{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":96000,"databundleVersionId":11397519,"sourceType":"competition"},{"sourceId":12581365,"sourceType":"datasetVersion","datasetId":7945927}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset, random_split\nfrom torchvision import transforms, models, datasets\nfrom PIL import Image\nfrom sklearn.preprocessing import LabelEncoder\nfrom tqdm import tqdm\nimport re\nimport matplotlib.pyplot as plt\n\ntrain_dir = \"/kaggle/input/butterflies/train_butterflies/\"\ntest_dir = \"/kaggle/input/butterflies/test_butterflies/\"\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nbatch_size = 16","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:56:59.440157Z","iopub.execute_input":"2025-07-31T08:56:59.440471Z","iopub.status.idle":"2025-07-31T08:56:59.445816Z","shell.execute_reply.started":"2025-07-31T08:56:59.44045Z","shell.execute_reply":"2025-07-31T08:56:59.444902Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"# функция для рисования кривых обучения\ndef plot_training_curves(train_loss, val_loss, train_acc, val_acc):\n    fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n    axes[0].plot(train_loss, label='Train Loss', linewidth=2)\n    axes[0].plot(val_loss, label='Val Loss', linewidth=2)\n    axes[0].set_title('Loss per Epoch', fontsize=14)\n    axes[0].set_xlabel('Epoch', fontsize=12)\n    axes[0].set_ylabel('Loss', fontsize=12)\n    axes[0].legend(loc='upper right', fontsize=10)\n\n    axes[1].plot(train_acc, label='Train Accuracy', linewidth=2)\n    axes[1].plot(val_acc, label='Val Accuracy', linewidth=2)\n    axes[1].set_title('Accuracy per Epoch', fontsize=14)\n    axes[1].set_xlabel('Epoch', fontsize=12)\n    axes[1].set_ylabel('Accuracy', fontsize=12)\n    axes[1].legend(loc='lower right', fontsize=10)\n\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:56:59.44713Z","iopub.execute_input":"2025-07-31T08:56:59.447406Z","iopub.status.idle":"2025-07-31T08:56:59.475756Z","shell.execute_reply.started":"2025-07-31T08:56:59.447388Z","shell.execute_reply":"2025-07-31T08:56:59.475013Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# тренировка модели\ndef train_model(model, num_epochs, optimizer, criterion, name, scheduler=None):\n    max_acc = 0\n    \n    train_loss_list = []\n    val_loss_list = []\n    train_acc_list = []\n    val_acc_list = []\n    \n    for epoch in range(num_epochs):\n        # тренировка\n        model.train()\n        train_loss = 0.0\n        correct = 0\n        total = 0\n        \n        for inputs, labels in tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\"):\n            inputs, labels = inputs.to(device), labels.to(device)\n            \n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            \n            train_loss += loss.item()\n            _, predicted = outputs.max(1)\n            total += labels.size(0)\n            correct += predicted.eq(labels).sum().item()\n        \n        train_acc = 100. * correct / total\n        train_loss /= len(train_loader)\n        \n        # валидация\n        model.eval()\n        val_loss = 0.0\n        correct = 0\n        total = 0\n        \n        with torch.no_grad():\n            for inputs, labels in val_loader:\n                inputs, labels = inputs.to(device), labels.to(device)\n                outputs = model(inputs)\n                loss = criterion(outputs, labels)\n                \n                val_loss += loss.item()\n                _, predicted = outputs.max(1)\n                total += labels.size(0)\n                correct += predicted.eq(labels).sum().item()\n        \n        val_acc = 100. * correct / total\n        val_loss /= len(val_loader)\n\n        if scheduler is not None:\n            scheduler.step(val_loss)\n        \n        print(f\"Train Loss: {train_loss:.4f} | Acc: {train_acc:.2f}%\")\n        print(f\"Val Loss: {val_loss:.4f} | Acc: {val_acc:.2f}%\")\n    \n        train_loss_list.append(train_loss)\n        val_loss_list.append(val_loss)\n        train_acc_list.append(train_acc)\n        val_acc_list.append(val_acc)\n    \n        if max_acc < val_acc:\n            max_acc = val_acc\n            torch.save(model.state_dict(), name)\n\n    plot_training_curves(train_loss_list, val_loss_list, train_acc_list, val_acc_list)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:56:59.476399Z","iopub.execute_input":"2025-07-31T08:56:59.47662Z","iopub.status.idle":"2025-07-31T08:56:59.493978Z","shell.execute_reply.started":"2025-07-31T08:56:59.476604Z","shell.execute_reply":"2025-07-31T08:56:59.493253Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"# первая модель в ансамбле\nclass ButterflyClassifierUpdated(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.base_model = models.efficientnet_b3(pretrained=True)\n        self.base_model.classifier = nn.Sequential(\n            nn.Dropout(p=0.4, inplace=True),\n            nn.Linear(self.base_model.classifier[1].in_features, 50)\n        )\n    \n    def forward(self, x):\n        return self.base_model(x)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:56:59.49537Z","iopub.execute_input":"2025-07-31T08:56:59.495599Z","iopub.status.idle":"2025-07-31T08:56:59.514714Z","shell.execute_reply.started":"2025-07-31T08:56:59.495583Z","shell.execute_reply":"2025-07-31T08:56:59.514104Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"transform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # статистика ImageNet\n])\n\naug_transform = transforms.Compose([\n    transforms.RandomAffine(degrees=(-20, 20), translate=(0.1, 0.1)),\n    transforms.RandomHorizontalFlip(p=0.5),\n    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n    transforms.RandomResizedCrop(224, scale=(0.8, 1.)),\n    transforms.RandomApply([transforms.GaussianBlur(3)], p=0.1),\n    transforms.RandomAdjustSharpness(1.5, p=0.3),\n    \n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\n#\ntrain_dataset = datasets.ImageFolder(\n    root=train_dir,\n    transform=None\n)\n\ntrain_tmp, val_tmp = random_split(train_dataset, [0.8, 0.2])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:56:59.515273Z","iopub.execute_input":"2025-07-31T08:56:59.515452Z","iopub.status.idle":"2025-07-31T08:57:02.262574Z","shell.execute_reply.started":"2025-07-31T08:56:59.515438Z","shell.execute_reply":"2025-07-31T08:57:02.261949Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"class TransformSubset(torch.utils.data.Dataset):\n    def __init__(self, subset, transform=None):\n        self.subset = subset\n        self.transform = transform\n\n    def __getitem__(self, index):\n        x, y = self.subset[index]\n        if self.transform:\n            x = self.transform(x)\n        return x, y\n\n    def __len__(self):\n        return len(self.subset)\n\ntrain_ds = TransformSubset(train_tmp, aug_transform)\nval_ds = TransformSubset(val_tmp, transform)\n\ntrain_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\nval_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:57:02.26374Z","iopub.execute_input":"2025-07-31T08:57:02.263966Z","iopub.status.idle":"2025-07-31T08:57:02.26971Z","shell.execute_reply.started":"2025-07-31T08:57:02.263949Z","shell.execute_reply":"2025-07-31T08:57:02.268967Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"model1 = ButterflyClassifierUpdated().to(device)\nopt = optim.Adam(model1.parameters(), lr=0.00005, amsgrad=True)\nloss_func = nn.CrossEntropyLoss()\ntrain_model(model1, 20, opt, loss_func, \"first_model.pth\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T08:57:02.270502Z","iopub.execute_input":"2025-07-31T08:57:02.270727Z","iopub.status.idle":"2025-07-31T09:21:07.125167Z","shell.execute_reply.started":"2025-07-31T08:57:02.27071Z","shell.execute_reply":"2025-07-31T09:21:07.124526Z"}},"outputs":[{"name":"stderr","text":"Epoch 1/20: 100%|██████████| 248/248 [01:30<00:00,  2.76it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 3.6221 | Acc: 18.92%\nVal Loss: 9.4899 | Acc: 57.42%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2/20: 100%|██████████| 248/248 [01:07<00:00,  3.68it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 2.2420 | Acc: 63.07%\nVal Loss: 10.8955 | Acc: 76.99%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3/20: 100%|██████████| 248/248 [01:07<00:00,  3.70it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 1.0800 | Acc: 81.63%\nVal Loss: 4.0437 | Acc: 83.65%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4/20: 100%|██████████| 248/248 [01:07<00:00,  3.68it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.6534 | Acc: 87.46%\nVal Loss: 1.2669 | Acc: 88.40%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 5/20: 100%|██████████| 248/248 [01:07<00:00,  3.67it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.4503 | Acc: 90.74%\nVal Loss: 0.7643 | Acc: 90.41%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 6/20: 100%|██████████| 248/248 [01:07<00:00,  3.70it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.3387 | Acc: 92.56%\nVal Loss: 0.6933 | Acc: 92.43%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 7/20: 100%|██████████| 248/248 [01:06<00:00,  3.71it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.2798 | Acc: 94.15%\nVal Loss: 0.9791 | Acc: 92.43%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 8/20: 100%|██████████| 248/248 [01:07<00:00,  3.68it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.2421 | Acc: 94.35%\nVal Loss: 0.3639 | Acc: 92.63%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 9/20: 100%|██████████| 248/248 [01:06<00:00,  3.73it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1938 | Acc: 95.84%\nVal Loss: 0.2873 | Acc: 93.74%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 10/20: 100%|██████████| 248/248 [01:06<00:00,  3.72it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1580 | Acc: 96.57%\nVal Loss: 0.2442 | Acc: 93.54%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 11/20: 100%|██████████| 248/248 [01:06<00:00,  