From f9c5af508af110ff944d82ed8651be0c1af08cfd Mon Sep 17 00:00:00 2001
From: DimaRus05 <dimarus06122005@gmail.com>
Date: Sun, 30 Nov 2025 01:02:15 +0300
Subject: [PATCH] Add hw3 task solution

---
 upgrade.ipynb | 1422 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1422 insertions(+)
 create mode 100644 upgrade.ipynb

diff --git a/upgrade.ipynb b/upgrade.ipynb
new file mode 100644
index 0000000..443a876
--- /dev/null
+++ b/upgrade.ipynb
@@ -0,0 +1,1422 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4c4bde13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Device: cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, random, math, time\n",
+    "from dataclasses import dataclass\n",
+    "from typing import List, Tuple, Dict, Any, Callable\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import DataLoader, Subset\n",
+    "\n",
+    "from datasets import load_dataset\n",
+    "import torchvision.transforms as T\n",
+    "from torchvision.transforms import InterpolationMode\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "def set_seed(seed: int = 42) -> None:\n",
+    "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
+    "    random.seed(seed)\n",
+    "    np.random.seed(seed)\n",
+    "    torch.manual_seed(seed)\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.manual_seed_all(seed)\n",
+    "        torch.backends.cudnn.deterministic = True\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "\n",
+    "set_seed(42)\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1e32c881",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Config(image_size=64, batch_size=64, num_workers=0, epochs=8, lr=0.001, num_classes=15, seed=42)\n"
+     ]
+    }
+   ],
+   "source": [
+    "@dataclass\n",
+    "class Config:\n",
+    "    image_size: int = 64\n",
+    "    batch_size: int = 64\n",
+    "    num_workers: int = 0\n",
+    "    epochs: int = 8\n",
+    "    lr: float = 1e-3\n",
+    "    num_classes: int = 15\n",
+    "    seed: int = 42\n",
+    "\n",
+    "cfg = Config()\n",
+    "print(cfg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "77bb3f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбрано 15 классов: ['pizza', 'hamburger', 'french_fries', 'hot_dog', 'sushi', 'ice_cream', 'apple_pie', 'chocolate_cake', 'caesar_salad', 'steak', 'tacos', 'ramen', 'pad_thai', 'fried_rice', 'omelette']\n"
+     ]
+    }
+   ],
+   "source": [
+    "set_seed(42)\n",
+    "\n",
+    "os.makedirs(\"./data/hf_cache\", exist_ok=True)\n",
+    "ds = load_dataset(\"food101\", cache_dir=\"./data/hf_cache\")\n",
+    "all_labels = ds[\"train\"].features[\"label\"].names\n",
+    "\n",
+    "selected_classes = [\n",
+    "    \"pizza\", \"hamburger\", \"french_fries\", \"hot_dog\", \"sushi\",\n",
+    "    \"ice_cream\", \"apple_pie\", \"chocolate_cake\", \"caesar_salad\", \"steak\",\n",
+    "    \"tacos\", \"ramen\", \"pad_thai\", \"fried_rice\", \"omelette\"\n",
+    "]\n",
+    "\n",
+    "class_to_idx = {name: idx for idx, name in enumerate(all_labels)}\n",
+    "selected_indices = [class_to_idx[name] for name in selected_classes]\n",
+    "\n",
+    "old_to_new = {old_idx: new_idx for new_idx, old_idx in enumerate(selected_indices)}\n",
+    "\n",
+    "print(f\"Выбрано {len(selected_classes)} классов: {selected_classes}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a762e6b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\user\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\PIL\\TiffImagePlugin.py:949: UserWarning: Truncated File Read\n",
+      "  warnings.warn(str(msg))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train: 750 изображений\n",
+      "Val: 300 изображений\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Фильтрация датасета и ограничение размера\n",
+    "def filter_dataset(split, max_per_class=100):\n",
+    "    filtered_images = []\n",
+    "    filtered_labels = []\n",
+    "    class_counts = {idx: 0 for idx in selected_indices}\n",
+    "    \n",
+    "    for item in split:\n",
+    "        label = item[\"label\"]\n",
+    "        if label in selected_indices and class_counts[label] < max_per_class:\n",
+    "            filtered_images.append(item[\"image\"])\n",
+    "            filtered_labels.append(old_to_new[label])  # новый индекс 0-14\n",
+    "            class_counts[label] += 1\n",
+    "    \n",
+    "    return filtered_images, filtered_labels\n",
+    "\n",
+    "train_images, train_labels = filter_dataset(ds[\"train\"], max_per_class=50)\n",
+    "val_images, val_labels = filter_dataset(ds[\"validation\"], max_per_class=20)\n",
+    "\n",
+    "print(f\"Train: {len(train_images)} изображений\")\n",
+    "print(f\"Val: {len(val_images)} изображений\")\n",
