loss: 1.1923 - accuracy: 0.9965 - F1_score: 0.9967 - val_loss: 1.0756 - val_accuracy: 0.9936 - val_F1_score: 0.9942\n","Epoch 12/40\n","67/67 [==============================] - ETA: 0s - loss: 1.0007 - accuracy: 0.9990 - F1_score: 0.9982\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 0.8963 is 16.6732 % below lowest loss, saving weights from epoch 12 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 670ms/step - loss: 1.0007 - accuracy: 0.9990 - F1_score: 0.9982 - val_loss: 0.8963 - val_accuracy: 0.9963 - val_F1_score: 0.9955\n","Epoch 13/40\n","67/67 [==============================] - ETA: 0s - loss: 0.8447 - accuracy: 0.9990 - F1_score: 0.9990\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 0.7596 is 15.2530 % below lowest loss, saving weights from epoch 13 as best weights\n","\u001b[0m\n","67/67 [==============================] - 46s 680ms/step - loss: 0.8447 - accuracy: 0.9990 - F1_score: 0.9990 - val_loss: 0.7596 - val_accuracy: 0.9952 - val_F1_score: 0.9958\n","Epoch 14/40\n","67/67 [==============================] - ETA: 0s - loss: 0.7227 - accuracy: 0.9975 - F1_score: 0.9960\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 0.6397 is 15.7778 % below lowest loss, saving weights from epoch 14 as best weights\n","\u001b[0m\n","67/67 [==============================] - 45s 673ms/step - loss: 0.7227 - accuracy: 0.9975 - F1_score: 0.9960 - val_loss: 0.6397 - val_accuracy: 0.9979 - val_F1_score: 0.9976\n","Epoch 15/40\n","67/67 [==============================] - ETA: 0s - loss: 0.6101 - accuracy: 1.0000 - F1_score: 0.9995\u001b[38;2;0;255;0;48;2;100;100;100m\n"," validation loss of 0.5478 is 14.3690 % below lowest loss, saving weights from epoch 15 as best weights\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100mpress enter to continue or enter a comment below \n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" H\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;155;245;66;48;2;100;100;100mH\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100m\n"," Enter H to end training or an integer for the number of additional epochs to run then ask again\n","\u001b[0m\n"]},{"name":"stdout","output_type":"stream","text":[" H\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[38;2;0;255;255;48;2;100;100;100myou entered H, Training halted on epoch 15 due to user input\n","\n","\u001b[0m\n","67/67 [==============================] - 181s 3s/step - loss: 0.6101 - accuracy: 1.0000 - F1_score: 0.9995 - val_loss: 0.5478 - val_accuracy: 0.9957 - val_F1_score: 0.9965\n","\u001b[38;2;0;255;255;48;2;100;100;100mloading model with weights from epoch 15\n","\u001b[0m\n","\u001b[38;2;0;255;255;48;2;100;100;100mtraining elapsed time was 0.0 hours, 16.0 minutes, 46.89 seconds)\n","\u001b[0m\n"]}],"source":["history=model.fit(x=train_gen, epochs=epochs, verbose=1, callbacks=callbacks, validation_data=valid_gen,\n"," shuffle=False, initial_epoch=0)"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:02:16.406867Z","iopub.status.busy":"2023-03-03T22:02:16.406363Z","iopub.status.idle":"2023-03-03T22:02:17.236850Z","shell.execute_reply":"2023-03-03T22:02:17.235848Z","shell.execute_reply.started":"2023-03-03T22:02:16.406829Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["def tr_plot(tr_data, start_epoch):\n"," #Plot the training and validation data\n"," tacc=tr_data.history['accuracy']\n"," tloss=tr_data.history['loss']\n"," vacc=tr_data.history['val_accuracy']\n"," vloss=tr_data.history['val_loss']\n"," tf1=tr_data.history['F1_score']\n"," vf1=tr_data.history['val_F1_score']\n"," Epoch_count=len(tacc)+ start_epoch\n"," Epochs=[]\n"," for i in range (start_epoch ,Epoch_count):\n"," Epochs.append(i+1) \n"," index_loss=np.argmin(vloss)# this is the epoch with the lowest validation loss\n"," val_lowest=vloss[index_loss]\n"," index_acc=np.argmax(vacc)\n"," acc_highest=vacc[index_acc]\n"," plt.style.use('fivethirtyeight')\n"," sc_label='best epoch= '+ str(index_loss+1 +start_epoch)\n"," vc_label='best epoch= '+ str(index_acc + 1+ start_epoch)\n"," fig,axes=plt.subplots(nrows=1, ncols=3, figsize=(25,10))\n"," axes[0].plot(Epochs,tloss, 'r', label='Training loss')\n"," axes[0].plot(Epochs,vloss,'g',label='Validation loss' )\n"," axes[0].scatter(index_loss+1 +start_epoch,val_lowest, s=150, c= 'blue', label=sc_label)\n"," axes[0].scatter(Epochs, tloss, s=100, c='red') \n"," axes[0].set_title('Training and Validation Loss')\n"," axes[0].set_xlabel('Epochs', fontsize=18)\n"," axes[0].set_ylabel('Loss', fontsize=18)\n"," axes[0].legend()\n"," axes[1].plot (Epochs,tacc,'r',label= 'Training Accuracy')\n"," axes[1].scatter(Epochs, tacc, s=100, c='red')\n"," axes[1].plot (Epochs,vacc,'g',label= 'Validation Accuracy')\n"," axes[1].scatter(index_acc+1 +start_epoch,acc_highest, s=150, c= 'blue', label=vc_label)\n"," axes[1].set_title('Training and Validation Accuracy')\n"," axes[1].set_xlabel('Epochs', fontsize=18)\n"," axes[1].set_ylabel('Accuracy', fontsize=18)\n"," axes[1].legend()\n"," axes[2].plot (Epochs,tf1,'r',label= 'Training F1 score') \n"," axes[2].plot (Epochs,vf1,'g',label= 'Validation F1 score')\n"," index_tf1=np.argmax(tf1)# this is the epoch with the highest training F1 score\n"," tf1max=tf1[index_tf1]\n"," index_vf1=np.argmax(vf1)# thisiis the epoch with the highest validation F1 score\n"," vf1max=vf1[index_vf1]\n"," #axes[2].scatter(index_vf1+1 +start_epoch,vf1max, s=150, c= 'blue', label=vc_label)\n"," #axes[2].scatter(Epochs, tf1max, s=100, c='red')\n"," axes[2].set_title('Training and Validation F1 score')\n"," axes[2].set_xlabel('Epochs', fontsize=18)\n"," axes[2].set_ylabel('F1 score', fontsize=18)\n"," axes[2].legend()\n"," \n"," plt.tight_layout \n"," plt.show()\n"," return index_loss\n"," \n","loss_index=tr_plot(history,0)"]},{"cell_type":"markdown","metadata":{},"source":["Make Predictions on the test set"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:01:36.872568Z","iopub.status.busy":"2023-03-03T22:01:36.871554Z","iopub.status.idle":"2023-03-03T22:01:36.886004Z","shell.execute_reply":"2023-03-03T22:01:36.884711Z","shell.execute_reply.started":"2023-03-03T22:01:36.872528Z"},"trusted":true},"outputs":[],"source":["def predictor(test_gen): \n"," y_pred= []\n"," error_list=[]\n"," error_pred_list = []\n"," y_true=test_gen.labels\n"," classes=list(test_gen.class_indices.keys())\n"," class_count=len(classes)\n"," errors=0\n"," preds=model.predict(test_gen, verbose=1)\n"," tests=len(preds) \n"," for i, p in enumerate(preds): \n"," pred_index=np.argmax(p) \n"," true_index=test_gen.labels[i] # labels are integer values \n"," if pred_index != true_index: # a misclassification has occurred \n"," errors=errors + 1\n"," file=test_gen.filenames[i]\n"," error_list.append(file)\n"," error_class=classes[pred_index]\n"," error_pred_list.append(error_class)\n"," y_pred.append(pred_index)\n"," \n"," acc=( 1-errors/tests) * 100\n"," msg=f'there were {errors} errors in {tests} tests for an accuracy of {acc:6.2f}'\n"," print_in_color(msg, (0,255,255), (100,100,100)) # cyan foreground\n"," ypred=np.array(y_pred)\n"," ytrue=np.array(y_true)\n"," f1score=f1_score(ytrue, ypred, average='weighted')* 100\n"," if class_count <=30:\n"," cm = confusion_matrix(ytrue, ypred )\n"," # plot the confusion matrix\n"," plt.figure(figsize=(10, 8))\n"," sns.heatmap(cm, annot=True, vmin=0, fmt='g', cmap='Blues', cbar=False) \n"," plt.xticks(np.arange(class_count)+.5, classes, rotation=90)\n"," plt.yticks(np.arange(class_count)+.5, classes, rotation=0)\n"," plt.xlabel(\"Predicted\")\n"," plt.ylabel(\"Actual\")\n"," plt.title(\"Confusion