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**Workflow**","metadata":{}},{"cell_type":"markdown","source":"![WORKFLOW 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**Extracting the train and test data**","metadata":{}},{"cell_type":"code","source":"# Extractig the training data\nimport zipfile\nfrom tqdm import tqdm\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\") as zf:\n    for member in tqdm(zf.infolist(), desc='Extracting '):\n        try:\n            zf.extract(member, \"./\")\n        except zipfile.error as e:\n            pass","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:32.799226Z","iopub.execute_input":"2021-10-17T11:57:32.799916Z","iopub.status.idle":"2021-10-17T11:57:45.21041Z","shell.execute_reply.started":"2021-10-17T11:57:32.799878Z","shell.execute_reply":"2021-10-17T11:57:45.209149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Extracting the testing data\nwith zipfile.ZipFile(\"../input/dogs-vs-cats/test1.zip\") as zf:\n    for member in tqdm(zf.infolist(), desc='Extracting '):\n        try:\n            zf.extract(member, \"./\")\n        except zipfile.error as e:\n            pass","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:45.212221Z","iopub.execute_input":"2021-10-17T11:57:45.212498Z","iopub.status.idle":"2021-10-17T11:57:50.636053Z","shell.execute_reply.started":"2021-10-17T11:57:45.212459Z","shell.execute_reply":"2021-10-17T11:57:50.634564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nos.listdir('./')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:50.637179Z","iopub.execute_input":"2021-10-17T11:57:50.637592Z","iopub.status.idle":"2021-10-17T11:57:50.643923Z","shell.execute_reply.started":"2021-10-17T11:57:50.637552Z","shell.execute_reply":"2021-10-17T11:57:50.643098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **write the datafame with each image path and respective image class (0 or 1)**","metadata":{}},{"cell_type":"code","source":"# create separate list to store target class (0 or 1) and respective image path\ntrain_filenames=os.listdir('./train')\npath=[]\ntarget=[]\nfor i in train_filenames:\n    temp=i.split('.')[0]\n    if temp=='cat':\n        target.append(0)\n    else:\n        target.append(1)\n    path.append('./train/'+i)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:50.645734Z","iopub.execute_input":"2021-10-17T11:57:50.646375Z","iopub.status.idle":"2021-10-17T11:57:50.68482Z","shell.execute_reply.started":"2021-10-17T11:57:50.646338Z","shell.execute_reply":"2021-10-17T11:57:50.684041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\ndata={'Image': path,'Target': target}\ndf=pd.DataFrame(data)\ndf.head()\n","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:50.685948Z","iopub.execute_input":"2021-10-17T11:57:50.686375Z","iopub.status.idle":"2021-10-17T11:57:50.720608Z","shell.execute_reply.started":"2021-10-17T11:57:50.686336Z","shell.execute_reply":"2021-10-17T11:57:50.719783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:57:54.875723Z","iopub.execute_input":"2021-10-17T11:57:54.87604Z","iopub.status.idle":"2021-10-17T11:57:54.915535Z","shell.execute_reply.started":"2021-10-17T11:57:54.876009Z","shell.execute_reply":"2021-10-17T11:57:54.912467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Visualisation**","metadata":{}},{"cell_type":"markdown","source":"### **Check all the images are of same size using simple visualisation**","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom matplotlib.image import imread\nimport random\nfig = plt.figure(figsize=(10,10))\n\nrandom.seed(15)\nrandom_list=random.sample(range(0,24999),9)\nfor i,index in enumerate(random_list):\n    plt.subplot(330+1+i)\n    file_path=df.loc[index]['Image']\n    img=imread(file_path)\n    plt.imshow(img)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:58:02.244816Z","iopub.execute_input":"2021-10-17T11:58:02.245089Z","iopub.status.idle":"2021-10-17T11:58:03.354501Z","shell.execute_reply.started":"2021-10-17T11:58:02.245062Z","shell.execute_reply":"2021-10-17T11:58:03.353733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"####  ***Note: Not all the images are of same sizes***","metadata":{}},{"cell_type":"markdown","source":"### **Check the class