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Load and prepare data","metadata":{"id":"0fjmgwCW2gHq"}},{"cell_type":"code","source":"import os\n\n# Показать файлы в директории данных\ndata_dir = \"/kaggle/input\"\nfor dirname, _, filenames in os.walk(data_dir):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:26:55.666309Z","iopub.execute_input":"2024-12-19T18:26:55.666644Z","iopub.status.idle":"2024-12-19T18:26:55.677035Z","shell.execute_reply.started":"2024-12-19T18:26:55.666612Z","shell.execute_reply":"2024-12-19T18:26:55.675771Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\ntrain_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')","metadata":{"id":"ke897ziBBldt","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:26:55.678936Z","iopub.execute_input":"2024-12-19T18:26:55.679295Z","iopub.status.idle":"2024-12-19T18:27:01.317535Z","shell.execute_reply.started":"2024-12-19T18:26:55.679264Z","shell.execute_reply":"2024-12-19T18:27:01.316203Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_labels = train_data['label']\ntrain_pixel = train_data.drop(columns=['label'])","metadata":{"id":"5Bzj7IWUCf3W","executionInfo":{"status":"ok","timestamp":1734191762584,"user_tz":-300,"elapsed":10,"user":{"displayName":"cvv s","userId":"10443969861340804968"}},"outputId":"c63d8e48-1437-45d5-eec9-d0709997d937","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:16.629824Z","iopub.execute_input":"2024-12-19T18:27:16.630158Z","iopub.status.idle":"2024-12-19T18:27:16.746589Z","shell.execute_reply.started":"2024-12-19T18:27:16.630130Z","shell.execute_reply":"2024-12-19T18:27:16.745476Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\ntrain_imgs = torch.tensor(train_pixel.values.reshape(-1, 28,28), dtype=torch.float32).unsqueeze(1)\ntest_imgs = torch.tensor(test_data.values.reshape(-1,28,28), dtype=torch.float32).unsqueeze(1)\ntrain_imgs.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:17.360029Z","iopub.execute_input":"2024-12-19T18:27:17.360386Z","iopub.status.idle":"2024-12-19T18:27:17.492295Z","shell.execute_reply.started":"2024-12-19T18:27:17.360327Z","shell.execute_reply":"2024-12-19T18:27:17.491371Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from torch.utils.data import Dataset\n\nclass mnist_dataset(Dataset):\n    def __init__(self, imgs, labels):\n        super().__init__()\n        self.imgs = imgs\n        self.len = len(imgs)\n        self.labels = labels\n        \n    def __getitem__(self, idx):\n        return (self.imgs[idx], self.labels[idx])\n        \n    def __len__(self):\n        return self.len\n        \n\ntrain_data = mnist_dataset(train_imgs, train_labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:18.076833Z","iopub.execute_input":"2024-12-19T18:27:18.077173Z","iopub.status.idle":"2024-12-19T18:27:18.083687Z","shell.execute_reply.started":"2024-12-19T18:27:18.077144Z","shell.execute_reply":"2024-12-19T18:27:18.082483Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimg1, lbl1 = train_data[0]\nimg1 = img1.squeeze(0)\nlbl1 = lbl1\n\nimg2, lbl2 = train_data[3100]\nimg2 = img2.squeeze(0)\nlbl2 = lbl2\n\nplt.figure(figsize=(10, 5))\n\nplt.subplot(1, 2, 1)\nplt.imshow(img1, cmap='gray')\nplt.title(\"Train Image 1\")\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(img2, cmap='gray')\nplt.title(\"Train Image 31000\")\nplt.axis('off')\n\nprint(lbl1, lbl2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:18.845882Z","iopub.execute_input":"2024-12-19T18:27:18.846242Z","iopub.status.idle":"2024-12-19T18:27:19.265579Z","shell.execute_reply.started":"2024-12-19T18:27:18.846213Z","shell.execute_reply":"2024-12-19T18:27:19.264432Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# dataloader","metadata":{"id":"Cni3fs_wEAlG"}},{"cell_type":"code","source":"from torchvision.transforms import v2\n\n\ntransform = v2.Compose([\n    v2.ToImage(),\n    v2.ToDtype(torch.float32, scale=True), # to dtype torch.float32 + scale to [0,1]\n    v2.Normalize(mean=[0.5], std=[0.5]) # scale to [-1:1]\n])","metadata":{"id":"KQyWWxJFCmwe","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:19.564792Z","iopub.execute_input":"2024-12-19T18:27:19.565159Z","iopub.status.idle":"2024-12-19T18:27:21.133338Z","shell.execute_reply.started":"2024-12-19T18:27:19.565128Z","shell.execute_reply":"2024-12-19T18:27:21.132165Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from torch.utils.data import DataLoader\nfrom torch.utils.data import random_split\n\ntrain_data, val_data = random_split(train_data, [0.8, 0.2])\n\ntrain_loader = DataLoader(train_data, batch_size=32, shuffle=True)\ntest_loader = DataLoader(test_imgs, batch_size=32, shuffle=False)\nval_loader = DataLoader(val_data, batch_size=32, shuffle=False)\n\nlen(train_loader), len(test_loader), len(val_loader)","metadata":{"id":"VQoKSfjz2fAG","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:21.134928Z","iopub.execute_input":"2024-12-19T18:27:21.135464Z","iopub.status.idle":"2024-12-19T18:27:21.159048Z","shell.execute_reply.started":"2024-12-19T18:27:21.135432Z","shell.execute_reply":"2024-12-19T18:27:21.158035Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for img, label in train_loader:\n    print(img.shape)\n    plt.imshow(img[0].squeeze(0), 'gray')\n    print(label[0])\n    break","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:23.526244Z","iopub.execute_input":"2024-12-19T18:27:23.526617Z","iopub.status.idle":"2024-12-19T18:27:23.737586Z","shell.execute_reply.started":"2024-12-19T18:27:23.526587Z","shell.execute_reply":"2024-12-19T18:27:23.736465Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Learning","metadata":{"id":"GzfMivIk2ioZ"}},{"cell_type":"code","source":"from tqdm.notebook import tqdm\nimport numpy as np\n\ndef train_cicle(model, train_loader, loss_function, train_loss_list_batch = np.array([])):\n\n  train_loop = tqdm(train_loader, leave=False)\n  counter_true_answer = 0\n\n  model.train()\n  for i, (x_b, y_b) in enumerate(train_loop):\n    x_b = x_b.to(torch.float32).to(device)\n    y_b = y_b.to(device)\n    pred = model(x_b)\n    loss = loss_function(pred, y_b)\n    loss.backward()\n    opt.step()\n    opt.zero_grad()\n\n    counter_true_answer += (pred.max(dim=1)[1] == y_b).sum().item()\n    train_loss_list_batch = np.append(train_loss_list_batch, loss.item())\n\n    train_loop.set_description(f'train_loss = {train_loss_list_batch.mean():.4f}')\n\n  return counter_true_answer, train_loss_list_batch\n\n\ndef val_cicle(model, val_loader, loss_function, val_loss_list_batch = np.array([])):\n\n  counter_true_answer = 0\n\n  model.eval()\n  with torch.no_grad():\n    for i, (x_b, y_b) in enumerate(val_loader):\n      x_b = x_b.to(torch.float32).to(device)\n      y_b = y_b.to(device)\n      pred = model(x_b)\n      loss = loss_function(pred, y_b)\n\n      counter_true_answer += (pred.max(dim=1)[1] == y_b).sum().item()\n\n      val_loss_list_batch = np.append(val_loss_list_batch, loss.item())\n\n  return counter_true_answer, val_loss_list_batch","metadata":{"id":"I8jA-A1DnzK9","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:42.159791Z","iopub.execute_input":"2024-12-19T18:27:42.160150Z","iopub.status.idle":"2024-12-19T18:27:42.168953Z","shell.execute_reply.started":"2024-12-19T18:27:42.160121Z","shell.execute_reply":"2024-12-19T18:27:42.167844Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"device = 'cuda' if torch.cuda.is_available() else 'cpu'\ndevice","metadata":{"id":"LR_9JPdB56Gg","executionInfo":{"status":"ok","timestamp":1734193955360,"user_tz":-300,"elapsed":5,"user":{"displayName":"cvv s","userId":"10443969861340804968"}},"outputId":"9377f999-dea1-4027-f17d-9068f06d94b8","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:27:44.506471Z","iopub.execute_input":"2024-12-19T18:27:44.506849Z","iopub.status.idle":"2024-12-19T18:27:44.518086Z","shell.execute_reply.started":"2024-12-19T18:27:44.506814Z","shell.execute_reply":"2024-12-19T18:27:44.516581Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from torch import nn\nfrom torch import optim\n\nclass my_conv_nn(nn.Module):\n  def __init__(self):\n    super().