Matrix\")\n"," plt.show()\n"," clr = classification_report(y_true, y_pred, target_names=classes, digits= 4) # create classification report\n"," print(\"Classification Report:\\n----------------------\\n\", clr)\n"," return errors, tests, error_list, error_pred_list, f1score\n","\n"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:01:36.888528Z","iopub.status.busy":"2023-03-03T22:01:36.887926Z","iopub.status.idle":"2023-03-03T22:02:16.372068Z","shell.execute_reply":"2023-03-03T22:02:16.371055Z","shell.execute_reply.started":"2023-03-03T22:01:36.888485Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1867/1867 [==============================] - 39s 20ms/step\n","\u001b[38;2;0;255;255;48;2;100;100;100mthere were 10 errors in 1867 tests for an accuracy of 99.46\n","\u001b[0m\n"]},{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Classification Report:\n","----------------------\n"," precision recall f1-score support\n","\n"," Cyst 0.9946 0.9928 0.9937 556\n"," Normal 0.9987 0.9948 0.9967 762\n"," Stone 0.9810 0.9952 0.9880 207\n"," Tumor 0.9942 0.9971 0.9956 342\n","\n"," accuracy 0.9946 1867\n"," macro avg 0.9921 0.9950 0.9935 1867\n","weighted avg 0.9947 0.9946 0.9947 1867\n","\n"]}],"source":["errors, tests, error_list, error_pred_list, f1score =predictor(test_gen)"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:02:48.771684Z","iopub.status.busy":"2023-03-03T22:02:48.770695Z","iopub.status.idle":"2023-03-03T22:02:48.779047Z","shell.execute_reply":"2023-03-03T22:02:48.777695Z","shell.execute_reply.started":"2023-03-03T22:02:48.771631Z"},"trusted":true},"outputs":[],"source":["# Image Mis Predicted are \n","def print_errors(error_list):\n"," if len(error_list) == 0:\n"," print_in_color('There were no errors in predicting the test set')\n"," else:\n"," if len(error_list)<50:\n"," print ('Below is a list of test files that were miss classified \\n')\n"," print ('{0:^30s}{1:^30s}'.format('Test File', ' Predicted as')) \n"," for i in range(len(error_list)):\n"," fpath=error_list[i] \n"," split=fpath.split('/') \n"," f=split[4]+ '-' + split[5]\n"," print(f'{f:^30s}{error_pred_list[i]:^30s}')"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:02:51.574546Z","iopub.status.busy":"2023-03-03T22:02:51.573498Z","iopub.status.idle":"2023-03-03T22:02:51.580093Z","shell.execute_reply":"2023-03-03T22:02:51.579012Z","shell.execute_reply.started":"2023-03-03T22:02:51.574506Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Below is a list of test files that were miss classified \n","\n"," Test File Predicted as \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Cyst Stone \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Cyst Stone \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Cyst Stone \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Tumor Normal \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Normal Tumor \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Stone Cyst \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Cyst Stone \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Normal Cyst \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Normal Cyst \n","CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone-Normal Tumor \n"]}],"source":["print_errors(error_list)\n"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:14:57.185517Z","iopub.status.busy":"2023-03-03T22:14:57.184860Z","iopub.status.idle":"2023-03-03T22:14:57.190780Z","shell.execute_reply":"2023-03-03T22:14:57.189633Z","shell.execute_reply.started":"2023-03-03T22:14:57.185476Z"},"trusted":true},"outputs":[],"source":["from tensorflow.keras.models import load_model\n"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T22:27:07.629750Z","iopub.status.busy":"2023-03-03T22:27:07.629305Z","iopub.status.idle":"2023-03-03T22:27:08.766873Z","shell.execute_reply":"2023-03-03T22:27:08.765763Z","shell.execute_reply.started":"2023-03-03T22:27:07.629711Z"},"trusted":true},"outputs":[],"source":["model.save('Final.h5')\n","## F1_score"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T23:16:26.486657Z","iopub.status.busy":"2023-03-03T23:16:26.485535Z","iopub.status.idle":"2023-03-03T23:16:30.725152Z","shell.execute_reply":"2023-03-03T23:16:30.724015Z","shell.execute_reply.started":"2023-03-03T23:16:26.486618Z"},"trusted":true},"outputs":[],"source":["model=load_model(\"/kaggle/working/Final.h5\",custom_objects={\"F1_score\":f1_score})\n"]},{"cell_type":"code","execution_count":54,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T23:17:01.413011Z","iopub.status.busy":"2023-03-03T23:17:01.412077Z","iopub.status.idle":"2023-03-03T23:17:02.474169Z","shell.execute_reply":"2023-03-03T23:17:02.473291Z","shell.execute_reply.started":"2023-03-03T23:17:01.412971Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type) Output Shape Param # Connected to \n","==================================================================================================\n"," input_1 (InputLayer) [(None, 224, 250, 3 0 [] \n"," )] \n"," \n"," rescaling (Rescaling) (None, 224, 250, 3) 0 ['input_1[0][0]'] \n"," \n"," normalization (Normalization) (None, 224, 250, 3) 7 ['rescaling[0][0]'] \n"," \n"," tf.math.truediv (TFOpLambda) (None, 224, 250, 3) 0 ['normalization[0][0]'] \n"," \n"," stem_conv_pad (ZeroPadding2D) (None, 225, 251, 3) 0 ['tf.math.truediv[0][0]'] \n"," \n"," stem_conv (Conv2D) (None, 112, 125, 40 1080 ['stem_conv_pad[0][0]'] \n"," ) \n"," \n"," stem_bn (BatchNormalization) (None, 112, 125, 40 160 ['stem_conv[0][0]'] \n"," ) \n"," \n"," stem_activation (Activation) (None, 112, 125, 40 0 ['stem_bn[0][0]'] \n"," ) \n"," \n"," block1a_dwconv (DepthwiseConv2 (None, 112, 125, 40 360 ['stem_activation[0][0]'] \n"," D) ) \n"," \n"," block1a_bn (BatchNormalization (None, 112, 125, 40 160 ['block1a_dwconv[0][0]'] \n"," ) ) \n"," \n"," block1a_activation (Activation (None, 112, 125, 40 0 ['block1a_bn[0][0]'] \n"," ) ) \n"," \n"," block1a_se_squeeze (GlobalAver (None, 40) 0 ['block1a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block1a_se_reshape (Reshape) (None, 1, 1, 40) 0 ['block1a_se_squeeze[0][0]'] \n"," \n"," block1a_se_reduce (Conv2D) (None, 1, 1, 10) 410 ['block1a_se_reshape[0][0]'] \n"," \n"," block1a_se_expand (Conv2D) (None, 1, 1, 40) 440 ['block1a_se_reduce[0][0]'] \n"," \n"," block1a_se_excite (Multiply) (None, 112, 125, 40 0 ['block1a_activation[0][0]', \n"," ) 'block1a_se_expand[0][0]'] \n"," \n"," block1a_project_conv (Conv2D) (None, 112, 125, 24 960 ['block1a_se_excite[0][0]'] \n"," ) \n"," \n"," block1a_project_bn (BatchNorma (None, 112, 125, 24 96 ['block1a_project_conv[0][0]'] \n"," lization) ) \n"," \n"," block1b_dwconv (DepthwiseConv2 (None, 112, 125, 24 216 ['block1a_project_bn[0][0]'] \n"," D) ) \n"," \n"," block1b_bn (BatchNormalization (None, 112, 125, 24 96 ['block1b_dwconv[0][0]'] \n"," ) ) \n"," \n"," block1b_activation (Activation (None, 112, 125, 24 0 ['block1b_bn[0][0]'] \n"," ) ) \n"," \n"," block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] \n"," \n"," block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] \n"," \n"," block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] \n"," \n"," block1b_se_excite (Multiply) (None, 112, 125, 24 0 ['block1b_activation[0][0]', \n"," ) 'block1b_se_expand[0][0]'] \n"," \n"," block1b_project_conv (Conv2D) (None, 112, 125, 