+    "\n",
+    "label_names = selected_classes\n",
+    "num_classes = len(label_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "adff6569",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Batch shape: torch.Size([64, 3, 64, 64]), Labels: torch.Size([64])\n"
+     ]
+    }
+   ],
+   "source": [
+    "set_seed(42)\n",
+    "\n",
+    "train_transform = T.Compose([\n",
+    "    T.Resize((cfg.image_size, cfg.image_size), interpolation=InterpolationMode.BILINEAR, antialias=True),\n",
+    "    T.RandomHorizontalFlip(p=0.5),\n",
+    "    T.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "eval_transform = T.Compose([\n",
+    "    T.Resize((cfg.image_size, cfg.image_size), interpolation=InterpolationMode.BILINEAR, antialias=True),\n",
+    "    T.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "class SimpleDataset(torch.utils.data.Dataset):\n",
+    "    def __init__(self, images, labels, transform):\n",
+    "        self.images = images\n",
+    "        self.labels = labels\n",
+    "        self.transform = transform\n",
+    "    \n",
+    "    def __len__(self):\n",
+    "        return len(self.images)\n",
+    "    \n",
+    "    def __getitem__(self, idx):\n",
+    "        img = self.images[idx].convert(\"RGB\")\n",
+    "        label = self.labels[idx]\n",
+    "        img = self.transform(img)\n",
+    "        return img, label\n",
+    "\n",
+    "train_dataset = SimpleDataset(train_images, train_labels, train_transform)\n",
+    "val_dataset = SimpleDataset(val_images, val_labels, eval_transform)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=0)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=cfg.batch_size, shuffle=False, num_workers=0)\n",
+    "\n",
+    "xb, yb = next(iter(train_loader))\n",
+    "print(f\"Batch shape: {xb.shape}, Labels: {yb.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04b9b7e5",
+   "metadata": {},
+   "source": [
+    "## Определение моделей и метрик"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a159c10b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Baseline модель (без регуляризации)\n",
+    "class BaselineCNN(nn.Module):\n",
+    "    def __init__(self, num_classes: int):\n",
+    "        super().__init__()\n",
+    "        self.features = nn.Sequential(\n",
+    "            nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "            \n",
+    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "            \n",
+    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "        )\n",
+    "        # После 3 MaxPool: 64 -> 32 -> 16 -> 8\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Flatten(),\n",
+    "            nn.Linear(128 * 8 * 8, 128),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Linear(128, num_classes),\n",
+    "        )\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = self.features(x)\n",
+    "        x = self.classifier(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b4339e2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Улучшенная модель (с Dropout и BatchNorm)\n",
+    "class ImprovedCNN(nn.Module):\n",
+    "    def __init__(self, num_classes: int, dropout_rate: float = 0.3):\n",
+    "        super().__init__()\n",
+    "        self.features = nn.Sequential(\n",
+    "            nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
+    "            nn.BatchNorm2d(32),  # BatchNorm для стабилизации\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "            nn.Dropout2d(dropout_rate),  # Dropout\n",
+    "            \n",
+    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "            nn.Dropout2d(dropout_rate),\n",
+    "            \n",
+    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
+    "            nn.BatchNorm2d(128),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(2),\n",
+    "        )\n",
+    "        # После 3 MaxPool: 64 -> 32 -> 16 -> 8\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Flatten(),\n",
+    "            nn.Linear(128 * 8 * 8, 128),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Dropout(dropout_rate),  # Dropout в FC слое\n",
+    "            nn.Linear(128, num_classes),\n",
+    "        )\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = self.features(x)\n",
+    "        x = self.classifier(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d084f022",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция вычисления ECE (Expected Calibration Error)\n",
+    "def compute_ece(probs: np.ndarray, labels: np.ndarray, n_bins: int = 10) -> float:\n",
+    "    confidences = probs.max(axis=1)\n",
+    "    predictions = probs.argmax(axis=1)\n",
+    "    accuracies = (predictions == labels).astype(float)\n",
+    "    \n",
