Bald_Classifier(Biased).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":8109847,"sourceType":"datasetVersion","datasetId":4790501}],"dockerImageVersionId":30683,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport glob\nimport numpy as np\nfrom PIL import Image \nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch import optim\nfrom torch import nn\nfrom torch.utils.data.sampler import SubsetRandomSampler\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.datasets as dataset\nimport torchvision.transforms as transforms","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:19.418488Z","iopub.execute_input":"2024-04-13T16:38:19.418851Z","iopub.status.idle":"2024-04-13T16:38:26.123373Z","shell.execute_reply.started":"2024-04-13T16:38:19.418822Z","shell.execute_reply":"2024-04-13T16:38:26.122575Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"# Check if GPU is available\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:27.788308Z","iopub.execute_input":"2024-04-13T16:38:27.789061Z","iopub.status.idle":"2024-04-13T16:38:27.816163Z","shell.execute_reply.started":"2024-04-13T16:38:27.789025Z","shell.execute_reply":"2024-04-13T16:38:27.815021Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"device","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:29.185388Z","iopub.execute_input":"2024-04-13T16:38:29.185753Z","iopub.status.idle":"2024-04-13T16:38:29.193197Z","shell.execute_reply.started":"2024-04-13T16:38:29.185724Z","shell.execute_reply":"2024-04-13T16:38:29.192167Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"device(type='cuda')"},"metadata":{}}]},{"cell_type":"code","source":"transform = transforms.Compose([\n    transforms.Resize((224, 224)),  \n    transforms.ToTensor(),           \n    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) \n])","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:30.856562Z","iopub.execute_input":"2024-04-13T16:38:30.856934Z","iopub.status.idle":"2024-04-13T16:38:30.863148Z","shell.execute_reply.started":"2024-04-13T16:38:30.856904Z","shell.execute_reply":"2024-04-13T16:38:30.861902Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"train_dir = '/kaggle/input/baldnotbald/Train/Train'\ntest_dir = '/kaggle/input/baldnotbald/Test/Test'","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:32.255180Z","iopub.execute_input":"2024-04-13T16:38:32.255586Z","iopub.status.idle":"2024-04-13T16:38:32.260158Z","shell.execute_reply.started":"2024-04-13T16:38:32.255553Z","shell.execute_reply":"2024-04-13T16:38:32.259124Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"train_dataset = dataset.ImageFolder(root=train_dir, transform=transform)\ntest_dataset = dataset.ImageFolder(root=test_dir, transform=transform)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:33.839013Z","iopub.execute_input":"2024-04-13T16:38:33.839456Z","iopub.status.idle":"2024-04-13T16:38:37.044531Z","shell.execute_reply.started":"2024-04-13T16:38:33.839383Z","shell.execute_reply":"2024-04-13T16:38:37.043672Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"train_dataset","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:37.046318Z","iopub.execute_input":"2024-04-13T16:38:37.047251Z","iopub.status.idle":"2024-04-13T16:38:37.053732Z","shell.execute_reply.started":"2024-04-13T16:38:37.047224Z","shell.execute_reply":"2024-04-13T16:38:37.052640Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"Dataset ImageFolder\n    Number of datapoints: 5672\n    Root location: /kaggle/input/baldnotbald/Train/Train\n    StandardTransform\nTransform: Compose(\n               Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=warn)\n               ToTensor()\n               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n           )"},"metadata":{}}]},{"cell_type":"code","source":"test_dataset","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:38.184578Z","iopub.execute_input":"2024-04-13T16:38:38.185006Z","iopub.status.idle":"2024-04-13T16:38:38.192660Z","shell.execute_reply.started":"2024-04-13T16:38:38.184972Z","shell.execute_reply":"2024-04-13T16:38:38.191437Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Dataset