grapevine-leaves-livedemo-start.ipynb

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   "source": [
    "# Pytorch Grapevine Leaf Classifier\n",
    "\n",
    "Taken with very little modification (just path and use pyplot) from Maxim Vlah\n",
    "\n",
    "Dataset and code (c) 2021 Maxim Vlah (maximvlah@gmail.com), Licensed CC-BY-SA-NC 4.0 and used with permission.\n",
    "\n",
    "Dataset: https://www.kaggle.com/maximvlah/grapevine-leaves\n",
    "\n",
    "Original notebook: https://www.kaggle.com/maximvlah/pytorch-resnet18-94-f1\n",
    "\n",
    "**Important notice from Thomas**: I picked this notebook because I liked the dataset and the code was reasonable and protypical of a lot of code I see in my work. Creating, curating and releasing a useful dataset (from your own images, too) is a great accomplishment, and a demonstration of the dataset with simple demo code is cool, too. I do not think of this code as primarily _unoptimized_ but rather as primarily _worth optimizing_. Thank you Maxim!\n"
   ]
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   "source": [
    "'''Load librarires'''\n",
    "import pickle\n",
    "import time\n",
    "import random\n",
    "import ntpath\n",
    "import glob\n",
    "import os\n",
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "from collections import defaultdict\n",
    "\n",
    "from matplotlib import pyplot\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import torchvision\n",
    "from torchvision import models, transforms, utils\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score"
   ]
  },
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   "execution_count": 3,
   "id": "composite-bracelet",
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     "text": [
      "You are using -> cuda:0\n"
     ]
    }
   ],
   "source": [
    "class CFG:\n",
    "\n",
    "    '''Store all hyperparameters here.''' \n",
    "\n",
    "    SEED = 420\n",
    "    TEST_SIZE = 0.2\n",
    "    VAL_SIZE = 0.25\n",
    "    CLASSES = None #Need to update manually\n",
    "    OUTPUT_FEATURES = None #Need to update manually\n",
    "    \n",
    "    #transforms\n",
    "    TRAIN_TRANSFORMS = transforms.Compose([\n",
    "        #Rotate the image by given angle.\n",
    "        transforms.RandomRotation(5),\n",
    "        #Crop the given PIL Image to random size and aspect ratio.\n",
    "        transforms.Resize((224,224)),\n",
    "        #Horizontally flip the given PIL Image randomly with a given probability.\n",
    "        transforms.RandomHorizontalFlip(p = 0.2),\n",
    "        #Convert a PIL Image or numpy.ndarray to tensor.\n",
    "        transforms.ToTensor(),\n",
    "        #Normalize a tensor image with mean and standard deviation\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                             std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "\n",
    "    VAL_TRANSFORMS = transforms.Compose([\n",
    "        #Rotate the image by given angle.\n",
    "        transforms.RandomRotation(5),\n",
    "        #Crop the given PIL Image to random size and aspect ratio.\n",
    "        transforms.Resize((224,224)),\n",
    "        #Horizontally flip the given PIL Image randomly with a given probability.\n",
    "        transforms.RandomHorizontalFlip(p = 0.2),\n",
    "        #Convert a PIL Image or numpy.ndarray to tensor.\n",
    "        transforms.ToTensor(),\n",
    "        #Normalize a tensor image with mean and standard deviation\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                             std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "    \n",
    "    TEST_TRANSFORMS = transforms.Compose([\n",
    "        #Crop the given PIL Image to random size and aspect ratio.\n",
    "        transforms.Resize((224,224)),\n",
    "        #Convert a PIL Image or numpy.ndarray to tensor.\n",
    "        transforms.ToTensor(),\n",
    "        #Normalize a tensor image with mean and standard deviation\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                             std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "    \n",
    "    #models\n",
    "    \n",
    "    # MODEL1 = {\n",
    "    #     'name': 'custom_cnn',\n",
    "    #     'transfer': False,\n",
    "    #     'architecture': nn.Sequential(\n",