+    "    bin_boundaries = np.linspace(0, 1, n_bins + 1)\n",
+    "    ece = 0.0\n",
+    "    \n",
+    "    for i in range(n_bins):\n",
+    "        in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n",
+    "        prop_in_bin = in_bin.mean()\n",
+    "        \n",
+    "        if prop_in_bin > 0:\n",
+    "            avg_confidence = confidences[in_bin].mean()\n",
+    "            avg_accuracy = accuracies[in_bin].mean()\n",
+    "            ece += np.abs(avg_accuracy - avg_confidence) * prop_in_bin\n",
+    "    \n",
+    "    return ece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6a54b87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функции обучения и оценки\n",
+    "@torch.no_grad()\n",
+    "def collect_preds_targets(model: nn.Module, loader: DataLoader):\n",
+    "    model.eval()\n",
+    "    all_true, all_pred, all_prob = [], [], []\n",
+    "    for images, targets in loader:\n",
+    "        images = images.to(device)\n",
+    "        logits = model(images)\n",
+    "        probs = torch.softmax(logits, dim=1)\n",
+    "        preds = probs.argmax(dim=1)\n",
+    "        all_true.append(targets.cpu())\n",
+    "        all_pred.append(preds.cpu())\n",
+    "        all_prob.append(probs.cpu())\n",
+    "    return torch.cat(all_true).numpy(), torch.cat(all_pred).numpy(), torch.cat(all_prob).numpy()\n",
+    "\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def evaluate_model(model: nn.Module, loader: DataLoader, criterion: nn.Module):\n",
+    "    model.eval()\n",
+    "    total_loss, total_items = 0.0, 0\n",
+    "    all_true, all_pred = [], []\n",
+    "    \n",
+    "    for images, targets in loader:\n",
+    "        images, targets = images.to(device), targets.to(device)\n",
+    "        logits = model(images)\n",
+    "        loss = criterion(logits, targets)\n",
+    "        \n",
+    "        bs = targets.size(0)\n",
+    "        total_loss += loss.item() * bs\n",
+    "        total_items += bs\n",
+    "        all_true.append(targets.cpu())\n",
+    "        all_pred.append(logits.argmax(dim=1).cpu())\n",
+    "    \n",
+    "    y_true = torch.cat(all_true).numpy()\n",
+    "    y_pred = torch.cat(all_pred).numpy()\n",
+    "    acc = accuracy_score(y_true, y_pred)\n",
+    "    return {\"loss\": total_loss / max(total_items, 1), \"acc\": acc}\n",
+    "\n",
+    "\n",
+    "def train_one_epoch(model: nn.Module, loader: DataLoader, optimizer, criterion):\n",
+    "    model.train()\n",
+    "    total_loss, total_items = 0.0, 0\n",
+    "    all_true, all_pred = [], []\n",
+    "    \n",
+    "    for images, targets in loader:\n",
+    "        images, targets = images.to(device), targets.to(device)\n",
+    "        \n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "        logits = model(images)\n",
+    "        loss = criterion(logits, targets)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        bs = targets.size(0)\n",
+    "        total_loss += loss.item() * bs\n",
+    "        total_items += bs\n",
+    "        all_true.append(targets.cpu())\n",
+    "        all_pred.append(logits.detach().argmax(dim=1).cpu())\n",
+    "    \n",
+    "    y_true = torch.cat(all_true).numpy()\n",
+    "    y_pred = torch.cat(all_pred).numpy()\n",
+    "    acc = accuracy_score(y_true, y_pred)\n",
+    "    return {\"loss\": total_loss / max(total_items, 1), \"acc\": acc}\n",
+    "\n",
+    "\n",
+    "def plot_reliability_diagram(probs, labels, title, n_bins=10):\n",
+    "    confidences = probs.max(axis=1)\n",
+    "    predictions = probs.argmax(axis=1)\n",
+    "    accuracies = (predictions == labels).astype(float)\n",
+    "    \n",
+    "    bin_boundaries = np.linspace(0, 1, n_bins + 1)\n",
+    "    bin_centers = (bin_boundaries[:-1] + bin_boundaries[1:]) / 2\n",
+    "    \n",
+    "    bin_accs = []\n",
+    "    bin_confs = []\n",
+    "    bin_counts = []\n",
+    "    \n",
+    "    for i in range(n_bins):\n",
+    "        in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n",
+    "        if in_bin.sum() > 0:\n",
+    "            bin_accs.append(accuracies[in_bin].mean())\n",
+    "            bin_confs.append(confidences[in_bin].mean())\n",
+    "            bin_counts.append(in_bin.sum())\n",
+    "        else:\n",
+    "            bin_accs.append(0)\n",
+    "            bin_confs.append(0)\n",
+    "            bin_counts.append(0)\n",
+    "    \n",
+    "    plt.figure(figsize=(6, 5))\n",
+    "    plt.bar(bin_centers, bin_accs, width=0.08, alpha=0.7, label='Accuracy')\n",
+    "    plt.plot([0, 1], [0, 1], 'r--', label='Perfect calibration')\n",
+    "    plt.xlabel('Confidence')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title(title)\n",