__init__()\n    self.relu = nn.ReLU()\n    self.conv1 = nn.Conv2d(1, 6, 3, bias=False) # 1x28x28 -> 6x26x26\n    self.bn1 = nn.BatchNorm2d(6)\n    self.pool1 = nn.MaxPool2d(2)  # 6x26x26 -> 6x13x13\n    self.conv2 = nn.Conv2d(6, 15, 3, bias=False) # 6x13x13 -> 15x11x11\n    self.bn2 = nn.BatchNorm2d(15)\n    self.pool2 = nn.MaxPool2d(3, stride=1) # 15x11x11 -> 15x5x5\n      \n    self.linear1 = nn.Linear(15*5*5, 256, bias=False)\n    self.bn3 = nn.BatchNorm1d(256)\n    self.linear2 = nn.Linear(256, 10)\n\n  def forward(self, x):\n    x = self.conv1(x)\n    x = self.relu(x)\n    x = self.bn1(x)\n    x = self.pool1(x)\n    x = self.conv2(x)\n    x = self.relu(x)\n    x = self.bn2(x)\n    x = self.pool1(x)\n    x = torch.flatten(x, start_dim=1)\n    x = self.linear1(x)\n    x = self.relu(x)\n    x = self.bn3(x)\n    out = self.linear2(x)\n    return out\n\nmodel = my_conv_nn().to(device)\nopt = optim.Adam(model.parameters(), lr=0.001)\nloss_f = nn.CrossEntropyLoss()\nlr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(opt, mode='min', factor=0.1, patience=3, threshold=0.1, threshold_mode='rel')","metadata":{"id":"dVnOiay3Von7","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:29:33.948797Z","iopub.execute_input":"2024-12-19T18:29:33.949153Z","iopub.status.idle":"2024-12-19T18:29:33.965371Z","shell.execute_reply.started":"2024-12-19T18:29:33.949122Z","shell.execute_reply":"2024-12-19T18:29:33.964213Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# naming:\n# train/val/test____loss/acc/*____list - список значений на эпохах\n# train/val/test____loss/acc/*____per_epoch - значение на текущей эпохе\n# train/val/test____loss/acc/*____list_batch - список значений с каждого батча на текущей эпохе\n\nepochs = 50\nlr_list = []\ntrain_acc_list, val_acc_list = np.array([]), np.array([])\ntrain_loss_list, val_loss_list = np.array([]), np.array([])\nbest_loss, count_no_changes, patience = None, 0, 5\n\nfor epoch in range(1, epochs+1):\n\n  # train_cicle\n  counter_true_answer, train_loss_list_batch = train_cicle(model, train_loader, loss_f)\n\n  # acc\n  train_acc_per_epoch = counter_true_answer / len(train_data)\n  train_acc_list = np.append(train_acc_list, train_acc_per_epoch)\n  # loss\n  train_loss_per_epoch = train_loss_list_batch.mean()\n  train_loss_list = np.append(train_loss_list, train_loss_per_epoch)\n\n  # test_cicle\n  counter_true_answer, val_loss_list_batch = val_cicle(model, val_loader, loss_f)\n\n  # acc\n  val_acc_per_epoch = counter_true_answer / len(val_data)\n  val_acc_list = np.append(val_acc_list, val_acc_per_epoch)\n  # loss\n  val_loss_per_epoch = val_loss_list_batch.mean()\n  val_loss_list = np.append(val_loss_list, val_loss_per_epoch)\n\n  # lr schedule\n  lr_scheduler.step(val_loss_per_epoch)\n  lr_list.append(lr_scheduler._last_lr)\n\n  # best_model save + early stop\n  count_no_changes += 1\n  if best_loss == None or best_loss - val_loss_per_epoch > best_loss * 0.1:\n    print(f'сохраняю модель st_dict_{epoch}_epoch.pt')\n    torch.save(model.state_dict(), f'st_dict_{epoch}_epoch.pt')\n    best_loss = val_loss_per_epoch\n    count_no_changes = 0\n\n  # result print\n  print(f'epoch {epoch}, train/val loss = {train_loss_per_epoch:.4f}/{val_loss_per_epoch:.4f}, train/val accuracy = {train_acc_per_epoch:.4f}/{val_acc_per_epoch:.4f}, no_change={count_no_changes+1}')\n\n  # early stop\n  if count_no_changes > patience:\n    print(f'обучение остановлено на эпохе {epoch}')\n    break\n\n","metadata":{"id":"yA4Y37MHUSRe","executionInfo":{"status":"ok","timestamp":1734195625532,"user_tz":-300,"elapsed":720048,"user":{"displayName":"cvv s","userId":"10443969861340804968"}},"outputId":"cbe34917-1be1-48b1-a9d3-bb9c674f0f17","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:29:34.491071Z","iopub.execute_input":"2024-12-19T18:29:34.491432Z","iopub.status.idle":"2024-12-19T18:32:09.808317Z","shell.execute_reply.started":"2024-12-19T18:29:34.491401Z","shell.execute_reply":"2024-12-19T18:32:09.807211Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nprint(lr_list)\nplt.plot(lr_list)","metadata":{"id":"seR_PMXWlNlf","executionInfo":{"status":"ok","timestamp":1734197943506,"user_tz":-300,"elapsed":367,"user":{"displayName":"cvv