    "    #       nn.Conv2d(1,20,5),\n",
    "    #       nn.ReLU(),\n",
    "    #       nn.Conv2d(20,64,5),\n",
    "    #       nn.ReLU()\n",
    "    #     ),\n",
    "    #     'criterion': nn.CrossEntropyLoss(),\n",
    "    #     'optimizer': optim.SGD,\n",
    "    #     'momentum': 0.9,\n",
    "    #     'lr': 0.003,\n",
    "    #     'history': None\n",
    "    # }\n",
    "\n",
    "    MODEL2 = {\n",
    "        'name': 'resnet18',\n",
    "        'transfer': True,\n",
    "        'architecture': models.resnet18(pretrained=True), # ResNet18\n",
    "        'criterion': nn.CrossEntropyLoss(),\n",
    "        'optimizer': optim.SGD,\n",
    "        'momentum': 0.9,\n",
    "        'lr': 0.002,\n",
    "        'history': None\n",
    "    }\n",
    "\n",
    "    # MODELS =[MODEL1,MODEL2]\n",
    "\n",
    "    BATCH_SIZE = 64\n",
    "    EPOCHS = 120\n",
    "    \n",
    "    DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "    print('You are using ->', DEVICE)    "
   ]
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   "id": "swedish-girlfriend",
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   "source": [
    "def seed_everything(seed):\n",
    "    '''Make the results reproducible'''\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = True \n",
    "\n",
    "seed_everything(CFG.SEED)"
   ]
  },
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   "id": "engaging-ranch",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/mnt/data/vision/plants-wine/data/Merlot/IMG_1...</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                path  label\n",
       "0  /mnt/data/vision/plants-wine/data/Merlot/IMG_1...      4\n",
       "1  /mnt/data/vision/plants-wine/data/Merlot/IMG_1...      4\n",
       "2  /mnt/data/vision/plants-wine/data/Merlot/IMG_0...      4\n",
       "3  /mnt/data/vision/plants-wine/data/Merlot/IMG_1...      4\n",
       "4  /mnt/data/vision/plants-wine/data/Merlot/IMG_1...      4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Store image paths and their labels in pandas dataframe. Will be used to create pytorch datasets. '''\n",
    "\n",
    "paths = glob.glob('/mnt/data/vision/plants-wine/data/*/*' )\n",
    "meta = pd.DataFrame([(path, ntpath.basename(ntpath.dirname(path))) for path in paths], columns = ['path','label'])\n",
    "#get class mappings\n",
    "classes = dict(enumerate(meta.label.astype('category').cat.categories))\n",
    "CFG.CLASSES = classes\n",
    "CFG.OUTPUT_FEATURES = len(CFG.CLASSES)\n",
    "\n",
    "meta.label = meta.label.astype('category').cat.codes\n",
    "meta.head()"
   ]
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   "id": "valid-scanning",
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    {
     "data": {
      "text/plain": [
       "{0: 'Auxerrois',\n",
       " 1: 'Cabernet Franc',\n",
       " 2: 'Cabernet Sauvignon',\n",
       " 3: 'Chardonnay',\n",
       " 4: 'Merlot',\n",
       " 5: 'Muller Thurgau',\n",
       " 6: 'Pinot Noir',\n",
       " 7: 'Riesling',\n",
       " 8: 'Sauvignon Blanc',\n",
       " 9: 'Syrah',\n",
       " 10: 'Tempranillo'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "classes"
   ]
  },
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   "id": "analyzed-student",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train length -> 605\n",
      "Val length -> 202\n",
      "Test length -> 202\n"
     ]
    }
   ],
   "source": [
    "'''Split data into train, validation and test sets'''\n",
    "\n",
    "X = list(meta.path)\n",
    "y = list(meta.label)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=CFG.TEST_SIZE, \n",
    "                                                    random_state=CFG.SEED, \n",
    "                                                    stratify=y) #stratified split\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train,\n",
    "                                                  y_train,\n",
    "                                                  test_size=CFG.VAL_SIZE,\n",
    "                                                  random_state=CFG.SEED,\n",
    "                                                  stratify=y_train) #stratified split\n",
    "\n",
    "print(f'Train length -> {len(X_train)}')\n",
    "print(f'Val length -> {len(X_val)}')\n",