24 576 ['block1b_se_excite[0][0]'] \n"," ) \n"," \n"," block1b_project_bn (BatchNorma (None, 112, 125, 24 96 ['block1b_project_conv[0][0]'] \n"," lization) ) \n"," \n"," block1b_drop (Dropout) (None, 112, 125, 24 0 ['block1b_project_bn[0][0]'] \n"," ) \n"," \n"," block1b_add (Add) (None, 112, 125, 24 0 ['block1b_drop[0][0]', \n"," ) 'block1a_project_bn[0][0]'] \n"," \n"," block2a_expand_conv (Conv2D) (None, 112, 125, 14 3456 ['block1b_add[0][0]'] \n"," 4) \n"," \n"," block2a_expand_bn (BatchNormal (None, 112, 125, 14 576 ['block2a_expand_conv[0][0]'] \n"," ization) 4) \n"," \n"," block2a_expand_activation (Act (None, 112, 125, 14 0 ['block2a_expand_bn[0][0]'] \n"," ivation) 4) \n"," \n"," block2a_dwconv_pad (ZeroPaddin (None, 113, 127, 14 0 ['block2a_expand_activation[0][0]\n"," g2D) 4) '] \n"," \n"," block2a_dwconv (DepthwiseConv2 (None, 56, 63, 144) 1296 ['block2a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block2a_bn (BatchNormalization (None, 56, 63, 144) 576 ['block2a_dwconv[0][0]'] \n"," ) \n"," \n"," block2a_activation (Activation (None, 56, 63, 144) 0 ['block2a_bn[0][0]'] \n"," ) \n"," \n"," block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] \n"," \n"," block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] \n"," \n"," block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] \n"," \n"," block2a_se_excite (Multiply) (None, 56, 63, 144) 0 ['block2a_activation[0][0]', \n"," 'block2a_se_expand[0][0]'] \n"," \n"," block2a_project_conv (Conv2D) (None, 56, 63, 32) 4608 ['block2a_se_excite[0][0]'] \n"," \n"," block2a_project_bn (BatchNorma (None, 56, 63, 32) 128 ['block2a_project_conv[0][0]'] \n"," lization) \n"," \n"," block2b_expand_conv (Conv2D) (None, 56, 63, 192) 6144 ['block2a_project_bn[0][0]'] \n"," \n"," block2b_expand_bn (BatchNormal (None, 56, 63, 192) 768 ['block2b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block2b_expand_activation (Act (None, 56, 63, 192) 0 ['block2b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block2b_dwconv (DepthwiseConv2 (None, 56, 63, 192) 1728 ['block2b_expand_activation[0][0]\n"," D) '] \n"," \n"," block2b_bn (BatchNormalization (None, 56, 63, 192) 768 ['block2b_dwconv[0][0]'] \n"," ) \n"," \n"," block2b_activation (Activation (None, 56, 63, 192) 0 ['block2b_bn[0][0]'] \n"," ) \n"," \n"," block2b_se_squeeze (GlobalAver (None, 192) 0 ['block2b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2b_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2b_se_squeeze[0][0]'] \n"," \n"," block2b_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2b_se_reshape[0][0]'] \n"," \n"," block2b_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2b_se_reduce[0][0]'] \n"," \n"," block2b_se_excite (Multiply) (None, 56, 63, 192) 0 ['block2b_activation[0][0]', \n"," 'block2b_se_expand[0][0]'] \n"," \n"," block2b_project_conv (Conv2D) (None, 56, 63, 32) 6144 ['block2b_se_excite[0][0]'] \n"," \n"," block2b_project_bn (BatchNorma (None, 56, 63, 32) 128 ['block2b_project_conv[0][0]'] \n"," lization) \n"," \n"," block2b_drop (Dropout) (None, 56, 63, 32) 0 ['block2b_project_bn[0][0]'] \n"," \n"," block2b_add (Add) (None, 56, 63, 32) 0 ['block2b_drop[0][0]', \n"," 'block2a_project_bn[0][0]'] \n"," \n"," block2c_expand_conv (Conv2D) (None, 56, 63, 192) 6144 ['block2b_add[0][0]'] \n"," \n"," block2c_expand_bn (BatchNormal (None, 56, 63, 192) 768 ['block2c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block2c_expand_activation (Act (None, 56, 63, 192) 0 ['block2c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block2c_dwconv (DepthwiseConv2 (None, 56, 63, 192) 1728 ['block2c_expand_activation[0][0]\n"," D) '] \n"," \n"," block2c_bn (BatchNormalization (None, 56, 63, 192) 768 ['block2c_dwconv[0][0]'] \n"," ) \n"," \n"," block2c_activation (Activation (None, 56, 63, 192) 0 ['block2c_bn[0][0]'] \n"," ) \n"," \n"," block2c_se_squeeze (GlobalAver (None, 192) 0 ['block2c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block2c_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2c_se_squeeze[0][0]'] \n"," \n"," block2c_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2c_se_reshape[0][0]'] \n"," \n"," block2c_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2c_se_reduce[0][0]'] \n"," \n"," block2c_se_excite (Multiply) (None, 56, 63, 192) 0 ['block2c_activation[0][0]', \n"," 'block2c_se_expand[0][0]'] \n"," \n"," block2c_project_conv (Conv2D) (None, 56, 63, 32) 6144 ['block2c_se_excite[0][0]'] \n"," \n"," block2c_project_bn (BatchNorma (None, 56, 63, 32) 128 ['block2c_project_conv[0][0]'] \n"," lization) \n"," \n"," block2c_drop (Dropout) (None, 56, 63, 32) 0 ['block2c_project_bn[0][0]'] \n"," \n"," block2c_add (Add) (None, 56, 63, 32) 0 ['block2c_drop[0][0]', \n"," 'block2b_add[0][0]'] \n"," \n"," block3a_expand_conv (Conv2D) (None, 56, 63, 192) 6144 ['block2c_add[0][0]'] \n"," \n"," block3a_expand_bn (BatchNormal (None, 56, 63, 192) 768 ['block3a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3a_expand_activation (Act (None, 56, 63, 192) 0 ['block3a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3a_dwconv_pad (ZeroPaddin (None, 59, 67, 192) 0 ['block3a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block3a_dwconv (DepthwiseConv2 (None, 28, 32, 192) 4800 ['block3a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block3a_bn (BatchNormalization (None, 28, 32, 192) 768 ['block3a_dwconv[0][0]'] \n"," ) \n"," \n"," block3a_activation (Activation (None, 28, 32, 192) 0 ['block3a_bn[0][0]'] \n"," ) \n"," \n"," block3a_se_squeeze (GlobalAver (None, 192) 0 ['block3a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block3a_se_squeeze[0][0]'] \n"," \n"," block3a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block3a_se_reshape[0][0]'] \n"," \n"," block3a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block3a_se_reduce[0][0]'] \n"," \n"," block3a_se_excite (Multiply) (None, 28, 32, 192) 0 ['block3a_activation[0][0]', \n"," 'block3a_se_expand[0][0]'] \n"," \n"," block3a_project_conv (Conv2D) (None, 28, 32, 48) 9216 ['block3a_se_excite[0][0]'] \n"," \n"," block3a_project_bn (BatchNorma (None, 28, 32, 48) 192 ['block3a_project_conv[0][0]'] \n"," lization) \n"," \n"," block3b_expand_conv (Conv2D) (None, 28, 32, 288) 13824 ['block3a_project_bn[0][0]'] \n"," \n"," block3b_expand_bn (BatchNormal (None, 28, 32, 288) 1152 ['block3b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3b_expand_activation (Act (None, 28, 32, 288) 0 ['block3b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3b_dwconv (DepthwiseConv2 (None, 28, 32, 288) 7200 ['block3b_expand_activation[0][0]\n"," D) '] \n"," \n"," block3b_bn (BatchNormalization (None, 28, 32, 288) 1152 ['block3b_dwconv[0][0]'] \n"," ) \n"," \n"," block3b_activation (Activation (None, 28, 32, 288) 0 ['block3b_bn[0][0]'] \n"," ) \n"," \n"," block3b_se_squeeze (GlobalAver (None, 288) 0 ['block3b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3b_se_squeeze[0][0]'] \n"," \n"," block3b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3b_se_reshape[0][0]'] \n"," \n"," block3b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3b_se_reduce[0][0]'] \n"," \n"," block3b_se_excite (Multiply) (None, 28, 32, 288) 0 ['block3b_activation[0][0]', \n"," 'block3b_se_expand[0][0]'] \n"," \n"," block3b_project_conv (Conv2D) (None, 28, 32, 48) 13824 ['block3b_se_excite[0][0]'] \n"," \n"," block3b_project_bn (BatchNorma (None, 28, 32, 48) 192 ['block3b_project_conv[0][0]'] \n"," lization) \n"," \n"," block3b_drop (Dropout) (None, 28, 32, 48) 0 ['block3b_project_bn[0][0]'] \n"," \n"," block3b_add (Add) (None, 28, 32, 48) 0 ['block3b_drop[0][0]', \n"," 'block3a_project_bn[0][0]'] \n"," \n"," block3c_expand_conv (Conv2D) (None, 28, 32, 288) 13824 ['block3b_add[0][0]'] \n"," \n"," block3c_expand_bn (BatchNormal (None, 28, 32, 288) 1152 ['block3c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block3c_expand_activation (Act (None, 28, 32, 288) 0 ['block3c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block3c_dwconv (DepthwiseConv2 (None, 28, 32, 288) 7200 ['block3c_expand_activation[0][0]\n"," D) '] \n"," \n"," block3c_bn (BatchNormalization (None, 28, 32, 288) 1152 ['block3c_dwconv[0][0]'] \n"," ) \n"," \n"," block3c_activation (Activation (None, 28, 32, 288) 0 ['block3c_bn[0][0]'] \n"," ) \n"," \n"," block3c_se_squeeze (GlobalAver (None, 288) 0 ['block3c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block3c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3c_se_squeeze[0][0]'] \n"," \n"," block3c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3c_se_reshape[0][0]'] \n"," \n"," block3c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3c_se_reduce[0][0]'] \n"," \n"," block3c_se_excite (Multiply) (None, 28, 32, 288) 0 ['block3c_activation[0][0]', \n"," 'block3c_se_expand[0][0]'] \n"," \n"," block3c_project_conv (Conv2D) (None, 28, 32, 48) 13824 ['block3c_se_excite[0][0]'] \n"," \n"," block3c_project_bn (BatchNorma (None, 28, 32, 48) 192 ['block3c_project_conv[0][0]'] \n"," lization) \n"," \n"," block3c_drop (Dropout) (None, 28, 32, 48) 0 ['block3c_project_bn[0][0]'] \n"," \n"," block3c_add (Add) (None, 28, 32, 48) 0 ['block3c_drop[0][0]', \n"," 'block3b_add[0][0]'] \n"," \n"," block4a_expand_conv (Conv2D) (None, 28, 32, 288) 13824 ['block3c_add[0][0]'] \n"," \n"," block4a_expand_bn (BatchNormal (None, 28, 32, 288) 1152 ['block4a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4a_expand_activation (Act (None, 28, 32, 288) 0 ['block4a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4a_dwconv_pad (ZeroPaddin (None, 29, 33, 288) 0 ['block4a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block4a_dwconv (DepthwiseConv2 (None, 14, 16, 288) 2592 ['block4a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block4a_bn (BatchNormalization (None, 14, 16, 288) 1152 ['block4a_dwconv[0][0]'] \n"," ) \n"," \n"," block4a_activation (Activation (None, 14, 16, 288) 0 ['block4a_bn[0][0]'] \n"," ) \n"," \n"," block4a_se_squeeze (GlobalAver (None, 288) 0 ['block4a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block4a_se_squeeze[0][0]'] \n"," \n"," block4a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block4a_se_reshape[0][0]'] \n"," \n"," block4a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block4a_se_reduce[0][0]'] \n"," \n"," block4a_se_excite (Multiply) (None, 14, 16, 288) 0 ['block4a_activation[0][0]', \n"," 'block4a_se_expand[0][0]'] \n"," \n"," block4a_project_conv (Conv2D) (None, 14, 16, 96) 27648 ['block4a_se_excite[0][0]'] \n"," \n"," block4a_project_bn (BatchNorma (None, 14, 16, 96) 384 ['block4a_project_conv[0][0]'] \n"," lization) \n"," \n"," block4b_expand_conv (Conv2D) (None, 14, 16, 576) 55296 ['block4a_project_bn[0][0]'] \n"," \n"," block4b_expand_bn (BatchNormal (None, 14, 16, 576) 2304 ['block4b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4b_expand_activation (Act (None, 14, 16, 576) 0 ['block4b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4b_dwconv (DepthwiseConv2 (None, 14, 16, 576) 5184 ['block4b_expand_activation[0][0]\n"," D) '] \n"," \n"," block4b_bn (BatchNormalization (None, 14, 16, 576) 2304 ['block4b_dwconv[0][0]'] \n"," ) \n"," \n"," block4b_activation (Activation (None, 14, 16, 576) 0 ['block4b_bn[0][0]'] \n"," ) \n"," \n"," block4b_se_squeeze (GlobalAver (None, 576) 0 ['block4b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4b_se_reshape (Reshape) (None, 1, 1, 576) 0 ['block4b_se_squeeze[0][0]'] \n"," \n"," block4b_se_reduce (Conv2D) (None, 1, 1, 24) 13848 ['block4b_se_reshape[0][0]'] \n"," \n"," block4b_se_expand (Conv2D) (None, 1, 1, 576) 14400 ['block4b_se_reduce[0][0]'] \n"," \n"," block4b_se_excite (Multiply) (None, 14, 16, 576) 0 ['block4b_activation[0][0]', \n"," 'block4b_se_expand[0][0]'] \n"," \n"," block4b_project_conv (Conv2D) (None, 14, 16, 96) 55296 ['block4b_se_excite[0][0]'] \n"," \n"," block4b_project_bn (BatchNorma (None, 14, 16, 96) 384 ['block4b_project_conv[0][0]'] \n"," lization) \n"," \n"," block4b_drop (Dropout) (None, 14, 16, 96) 0 ['block4b_project_bn[0][0]'] \n"," \n"," block4b_add (Add) (None, 14, 16, 96) 0 ['block4b_drop[0][0]', \n"," 'block4a_project_bn[0][0]'] \n"," \n"," block4c_expand_conv (Conv2D) (None, 14, 16, 576) 55296 ['block4b_add[0][0]'] \n"," \n"," block4c_expand_bn (BatchNormal (None, 14, 16, 576) 2304 ['block4c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4c_expand_activation (Act (None, 14, 16, 576) 0 ['block4c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4c_dwconv (DepthwiseConv2 (None, 14, 16, 576) 5184 ['block4c_expand_activation[0][0]\n"," D) '] \n"," \n"," block4c_bn (BatchNormalization (None, 14, 16, 576) 2304 ['block4c_dwconv[0][0]'] \n"," ) \n"," \n"," block4c_activation (Activation (None, 14, 16, 576) 0 ['block4c_bn[0][0]'] \n"," ) \n"," \n"," block4c_se_squeeze (GlobalAver (None, 576) 0 ['block4c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4c_se_reshape (Reshape) (None, 1, 1, 576) 0 ['block4c_se_squeeze[0][0]'] \n"," \n"," block4c_se_reduce (Conv2D) (None, 1, 1, 24) 13848 ['block4c_se_reshape[0][0]'] \n"," \n"," block4c_se_expand (Conv2D) (None, 1, 1, 576) 14400 ['block4c_se_reduce[0][0]'] \n"," \n"," block4c_se_excite (Multiply) (None, 14, 16, 576) 0 ['block4c_activation[0][0]', \n"," 'block4c_se_expand[0][0]'] \n"," \n"," block4c_project_conv (Conv2D) (None, 14, 16, 96) 55296 ['block4c_se_excite[0][0]'] \n"," \n"," block4c_project_bn (BatchNorma (None, 14, 16, 96) 384 ['block4c_project_conv[0][0]'] \n"," lization) \n"," \n"," block4c_drop (Dropout) (None, 14, 16, 96) 0 ['block4c_project_bn[0][0]'] \n"," \n"," block4c_add (Add) (None, 14, 16, 96) 0 ['block4c_drop[0][0]', \n"," 'block4b_add[0][0]'] \n"," \n"," block4d_expand_conv (Conv2D) (None, 14, 16, 576) 55296 ['block4c_add[0][0]'] \n"," \n"," block4d_expand_bn (BatchNormal (None, 14, 16, 576) 2304 ['block4d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4d_expand_activation (Act (None, 14, 16, 576) 0 ['block4d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4d_dwconv (DepthwiseConv2 (None, 14, 16, 576) 5184 ['block4d_expand_activation[0][0]\n"," D) '] \n"," \n"," block4d_bn (BatchNormalization (None, 14, 16, 576) 2304 ['block4d_dwconv[0][0]'] \n"," ) \n"," \n"," block4d_activation (Activation (None, 14, 16, 576) 0 ['block4d_bn[0][0]'] \n"," ) \n"," \n"," block4d_se_squeeze (GlobalAver (None, 576) 0 ['block4d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4d_se_reshape (Reshape) (None, 1, 1, 576) 0 ['block4d_se_squeeze[0][0]'] \n"," \n"," block4d_se_reduce (Conv2D) (None, 1, 1, 24) 13848 ['block4d_se_reshape[0][0]'] \n"," \n"," block4d_se_expand (Conv2D) (None, 1, 1, 576) 14400 ['block4d_se_reduce[0][0]'] \n"," \n"," block4d_se_excite (Multiply) (None, 14, 16, 576) 0 ['block4d_activation[0][0]', \n"," 'block4d_se_expand[0][0]'] \n"," \n"," block4d_project_conv (Conv2D) (None, 14, 16, 96) 55296 ['block4d_se_excite[0][0]'] \n"," \n"," block4d_project_bn (BatchNorma (None, 14, 16, 