+    "    plt.legend()\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.xlim(0, 1)\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c4632ae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_experiment(model, optimizer, criterion, num_epochs=10, print_every=2):\n",
+    "    history = {\"epoch\": [], \"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\n",
+    "    \n",
+    "    for epoch in range(1, num_epochs + 1):\n",
+    "        train_metrics = train_one_epoch(model, train_loader, optimizer, criterion)\n",
+    "        val_metrics = evaluate_model(model, val_loader, criterion)\n",
+    "        \n",
+    "        history[\"epoch\"].append(epoch)\n",
+    "        history[\"train_loss\"].append(train_metrics[\"loss\"])\n",
+    "        history[\"train_acc\"].append(train_metrics[\"acc\"])\n",
+    "        history[\"val_loss\"].append(val_metrics[\"loss\"])\n",
+    "        history[\"val_acc\"].append(val_metrics[\"acc\"])\n",
+    "        \n",
+    "        if epoch % print_every == 0:\n",
+    "            print(f\"[epoch {epoch:02d}] train: loss={train_metrics['loss']:.4f}, acc={train_metrics['acc']:.3f} | \"\n",
+    "                  f\"val: loss={val_metrics['loss']:.4f}, acc={val_metrics['acc']:.3f}\")\n",
+    "    \n",
+    "    return history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b90d201",
+   "metadata": {},
+   "source": [
+    "## Baseline эксперимент"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "32e95537",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================================\n",
+      "BASELINE (без регуляризации)\n",
+      "==================================================\n",
+      "[epoch 02] train: loss=2.7064, acc=0.057 | val: loss=2.7030, acc=0.060\n",
+      "[epoch 02] train: loss=2.7064, acc=0.057 | val: loss=2.7030, acc=0.060\n",
+      "[epoch 04] train: loss=2.6078, acc=0.129 | val: loss=2.6046, acc=0.103\n",
+      "[epoch 04] train: loss=2.6078, acc=0.129 | val: loss=2.6046, acc=0.103\n",
+      "[epoch 06] train: loss=2.4852, acc=0.163 | val: loss=2.4916, acc=0.183\n",
+      "[epoch 06] train: loss=2.4852, acc=0.163 | val: loss=2.4916, acc=0.183\n",
+      "[epoch 08] train: loss=2.3398, acc=0.236 | val: loss=2.4544, acc=0.190\n",
+      "[epoch 08] train: loss=2.3398, acc=0.236 | val: loss=2.4544, acc=0.190\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Baseline: SimpleCNN без регуляризации\n",
+    "set_seed(42)\n",
+    "\n",
+    "model_baseline = BaselineCNN(num_classes=num_classes).to(device)\n",
+    "criterion_baseline = nn.CrossEntropyLoss()  # без label smoothing\n",
+    "optimizer_baseline = torch.optim.Adam(model_baseline.parameters(), lr=cfg.lr)\n",
+    "\n",
+    "print(\"=\" * 50)\n",
+    "print(\"BASELINE (без регуляризации)\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "history_baseline = run_experiment(\n",
+    "    model_baseline, optimizer_baseline, criterion_baseline,\n",
+    "    num_epochs=cfg.epochs, print_every=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "525e4b2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "BASELINE метрики (валидация):\n",
+      "==================================================\n",
+      "Top-1 Accuracy: 0.1900\n",
+      "F1-macro:       0.1552\n",
+      "ECE:            0.0177\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка Baseline: Top-1, F1-macro, ECE\n",
+    "y_true_bl, y_pred_bl, y_prob_bl = collect_preds_targets(model_baseline, val_loader)\n",
+    "\n",
+    "top1_baseline = accuracy_score(y_true_bl, y_pred_bl)\n",
+    "f1_baseline = f1_score(y_true_bl, y_pred_bl, average='macro')\n",
+    "ece_baseline = compute_ece(y_prob_bl, y_true_bl)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 50)\n",
+    "print(\"BASELINE метрики (валидация):\")\n",
+    "print(\"=\" * 50)\n",
+    "print(f\"Top-1 Accuracy: {top1_baseline:.4f}\")\n",
+    "print(f\"F1-macro:       {f1_baseline:.4f}\")\n",
+    "print(f\"ECE:            {ece_baseline:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e6e420",
+   "metadata": {},
+   "source": [
+    "## Улучшенная модель\n",
+    "\n",
+    "Применяемые методы улучшения:\n",
+    "\n",
+    "| Метод | Параметр | Назначение |\n",
+    "|-------|----------|------------|\n",
+    "| **Dropout** | 0.2 | Отключает 20% нейронов при обучении, снижая переобучение |\n",
+    "| **Weight Decay** | 0.005 | L2-регуляризация весов (AdamW) |\n",
+    "| **Label Smoothing** | 0.1 | Сглаживает one-hot метки: `[0, 1, 0]` → `[0.05, 0.9, 0.05]` |\n",
+    "| **BatchNorm** | — | Нормализация активаций после каждого Conv2d |\n",
+    "\n",
+    "**Ожидаемый эффект:**\n",
+    "- Уменьшение разрыва между train и val accuracy (меньше переобучение)\n",
