{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Fine Grained Image Classification","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Importing Modules and Fixing Seeds","metadata":{}},{"cell_type":"code","source":"!pip install torchsummary\n%matplotlib inline\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport random\nimport os\nfrom PIL import Image\n\nimport torch\nfrom torchsummary import summary\nfrom torch import nn, optim\nfrom torch.functional import F\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import transforms\nfrom torch.autograd import Variable","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2023-02-23T13:36:22.102062Z","iopub.execute_input":"2023-02-23T13:36:22.102372Z","iopub.status.idle":"2023-02-23T13:36:32.530992Z","shell.execute_reply.started":"2023-02-23T13:36:22.102339Z","shell.execute_reply":"2023-02-23T13:36:32.530150Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.style.use('seaborn-whitegrid')\nplt.rcParams['lines.linewidth'] = 2\nplt.rcParams['font.sans-serif'] = 'Arial'\nplt.rcParams['text.color'] = 'black'\nplt.rcParams['axes.labelcolor']= 'black'\nplt.rcParams['xtick.color'] = 'black'\nplt.rcParams['ytick.color'] = 'black'\nplt.rcParams['font.size']=12","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:40.610844Z","iopub.execute_input":"2023-02-23T13:36:40.611137Z","iopub.status.idle":"2023-02-23T13:36:40.616940Z","shell.execute_reply.started":"2023-02-23T13:36:40.611104Z","shell.execute_reply":"2023-02-23T13:36:40.615921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"SEED = 1234\n\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntorch.manual_seed(SEED)\ntorch.cuda.manual_seed(SEED)\ntorch.backends.cudnn.deterministic = True\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:44.320665Z","iopub.execute_input":"2023-02-23T13:36:44.320956Z","iopub.status.idle":"2023-02-23T13:36:44.380785Z","shell.execute_reply.started":"2023-02-23T13:36:44.320925Z","shell.execute_reply":"2023-02-23T13:36:44.379969Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Looking at The Data and Creating The Dataset For Deep Learning","metadata":{}},{"cell_type":"code","source":"data_dir = '../input/dog-breed-identification/'\nlabels = pd.read_csv(os.path.join(data_dir, 'labels.csv'))\nassert(len(os.listdir(os.path.join(data_dir, 'train'))) == len(labels))","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:47.962769Z","iopub.execute_input":"2023-02-23T13:36:47.963147Z","iopub.status.idle":"2023-02-23T13:36:48.267026Z","shell.execute_reply.started":"2023-02-23T13:36:47.963105Z","shell.execute_reply":"2023-02-23T13:36:48.266177Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:51.083377Z","iopub.execute_input":"2023-02-23T13:36:51.083670Z","iopub.status.idle":"2023-02-23T13:36:51.102535Z","shell.execute_reply.started":"2023-02-23T13:36:51.083636Z","shell.execute_reply":"2023-02-23T13:36:51.101718Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nlabels.breed = le.fit_transform(labels.breed)\nlabels.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:53.514600Z","iopub.execute_input":"2023-02-23T13:36:53.514888Z","iopub.status.idle":"2023-02-23T13:36:54.210020Z","shell.execute_reply.started":"2023-02-23T13:36:53.514858Z","shell.execute_reply":"2023-02-23T13:36:54.209318Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = labels.id\ny = labels.breed\n\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_valid, y_train, y_valid = train_test_split(X, y,test_size=0.4, random_state=SEED, stratify=y)\nX_valid, X_test, y_valid, y_test = train_test_split(X_valid, y_valid, test_size=0.5, random_state=SEED, stratify=y_valid)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:36:57.119107Z","iopub.execute_input":"2023-02-23T13:36:57.119410Z","iopub.status.idle":"2023-02-23T13:36:57.173866Z","shell.execute_reply.started":"2023-02-23T13:36:57.119377Z","shell.execute_reply":"2023-02-23T13:36:57.172934Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Dataset_Interpreter(Dataset):\n    def __init__(self, data_path, file_names, labels=None, transforms=None):\n        self.data_path = data_path\n        self.file_names = file_names\n        self.labels = labels\n        self.transforms = transforms\n        \n    def __len__(self):\n        return (len(self.file_names))\n    \n    def __getitem__(self, idx):\n        