    "print(f'Test length -> {len(X_test)}')"
   ]
  },
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   "id": "elder-turkish",
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   "source": [
    "'''Custom pytorch dataset implementation.'''\n",
    "class LeafDataset(Dataset):\n",
    "    def __init__(self,X,y, transform=None):\n",
    "        self.X = X\n",
    "        self.y = torch.tensor(y, dtype=torch.long)\n",
    "        self.transform = transform\n",
    "\n",
    "        assert len(self.X) == len(self.y), f'X and y have different lengths -> {len(self.X)} != {len(self.y)} '\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "\n",
    "    def __getitem__(self,idx):\n",
    "        img_path = self.X[idx]\n",
    "        img = Image.open(img_path)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        label = self.y[idx]\n",
    "        return (img,label) \n",
    "\n",
    "    def show_img(self,idx):\n",
    "        '''Plot image'''\n",
    "        img,label = self.__getitem__(idx)\n",
    "        img = img.numpy().transpose((1, 2, 0))\n",
    "        pyplot.figure(figsize=(16, 8))\n",
    "        pyplot.axis('off')\n",
    "        pyplot.imshow(img)\n",
    "        pyplot.title(CFG.CLASSES[int(label)]) #using CFG.CLASSES dict\n",
    "        pyplot.pause(0.001)"
   ]
  },
  {
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   "execution_count": 14,
   "id": "personalized-inside",
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   "source": [
    "'''Instantiate pytorch train, validation and test sets'''\n",
    "TRAIN = LeafDataset(X_train,y_train, CFG.TRAIN_TRANSFORMS)\n",
    "VAL = LeafDataset(X_val,y_val, CFG.VAL_TRANSFORMS)\n",
    "TEST = LeafDataset(X_test,y_test, CFG.TEST_TRANSFORMS)\n",
    "\n",
    "'''Instantiate Dataloaders'''\n",
    "TRAIN_LOADER = DataLoader(TRAIN,CFG.BATCH_SIZE)\n",
    "VAL_LOADER = DataLoader(VAL,CFG.BATCH_SIZE)\n",
    "TEST_LOADER = DataLoader(TEST,CFG.BATCH_SIZE)"
   ]
  },
  {
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   "id": "sorted-teens",
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   "source": [
    "class Net(nn.Module):\n",
    "    '''\n",
    "    ========================\n",
    "          NEURAL NET\n",
    "    ========================\n",
    "    \n",
    "    Args:\n",
    "        model_dict(dict): configuration dict containing the model architecture\n",
    "        output_features(int): length of output tensor; for classification equals to number of classes\n",
    "    '''\n",
    "    def __init__(self, model_dict, output_features):\n",
    "        super().__init__()\n",
    "        self.__dict__.update(model_dict) #unpack model dict from CFG into this class\n",
    "        \n",
    "        if self.transfer:\n",
    "            model = self.architecture\n",
    "            num_ftrs = model.fc.in_features\n",
    "            model.fc = nn.Linear(num_ftrs, output_features)\n",
    "            self.model = model\n",
    "        else:\n",
    "            self.model = self.architecture\n",
    "        \n",
    "        self.output_features = output_features\n",
    "        #optimizer\n",
    "        self.optimizer = self.optimizer(self.model.parameters(),self.lr)\n",
    "        #path where to save model\n",
    "        self.save_path = 'models'\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def fit(self,\n",
    "            train_loader,\n",
    "            val_loader,\n",
    "            epochs = 5,\n",
    "            batch_size = 32,\n",
    "            device = 'cpu'):\n",
    "        '''\n",
    "        =============================\n",
    "            OPTIMIZATION LOOP\n",
    "        =============================\n",
    "\n",
    "        Args:\n",
    "            train_loader(torch dataloader)\n",
    "            val_loader(torch dataloader)\n",
    "            epochs(int)\n",
    "            batch_size(int)\n",
    "            device(str)\n",
    "\n",
    "            \n",
    "        Output style inspired by the skorch fit() method\n",
    "\n",
    "        '''\n",
    "        #may be changed if lrscheduler is used???\n",
    "        lr = deepcopy(self.lr)\n",
    "\n",
    "        #get model training history\n",
    "        history = self.history\n",