+    "- Улучшение калибровки модели (ECE ↓)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6348a068",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================================\n",
+      "IMPROVED (Dropout + Weight Decay + Label Smoothing)\n",
+      "==================================================\n",
+      "[epoch 02] train: loss=2.6852, acc=0.101 | val: loss=2.6856, acc=0.110\n",
+      "[epoch 02] train: loss=2.6852, acc=0.101 | val: loss=2.6856, acc=0.110\n",
+      "[epoch 04] train: loss=2.6244, acc=0.145 | val: loss=2.6163, acc=0.137\n",
+      "[epoch 04] train: loss=2.6244, acc=0.145 | val: loss=2.6163, acc=0.137\n",
+      "[epoch 06] train: loss=2.5665, acc=0.164 | val: loss=2.5647, acc=0.160\n",
+      "[epoch 06] train: loss=2.5665, acc=0.164 | val: loss=2.5647, acc=0.160\n",
+      "[epoch 08] train: loss=2.5177, acc=0.160 | val: loss=2.5065, acc=0.183\n",
+      "[epoch 08] train: loss=2.5177, acc=0.160 | val: loss=2.5065, acc=0.183\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Улучшенная модель: Dropout + Weight Decay + Label Smoothing\n",
+    "set_seed(42)\n",
+    "\n",
+    "model_improved = ImprovedCNN(num_classes=num_classes, dropout_rate=0.2).to(device)  # было 0.3\n",
+    "\n",
+    "# Label Smoothing в функции потерь\n",
+    "criterion_improved = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
+    "\n",
+    "# Weight Decay в оптимизаторе (уменьшили)\n",
+    "optimizer_improved = torch.optim.AdamW(\n",
+    "    model_improved.parameters(),\n",
+    "    lr=cfg.lr,\n",
+    "    weight_decay=0.005  # было 0.01 - слишком агрессивно\n",
+    ")\n",
+    "\n",
+    "print(\"=\" * 50)\n",
+    "print(\"IMPROVED (Dropout + Weight Decay + Label Smoothing)\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "history_improved = run_experiment(\n",
+    "    model_improved, optimizer_improved, criterion_improved,\n",
+    "    num_epochs=cfg.epochs, print_every=2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "11d03e8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "IMPROVED метрики (валидация):\n",
+      "==================================================\n",
+      "Top-1 Accuracy: 0.1833\n",
+      "F1-macro:       0.1590\n",
+      "ECE:            0.0316\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_true_imp, y_pred_imp, y_prob_imp = collect_preds_targets(model_improved, val_loader)\n",
+    "\n",
+    "top1_improved = accuracy_score(y_true_imp, y_pred_imp)\n",
+    "f1_improved = f1_score(y_true_imp, y_pred_imp, average='macro')\n",
+    "ece_improved = compute_ece(y_prob_imp, y_true_imp)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 50)\n",
+    "print(\"IMPROVED метрики (валидация):\")\n",
+    "print(\"=\" * 50)\n",
+    "print(f\"Top-1 Accuracy: {top1_improved:.4f}\")\n",
+    "print(f\"F1-macro:       {f1_improved:.4f}\")\n",
+    "print(f\"ECE:            {ece_improved:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96b3c334",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train loader обновлён: усиленные аугментации применяются.\n",
+      "Модель TransferResNet создана. Кол-во обучаемых параметров: 8528911\n",
+      "\n",
+      "=== TRAIN Transfer model ===\n",
+      "Модель TransferResNet создана. Кол-во обучаемых параметров: 8528911\n",
+      "\n",
+      "=== TRAIN Transfer model ===\n",
+      "[epoch 02] train: loss=2.5934, acc=0.177 | val: loss=2.5377, acc=0.237\n",
+      "[epoch 02] train: loss=2.5934, acc=0.177 | val: loss=2.5377, acc=0.237\n",
+      "[epoch 04] train: loss=2.3646, acc=0.300 | val: loss=2.3251, acc=0.303\n",
+      "[epoch 04] train: loss=2.3646, acc=0.300 | val: loss=2.3251, acc=0.303\n",
+      "[epoch 06] train: loss=2.1617, acc=0.391 | val: loss=2.1439, acc=0.360\n",
+      "[epoch 06] train: loss=2.1617, acc=0.391 | val: loss=2.1439, acc=0.360\n",
+      "[epoch 08] train: loss=1.9336, acc=0.468 | val: loss=1.9709, acc=0.437\n",
+      "[epoch 08] train: loss=1.9336, acc=0.468 | val: loss=1.9709, acc=0.437\n",
+      "\n",
+      "Transfer model metrics:\n",
+      "Top-1 Accuracy: 0.4367\n",
+      "F1-macro:       0.4275\n",
+      "ECE:            0.1523\n",
+      "\n",
+      "============================================================\n",
+      "СРАВНЕНИЕ BASELINE vs IMPROVED vs TRANSFER\n",
+      "============================================================\n",
+      "\n",
+      "Transfer model metrics:\n",
+      "Top-1 Accuracy: 0.4367\n",
+      "F1-macro:       0.4275\n",
+      "ECE:            0.1523\n",
+      "\n",
+      "============================================================\n",