imbalancing**","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nax=sns.countplot(data=df,x='Target')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:58:27.138533Z","iopub.execute_input":"2021-10-17T11:58:27.139216Z","iopub.status.idle":"2021-10-17T11:58:28.02106Z","shell.execute_reply.started":"2021-10-17T11:58:27.139179Z","shell.execute_reply":"2021-10-17T11:58:28.020168Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### ***Note: Dataset is Balanced***","metadata":{}},{"cell_type":"markdown","source":"### **Split the dataset to train(60%),validation(20%),test(20%)**","metadata":{}},{"cell_type":"code","source":"import sklearn.model_selection as sk\nimport numpy as np\n\ntrain_x, val1_x, train_y, val1_y = sk.train_test_split(path,target,test_size=0.4, random_state=42)\nval_x,test_x,val_y,test_y=sk.train_test_split(val1_x,val1_y,test_size=0.5, random_state=42)\nprint('Training data:', len(train_x))\nprint('Training Label:', len(train_y))\nprint('Validation Data:',len(val_x))\nprint('Validation Label:', len(val_y))\nprint('Test Data:',len(test_x))\nprint('Test Label:', len(test_y))\nprint('-------------------------------------------')\nprint('Total Data:', len(train_x)+len(val_x)+len(test_x))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:11:32.954344Z","iopub.execute_input":"2021-10-17T12:11:32.954892Z","iopub.status.idle":"2021-10-17T12:11:32.990006Z","shell.execute_reply.started":"2021-10-17T12:11:32.954853Z","shell.execute_reply":"2021-10-17T12:11:32.989301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Preprocessing of the image**","metadata":{}},{"cell_type":"code","source":"import cv2\nfrom albumentations import (\n    Compose, ToFloat\n)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:11:37.623188Z","iopub.execute_input":"2021-10-17T12:11:37.623919Z","iopub.status.idle":"2021-10-17T12:11:37.628508Z","shell.execute_reply.started":"2021-10-17T12:11:37.623878Z","shell.execute_reply":"2021-10-17T12:11:37.62761Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Changing all the pixel values to float**\n\n> I preprocessed using albumentation, If data quantity is low we can include augmentation inside `AUGMENTATIONS_TRAIN` and use it for a real time augmentation.","metadata":{}},{"cell_type":"code","source":"AUGMENTATIONS_TRAIN = Compose([\n    ToFloat(max_value=255)\n])","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:38:43.61774Z","iopub.execute_input":"2021-10-17T12:38:43.618027Z","iopub.status.idle":"2021-10-17T12:38:43.622265Z","shell.execute_reply.started":"2021-10-17T12:38:43.617997Z","shell.execute_reply":"2021-10-17T12:38:43.621232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"AUGMENTATIONS_TEST = Compose([\n    # CLAHE(p=1.0, clip_limit=2.0),\n    ToFloat(max_value=255)\n])","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:08:04.66675Z","iopub.execute_input":"2021-10-17T12:08:04.66701Z","iopub.status.idle":"2021-10-17T12:08:04.67123Z","shell.execute_reply.started":"2021-10-17T12:08:04.666981Z","shell.execute_reply":"2021-10-17T12:08:04.67036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Data Generators inherits from keras.sequence class**\n> \n> **Our dataset is too large to fit in memory, we have to load the dataset from the hard disk in batches to our memory.**\n>  \n> * `keras.sequence` class is inherited in `Cat_Dog_Sequence` to split the data to fit into the memory dynamically.\n> * Split the data into batches(for each batch 16 images).\n> * Resize the Image to 312*312 \n","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.utils import Sequence\nimport cv2","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:08:16.973669Z","iopub.execute_input":"2021-10-17T12:08:16.974434Z","iopub.status.idle":"2021-10-17T12:08:16.978448Z","shell.execute_reply.started":"2021-10-17T12:08:16.97439Z","shell.execute_reply":"2021-10-17T12:08:16.977524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for training data\nclass Cat_Dog_Sequence(Sequence):\n    def __init__(self, x_set, y_set, batch_size, augmentations, shape):\n        self.x, self.y = x_set, y_set\n        self.batch_size = batch_size\n        self.augment = augmentations\n        self.shape = shape\n\n    def __len__(self):\n        return