96) 384 ['block4d_project_conv[0][0]'] \n"," lization) \n"," \n"," block4d_drop (Dropout) (None, 14, 16, 96) 0 ['block4d_project_bn[0][0]'] \n"," \n"," block4d_add (Add) (None, 14, 16, 96) 0 ['block4d_drop[0][0]', \n"," 'block4c_add[0][0]'] \n"," \n"," block4e_expand_conv (Conv2D) (None, 14, 16, 576) 55296 ['block4d_add[0][0]'] \n"," \n"," block4e_expand_bn (BatchNormal (None, 14, 16, 576) 2304 ['block4e_expand_conv[0][0]'] \n"," ization) \n"," \n"," block4e_expand_activation (Act (None, 14, 16, 576) 0 ['block4e_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block4e_dwconv (DepthwiseConv2 (None, 14, 16, 576) 5184 ['block4e_expand_activation[0][0]\n"," D) '] \n"," \n"," block4e_bn (BatchNormalization (None, 14, 16, 576) 2304 ['block4e_dwconv[0][0]'] \n"," ) \n"," \n"," block4e_activation (Activation (None, 14, 16, 576) 0 ['block4e_bn[0][0]'] \n"," ) \n"," \n"," block4e_se_squeeze (GlobalAver (None, 576) 0 ['block4e_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block4e_se_reshape (Reshape) (None, 1, 1, 576) 0 ['block4e_se_squeeze[0][0]'] \n"," \n"," block4e_se_reduce (Conv2D) (None, 1, 1, 24) 13848 ['block4e_se_reshape[0][0]'] \n"," \n"," block4e_se_expand (Conv2D) (None, 1, 1, 576) 14400 ['block4e_se_reduce[0][0]'] \n"," \n"," block4e_se_excite (Multiply) (None, 14, 16, 576) 0 ['block4e_activation[0][0]', \n"," 'block4e_se_expand[0][0]'] \n"," \n"," block4e_project_conv (Conv2D) (None, 14, 16, 96) 55296 ['block4e_se_excite[0][0]'] \n"," \n"," block4e_project_bn (BatchNorma (None, 14, 16, 96) 384 ['block4e_project_conv[0][0]'] \n"," lization) \n"," \n"," block4e_drop (Dropout) (None, 14, 16, 96) 0 ['block4e_project_bn[0][0]'] \n"," \n"," block4e_add (Add) (None, 14, 16, 96) 0 ['block4e_drop[0][0]', \n"," 'block4d_add[0][0]'] \n"," \n"," block5a_expand_conv (Conv2D) (None, 14, 16, 576) 55296 ['block4e_add[0][0]'] \n"," \n"," block5a_expand_bn (BatchNormal (None, 14, 16, 576) 2304 ['block5a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5a_expand_activation (Act (None, 14, 16, 576) 0 ['block5a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5a_dwconv (DepthwiseConv2 (None, 14, 16, 576) 14400 ['block5a_expand_activation[0][0]\n"," D) '] \n"," \n"," block5a_bn (BatchNormalization (None, 14, 16, 576) 2304 ['block5a_dwconv[0][0]'] \n"," ) \n"," \n"," block5a_activation (Activation (None, 14, 16, 576) 0 ['block5a_bn[0][0]'] \n"," ) \n"," \n"," block5a_se_squeeze (GlobalAver (None, 576) 0 ['block5a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5a_se_reshape (Reshape) (None, 1, 1, 576) 0 ['block5a_se_squeeze[0][0]'] \n"," \n"," block5a_se_reduce (Conv2D) (None, 1, 1, 24) 13848 ['block5a_se_reshape[0][0]'] \n"," \n"," block5a_se_expand (Conv2D) (None, 1, 1, 576) 14400 ['block5a_se_reduce[0][0]'] \n"," \n"," block5a_se_excite (Multiply) (None, 14, 16, 576) 0 ['block5a_activation[0][0]', \n"," 'block5a_se_expand[0][0]'] \n"," \n"," block5a_project_conv (Conv2D) (None, 14, 16, 136) 78336 ['block5a_se_excite[0][0]'] \n"," \n"," block5a_project_bn (BatchNorma (None, 14, 16, 136) 544 ['block5a_project_conv[0][0]'] \n"," lization) \n"," \n"," block5b_expand_conv (Conv2D) (None, 14, 16, 816) 110976 ['block5a_project_bn[0][0]'] \n"," \n"," block5b_expand_bn (BatchNormal (None, 14, 16, 816) 3264 ['block5b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5b_expand_activation (Act (None, 14, 16, 816) 0 ['block5b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5b_dwconv (DepthwiseConv2 (None, 14, 16, 816) 20400 ['block5b_expand_activation[0][0]\n"," D) '] \n"," \n"," block5b_bn (BatchNormalization (None, 14, 16, 816) 3264 ['block5b_dwconv[0][0]'] \n"," ) \n"," \n"," block5b_activation (Activation (None, 14, 16, 816) 0 ['block5b_bn[0][0]'] \n"," ) \n"," \n"," block5b_se_squeeze (GlobalAver (None, 816) 0 ['block5b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5b_se_reshape (Reshape) (None, 1, 1, 816) 0 ['block5b_se_squeeze[0][0]'] \n"," \n"," block5b_se_reduce (Conv2D) (None, 1, 1, 34) 27778 ['block5b_se_reshape[0][0]'] \n"," \n"," block5b_se_expand (Conv2D) (None, 1, 1, 816) 28560 ['block5b_se_reduce[0][0]'] \n"," \n"," block5b_se_excite (Multiply) (None, 14, 16, 816) 0 ['block5b_activation[0][0]', \n"," 'block5b_se_expand[0][0]'] \n"," \n"," block5b_project_conv (Conv2D) (None, 14, 16, 136) 110976 ['block5b_se_excite[0][0]'] \n"," \n"," block5b_project_bn (BatchNorma (None, 14, 16, 136) 544 ['block5b_project_conv[0][0]'] \n"," lization) \n"," \n"," block5b_drop (Dropout) (None, 14, 16, 136) 0 ['block5b_project_bn[0][0]'] \n"," \n"," block5b_add (Add) (None, 14, 16, 136) 0 ['block5b_drop[0][0]', \n"," 'block5a_project_bn[0][0]'] \n"," \n"," block5c_expand_conv (Conv2D) (None, 14, 16, 816) 110976 ['block5b_add[0][0]'] \n"," \n"," block5c_expand_bn (BatchNormal (None, 14, 16, 816) 3264 ['block5c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5c_expand_activation (Act (None, 14, 16, 816) 0 ['block5c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5c_dwconv (DepthwiseConv2 (None, 14, 16, 816) 20400 ['block5c_expand_activation[0][0]\n"," D) '] \n"," \n"," block5c_bn (BatchNormalization (None, 14, 16, 816) 3264 ['block5c_dwconv[0][0]'] \n"," ) \n"," \n"," block5c_activation (Activation (None, 14, 16, 816) 0 ['block5c_bn[0][0]'] \n"," ) \n"," \n"," block5c_se_squeeze (GlobalAver (None, 816) 0 ['block5c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5c_se_reshape (Reshape) (None, 1, 1, 816) 0 ['block5c_se_squeeze[0][0]'] \n"," \n"," block5c_se_reduce (Conv2D) (None, 1, 1, 34) 27778 ['block5c_se_reshape[0][0]'] \n"," \n"," block5c_se_expand (Conv2D) (None, 1, 1, 816) 28560 ['block5c_se_reduce[0][0]'] \n"," \n"," block5c_se_excite (Multiply) (None, 14, 16, 816) 0 ['block5c_activation[0][0]', \n"," 'block5c_se_expand[0][0]'] \n"," \n"," block5c_project_conv (Conv2D) (None, 14, 16, 136) 110976 ['block5c_se_excite[0][0]'] \n"," \n"," block5c_project_bn (BatchNorma (None, 14, 16, 136) 544 ['block5c_project_conv[0][0]'] \n"," lization) \n"," \n"," block5c_drop (Dropout) (None, 14, 16, 136) 0 ['block5c_project_bn[0][0]'] \n"," \n"," block5c_add (Add) (None, 14, 16, 136) 0 ['block5c_drop[0][0]', \n"," 'block5b_add[0][0]'] \n"," \n"," block5d_expand_conv (Conv2D) (None, 14, 16, 816) 110976 ['block5c_add[0][0]'] \n"," \n"," block5d_expand_bn (BatchNormal (None, 14, 16, 816) 3264 ['block5d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5d_expand_activation (Act (None, 14, 16, 816) 0 ['block5d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5d_dwconv (DepthwiseConv2 (None, 14, 16, 816) 20400 ['block5d_expand_activation[0][0]\n"," D) '] \n"," \n"," block5d_bn (BatchNormalization (None, 14, 16, 816) 3264 ['block5d_dwconv[0][0]'] \n"," ) \n"," \n"," block5d_activation (Activation (None, 14, 16, 816) 0 ['block5d_bn[0][0]'] \n"," ) \n"," \n"," block5d_se_squeeze (GlobalAver (None, 816) 0 ['block5d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5d_se_reshape (Reshape) (None, 1, 1, 816) 0 ['block5d_se_squeeze[0][0]'] \n"," \n"," block5d_se_reduce (Conv2D) (None, 1, 1, 34) 27778 ['block5d_se_reshape[0][0]'] \n"," \n"," block5d_se_expand (Conv2D) (None, 1, 1, 816) 28560 ['block5d_se_reduce[0][0]'] \n"," \n"," block5d_se_excite (Multiply) (None, 14, 16, 816) 0 ['block5d_activation[0][0]', \n"," 'block5d_se_expand[0][0]'] \n"," \n"," block5d_project_conv (Conv2D) (None, 14, 16, 136) 110976 ['block5d_se_excite[0][0]'] \n"," \n"," block5d_project_bn (BatchNorma (None, 14, 16, 136) 544 ['block5d_project_conv[0][0]'] \n"," lization) \n"," \n"," block5d_drop (Dropout) (None, 14, 16, 136) 0 ['block5d_project_bn[0][0]'] \n"," \n"," block5d_add (Add) (None, 14, 16, 136) 0 ['block5d_drop[0][0]', \n"," 'block5c_add[0][0]'] \n"," \n"," block5e_expand_conv (Conv2D) (None, 14, 16, 816) 110976 ['block5d_add[0][0]'] \n"," \n"," block5e_expand_bn (BatchNormal (None, 14, 16, 816) 3264 ['block5e_expand_conv[0][0]'] \n"," ization) \n"," \n"," block5e_expand_activation (Act (None, 14, 16, 816) 0 ['block5e_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block5e_dwconv (DepthwiseConv2 (None, 14, 16, 816) 20400 ['block5e_expand_activation[0][0]\n"," D) '] \n"," \n"," block5e_bn (BatchNormalization (None, 14, 16, 816) 3264 ['block5e_dwconv[0][0]'] \n"," ) \n"," \n"," block5e_activation (Activation (None, 14, 16, 816) 0 ['block5e_bn[0][0]'] \n"," ) \n"," \n"," block5e_se_squeeze (GlobalAver (None, 816) 0 ['block5e_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block5e_se_reshape (Reshape) (None, 1, 1, 816) 0 ['block5e_se_squeeze[0][0]'] \n"," \n"," block5e_se_reduce (Conv2D) (None, 1, 1, 34) 27778 ['block5e_se_reshape[0][0]'] \n"," \n"," block5e_se_expand (Conv2D) (None, 1, 1, 816) 28560 ['block5e_se_reduce[0][0]'] \n"," \n"," block5e_se_excite (Multiply) (None, 14, 16, 816) 0 ['block5e_activation[0][0]', \n"," 'block5e_se_expand[0][0]'] \n"," \n"," block5e_project_conv (Conv2D) (None, 14, 16, 136) 110976 ['block5e_se_excite[0][0]'] \n"," \n"," block5e_project_bn (BatchNorma (None, 14, 16, 136) 544 ['block5e_project_conv[0][0]'] \n"," lization) \n"," \n"," block5e_drop (Dropout) (None, 14, 16, 136) 0 ['block5e_project_bn[0][0]'] \n"," \n"," block5e_add (Add) (None, 14, 16, 136) 0 ['block5e_drop[0][0]', \n"," 'block5d_add[0][0]'] \n"," \n"," block6a_expand_conv (Conv2D) (None, 14, 16, 816) 110976 ['block5e_add[0][0]'] \n"," \n"," block6a_expand_bn (BatchNormal (None, 14, 16, 816) 3264 ['block6a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6a_expand_activation (Act (None, 14, 16, 816) 0 ['block6a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6a_dwconv_pad (ZeroPaddin (None, 17, 19, 816) 0 ['block6a_expand_activation[0][0]\n"," g2D) '] \n"," \n"," block6a_dwconv (DepthwiseConv2 (None, 7, 8, 816) 20400 ['block6a_dwconv_pad[0][0]'] \n"," D) \n"," \n"," block6a_bn (BatchNormalization (None, 7, 8, 816) 3264 ['block6a_dwconv[0][0]'] \n"," ) \n"," \n"," block6a_activation (Activation (None, 7, 8, 816) 0 ['block6a_bn[0][0]'] \n"," ) \n"," \n"," block6a_se_squeeze (GlobalAver (None, 816) 0 ['block6a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6a_se_reshape (Reshape) (None, 1, 1, 816) 0 ['block6a_se_squeeze[0][0]'] \n"," \n"," block6a_se_reduce (Conv2D) (None, 1, 1, 34) 27778 ['block6a_se_reshape[0][0]'] \n"," \n"," block6a_se_expand (Conv2D) (None, 1, 1, 816) 28560 ['block6a_se_reduce[0][0]'] \n"," \n"," block6a_se_excite (Multiply) (None, 7, 8, 816) 0 ['block6a_activation[0][0]', \n"," 'block6a_se_expand[0][0]'] \n"," \n"," block6a_project_conv (Conv2D) (None, 7, 8, 232) 189312 ['block6a_se_excite[0][0]'] \n"," \n"," block6a_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6a_project_conv[0][0]'] \n"," lization) \n"," \n"," block6b_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6a_project_bn[0][0]'] \n"," \n"," block6b_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block6b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6b_expand_activation (Act (None, 7, 8, 1392) 0 ['block6b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6b_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 34800 ['block6b_expand_activation[0][0]\n"," D) '] \n"," \n"," block6b_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block6b_dwconv[0][0]'] \n"," ) \n"," \n"," block6b_activation (Activation (None, 7, 8, 1392) 0 ['block6b_bn[0][0]'] \n"," ) \n"," \n"," block6b_se_squeeze (GlobalAver (None, 1392) 0 ['block6b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6b_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block6b_se_squeeze[0][0]'] \n"," \n"," block6b_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block6b_se_reshape[0][0]'] \n"," \n"," block6b_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block6b_se_reduce[0][0]'] \n"," \n"," block6b_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block6b_activation[0][0]', \n"," 'block6b_se_expand[0][0]'] \n"," \n"," block6b_project_conv (Conv2D) (None, 7, 8, 232) 322944 ['block6b_se_excite[0][0]'] \n"," \n"," block6b_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6b_project_conv[0][0]'] \n"," lization) \n"," \n"," block6b_drop (Dropout) (None, 7, 8, 232) 0 ['block6b_project_bn[0][0]'] \n"," \n"," block6b_add (Add) (None, 7, 8, 232) 0 ['block6b_drop[0][0]', \n"," 'block6a_project_bn[0][0]'] \n"," \n"," block6c_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6b_add[0][0]'] \n"," \n"," block6c_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block6c_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6c_expand_activation (Act (None, 7, 8, 1392) 0 ['block6c_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6c_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 34800 ['block6c_expand_activation[0][0]\n"," D) '] \n"," \n"," block6c_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block6c_dwconv[0][0]'] \n"," ) \n"," \n"," block6c_activation (Activation (None, 7, 8, 1392) 0 ['block6c_bn[0][0]'] \n"," ) \n"," \n"," block6c_se_squeeze (GlobalAver (None, 1392) 0 ['block6c_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6c_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block6c_se_squeeze[0][0]'] \n"," \n"," block6c_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block6c_se_reshape[0][0]'] \n"," \n"," block6c_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block6c_se_reduce[0][0]'] \n"," \n"," block6c_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block6c_activation[0][0]', \n"," 'block6c_se_expand[0][0]'] \n"," \n"," block6c_project_conv (Conv2D) (None, 7, 8, 232) 322944 ['block6c_se_excite[0][0]'] \n"," \n"," block6c_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6c_project_conv[0][0]'] \n"," lization) \n"," \n"," block6c_drop (Dropout) (None, 7, 8, 232) 0 ['block6c_project_bn[0][0]'] \n"," \n"," block6c_add (Add) (None, 7, 8, 232) 0 ['block6c_drop[0][0]', \n"," 'block6b_add[0][0]'] \n"," \n"," block6d_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6c_add[0][0]'] \n"," \n"," block6d_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block6d_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6d_expand_activation (Act (None, 7, 8, 1392) 0 ['block6d_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6d_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 34800 ['block6d_expand_activation[0][0]\n"," D) '] \n"," \n"," block6d_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block6d_dwconv[0][0]'] \n"," ) \n"," \n"," block6d_activation (Activation (None, 7, 8, 1392) 0 ['block6d_bn[0][0]'] \n"," ) \n"," \n"," block6d_se_squeeze (GlobalAver (None, 1392) 0 ['block6d_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6d_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block6d_se_squeeze[0][0]'] \n"," \n"," block6d_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block6d_se_reshape[0][0]'] \n"," \n"," block6d_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block6d_se_reduce[0][0]'] \n"," \n"," block6d_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block6d_activation[0][0]', \n"," 