img_name = f'{self.file_names.iloc[idx]}.jpg'\n        full_address = os.path.join(self.data_path, img_name)\n        image = Image.open(full_address)\n        label = self.labels.iloc[idx]\n        \n        if self.transforms is not None:\n            image = self.transforms(image)\n            \n        return np.array(image), label","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:01.406912Z","iopub.execute_input":"2023-02-23T13:37:01.407272Z","iopub.status.idle":"2023-02-23T13:37:01.424395Z","shell.execute_reply.started":"2023-02-23T13:37:01.407236Z","shell.execute_reply":"2023-02-23T13:37:01.422531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_images(images):\n\n    n_images = len(images)\n\n    rows = int(np.sqrt(n_images))\n    cols = int(np.sqrt(n_images))\n\n    fig = plt.figure(figsize=(20,10))\n    for i in range(rows*cols):\n        ax = fig.add_subplot(rows, cols, i+1)\n        ax.set_title(f'{le.inverse_transform([images[i][1]])}')\n        ax.imshow(np.array(images[i][0]))\n        ax.axis('off')\n        ","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:04.069455Z","iopub.execute_input":"2023-02-23T13:37:04.069738Z","iopub.status.idle":"2023-02-23T13:37:04.076292Z","shell.execute_reply.started":"2023-02-23T13:37:04.069708Z","shell.execute_reply":"2023-02-23T13:37:04.075505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"N_IMAGES = 9\n\ntrain_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_train, labels=y_train)\nimages = [(image, label) for image, label in [train_data[i] for i in range(N_IMAGES)]] \nplot_images(images)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:06.960026Z","iopub.execute_input":"2023-02-23T13:37:06.960332Z","iopub.status.idle":"2023-02-23T13:37:07.585211Z","shell.execute_reply.started":"2023-02-23T13:37:06.960287Z","shell.execute_reply":"2023-02-23T13:37:07.584477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Getting Data Ready For Deep Learning","metadata":{}},{"cell_type":"code","source":"normalize = transforms.Normalize(\n   mean=[0.485, 0.456, 0.406],\n   std=[0.229, 0.224, 0.225]\n)\ntransforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)\ntrain_transforms = transforms.Compose([transforms.Resize(32),\n                               transforms.CenterCrop(32),\n                               transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0),\n                               transforms.RandomHorizontalFlip(p=0.5),\n                               transforms.RandomVerticalFlip(p=0.5),\n                               transforms.RandomGrayscale(p=0.1), \n                               transforms.ToTensor(),\n                               normalize])\ntest_transforms = transforms.Compose([transforms.Resize(32),\n                               transforms.CenterCrop(32),\n                               transforms.ToTensor(),\n                               normalize])\n\ntrain_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_train, labels=y_train, transforms=train_transforms)\nvalid_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_valid, labels=y_valid, transforms=test_transforms)\ntest_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_test, labels=y_test, transforms=test_transforms)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:14.584638Z","iopub.execute_input":"2023-02-23T13:37:14.584928Z","iopub.status.idle":"2023-02-23T13:37:14.593491Z","shell.execute_reply.started":"2023-02-23T13:37:14.584898Z","shell.execute_reply":"2023-02-23T13:37:14.592747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'Number of training examples: {len(train_data)}')\nprint(f'Number of validation examples: {len(valid_data)}')\nprint(f'Number of testing examples: {len(test_data)}')","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:18.365647Z","iopub.execute_input":"2023-02-23T13:37:18.365927Z","iopub.status.idle":"2023-02-23T13:37:18.370866Z","shell.execute_reply.started":"2023-02-23T13:37:18.365899Z","shell.execute_reply":"2023-02-23T13:37:18.369674Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BATCH_SIZE = 64\n\ntrain_iterator = DataLoader(train_data, shuffle=True, batch_size= BATCH_SIZE)\nvalid_iterator = DataLoader(valid_data, batch_size=BATCH_SIZE)\ntest_iterator = DataLoader(test_data, batch_size = BATCH_SIZE)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:20.654058Z","iopub.execute_input":"2023-02-23T13:37:20.654351Z","iopub.status.idle":"2023-02-23T13:37:20.659097Z","shell.execute_reply.started":"2023-02-23T13:37:20.654321Z","shell.execute_reply":"2023-02-23T13:37:20.658228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\ndef epoch_time(start_time, end_time):\n    elapsed_time = end_time - start_time\n    elapsed_mins = int(elapsed_time / 60)\n    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n    return elapsed_mins, elapsed_secs\n\ndef calculate_accuracy(y_pred, y):\n    top_pred = y_pred.argmax(1, keepdim = True)\n    correct = top_pred.eq(y.view_as(top_pred)).sum()\n    acc = correct.float() / y.shape[0]\n    return acc","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:22.744331Z","iopub.execute_input":"2023-02-23T13:37:22.744649Z","iopub.status.idle":"2023-02-23T13:37:22.751487Z","shell.execute_reply.started":"2023-02-23T13:37:22.744618Z","shell.execute_reply":"2023-02-23T13:37:22.750432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training on LeNet-5 From Scratch","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"code","source":"class LeNet(nn.Module):\n    def __init__(self):\n        super(LeNet, self).__init__()\n        \n        self.features = nn.Sequential(\n            nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5), #stride=1, padding=0 is a default\n            nn.ReLU(),\n            nn.AvgPool2d(kernel_size=2, stride=2),\n            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),\n            nn.ReLU(),\n            nn.AvgPool2d(kernel_size=2, stride=2)\n        )\n        self.classifier = nn.Sequential(\n            nn.Linear(16*5*5, 120),\n            nn.ReLU(),\n            nn.Linear(120, 84),\n            nn.ReLU(),\n            nn.Linear(84, 120)   #num_classes = 120\n        )\n    \n    def forward(self, x):\n        batch_size = x.shape[0]\n        x = self.features(x)\n        x = x.view(batch_size, -1)\n        x = self.classifier(x)\n        \n        return x\nmodel = LeNet().to(device)\nsummary(model, (3, 32, 32))","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:27.265385Z","iopub.execute_input":"2023-02-23T13:37:27.265675Z","iopub.status.idle":"2023-02-23T13:37:31.275639Z","shell.execute_reply.started":"2023-02-23T13:37:27.265646Z","shell.execute_reply":"2023-02-23T13:37:31.274857Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\nmodel= LeNet().to(device)\nloss_criterion = nn.CrossEntropyLoss().to(device)\noptimizer=optim.Adam(model.parameters())\n\nprint(f'The model has {count_parameters(model):,} trainable parameters')","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:35.343264Z","iopub.execute_input":"2023-02-23T13:37:35.343616Z","iopub.status.idle":"2023-02-23T13:37:35.353407Z","shell.execute_reply.started":"2023-02-23T13:37:35.343585Z","shell.execute_reply":"2023-02-23T13:37:35.352353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train(model, iterator, optimizer, criterion, device):\n    \n    epoch_loss = 0\n    epoch_acc = 0\n    \n    model.train()\n    \n    for (x, y) in iterator:\n        \n        x = Variable(torch.FloatTensor(np.array(x))).to(device)\n        y = Variable(torch.LongTensor(y)).to(device)\n        \n        optimizer.zero_grad()\n                \n        y_pred = model(x)\n        \n        loss = criterion(y_pred, y)\n        \n        acc = calculate_accuracy(y_pred, y)\n        \n        loss.backward()\n        \n        optimizer.step()\n        \n        epoch_loss += loss.item()\n        epoch_acc += acc.item()\n        \n    return epoch_loss / len(iterator), epoch_acc / len(iterator)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:42.577192Z","iopub.execute_input":"2023-02-23T13:37:42.577521Z","iopub.status.idle":"2023-02-23T13:37:42.584890Z","shell.execute_reply.started":"2023-02-23T13:37:42.577491Z","shell.execute_reply":"2023-02-23T13:37:42.583824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def evaluate(model, iterator, criterion, device):\n    \n    epoch_loss = 0\n    epoch_acc = 0\n    \n    model.eval()\n    \n    with torch.no_grad():\n        \n        for (x, y) in iterator:\n\n            x = Variable(torch.FloatTensor(np.array(x))).to(device)\n            y = Variable(torch.LongTensor(y)).to(device)\n        \n            y_pred = model(x)\n\n            loss = criterion(y_pred, y)\n\n            acc = calculate_accuracy(y_pred, y)\n\n            epoch_loss += loss.item()\n            epoch_acc += acc.item()\n        \n    return epoch_loss / len(iterator), epoch_acc / len(iterator)","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:44.860835Z","iopub.execute_input":"2023-02-23T13:37:44.861126Z","iopub.status.idle":"2023-02-23T13:37:44.868143Z","shell.execute_reply.started":"2023-02-23T13:37:44.861095Z","shell.execute_reply":"2023-02-23T13:37:44.867365Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def fit_model(model, model_name, train_iterator, valid_iterator, optimizer, loss_criterion, device, epochs):\n    \"\"\" Fits a dataset to model\"\"\"\n    best_valid_loss = float('inf')\n    \n    train_losses = []\n    valid_losses = []\n    train_accs = []\n    valid_accs = []\n    \n    for epoch in range(epochs):\n    \n        start_time = time.time()\n    \n        train_loss, train_acc = train(model, train_iterator, optimizer, loss_criterion, device)\n        valid_loss, valid_acc = evaluate(model, valid_iterator, loss_criterion, device)\n        \n        train_losses.append(train_loss)\n        valid_losses.append(valid_loss)\n        train_accs.append(train_acc*100)\n        valid_accs.append(valid_acc*100)\n    \n        if valid_loss < best_valid_loss:\n            best_valid_loss = valid_loss\n            torch.save(model.state_dict(), f'{model_name}.pt')\n    \n        end_time = time.time()\n\n        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n    \n        print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n        print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n        print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')\n        \n    return pd.DataFrame({f'{model_name}_Training_Loss':train_losses, \n                        f'{model_name}_Training_Acc':train_accs, \n                        f'{model_name}_Validation_Loss':valid_losses, \n                        f'{model_name}_Validation_Acc':valid_accs})","metadata":{"execution":{"iopub.status.busy":"2023-02-23T13:37:47.286886Z","iopub.execute_input":"2023-02-23T13:37:47.287169Z","iopub.status.idle":"2023-02-23T13:37:47.296014Z","shell.execute_reply.started":"2023-02-23T13:37:47.287138Z","shell.execute_reply":"2023-02-23T13:37:47.295250Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_stats_LeNet = fit_model(model, 'LeNet', train_iterator, valid_iterator, optimizer, loss_criterion, device, epochs=20)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_training_statistics(train_stats, model_name):\n    \n    fig, axes = plt.subplots(2, figsize=(15,15))\n    axes[0].plot(train_stats[f'{model_name}_Training_Loss'], label=f'{model_name}_Training_Loss')\n    axes[0].plot(train_stats[f'{model_name}_Validation_Loss'], label=f'{model_name}_Validation_Loss')\n    axes[1].plot(train_stats[f'{model_name}_Training_Acc'], label=f'{model_name}_Training_Acc')\n    axes[1].plot(train_stats[f'{model_name}_Validation_Acc'], label=f'{model_name}_Validation_Acc')\n    \n    axes[0].set_xlabel(\"Number of Epochs\"), axes[0].set_ylabel(\"Loss\")\n    axes[1].set_xlabel(\"Number of Epochs\"), axes[1].set_ylabel(\"Accuracy in %\")\n    \n    axes[0].legend(), axes[1].legend()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_training_statistics(train_stats_LeNet, 'LeNet')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training on ResNet-18 with Transfer Learning","metadata":{}},{"cell_type":"code","source":"from torchvision import models\nmodel = models.resnet18(pretrained=True).to(device)\nprint(model)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You might not want to freeze the BatchNorm layers in a model, as they will be trained to approximate the mean and standard deviation of the dataset that the model was originally trained on, not the dataset that you want to fine-tune on. Some of the signal from your data may end up being lost as BatchNorm corrects your input. You can look at the model structure and freeze only layers that aren’t BatchNorm like this:","metadata":{"trusted":true}},{"cell_type":"code","source":"for name, param in model.named_parameters():\n    if(\"bn\" not in name):\n        param.requires_grad = False","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fc = nn.Linear(model.fc.in_features,120).to(device)\noptimizer = optim.Adam(model.parameters(), lr = 1e-2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"normalize = transforms.Normalize(\n   mean=[0.485, 0.456, 0.406],\n   std=[0.229, 0.224, 0.225]\n)\ntransforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)\ntrain_transforms = transforms.Compose([transforms.Resize(224),\n                               transforms.CenterCrop(224),\n                               transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0),\n                               transforms.RandomHorizontalFlip(p=0.5),\n                               transforms.RandomVerticalFlip(p=0.5),\n                               transforms.RandomGrayscale(p=0.1), \n                               transforms.ToTensor(),\n                               normalize])\ntest_transforms = transforms.Compose([transforms.Resize(224),\n                               transforms.CenterCrop(224),\n                               transforms.ToTensor(),\n                               normalize])\n\ntrain_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_train, labels=y_train, transforms=train_transforms)\nvalid_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_valid, labels=y_valid, transforms=test_transforms)\ntest_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_test, labels=y_test, transforms=test_transforms)\n\nBATCH_SIZE = 64\n\ntrain_iterator = DataLoader(train_data, shuffle=True, batch_size= BATCH_SIZE)\nvalid_iterator = DataLoader(valid_data, batch_size=BATCH_SIZE)\ntest_iterator = DataLoader(test_data, batch_size = BATCH_SIZE)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_stats_ResNet18 = fit_model(model, 'ResNet18', train_iterator, valid_iterator, optimizer, loss_criterion, device, epochs=20)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_training_statistics(train_stats_ResNet18, 'ResNet18')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training on ResNet-34 with Transfer Learning","metadata":{}},{"cell_type":"code","source":"from torchvision import models\nmodel = models.resnet34(pretrained=True).to(device)\nprint(model)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for name, param in model.named_parameters():\n    if(\"bn\" not in name):\n        param.requires_grad = False","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fc = nn.Linear(model.fc.in_features,120).to(device)\noptimizer = optim.Adam(model.parameters(), lr = 1e-2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"normalize = transforms.Normalize(\n   mean=[0.485, 0.456, 0.406],\n   std=[0.229, 0.224, 0.225]\n)\ntransforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)\ntrain_transforms = transforms.Compose([transforms.Resize(224),\n                               transforms.CenterCrop(224),\n                               transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0),\n                               transforms.RandomHorizontalFlip(p=0.5),\n                               transforms.RandomVerticalFlip(p=0.5),\n                               transforms.RandomGrayscale(p=0.1), \n                               transforms.ToTensor(),\n                               normalize])\ntest_transforms = transforms.Compose([transforms.Resize(224),\n                               transforms.CenterCrop(224),\n                               transforms.ToTensor(),\n                               normalize])\n\ntrain_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_train, labels=y_train, transforms=train_transforms)\nvalid_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_valid, labels=y_valid, transforms=test_transforms)\ntest_data = Dataset_Interpreter(data_path=data_dir+'train/', file_names=X_test, labels=y_test, transforms=test_transforms)\n\nBATCH_SIZE = 64\n\ntrain_iterator = DataLoader(train_data, shuffle=True, batch_size= BATCH_SIZE)\nvalid_iterator = DataLoader(valid_data, batch_size=BATCH_SIZE)\ntest_iterator = DataLoader(test_data, batch_size = BATCH_SIZE)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_stats_ResNet34 = fit_model(model, 'ResNet34', train_iterator, valid_iterator, optimizer, loss_criterion, device, epochs=20)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_training_statistics(train_stats_ResNet34, 'ResNet34')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}