int(np.ceil(len(self.x) / float(self.batch_size)))\n\n    def __getitem__(self, idx):\n        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        return np.stack([\n            self.augment(image=cv2.resize(cv2.imread(x, 1), self.shape))[\"image\"] for x in batch_x\n        ], axis=0), np.array(batch_y)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:08:19.513006Z","iopub.execute_input":"2021-10-17T12:08:19.513634Z","iopub.status.idle":"2021-10-17T12:08:19.524744Z","shell.execute_reply.started":"2021-10-17T12:08:19.513591Z","shell.execute_reply":"2021-10-17T12:08:19.52373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#for Testing data\nclass Cat_Dog_Test_Sequence(Sequence):\n    def __init__(self, x_set,batch_size, augmentations, shape):\n        self.x = x_set\n        self.batch_size = batch_size\n        self.augment = augmentations\n        self.shape = shape\n\n    def __len__(self):\n        return int(np.ceil(len(self.x) / float(self.batch_size)))\n\n    def __getitem__(self, idx):\n        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        #batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        return np.stack([\n            self.augment(image=cv2.resize(cv2.imread(x, 1), self.shape))[\"image\"] for x in batch_x\n        ], axis=0)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:41:19.294064Z","iopub.execute_input":"2021-10-17T13:41:19.294343Z","iopub.status.idle":"2021-10-17T13:41:19.303422Z","shell.execute_reply.started":"2021-10-17T13:41:19.294291Z","shell.execute_reply":"2021-10-17T13:41:19.302629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_y=np.array(train_y).reshape((len(train_y),1))\nval_y=np.array(val_y).reshape((len(val_y),1))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Initialise the data generators\ntrain_gen=Cat_Dog_Sequence(train_x,train_y,16, augmentations=AUGMENTATIONS_TRAIN,shape=(312,312))\nval_gen=Cat_Dog_Sequence(val_x,val_y,16, augmentations=AUGMENTATIONS_TEST,shape=(312,312))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:08:22.355538Z","iopub.execute_input":"2021-10-17T12:08:22.355795Z","iopub.status.idle":"2021-10-17T12:08:22.366434Z","shell.execute_reply.started":"2021-10-17T12:08:22.355766Z","shell.execute_reply":"2021-10-17T12:08:22.365551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Inception Resnet v2 model is used from keras.application to get the pretrained weights**\n\n> Pretrained Imagenet weights of InceptionResnetv2 is used.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import InceptionResNetV2\nmodel=InceptionResNetV2(include_top=False, weights='imagenet',input_shape=[312,312,3])","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:13:28.535248Z","iopub.execute_input":"2021-10-17T12:13:28.535677Z","iopub.status.idle":"2021-10-17T12:13:39.491443Z","shell.execute_reply.started":"2021-10-17T12:13:28.535634Z","shell.execute_reply":"2021-10-17T12:13:39.49066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:14:34.239875Z","iopub.execute_input":"2021-10-17T12:14:34.240138Z","iopub.status.idle":"2021-10-17T12:14:34.587742Z","shell.execute_reply.started":"2021-10-17T12:14:34.24011Z","shell.execute_reply":"2021-10-17T12:14:34.58695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Add a head to our classification**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D\nfrom tensorflow.keras import Model\nx=model.output\n\n#x=Flatten(name='Flatten')(x)\nx=GlobalAveragePooling2D(name='G_avg_pooling')(x)\n'''x=Dense(1000,activation=('relu'),name=\"top_activation_2\")(x)\nx=Dropout(0.4, name=\"top_dropout2\")(x)\n'''\nx=Dense(256,activation=('relu'), name=\"top_activation_3\")(x)\nx=Dropout(0.3, name=\"top_dropout3\")(x)\n\nx=Dense(1,activation=\"sigmoid\", name=\"pred\")(x)\n\nfinal_model=Model(model.input,x)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:16:55.021136Z","iopub.execute_input":"2021-10-17T12:16:55.021415Z","iopub.status.idle":"2021-10-17T12:16:55.094005Z","shell.execute_reply.started":"2021-10-17T12:16:55.021384Z","shell.execute_reply":"2021-10-17T12:16:55.093258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final_model.summary()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:17:01.102513Z","iopub.execute_input":"2021-10-17T12:17:01.102777Z","iopub.status.idle":"2021-10-17T12:17:01.445409Z","shell.execute_reply.started":"2021-10-17T12:17:01.10275Z","shell.execute_reply":"2021-10-17T12:17:01.444654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Intialise the callbacks for our Model**\n\n* `ModelCheckpoint` is used to save the best weights with low validation loss.