+      "СРАВНЕНИЕ BASELINE vs IMPROVED vs TRANSFER\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Модель",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Top-1 Accuracy",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "F1-macro",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "ECE",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Train-Val Gap (acc)",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Δ Top-1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Δ F1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Δ ECE",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "cdf273b5-df72-4ac8-8f7b-9d6a6c74acf7",
+       "rows": [
+        [
+         "0",
+         "Baseline",
+         "0.19",
+         "0.1552399036559363",
+         "0.017741344124078753",
+         "0.045999999999999985",
+         "",
+         "",
+         ""
+        ],
+        [
+         "1",
+         "Improved",
+         "0.18333333333333332",
+         "0.15897079366642777",
+         "0.03164389143387478",
+         "-0.023333333333333317",
+         "-0.67%",
+         "+0.37%",
+         "0.0139"
+        ],
+        [
+         "2",
+         "Transfer",
+         "0.43666666666666665",
+         "0.42748158860126245",
+         "0.15233263194561003",
+         "0.03133333333333338",
+         "+24.67%",
+         "+27.22%",
+         "0.1346"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 3
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        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>Модель</th>\n",
+       "      <th>Top-1 Accuracy</th>\n",
+       "      <th>F1-macro</th>\n",
+       "      <th>ECE</th>\n",
+       "      <th>Train-Val Gap (acc)</th>\n",
+       "      <th>Δ Top-1</th>\n",
+       "      <th>Δ F1</th>\n",
+       "      <th>Δ ECE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Baseline</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.155240</td>\n",
+       "      <td>0.017741</td>\n",
+       "      <td>0.046000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Improved</td>\n",
+       "      <td>0.183333</td>\n",
+       "      <td>0.158971</td>\n",
+       "      <td>0.031644</td>\n",
+       "      <td>-0.023333</td>\n",
+       "      <td>-0.67%</td>\n",
+       "      <td>+0.37%</td>\n",
+       "      <td>0.0139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Transfer</td>\n",
+       "      <td>0.436667</td>\n",
+       "      <td>0.427482</td>\n",
+       "      <td>0.152333</td>\n",
+       "      <td>0.031333</td>\n",
+       "      <td>+24.67%</td>\n",
+       "      <td>+27.22%</td>\n",
+       "      <td>0.1346</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Модель  Top-1 Accuracy  F1-macro       ECE  Train-Val Gap (acc)  Δ Top-1  \\\n",
+       "0  Baseline        0.190000  0.155240  0.017741             0.046000            \n",
+       "1  Improved        0.183333  0.158971  0.031644            -0.023333   -0.67%   \n",
+       "2  Transfer        0.436667  0.427482  0.152333             0.031333  +24.67%   \n",
+       "\n",
+       "      Δ F1   Δ ECE  \n",
+       "0                   \n",
+       "1   +0.37%  0.0139  \n",
+       "2  +27.22%  0.1346  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ---------------------------------------------\n",
+    "# Настройка усилённых аугментаций и обновлённый train_loader\n",
+    "# ---------------------------------------------\n",
+    "import torchvision.models as models\n",
+    "\n",
+    "# Сильные аугментации для трансферного обучения\n",
+    "train_transform_aug = T.Compose([\n",
+    "    T.RandomResizedCrop(cfg.image_size, scale=(0.6, 1.0), interpolation=InterpolationMode.BILINEAR),\n",
+    "    T.RandomHorizontalFlip(p=0.5),\n",
+    "    T.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.02),\n",
+    "    T.RandAugment(num_ops=2, magnitude=9),\n",
+    "    T.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "# Переопределим train_dataset и train_loader с усилёнными аугментациями\n",
+    "train_dataset = SimpleDataset(train_images, train_labels, train_transform_aug)\n",
+    "train_loader = DataLoader(train_dataset, batch_size=cfg.batch_size, shuffle=True, num_workers=0)\n",
+    "\n",
+    "print(\"Train loader обновлён: усиленные аугментации применяются.\")\n",
+    "\n",
+    "# ---------------------------------------------\n",
+    "# Transfer model: pretrained ResNet-18\n",
+    "# ---------------------------------------------\n",
+    "class TransferResNet(nn.Module):\n",
+    "    def __init__(self, num_classes: int, pretrained: bool = True, freeze_backbone: bool = True):\n",
+    "        super().__init__()\n",
+    "        self.backbone = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1 if pretrained else None)\n",