3.72it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1483 | Acc: 96.72%\nVal Loss: 0.2609 | Acc: 93.14%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 12/20: 100%|██████████| 248/248 [01:06<00:00,  3.71it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1290 | Acc: 97.07%\nVal Loss: 0.2407 | Acc: 93.95%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 13/20: 100%|██████████| 248/248 [01:06<00:00,  3.74it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1185 | Acc: 97.05%\nVal Loss: 0.2258 | Acc: 94.75%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 14/20: 100%|██████████| 248/248 [01:06<00:00,  3.72it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1083 | Acc: 97.38%\nVal Loss: 0.2235 | Acc: 94.65%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 15/20: 100%|██████████| 248/248 [01:06<00:00,  3.74it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0932 | Acc: 97.86%\nVal Loss: 0.2304 | Acc: 94.35%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 16/20: 100%|██████████| 248/248 [01:05<00:00,  3.77it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0860 | Acc: 97.86%\nVal Loss: 0.2383 | Acc: 93.74%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 17/20: 100%|██████████| 248/248 [01:07<00:00,  3.66it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0781 | Acc: 98.18%\nVal Loss: 0.2339 | Acc: 94.65%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 18/20: 100%|██████████| 248/248 [01:07<00:00,  3.69it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0717 | Acc: 98.23%\nVal Loss: 0.2309 | Acc: 94.35%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 19/20: 100%|██████████| 248/248 [01:07<00:00,  3.68it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0615 | Acc: 98.54%\nVal Loss: 0.2356 | Acc: 94.75%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 20/20: 100%|██████████| 248/248 [01:06<00:00,  3.75it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0622 | Acc: 98.56%\nVal Loss: 0.2279 | Acc: 95.06%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x500 with 2 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\n"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"# вторая модель\nclass ButterflyClassifierResnet(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.base_model = models.wide_resnet50_2(pretrained=True)\n        self.base_model.fc = nn.Sequential(\n            nn.Dropout(p=0.4, inplace=True),\n            nn.Linear(self.base_model.fc.in_features, 50)\n        )\n    \n    def forward(self, x):\n        return self.base_model(x)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T09:21:07.126051Z","iopub.execute_input":"2025-07-31T09:21:07.126593Z","iopub.status.idle":"2025-07-31T09:21:07.130792Z","shell.execute_reply.started":"2025-07-31T09:21:07.126573Z","shell.execute_reply":"2025-07-31T09:21:07.130192Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"model2 = ButterflyClassifierResnet().to(device)\nopt = optim.Adam(model2.parameters(), lr=0.0001, amsgrad=True)\nloss_func = nn.CrossEntropyLoss()\ntrain_model(model2, 20, opt, loss_func, \"second_model.pth\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T09:21:07.131398Z","iopub.execute_input":"2025-07-31T09:21:07.131605Z","iopub.status.idle":"2025-07-31T09:49:11.933083Z","shell.execute_reply.started":"2025-07-31T09:21:07.13158Z","shell.execute_reply":"2025-07-31T09:49:11.932367Z"}},"outputs":[{"name":"stderr","text":"Epoch 1/20: 100%|██████████| 248/248 [01:18<00:00,  3.17it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 1.6041 | Acc: 65.97%\nVal Loss: 0.3749 | Acc: 89.20%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2/20: 100%|██████████| 248/248 [01:18<00:00,  3.17it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.4322 | Acc: 89.68%\nVal Loss: 0.3203 | Acc: 90.31%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3/20: 100%|██████████| 248/248 [01:18<00:00,  3.15it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.3175 | Acc: 92.28%\nVal Loss: 0.3312 | Acc: 92.53%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4/20: 100%|██████████| 248/248 [01:18<00:00,  3.14it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.2418 | Acc: 93.82%\nVal Loss: 0.3002 | Acc: 92.63%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 5/20: 100%|██████████| 248/248 [01:19<00:00,  3.14it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1847 | Acc: 95.31%\nVal Loss: 0.3450 | Acc: 92.43%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 6/20: 100%|██████████| 248/248 [01:19<00:00,  3.12it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1560 | Acc: 95.99%\nVal Loss: 0.3223 | Acc: 92.84%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 7/20: 100%|██████████| 248/248 [01:19<00:00,  3.14it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1474 | Acc: 95.79%\nVal Loss: 0.3676 | Acc: 91.62%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 8/20: 100%|██████████| 248/248 [01:18<00:00,  3.16it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1404 | Acc: 96.44%\nVal Loss: 0.3870 | Acc: 91.62%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 9/20: 100%|██████████| 248/248 [01:19<00:00,  3.13it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1518 | Acc: 95.74%\nVal Loss: 0.3783 | Acc: 91.73%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 10/20: 100%|██████████| 248/248 [01:18<00:00,  3.16it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1224 | Acc: 96.72%\nVal Loss: 0.3314 | Acc: 92.33%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 11/20: 100%|██████████| 248/248 [01:19<00:00,  3.13it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.1016 | Acc: 97.05%\nVal Loss: 0.3055 | Acc: 94.25%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 12/20: 100%|██████████| 248/248 [01:18<00:00,  3.14it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0829 | Acc: 97.88%\nVal Loss: 0.3397 | Acc: 92.63%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 13/20: 100%|██████████| 248/248 [01:19<00:00,  3.13it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0693 | Acc: 98.39%\nVal Loss: 0.3530 | Acc: 93.95%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 14/20: 100%|██████████| 248/248 [01:18<00:00,  3.14it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0691 | Acc: 97.91%\nVal Loss: 0.3694 | Acc: 94.15%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 15/20: 100%|██████████| 248/248 [01:18<00:00,  3.15it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0515 | Acc: 98.59%\nVal Loss: 0.4075 | Acc: 93.44%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 16/20: 100%|██████████| 248/248 [01:19<00:00,  3.13it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0671 | Acc: 98.13%\nVal Loss: 0.3350 | Acc: 94.15%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 17/20: 100%|██████████| 248/248 [01:18<00:00,  3.15it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0459 | Acc: 98.54%\nVal Loss: 0.3253 | Acc: 94.45%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 18/20: 100%|██████████| 248/248 [01:18<00:00,  3.15it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0462 | Acc: 98.64%\nVal Loss: 0.3737 | Acc: 93.84%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 19/20: 100%|██████████| 248/248 [01:18<00:00,  3.18it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0443 | Acc: 98.84%\nVal Loss: 0.3481 | Acc: 94.55%\n","output_type":"stream"},{"name":"stderr","text":"Epoch 20/20: 100%|██████████| 248/248 [01:18<00:00,  3.16it/s]\n","output_type":"stream"},{"name":"stdout","text":"Train Loss: 0.0312 | Acc: 99.27%\nVal Loss: 0.3670 | Acc: 93.95%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x500 with 2 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\n"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"class FinalModel(nn.Module):\n    def __init__(self, model1, model2):\n        super().__init__()\n        self.model1 = model1\n        self.model2 = model2\n        \n    def forward(self, x):\n        preds1 = self.model1(x)\n        preds2 = self.model2(x), dim=1\n        \n        return (preds1 + preds2) / 2 # возвращаем логиты для корректной работы функции потерь","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T09:49:11.934888Z","iopub.execute_input":"2025-07-31T09:49:11.935094Z","iopub.status.idle":"2025-07-31T09:49:11.939441Z","shell.execute_reply.started":"2025-07-31T09:49:11.935077Z","shell.execute_reply":"2025-07-31T09:49:11.93892Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"model1.load_state_dict(torch.load('/kaggle/working/first_model.pth'))\nmodel2.load_state_dict(torch.load('/kaggle/working/second_model.pth'))\n\nmodel1.eval()\nmodel2.eval()\n\nfinal_model = FinalModel(model1, model2).to(device)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T09:49:11.940095Z","iopub.execute_input":"2025-07-31T09:49:11.940326Z","iopub.status.idle":"2025-07-31T09:49:12.362633Z","shell.execute_reply.started":"2025-07-31T09:49:11.940311Z","shell.execute_reply":"2025-07-31T09:49:12.362082Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"val_loss = 0.\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n    for inputs, labels in val_loader:\n        inputs, labels = inputs.to(device), labels.to(device)\n        outputs = final_model(inputs)\n        loss = loss_func(outputs, labels)\n        \n        val_loss += loss.item()\n        _, predicted = outputs.max(1)\n        total += labels.size(0)\n        correct += predicted.eq(labels).sum().item()\n\nval_acc = 100. * correct / total\nval_loss /= len(val_loader)\n\nprint(f\"Val Loss: {val_loss:.4f} | Acc: {val_acc:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-31T09:50:26.283496Z","iopub.execute_input":"2025-07-31T09:50:26.28382Z","iopub.status.idle":"2025-07-31T09:50:33.062291Z","shell.execute_reply.started":"2025-07-31T09:50:26.283798Z","shell.execute_reply":"2025-07-31T09:50:33.06164Z"}},"outputs":[{"name":"stdout","text":"Val Loss: 3.0100 | Acc: 95.26%\n","output_type":"stream"}],"execution_count":23}]}