\n* `EarlyStopping` is used for monitoring validation loss for 2 epochs\n* `ReduceLROnPlateau`(Reduce Learning Rate) is used if validation loss decreases.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras import losses, optimizers, callbacks\nfile_path='/kaggle/working/inception_resnet_v2.h5'\n\nlr_schedule=callbacks.ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=2, min_lr=0.0001,verbose=1)\ncheckpoint= callbacks.ModelCheckpoint(file_path, monitor='val_loss',verbose=1,save_best_only=True)\nearlystopping= callbacks.EarlyStopping(monitor='val_loss',patience=3,verbose=1)\nfinal_model.compile(optimizer= optimizers.SGD(learning_rate=0.01),loss='binary_crossentropy', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:45:02.479135Z","iopub.execute_input":"2021-10-17T12:45:02.479413Z","iopub.status.idle":"2021-10-17T12:45:02.508826Z","shell.execute_reply.started":"2021-10-17T12:45:02.479383Z","shell.execute_reply":"2021-10-17T12:45:02.508105Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Fit the inception resnet v2 model for our training data**\n\n* Due to the use pretrained weights, reduce the epochs to 5\n","metadata":{}},{"cell_type":"code","source":"history=final_model.fit(train_gen, epochs=5, callbacks=[lr_schedule,checkpoint,earlystopping],validation_data=val_gen,verbose=1,shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T12:45:18.792001Z","iopub.execute_input":"2021-10-17T12:45:18.792256Z","iopub.status.idle":"2021-10-17T13:14:55.827787Z","shell.execute_reply.started":"2021-10-17T12:45:18.792229Z","shell.execute_reply":"2021-10-17T13:14:55.826999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Inference**\n\n> * Due to Imagenet Pretrained weights, our model reach the  training accuracy of 0.9993 at third epochs, Due to the model checkpoint callback we took the epochs with low validation loss as our model weights.\n> * We have large dataset. So, We get this accuracy.\n> * We Need not to consider as a overfitting.","metadata":{}},{"cell_type":"code","source":"os.listdir('./')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:17:46.53473Z","iopub.execute_input":"2021-10-17T13:17:46.535335Z","iopub.status.idle":"2021-10-17T13:17:46.543574Z","shell.execute_reply.started":"2021-10-17T13:17:46.535276Z","shell.execute_reply":"2021-10-17T13:17:46.542719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Training loss vs validation loss**","metadata":{}},{"cell_type":"code","source":"import numpy as np\nplt.plot(history.history['loss'], color='b', label=\"Training loss\")\nplt.plot(history.history['val_loss'], color='r', label=\"validation loss\")\nplt.xticks(np.arange(1, 2, 1))\nplt.yticks(np.arange(0, 1, 0.1))\nplt.legend()\nplt.title('Training Loss VS Validation Loss')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:17:51.143144Z","iopub.execute_input":"2021-10-17T13:17:51.143729Z","iopub.status.idle":"2021-10-17T13:17:51.285262Z","shell.execute_reply.started":"2021-10-17T13:17:51.143689Z","shell.execute_reply":"2021-10-17T13:17:51.284507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Training Accuracy vs Validation Accuracy**","metadata":{}},{"cell_type":"code","source":"plt.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\nplt.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\nplt.xticks(np.arange(1, 2, 1))\nplt.title('Training Accuracy VS Validation Accuracy')\nplt.legend()f\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:17:59.503455Z","iopub.execute_input":"2021-10-17T13:17:59.50416Z","iopub.status.idle":"2021-10-17T13:17:59.691756Z","shell.execute_reply.started":"2021-10-17T13:17:59.504123Z","shell.execute_reply":"2021-10-17T13:17:59.691044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Test our model with our splitted test data (because csv submission is diabled)**","metadata":{}},{"cell_type":"code","source":"#load our model