+    "        # заменим classifier\n",
+    "        in_features = self.backbone.fc.in_features\n",
+    "        self.backbone.fc = nn.Identity()\n",
+    "        self.head = nn.Sequential(\n",
+    "            nn.Linear(in_features, 256),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Dropout(0.3),\n",
+    "            nn.Linear(256, num_classes)\n",
+    "        )\n",
+    "        if freeze_backbone:\n",
+    "            for name, param in self.backbone.named_parameters():\n",
+    "                param.requires_grad = False\n",
+    "            # разморозим последний блок layer4\n",
+    "            for param in self.backbone.layer4.parameters():\n",
+    "                param.requires_grad = True\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        z = self.backbone(x)\n",
+    "        out = self.head(z)\n",
+    "        return out\n",
+    "\n",
+    "# Создаём модель и оптимизатор (тонкая настройка)\n",
+    "set_seed(cfg.seed)\n",
+    "model_transfer = TransferResNet(num_classes=num_classes, pretrained=True, freeze_backbone=True).to(device)\n",
+    "\n",
+    "# Оптимизатор: только для размороженных параметров и головы\n",
+    "params_to_optimize = [p for p in model_transfer.parameters() if p.requires_grad]\n",
+    "optimizer_transfer = torch.optim.AdamW(params_to_optimize, lr=1e-4, weight_decay=1e-3)\n",
+    "criterion_transfer = nn.CrossEntropyLoss(label_smoothing=0.05)\n",
+    "\n",
+    "print(\"Модель TransferResNet создана. Кол-во обучаемых параметров:\", sum(p.numel() for p in params_to_optimize))\n",
+    "\n",
+    "# Обучение модели трансфера\n",
+    "print(\"\\n=== TRAIN Transfer model ===\")\n",
+    "history_transfer = run_experiment(model_transfer, optimizer_transfer, criterion_transfer, num_epochs=cfg.epochs, print_every=2)\n",
+    "\n",
+    "# Оценка transfer модели\n",
+    "y_true_tr, y_pred_tr, y_prob_tr = collect_preds_targets(model_transfer, val_loader)\n",
+    "top1_transfer = accuracy_score(y_true_tr, y_pred_tr)\n",
+    "f1_transfer = f1_score(y_true_tr, y_pred_tr, average='macro')\n",
+    "ece_transfer = compute_ece(y_prob_tr, y_true_tr)\n",
+    "\n",
+    "print(\"\\nTransfer model metrics:\")\n",
+    "print(f\"Top-1 Accuracy: {top1_transfer:.4f}\")\n",
+    "print(f\"F1-macro:       {f1_transfer:.4f}\")\n",
+    "print(f\"ECE:            {ece_transfer:.4f}\")\n",
+    "\n",
+    "# Добавим transfer результата в сравнение\n",
+    "comparison_df = pd.DataFrame({\n",
+    "    \"Модель\": [\"Baseline\", \"Improved\", \"Transfer\"],\n",
+    "    \"Top-1 Accuracy\": [top1_baseline, top1_improved, top1_transfer],\n",
+    "    \"F1-macro\": [f1_baseline, f1_improved, f1_transfer],\n",
+    "    \"ECE\": [ece_baseline, ece_improved, ece_transfer],\n",
+    "    \"Train-Val Gap (acc)\": [\n",
+    "        history_baseline[\"train_acc\"][-1] - history_baseline[\"val_acc\"][-1],\n",
+    "        history_improved[\"train_acc\"][-1] - history_improved[\"val_acc\"][-1],\n",
+    "        history_transfer[\"train_acc\"][-1] - history_transfer[\"val_acc\"][-1]\n",
+    "    ]\n",
+    "})\n",
+    "\n",
+    "# Дельты относительно baseline\n",
+    "comparison_df[\"Δ Top-1\"] = [\"\", f\"{(top1_improved - top1_baseline)*100:+.2f}%\", f\"{(top1_transfer - top1_baseline)*100:+.2f}%\"]\n",
+    "comparison_df[\"Δ F1\"] = [\"\", f\"{(f1_improved - f1_baseline)*100:+.2f}%\", f\"{(f1_transfer - f1_baseline)*100:+.2f}%\"]\n",
+    "comparison_df[\"Δ ECE\"] = [\"\", f\"{(ece_improved - ece_baseline):.4f}\", f\"{(ece_transfer - ece_baseline):.4f}\"]\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"СРАВНЕНИЕ BASELINE vs IMPROVED vs TRANSFER\")\n",
+    "print(\"=\" * 60)\n",
+    "display(comparison_df)\n",
+    "\n",
+    "# Покажем reliability diagram для transfer\n",
+    "plot_reliability_diagram(y_prob_tr, y_true_tr, f\"Transfer (ECE = {ece_transfer:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e701af63",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Добавим трансферное обучение с ResNet-18 (предобученная на ImageNet). Также усилим аугментации\n",
+    "(RandomResizedCrop, ColorJitter, RandAugment) и уменьшим LR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "880a3564",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "СРАВНЕНИЕ BASELINE vs IMPROVED\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Модель",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Top-1 Accuracy",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "F1-macro",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "ECE",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Train-Val Gap (acc)",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Δ Top-1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Δ F1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Δ ECE",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "2577a870-11c3-4d36-992c-e7d21a6d9144",