ImageFolder\n    Number of datapoints: 2087\n    Root location: /kaggle/input/baldnotbald/Test/Test\n    StandardTransform\nTransform: Compose(\n               Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=warn)\n               ToTensor()\n               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n           )"},"metadata":{}}]},{"cell_type":"code","source":"print(train_dataset.classes)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:39.833149Z","iopub.execute_input":"2024-04-13T16:38:39.833497Z","iopub.status.idle":"2024-04-13T16:38:39.838773Z","shell.execute_reply.started":"2024-04-13T16:38:39.833472Z","shell.execute_reply":"2024-04-13T16:38:39.837643Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"['Bald', 'NotBald']\n","output_type":"stream"}]},{"cell_type":"code","source":"train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)\ntest_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:43.670794Z","iopub.execute_input":"2024-04-13T16:38:43.671452Z","iopub.status.idle":"2024-04-13T16:38:43.678758Z","shell.execute_reply.started":"2024-04-13T16:38:43.671420Z","shell.execute_reply":"2024-04-13T16:38:43.677799Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"len(train_dataloader)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:45.662894Z","iopub.execute_input":"2024-04-13T16:38:45.663254Z","iopub.status.idle":"2024-04-13T16:38:45.669931Z","shell.execute_reply.started":"2024-04-13T16:38:45.663216Z","shell.execute_reply":"2024-04-13T16:38:45.668873Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"89"},"metadata":{}}]},{"cell_type":"code","source":"len(test_dataloader)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:46.306191Z","iopub.execute_input":"2024-04-13T16:38:46.306991Z","iopub.status.idle":"2024-04-13T16:38:46.313011Z","shell.execute_reply.started":"2024-04-13T16:38:46.306959Z","shell.execute_reply":"2024-04-13T16:38:46.311947Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"33"},"metadata":{}}]},{"cell_type":"code","source":"model = torchvision.models.resnet18(weights='IMAGENET1K_V1')","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:47.908195Z","iopub.execute_input":"2024-04-13T16:38:47.908624Z","iopub.status.idle":"2024-04-13T16:38:48.711556Z","shell.execute_reply.started":"2024-04-13T16:38:47.908591Z","shell.execute_reply":"2024-04-13T16:38:48.710669Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stderr","text":"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n100%|██████████| 44.7M/44.7M [00:00<00:00, 123MB/s] \n","output_type":"stream"}]},{"cell_type":"code","source":"in_features = model.fc.in_features\nout_features = 2\n\nclass SoftmaxClassifier(nn.Module):\n    def __init__(self, in_features, out_features):\n        super(SoftmaxClassifier, self).__init__()\n        self.fc = nn.Linear(in_features, out_features)\n\n    def forward(self, x):\n        x = self.fc(x)\n        x = F.softmax(x, dim=1)\n        return x\n\nmodel.fc = SoftmaxClassifier(in_features=in_features, out_features=out_features)\nprint(model)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:50.318488Z","iopub.execute_input":"2024-04-13T16:38:50.318837Z","iopub.status.idle":"2024-04-13T16:38:50.328502Z","shell.execute_reply.started":"2024-04-13T16:38:50.318809Z","shell.execute_reply":"2024-04-13T16:38:50.327448Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): SoftmaxClassifier(\n    (fc): Linear(in_features=512, out_features=2, bias=True)\n  )\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"model.to('cuda')","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:53.317916Z","iopub.execute_input":"2024-04-13T16:38:53.318277Z","iopub.status.idle":"2024-04-13T16:38:53.490842Z","shell.execute_reply.started":"2024-04-13T16:38:53.318251Z","shell.execute_reply":"2024-04-13T16:38:53.489896Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): SoftmaxClassifier(\n    (fc): Linear(in_features=512, out_features=2, bias=True)\n  )\n)"},"metadata":{}}]},{"cell_type":"code","source":"criterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.fc.parameters(), lr=1e-3)","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:38:56.600474Z","iopub.execute_input":"2024-04-13T16:38:56.600835Z","iopub.status.idle":"2024-04-13T16:38:56.606463Z","shell.execute_reply.started":"2024-04-13T16:38:56.600806Z","shell.execute_reply":"2024-04-13T16:38:56.605454Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"for epoch in range(20):\n    model.train()\n    print(\"Training\")\n    running_loss = 0\n    correct = 0\n    total = 0\n    \n    for data in train_dataloader:\n        img, labels = data\n        \n        img = img.to('cuda')\n        labels = labels.to('cuda')\n        \n        optimizer.zero_grad()\n        \n        outputs = model(img)\n        #pred = pred.to('cpu')\n        \n        loss = criterion(outputs, labels)\n        loss.backward()\n        \n        optimizer.step()\n        \n        # Compute accuracy\n        _, predicted = torch.max(outputs, 1)\n        #total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n        \n        running_loss += loss.item()\n        \n    train_loss = running_loss/(len(train_dataloader)*64)\n    train_acc = correct/(len(train_dataloader)*64)\n    \n    print(f\"Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc*100:.2f}%\")\n    ","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:39:07.166431Z","iopub.execute_input":"2024-04-13T16:39:07.167079Z","iopub.status.idle":"2024-04-13T16:48:04.049533Z","shell.execute_reply.started":"2024-04-13T16:39:07.167046Z","shell.execute_reply":"2024-04-13T16:48:04.048433Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Training\nEpoch 1, Train Loss: 0.0080, Train Accuracy: 82.57%\nTraining\nEpoch 2, Train Loss: 0.0068, Train Accuracy: 88.29%\nTraining\nEpoch 3, Train Loss: 0.0067, Train Accuracy: 89.36%\nTraining\nEpoch 4, Train Loss: 0.0065, Train Accuracy: 89.94%\nTraining\nEpoch 5, Train Loss: 0.0065, Train Accuracy: 90.41%\nTraining\nEpoch 6, Train Loss: 0.0064, Train Accuracy: 90.54%\nTraining\nEpoch 7, Train Loss: 0.0064, Train Accuracy: 90.41%\nTraining\nEpoch 8, Train Loss: 0.0064, Train Accuracy: 90.54%\nTraining\nEpoch 9, Train Loss: 0.0063, Train Accuracy: 90.64%\nTraining\nEpoch 10, Train Loss: 0.0063, Train Accuracy: 90.55%\nTraining\nEpoch 11, Train Loss: 0.0063, Train Accuracy: 90.78%\nTraining\nEpoch 12, Train Loss: 0.0063, Train Accuracy: 91.15%\nTraining\nEpoch 13, Train Loss: 0.0063, Train Accuracy: 90.85%\nTraining\nEpoch 14, Train Loss: 0.0062, Train Accuracy: 91.15%\nTraining\nEpoch 15, Train Loss: 0.0062, Train Accuracy: 91.70%\nTraining\nEpoch 16, Train Loss: 0.0062, Train Accuracy: 91.45%\nTraining\nEpoch 17, Train Loss: 0.0062, Train Accuracy: 91.10%\nTraining\nEpoch 18, Train Loss: 0.0062, Train Accuracy: 91.59%\nTraining\nEpoch 19, Train Loss: 0.0062, Train Accuracy: 91.19%\nTraining\nEpoch 20, Train Loss: 0.0062, Train Accuracy: 91.27%\n","output_type":"stream"}]},{"cell_type":"code","source":"checkpoint_path = '/kaggle/working/model_bald_checkpoint.pth' ","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:48:11.388854Z","iopub.execute_input":"2024-04-13T16:48:11.389221Z","iopub.status.idle":"2024-04-13T16:48:11.393759Z","shell.execute_reply.started":"2024-04-13T16:48:11.389190Z","shell.execute_reply":"2024-04-13T16:48:11.392726Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"torch.save({\n    'epoch': epoch,\n    'model_state_dict': model.state_dict(),\n    'optimizer_state_dict': optimizer.state_dict(),\n    'loss': train_loss,\n    'accuracy': train_acc,\n}, checkpoint_path)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-13T16:48:33.987247Z","iopub.execute_input":"2024-04-13T16:48:33.987741Z","iopub.status.idle":"2024-04-13T16:48:34.079223Z","shell.execute_reply.started":"2024-04-13T16:48:33.987704Z","shell.execute_reply":"2024-04-13T16:48:34.078253Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}