    "        if history == None:\n",
    "            history = defaultdict(list)\n",
    "        else:\n",
    "            pass\n",
    "        #get train and val sizes\n",
    "        train_size = len(train_loader.dataset)\n",
    "        val_size = len(val_loader.dataset)\n",
    "        #stuff for printing epoch metrics as a beautiful table\n",
    "        headers = ['epoch','train_loss','val_loss','val_acc','cp','lr','dur']\n",
    "        template = '{:<10} {:<10} {:<10} {:<10} {:<10} {:<10} {:<10}'\n",
    "        print(template.format(*headers))\n",
    "        print(template.replace(':', ':-').format('','','','','','',''))\n",
    "        cyan = \"\\033[96m{:<10}\\033[00m\" #cyan\n",
    "        purple = \"\\033[95m{:<10}\\033[00m\" #purple\n",
    "        green = \"\\033[92m{:<10}\\033[00m\" #green\n",
    "        white = \"\\033[0m{:<10}\\033[0m\" #white\n",
    "        #set model into train mode\n",
    "        self.model.train()\n",
    "        #send model to device\n",
    "        self.model.to(device)\n",
    "        #training loop\n",
    "        for epoch in range(epochs):\n",
    "            start_time = time.time()\n",
    "            train_loss = 0\n",
    "            val_loss, val_acc = 0, 0\n",
    "            #optimization  loop\n",
    "            for (X,y) in train_loader:\n",
    "                #Send training data to device\n",
    "                X,y = X.to(device), y.to(device)\n",
    "                #Forward propagation\n",
    "                pred = self.model(X)\n",
    "                loss = self.criterion(pred,y)\n",
    "                #Backpropagation\n",
    "                self.optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                #update loss\n",
    "                train_loss = loss.item()\n",
    "            #validation loop\n",
    "            with torch.no_grad():\n",
    "                for X, y in val_loader:\n",
    "                    X,y = X.to(device),y.to(device)\n",
    "                    pred = self.model(X)\n",
    "                    val_loss = self.criterion(pred,y).item()\n",
    "                    val_acc += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "            #calculate validation accuracy after the epoch\n",
    "            val_acc /= val_size\n",
    "            #append epoch results\n",
    "            history['epoch'].append(epoch+1)\n",
    "            history['train_loss'].append(train_loss)\n",
    "            history['val_loss'].append(val_loss)\n",
    "            history['val_acc'].append(val_acc)\n",
    "            \n",
    "            #colorize epoch's output if it improves\n",
    "            colortemp = template.split(' ')\n",
    "            # colorize train loss if it decreases\n",
    "            if history['train_loss'][-1] == min(history['train_loss']):\n",
    "                colortemp[1] = cyan\n",
    "            else:\n",
    "                colortemp[1] = white\n",
    "            #colorize validation loss if it decreases\n",
    "            if history['val_loss'][-1] == min(history['val_loss']):\n",
    "                colortemp[2] = purple\n",
    "            else:\n",
    "                colortemp[2] = white\n",
    "            # colorize validation accuracy & save best weights if it increases\n",
    "            if history['val_acc'][-1] == max(history['val_acc']):\n",
    "                #colorize       \n",
    "                colortemp[3] = green\n",
    "                #checkpoint\n",
    "                cp = '+'\n",
    "                if not os.path.exists(self.save_path):\n",
    "                    os.mkdir(self.save_path)\n",
    "                torch.save(self.model.state_dict(), Path(self.save_path,f'best_{self.name}.pth'))\n",
    "            else:\n",
    "                colortemp[3] = white\n",
    "                cp = '-'\n",
    "            colortemp = ' '.join(colortemp)\n",
    "\n",
    "            #calculate epoch duration (in seconds)\n",
    "            end_time = time.time()\n",
    "            dur = end_time - start_time\n",
    "            #append the rest of epoch results\n",
    "            history['cp'].append(cp)\n",
    "            history['lr'].append(lr)\n",
    "            history['dur'].append(dur)\n",
    "            #display the epoch results\n",