'block6d_se_expand[0][0]'] \n"," \n"," block6d_project_conv (Conv2D) (None, 7, 8, 232) 322944 ['block6d_se_excite[0][0]'] \n"," \n"," block6d_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6d_project_conv[0][0]'] \n"," lization) \n"," \n"," block6d_drop (Dropout) (None, 7, 8, 232) 0 ['block6d_project_bn[0][0]'] \n"," \n"," block6d_add (Add) (None, 7, 8, 232) 0 ['block6d_drop[0][0]', \n"," 'block6c_add[0][0]'] \n"," \n"," block6e_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6d_add[0][0]'] \n"," \n"," block6e_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block6e_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6e_expand_activation (Act (None, 7, 8, 1392) 0 ['block6e_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6e_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 34800 ['block6e_expand_activation[0][0]\n"," D) '] \n"," \n"," block6e_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block6e_dwconv[0][0]'] \n"," ) \n"," \n"," block6e_activation (Activation (None, 7, 8, 1392) 0 ['block6e_bn[0][0]'] \n"," ) \n"," \n"," block6e_se_squeeze (GlobalAver (None, 1392) 0 ['block6e_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6e_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block6e_se_squeeze[0][0]'] \n"," \n"," block6e_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block6e_se_reshape[0][0]'] \n"," \n"," block6e_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block6e_se_reduce[0][0]'] \n"," \n"," block6e_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block6e_activation[0][0]', \n"," 'block6e_se_expand[0][0]'] \n"," \n"," block6e_project_conv (Conv2D) (None, 7, 8, 232) 322944 ['block6e_se_excite[0][0]'] \n"," \n"," block6e_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6e_project_conv[0][0]'] \n"," lization) \n"," \n"," block6e_drop (Dropout) (None, 7, 8, 232) 0 ['block6e_project_bn[0][0]'] \n"," \n"," block6e_add (Add) (None, 7, 8, 232) 0 ['block6e_drop[0][0]', \n"," 'block6d_add[0][0]'] \n"," \n"," block6f_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6e_add[0][0]'] \n"," \n"," block6f_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block6f_expand_conv[0][0]'] \n"," ization) \n"," \n"," block6f_expand_activation (Act (None, 7, 8, 1392) 0 ['block6f_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block6f_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 34800 ['block6f_expand_activation[0][0]\n"," D) '] \n"," \n"," block6f_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block6f_dwconv[0][0]'] \n"," ) \n"," \n"," block6f_activation (Activation (None, 7, 8, 1392) 0 ['block6f_bn[0][0]'] \n"," ) \n"," \n"," block6f_se_squeeze (GlobalAver (None, 1392) 0 ['block6f_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block6f_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block6f_se_squeeze[0][0]'] \n"," \n"," block6f_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block6f_se_reshape[0][0]'] \n"," \n"," block6f_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block6f_se_reduce[0][0]'] \n"," \n"," block6f_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block6f_activation[0][0]', \n"," 'block6f_se_expand[0][0]'] \n"," \n"," block6f_project_conv (Conv2D) (None, 7, 8, 232) 322944 ['block6f_se_excite[0][0]'] \n"," \n"," block6f_project_bn (BatchNorma (None, 7, 8, 232) 928 ['block6f_project_conv[0][0]'] \n"," lization) \n"," \n"," block6f_drop (Dropout) (None, 7, 8, 232) 0 ['block6f_project_bn[0][0]'] \n"," \n"," block6f_add (Add) (None, 7, 8, 232) 0 ['block6f_drop[0][0]', \n"," 'block6e_add[0][0]'] \n"," \n"," block7a_expand_conv (Conv2D) (None, 7, 8, 1392) 322944 ['block6f_add[0][0]'] \n"," \n"," block7a_expand_bn (BatchNormal (None, 7, 8, 1392) 5568 ['block7a_expand_conv[0][0]'] \n"," ization) \n"," \n"," block7a_expand_activation (Act (None, 7, 8, 1392) 0 ['block7a_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block7a_dwconv (DepthwiseConv2 (None, 7, 8, 1392) 12528 ['block7a_expand_activation[0][0]\n"," D) '] \n"," \n"," block7a_bn (BatchNormalization (None, 7, 8, 1392) 5568 ['block7a_dwconv[0][0]'] \n"," ) \n"," \n"," block7a_activation (Activation (None, 7, 8, 1392) 0 ['block7a_bn[0][0]'] \n"," ) \n"," \n"," block7a_se_squeeze (GlobalAver (None, 1392) 0 ['block7a_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block7a_se_reshape (Reshape) (None, 1, 1, 1392) 0 ['block7a_se_squeeze[0][0]'] \n"," \n"," block7a_se_reduce (Conv2D) (None, 1, 1, 58) 80794 ['block7a_se_reshape[0][0]'] \n"," \n"," block7a_se_expand (Conv2D) (None, 1, 1, 1392) 82128 ['block7a_se_reduce[0][0]'] \n"," \n"," block7a_se_excite (Multiply) (None, 7, 8, 1392) 0 ['block7a_activation[0][0]', \n"," 'block7a_se_expand[0][0]'] \n"," \n"," block7a_project_conv (Conv2D) (None, 7, 8, 384) 534528 ['block7a_se_excite[0][0]'] \n"," \n"," block7a_project_bn (BatchNorma (None, 7, 8, 384) 1536 ['block7a_project_conv[0][0]'] \n"," lization) \n"," \n"," block7b_expand_conv (Conv2D) (None, 7, 8, 2304) 884736 ['block7a_project_bn[0][0]'] \n"," \n"," block7b_expand_bn (BatchNormal (None, 7, 8, 2304) 9216 ['block7b_expand_conv[0][0]'] \n"," ization) \n"," \n"," block7b_expand_activation (Act (None, 7, 8, 2304) 0 ['block7b_expand_bn[0][0]'] \n"," ivation) \n"," \n"," block7b_dwconv (DepthwiseConv2 (None, 7, 8, 2304) 20736 ['block7b_expand_activation[0][0]\n"," D) '] \n"," \n"," block7b_bn (BatchNormalization (None, 7, 8, 2304) 9216 ['block7b_dwconv[0][0]'] \n"," ) \n"," \n"," block7b_activation (Activation (None, 7, 8, 2304) 0 ['block7b_bn[0][0]'] \n"," ) \n"," \n"," block7b_se_squeeze (GlobalAver (None, 2304) 0 ['block7b_activation[0][0]'] \n"," agePooling2D) \n"," \n"," block7b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7b_se_squeeze[0][0]'] \n"," \n"," block7b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7b_se_reshape[0][0]'] \n"," \n"," block7b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7b_se_reduce[0][0]'] \n"," \n"," block7b_se_excite (Multiply) (None, 7, 8, 2304) 0 ['block7b_activation[0][0]', \n"," 'block7b_se_expand[0][0]'] \n"," \n"," block7b_project_conv (Conv2D) (None, 7, 8, 384) 884736 ['block7b_se_excite[0][0]'] \n"," \n"," block7b_project_bn (BatchNorma (None, 7, 8, 384) 1536 ['block7b_project_conv[0][0]'] \n"," lization) \n"," \n"," block7b_drop (Dropout) (None, 7, 8, 384) 0 ['block7b_project_bn[0][0]'] \n"," \n"," block7b_add (Add) (None, 7, 8, 384) 0 ['block7b_drop[0][0]', \n"," 'block7a_project_bn[0][0]'] \n"," \n"," top_conv (Conv2D) (None, 7, 8, 1536) 589824 ['block7b_add[0][0]'] \n"," \n"," top_bn (BatchNormalization) (None, 7, 8, 1536) 6144 ['top_conv[0][0]'] \n"," \n"," top_activation (Activation) (None, 7, 8, 1536) 0 ['top_bn[0][0]'] \n"," \n"," max_pool (GlobalMaxPooling2D) (None, 1536) 0 ['top_activation[0][0]'] \n"," \n"," batch_normalization (BatchNorm (None, 1536) 6144 ['max_pool[0][0]'] \n"," alization) \n"," \n"," dense (Dense) (None, 256) 393472 ['batch_normalization[0][0]'] \n"," \n"," dropout (Dropout) (None, 256) 0 ['dense[0][0]'] \n"," \n"," dense_1 (Dense) (None, 4) 1028 ['dropout[0][0]'] \n"," \n","==================================================================================================\n","Total params: 11,184,179\n","Trainable params: 11,093,804\n","Non-trainable params: 90,375\n","__________________________________________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model.predict(img)"]},{"cell_type":"code","execution_count":55,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T23:18:49.398695Z","iopub.status.busy":"2023-03-03T23:18:49.398049Z","iopub.status.idle":"2023-03-03T23:19:03.595728Z","shell.execute_reply":"2023-03-03T23:19:03.594401Z","shell.execute_reply.started":"2023-03-03T23:18:49.398641Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting Image\n"," Downloading image-1.5.33.tar.gz (15 kB)\n"," Preparing metadata (setup.py) ... \u001b[?25ldone\n","\u001b[?25hRequirement already satisfied: pillow in /opt/conda/lib/python3.7/site-packages (from Image) (9.4.0)\n","Collecting django\n"," Downloading Django-3.2.18-py3-none-any.whl (7.9 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m46.