weights\nfinal_model.load_weights('./inception_resnet_v2.h5')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:25:34.098293Z","iopub.execute_input":"2021-10-17T13:25:34.098961Z","iopub.status.idle":"2021-10-17T13:25:35.043522Z","shell.execute_reply.started":"2021-10-17T13:25:34.098922Z","shell.execute_reply":"2021-10-17T13:25:35.042737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#change the test data to an array\ntest_y=np.array(test_y).reshape((len(test_y),1))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:26:55.030128Z","iopub.execute_input":"2021-10-17T13:26:55.030409Z","iopub.status.idle":"2021-10-17T13:26:55.035658Z","shell.execute_reply.started":"2021-10-17T13:26:55.030378Z","shell.execute_reply":"2021-10-17T13:26:55.03481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#use generators to convert the test data\ntest_gen=Cat_Dog_Sequence(test_x,test_y,16, augmentations=AUGMENTATIONS_TRAIN,shape=(312,312))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:28:56.061097Z","iopub.execute_input":"2021-10-17T13:28:56.06139Z","iopub.status.idle":"2021-10-17T13:28:56.066547Z","shell.execute_reply.started":"2021-10-17T13:28:56.061359Z","shell.execute_reply":"2021-10-17T13:28:56.065662Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# calculate loss and accuracy for our model\nloss,acc=final_model.evaluate(test_gen,verbose=1)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:29:23.887432Z","iopub.execute_input":"2021-10-17T13:29:23.887949Z","iopub.status.idle":"2021-10-17T13:30:04.954881Z","shell.execute_reply.started":"2021-10-17T13:29:23.887914Z","shell.execute_reply":"2021-10-17T13:30:04.954097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Model with 98.92% of accuracy**","metadata":{}},{"cell_type":"code","source":"print('Accuracy for our test_data:',str(acc*100)+'%')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:31:00.203051Z","iopub.execute_input":"2021-10-17T13:31:00.2034Z","iopub.status.idle":"2021-10-17T13:31:00.211987Z","shell.execute_reply.started":"2021-10-17T13:31:00.203276Z","shell.execute_reply":"2021-10-17T13:31:00.211119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **Checking our model using precision and recall other accuracy**","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import classification_report\n\ntest_gen=Cat_Dog_Test_Sequence(test_x, 16, augmentations=AUGMENTATIONS_TEST,shape=(312,312))\noutput=final_model.predict(test_gen,verbose=1)\noutput=output.round().astype(np.int32).reshape(output.shape[0])\ntarget_names=('Cat','Dog')\nprint(classification_report(test_y,output, target_names=target_names))\n\n## According to f1 score, We conclude this as a good model","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:44:20.608986Z","iopub.execute_input":"2021-10-17T13:44:20.609249Z","iopub.status.idle":"2021-10-17T13:44:52.528231Z","shell.execute_reply.started":"2021-10-17T13:44:20.609221Z","shell.execute_reply":"2021-10-17T13:44:52.527343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### **According to f1 score, We conclude this as a good model.**","metadata":{}},{"cell_type":"markdown","source":"### **Test the model and submit the results in submission.csv (Optional)**","metadata":{}},{"cell_type":"code","source":"class Cat_Dog_Test_Sequence(Sequence):\n    def __init__(self, x_set,batch_size, augmentations, shape):\n        self.x = x_set\n        self.batch_size = batch_size\n        self.augment = augmentations\n        self.shape = shape\n\n    def __len__(self):\n        return int(np.ceil(len(self.x) / float(self.batch_size)))\n\n    def __getitem__(self, idx):\n        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        #batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        return np.stack([\n            self.augment(image=cv2.resize(cv2.imread(x, 1), self.shape))[\"image\"] for x in batch_x\n        ], axis=0)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T13:39:20.895395Z","iopub.execute_input":"2021-10-17T13:39:20.895997Z","iopub.status.idle":"2021-10-17T13:39:20.902445Z","shell.execute_reply.started":"2021-10-17T13:39:20.895959Z","shell.execute_reply":"2021-10-17T13:39:20.901737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create separate list to store target class (0 or 1) and respective image path\ntest_filenames=os.listdir('./test1')\ntest_path=[]\nid=[]\nfor i in test_filenames:\n    temp=i.split('.')