    "            print(colortemp.format(*f'{epoch+1}/{epochs} {train_loss:.4f} {val_loss:.4f} {val_acc:.2f} {cp} {lr} {dur:.2f}'.split(' ')))\n",
    "        #update epoch number of the entire training history\n",
    "        history['epoch'] = [e+1 for e in range(len(history['epoch']))]\n",
    "        #update model's training history\n",
    "        self.history = history\n",
    "        #save training history as csv\n",
    "        self.save_history()\n",
    "\n",
    "    def eval_model(self,dataloader,avg=None,device ='cpu'):\n",
    "        '''\n",
    "        ==================================\n",
    "           ACCURACY PRECISION RECALL F1\n",
    "        ==================================\n",
    "        '''\n",
    "        labels = [l for l in range(self.output_features)]\n",
    "        loader_size = len(dataloader)\n",
    "        dataset_size = len(dataloader.dataset)\n",
    "\n",
    "        acc =0\n",
    "        precision = 0\n",
    "        recall = 0\n",
    "        f1 = 0\n",
    "\n",
    "        #set model to evaluation mode\n",
    "        self.model.eval()\n",
    "        #model to device, default cpu\n",
    "        self.model.to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for X, y in dataloader:\n",
    "                pred = self.model(X)\n",
    "                #accuracy\n",
    "                acc += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "\n",
    "                pred = pred.argmax(1)\n",
    "                pred,y = list(pred), list(y)\n",
    "                #precision\n",
    "                p = precision_score(y, pred, labels = labels, zero_division = 1, average = avg)\n",
    "                precision+=p\n",
    "                #recall\n",
    "                r = recall_score(y, pred, labels = labels, zero_division = 1,  average = avg)\n",
    "                recall+=r\n",
    "                #f1 score\n",
    "                f = f1_score(y, pred, labels = labels, zero_division = 1,  average = avg)\n",
    "                f1 += f\n",
    "\n",
    "        acc /= dataset_size\n",
    "        precision /= loader_size\n",
    "        recall /= loader_size\n",
    "        f1 /= loader_size\n",
    "\n",
    "        print(f\" Accuracy: {(100*acc):>0.1f}%\")\n",
    "        print(f\"Precision: {(100*np.mean(precision)):>0.1f}%\")\n",
    "        print(f\"   Recall: {(100*np.mean(recall)):>0.1f}%\")\n",
    "        print(f\" F1 Score: {(100*np.mean(f1)):>0.1f}%\")\n",
    "\n",
    "        \n",
    "    def plot_loss_history(self):\n",
    "        '''\n",
    "        Plot loss history\n",
    "        '''\n",
    "        assert self.history != None, 'No history to plot -> the model has not been trained yet!'\n",
    "        \n",
    "        df = pd.DataFrame(self.history)\n",
    "        fig = pyplot.figure()\n",
    "        pyplot.plot(df.epoch, df.train_loss, label='train loss')\n",
    "        pyplot.plot(df.epoch, df.val_loss, label='val loss')\n",
    "        pyplot.title('Loss History')\n",
    "        pyplot.legend()\n",
    "        pyplot.show()\n",
    "\n",
    "    def save_history(self):\n",
    "        '''Save model's training history'''\n",
    "        assert self.history != None, 'No history to save -> the model has not been trained yet!'\n",
    "        #save as csv\n",
    "        pd.DataFrame(self.history).to_csv(Path(f'models/{self.name}_history.csv')) \n",
    "        #save as pickle file\n",
    "        with open(Path(f'models/{self.name}_history.pkl'), 'wb') as f:\n",
    "            pickle.dump(self.history, f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "    def save_model(self):\n",
    "        torch.save(self.model.state_dict(), Path(self.save_path,f'latest_{self.name}.pth'))\n",
    "\n",
    "    def load_model(self,path = 'models'):\n",
    "        '''Load model'''\n",
    "        try:\n",
    "            #load model weights\n",
    "            p = Path(path,f'best_{self.name}.pth')\n",
    "            self.model.load_state_dict(torch.load(p))\n",
    "            #load model training history\n",
    "            with open(Path(path,f'{self.name}_history.pkl'), 'rb') as h: \n",
    "                self.history = pickle.load(h)\n",
    "        except:\n",