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n","\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from Image) (1.16.0)\n","Collecting asgiref<4,>=3.3.2\n"," Downloading asgiref-3.6.0-py3-none-any.whl (23 kB)\n","Requirement already satisfied: pytz in /opt/conda/lib/python3.7/site-packages (from django->Image) (2022.7.1)\n","Requirement already satisfied: sqlparse>=0.2.2 in /opt/conda/lib/python3.7/site-packages (from django->Image) (0.4.3)\n","Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from asgiref<4,>=3.3.2->django->Image) (4.4.0)\n","Building wheels for collected packages: Image\n"," Building wheel for Image (setup.py) ... \u001b[?25ldone\n","\u001b[?25h Created wheel for Image: filename=image-1.5.33-py2.py3-none-any.whl size=19494 sha256=026e5c2b13e830a31c22e45a95c662a8511d361969aa66e8b5e4c4fcef67b699\n"," Stored in directory: /root/.cache/pip/wheels/23/29/74/b2ac5172832c36fb4cb4126f66765568099d9ddaf8a8613502\n","Successfully built Image\n","Installing collected packages: asgiref, django, Image\n","Successfully installed Image-1.5.33 asgiref-3.6.0 django-3.2.18\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]}],"source":["!pip install Image"]},{"cell_type":"code","execution_count":64,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T23:34:46.887423Z","iopub.status.busy":"2023-03-03T23:34:46.886413Z","iopub.status.idle":"2023-03-03T23:34:46.897792Z","shell.execute_reply":"2023-03-03T23:34:46.896750Z","shell.execute_reply.started":"2023-03-03T23:34:46.887354Z"},"trusted":true},"outputs":[],"source":["from PIL import Image\n","img = Image.open(\"/kaggle/input/images/images (3).jfif\")\n","new_image = img.resize((240, 250))"]},{"cell_type":"code","execution_count":100,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:25:17.029168Z","iopub.status.busy":"2023-03-04T00:25:17.028351Z","iopub.status.idle":"2023-03-04T00:25:17.450487Z","shell.execute_reply":"2023-03-04T00:25:17.449356Z","shell.execute_reply.started":"2023-03-04T00:25:17.029129Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Input image shape: (1, 224, 250, 3)\n"]},{"data":{"text/plain":[""]},"execution_count":100,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["import cv2\n","import numpy as np\n","from matplotlib.pyplot import imread\n","from matplotlib.pyplot import imshow\n","from tensorflow.keras.preprocessing import image\n","from tensorflow.keras.applications.imagenet_utils import decode_predictions\n","from tensorflow.keras.applications.imagenet_utils import preprocess_input\n","\n","\n","img_path = '/kaggle/input/images/images (3).jfif'\n","\n","img = cv2.imread(img_path)\n","img = cv2.resize(img, (250, 224))\n","\n","x = np.expand_dims(img, axis=0)\n","x = preprocess_input(x)\n","\n","print('Input image shape:', x.shape)\n","\n","my_image = imread(img_path)\n","imshow(my_image)"]},{"cell_type":"code","execution_count":101,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:25:21.515272Z","iopub.status.busy":"2023-03-04T00:25:21.513892Z","iopub.status.idle":"2023-03-04T00:25:23.814160Z","shell.execute_reply":"2023-03-04T00:25:23.813117Z","shell.execute_reply.started":"2023-03-04T00:25:21.515226Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 2s 2s/step\n","predicted class: [[0. 0. 0. 1.]]\n"]}],"source":["preds=model.predict(x)\n","print(\"predicted class: \",preds ) \n","print "]},{"cell_type":"code","execution_count":102,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:26:23.077153Z","iopub.status.busy":"2023-03-04T00:26:23.076036Z","iopub.status.idle":"2023-03-04T00:26:23.084285Z","shell.execute_reply":"2023-03-04T00:26:23.083209Z","shell.execute_reply.started":"2023-03-04T00:26:23.077100Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array(['Tumor', 'Cyst', 'Normal', 'Stone'], dtype=object)"]},"execution_count":102,"metadata":{},"output_type":"execute_result"}],"source":["classes"]},{"cell_type":"code","execution_count":105,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:29:56.694231Z","iopub.status.busy":"2023-03-04T00:29:56.693181Z","iopub.status.idle":"2023-03-04T00:29:56.770825Z","shell.execute_reply":"2023-03-04T00:29:56.769730Z","shell.execute_reply.started":"2023-03-04T00:29:56.694190Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 31ms/step\n","The predicted class of the image is: [3]\n","The predicted class is ['Stone']\n"]}],"source":["prediction = model.predict(x)\n","\n","#convert the prediction to a class label\n","predicted_class = np.argmax(prediction, axis=1)\n","print(\"The predicted class of the image is: \", predicted_class)\n","print(\"The predicted class is \",classes[predicted_class])"]},{"cell_type":"code","execution_count":126,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:54:52.480048Z","iopub.status.busy":"2023-03-04T00:54:52.479633Z","iopub.status.idle":"2023-03-04T00:54:52.487519Z","shell.execute_reply":"2023-03-04T00:54:52.486296Z","shell.execute_reply.started":"2023-03-04T00:54:52.480014Z"},"trusted":true},"outputs":[],"source":["def Pred(img_path):\n"," from tensorflow.keras.applications.imagenet_utils import preprocess_input\n","\n"," img = cv2.imread(img_path)\n"," img = cv2.resize(img, (250, 224))\n"," x = np.expand_dims(img, axis=0)\n"," x = preprocess_input(x)\n","\n"," my_image = imread(img_path)\n"," prediction = model.predict(x)\n","\n"," #convert the prediction to a class label\n"," classes=['Tumor', 'Cyst', 'Normal', 'Stone']\n"," predicted_class = classes[np.argmax(prediction[0])]\n"," confidence = round(100 * (np.max(prediction[0])), 2)\n"," \n"," return predicted_class, confidence"]},{"cell_type":"code","execution_count":127,"metadata":{"execution":{"iopub.execute_input":"2023-03-04T00:54:55.118461Z","iopub.status.busy":"2023-03-04T00:54:55.117367Z","iopub.status.idle":"2023-03-04T00:54:55.214004Z","shell.execute_reply":"2023-03-04T00:54:55.213031Z","shell.execute_reply.started":"2023-03-04T00:54:55.118415Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 30ms/step\n"]},{"data":{"text/plain":["('Cyst', 100.0)"]},"execution_count":127,"metadata":{},"output_type":"execute_result"}],"source":["Pred(\"/kaggle/input/images-new/MRI-of-the-cyst-in-left-kidney-MRI-Magnetic-resonance-imaging.png\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2023-03-03T21:44:05.609259Z","iopub.status.busy":"2023-03-03T21:44:05.608517Z","iopub.status.idle":"2023-03-03T21:44:05.614589Z","shell.execute_reply":"2023-03-03T21:44:05.613530Z","shell.execute_reply.started":"2023-03-03T21:44:05.609223Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["2.9.1\n"]}],"source":["print(tf.__version__)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.7"},"vscode":{"interpreter":{"hash":"213524bb45a1aeaf737b1d8c77d7b8db5d425938d9dffc5f4bc6fe6dd3324700"}}},"nbformat":4,"nbformat_minor":4}