s","userId":"10443969861340804968"}},"outputId":"100e31d6-33b2-4fa3-c770-a5e7e3d24ff1","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:32:09.809891Z","iopub.execute_input":"2024-12-19T18:32:09.810293Z","iopub.status.idle":"2024-12-19T18:32:10.054410Z","shell.execute_reply.started":"2024-12-19T18:32:09.810255Z","shell.execute_reply":"2024-12-19T18:32:10.053132Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(train_loss_list, c='r')\nplt.plot(val_loss_list, c='b')","metadata":{"id":"eYLKPNKE8iyn","executionInfo":{"status":"ok","timestamp":1734197969414,"user_tz":-300,"elapsed":394,"user":{"displayName":"cvv s","userId":"10443969861340804968"}},"outputId":"06961010-8565-4568-a742-bd5afa821daa","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:32:10.056246Z","iopub.execute_input":"2024-12-19T18:32:10.056664Z","iopub.status.idle":"2024-12-19T18:32:10.263468Z","shell.execute_reply.started":"2024-12-19T18:32:10.056621Z","shell.execute_reply":"2024-12-19T18:32:10.262120Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"folder_path = \".\"\n# Получаем список файлов в папке\nfiles = os.listdir(folder_path)\n\n# Фильтруем файлы, соответствующие шаблону \"st_dict_i_epoch.pt\"\nepoch_files = [f for f in files if f.startswith(\"st_dict_\") and f.endswith(\"_epoch.pt\")]\n\n# Извлекаем номер эпохи (i) из имени файла и находим файл с максимальным номером\nmax_epoch_file = max(epoch_files, key=lambda x: int(x.split('_')[2]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:59:45.134267Z","iopub.execute_input":"2024-12-19T18:59:45.134684Z","iopub.status.idle":"2024-12-19T18:59:45.142135Z","shell.execute_reply.started":"2024-12-19T18:59:45.134651Z","shell.execute_reply":"2024-12-19T18:59:45.141069Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = my_conv_nn()\nparams = torch.load(max_epoch_file, weights_only=True)\nmodel.load_state_dict(params)","metadata":{"id":"_PpfFLK_0azL","trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:39:52.132402Z","iopub.execute_input":"2024-12-19T18:39:52.132733Z","iopub.status.idle":"2024-12-19T18:39:52.147115Z","shell.execute_reply.started":"2024-12-19T18:39:52.132698Z","shell.execute_reply":"2024-12-19T18:39:52.146162Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"preds = model(test_imgs)\npreds.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:41:23.878957Z","iopub.execute_input":"2024-12-19T18:41:23.879310Z","iopub.status.idle":"2024-12-19T18:41:27.197552Z","shell.execute_reply.started":"2024-12-19T18:41:23.879280Z","shell.execute_reply":"2024-12-19T18:41:27.196182Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"labels = torch.argmax(preds, dim=1).numpy()\nlabels.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:41:38.622095Z","iopub.execute_input":"2024-12-19T18:41:38.622506Z","iopub.status.idle":"2024-12-19T18:41:38.631506Z","shell.execute_reply.started":"2024-12-19T18:41:38.622473Z","shell.execute_reply":"2024-12-19T18:41:38.630535Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"image_ids = range(1, len(labels) + 1)  # Нумерация с 1\nsubmission = pd.DataFrame({\n    'ImageId': image_ids,\n    'Label': labels\n})\n\n# Сохраняем в CSV\nsubmission.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:41:55.731783Z","iopub.execute_input":"2024-12-19T18:41:55.732133Z","iopub.status.idle":"2024-12-19T18:41:55.766340Z","shell.execute_reply.started":"2024-12-19T18:41:55.732107Z","shell.execute_reply":"2024-12-19T18:41:55.765332Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!head submission.csv","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-19T18:42:11.718166Z","iopub.execute_input":"2024-12-19T18:42:11.718538Z","iopub.status.idle":"2024-12-19T18:42:11.902676Z","shell.execute_reply.started":"2024-12-19T18:42:11.718509Z","shell.execute_reply":"2024-12-19T18:42:11.901289Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}