    "            print('No model to load!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "norman-national",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-08T11:34:19.600420Z",
     "iopub.status.busy": "2021-05-08T11:34:19.599609Z",
     "iopub.status.idle": "2021-05-08T11:34:19.602614Z",
     "shell.execute_reply": "2021-05-08T11:34:19.602163Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#instantiate model and send to device\n",
    "Resnet18 = Net(CFG.MODEL2,CFG.OUTPUT_FEATURES)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "wooden-baptist",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-08T11:34:19.640153Z",
     "iopub.status.busy": "2021-05-08T11:34:19.639534Z",
     "iopub.status.idle": "2021-05-08T13:39:36.915991Z",
     "shell.execute_reply": "2021-05-08T13:39:36.916582Z"
    },
    "papermill": {
     "duration": 7517.298847,
     "end_time": "2021-05-08T13:39:36.916788",
     "exception": false,
     "start_time": "2021-05-08T11:34:19.617941",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      train_loss val_loss   val_acc    cp         lr         dur       \n",
      "---------- ---------- ---------- ---------- ---------- ---------- ----------\n",
      "1/3        \u001b[96m2.4043    \u001b[00m \u001b[95m2.3869    \u001b[00m \u001b[92m0.16      \u001b[00m +          0.002      21.43     \n",
      "2/3        \u001b[96m2.3234    \u001b[00m \u001b[95m2.3110    \u001b[00m \u001b[92m0.24      \u001b[00m +          0.002      20.02     \n",
      "3/3        \u001b[96m2.2311    \u001b[00m \u001b[95m2.3061    \u001b[00m \u001b[92m0.28      \u001b[00m +          0.002      19.85     \n"
     ]
    }
   ],
   "source": [
    "Resnet18.fit(TRAIN_LOADER,\n",
    "             VAL_LOADER,\n",
    "             3, CFG.EPOCHS,\n",
    "             CFG.BATCH_SIZE,\n",
    "             CFG.DEVICE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "powerful-nevada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-08T13:39:37.009864Z",
     "iopub.status.busy": "2021-05-08T13:39:37.009314Z",
     "iopub.status.idle": "2021-05-08T13:39:37.901738Z",
     "shell.execute_reply": "2021-05-08T13:39:37.901249Z"
    },
    "papermill": {
     "duration": 0.940682,
     "end_time": "2021-05-08T13:39:37.901891",
     "exception": false,
     "start_time": "2021-05-08T13:39:36.961209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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   ],
   "source": [
    "Resnet18.plot_loss_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "renewable-secretariat",
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     "exception": false,
     "start_time": "2021-05-08T13:39:37.946698",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model:\n",
      "--------------------\n",
      " Accuracy: 92.6%\n",
      "Precision: 94.3%\n",
      "   Recall: 94.5%\n",
      " F1 Score: 93.8%\n",
      "Current Model:\n",
      "--------------------\n",
      " Accuracy: 92.6%\n",
      "Precision: 94.3%\n",
      "   Recall: 94.5%\n",
      " F1 Score: 93.8%\n"
     ]
    }
   ],
   "source": [
    "#evaluate best model's performance on the test set\n",
    "print('Best Model:')\n",
    "print('-'*20)\n",
    "best_model = deepcopy(Resnet18)\n",
    "best_model.load_model()\n",
    "best_model.eval_model(TEST_LOADER)\n",
    "#evaluate current model's performance on the test set\n",
    "print('Current Model:')\n",
    "print('-'*20)\n",
    "Resnet18.eval_model(TEST_LOADER)"
   ]
  },
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   "cell_type": "markdown",
   "id": "eleven-iceland",
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     "start_time": "2021-05-08T13:40:34.181670",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# What's next?\n",
    "\n",
    "- Train function as model class method\n",
    "- LR Scheduler\n",
    "- Train Custom Model\n",
    "- Confusion Matrix\n",
    "- Random & Grid Search\n",
    "- Cross-Validation\n",
    "- Ensemble Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "postal-lindsay",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def cross_val():\n",
    "    '''\n",
    "    ======================\n",
    "       CROSS VALIDATION\n",
    "    ======================\n",
    "    '''\n",
    "    pass"
   ]
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