[0]\n    id.append(temp)\n    test_path.append('./test1/'+i)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:37:26.156912Z","iopub.execute_input":"2021-10-17T08:37:26.157189Z","iopub.status.idle":"2021-10-17T08:37:26.179883Z","shell.execute_reply.started":"2021-10-17T08:37:26.15716Z","shell.execute_reply":"2021-10-17T08:37:26.179195Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('No.of.Test Images:',len(test_path))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:21:32.590496Z","iopub.execute_input":"2021-10-17T08:21:32.591162Z","iopub.status.idle":"2021-10-17T08:21:32.595519Z","shell.execute_reply.started":"2021-10-17T08:21:32.591125Z","shell.execute_reply":"2021-10-17T08:21:32.594794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_gen=Cat_Dog_Test_Sequence(test_path, 16, augmentations=AUGMENTATIONS_TEST,shape=(312,312))","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:22:07.180128Z","iopub.execute_input":"2021-10-17T08:22:07.180466Z","iopub.status.idle":"2021-10-17T08:22:07.189263Z","shell.execute_reply.started":"2021-10-17T08:22:07.18042Z","shell.execute_reply":"2021-10-17T08:22:07.188544Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output=final_model.predict(test_gen,verbose=1)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:28:19.937922Z","iopub.execute_input":"2021-10-17T08:28:19.938743Z","iopub.status.idle":"2021-10-17T08:29:38.583946Z","shell.execute_reply.started":"2021-10-17T08:28:19.938694Z","shell.execute_reply":"2021-10-17T08:29:38.583182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output.shape","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:29:50.874056Z","iopub.execute_input":"2021-10-17T08:29:50.874317Z","iopub.status.idle":"2021-10-17T08:29:50.882832Z","shell.execute_reply.started":"2021-10-17T08:29:50.874289Z","shell.execute_reply":"2021-10-17T08:29:50.882036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output=output.round().astype(np.int32).reshape(output.shape[0])","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:33:57.70397Z","iopub.execute_input":"2021-10-17T08:33:57.704231Z","iopub.status.idle":"2021-10-17T08:33:57.708779Z","shell.execute_reply.started":"2021-10-17T08:33:57.704201Z","shell.execute_reply":"2021-10-17T08:33:57.707825Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output.shape","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:34:08.925876Z","iopub.execute_input":"2021-10-17T08:34:08.926142Z","iopub.status.idle":"2021-10-17T08:34:08.933827Z","shell.execute_reply.started":"2021-10-17T08:34:08.926113Z","shell.execute_reply":"2021-10-17T08:34:08.93281Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output_csv={'id':id,'label':output}\no=pd.DataFrame(output_csv)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:39:12.786911Z","iopub.execute_input":"2021-10-17T08:39:12.787216Z","iopub.status.idle":"2021-10-17T08:39:12.793164Z","shell.execute_reply.started":"2021-10-17T08:39:12.787185Z","shell.execute_reply":"2021-10-17T08:39:12.792509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"o.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2021-10-17T08:41:38.368312Z","iopub.execute_input":"2021-10-17T08:41:38.368854Z","iopub.status.idle":"2021-10-17T08:41:38.399071Z","shell.execute_reply.started":"2021-10-17T08:41:38.368815Z","shell.execute_reply":"2021-10-17T08:41:38.398283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nos.listdir('./')","metadata":{"execution":{"iopub.status.busy":"2021-10-17T11:55:50.759894Z","iopub.execute_input":"2021-10-17T11:55:50.760187Z","iopub.status.idle":"2021-10-17T11:55:50.767477Z","shell.execute_reply.started":"2021-10-17T11:55:50.760156Z","shell.execute_reply":"2021-10-17T11:55:50.766619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!jupyter nbconvert --execute --to pdf __notebook_source__.ipynb","metadata":{"execution":{"iopub.status.busy":"2021-10-17T14:36:20.015032Z","iopub.execute_input":"2021-10-17T14:36:20.015353Z","iopub.status.idle":"2021-10-17T14:36:34.655975Z","shell.execute_reply.started":"2021-10-17T14:36:20.015319Z","shell.execute_reply":"2021-10-17T14:36:34.654903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}