+       "rows": [
+        [
+         "0",
+         "Baseline",
+         "0.19",
+         "0.1552399036559363",
+         "0.017741344124078753",
+         "0.045999999999999985",
+         "",
+         "",
+         ""
+        ],
+        [
+         "1",
+         "Improved",
+         "0.18333333333333332",
+         "0.15897079366642777",
+         "0.03164389143387478",
+         "-0.023333333333333317",
+         "-0.67%",
+         "+0.37%",
+         "0.0139"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 2
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        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>Модель</th>\n",
+       "      <th>Top-1 Accuracy</th>\n",
+       "      <th>F1-macro</th>\n",
+       "      <th>ECE</th>\n",
+       "      <th>Train-Val Gap (acc)</th>\n",
+       "      <th>Δ Top-1</th>\n",
+       "      <th>Δ F1</th>\n",
+       "      <th>Δ ECE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Baseline</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.155240</td>\n",
+       "      <td>0.017741</td>\n",
+       "      <td>0.046000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Improved</td>\n",
+       "      <td>0.183333</td>\n",
+       "      <td>0.158971</td>\n",
+       "      <td>0.031644</td>\n",
+       "      <td>-0.023333</td>\n",
+       "      <td>-0.67%</td>\n",
+       "      <td>+0.37%</td>\n",
+       "      <td>0.0139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Модель  Top-1 Accuracy  F1-macro       ECE  Train-Val Gap (acc) Δ Top-1  \\\n",
+       "0  Baseline        0.190000  0.155240  0.017741             0.046000           \n",
+       "1  Improved        0.183333  0.158971  0.031644            -0.023333  -0.67%   \n",
+       "\n",
+       "     Δ F1   Δ ECE  \n",
+       "0                  \n",
+       "1  +0.37%  0.0139  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Таблица сравнения метрик\n",
+    "comparison_df = pd.DataFrame({\n",
+    "    \"Модель\": [\"Baseline\", \"Improved\"],\n",
+    "    \"Top-1 Accuracy\": [top1_baseline, top1_improved],\n",
+    "    \"F1-macro\": [f1_baseline, f1_improved],\n",
+    "    \"ECE\": [ece_baseline, ece_improved],\n",
+    "    \"Train-Val Gap (acc)\": [\n",
+    "        history_baseline[\"train_acc\"][-1] - history_baseline[\"val_acc\"][-1],\n",
+    "        history_improved[\"train_acc\"][-1] - history_improved[\"val_acc\"][-1]\n",
+    "    ]\n",
+    "})\n",
+    "\n",
+    "comparison_df[\"Δ Top-1\"] = [\"\", f\"{(top1_improved - top1_baseline)*100:+.2f}%\"]\n",
+    "comparison_df[\"Δ F1\"] = [\"\", f\"{(f1_improved - f1_baseline)*100:+.2f}%\"]\n",
+    "comparison_df[\"Δ ECE\"] = [\"\", f\"{(ece_improved - ece_baseline):.4f}\"]\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 60)\n",
+    "print(\"СРАВНЕНИЕ BASELINE vs IMPROVED\")\n",
+    "print(\"=\" * 60)\n",
+    "display(comparison_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "201f7d3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Графики: Loss и Accuracy\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "# Loss\n",
+    "axes[0].plot(history_baseline[\"epoch\"], history_baseline[\"train_loss\"], 'b-', label=\"Baseline train\")\n",
+    "axes[0].plot(history_baseline[\"epoch\"], history_baseline[\"val_loss\"], 'b--', label=\"Baseline val\")\n",
+    "axes[0].plot(history_improved[\"epoch\"], history_improved[\"train_loss\"], 'r-', label=\"Improved train\")\n",
+    "axes[0].plot(history_improved[\"epoch\"], history_improved[\"val_loss\"], 'r--', label=\"Improved val\")\n",
+    "axes[0].set_xlabel(\"Epoch\")\n",
+    "axes[0].set_ylabel(\"Loss\")\n",
+    "axes[0].set_title(\"Динамика Loss\")\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Accuracy\n",
+    "axes[1].plot(history_baseline[\"epoch\"], history_baseline[\"train_acc\"], 'b-', label=\"Baseline train\")\n",
+    "axes[1].plot(history_baseline[\"epoch\"], history_baseline[\"val_acc\"], 'b--', label=\"Baseline val\")\n",
+    "axes[1].plot(history_improved[\"epoch\"], history_improved[\"train_acc\"], 'r-', label=\"Improved train\")\n",
+    "axes[1].plot(history_improved[\"epoch\"], history_improved[\"val_acc\"], 'r--', label=\"Improved val\")\n",
+    "axes[1].set_xlabel(\"Epoch\")\n",
+    "axes[1].set_ylabel(\"Accuracy\")\n",
+    "axes[1].set_title(\"Динамика Accuracy\")\n",
+    "axes[1].legend()\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "41434544",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion Matrix\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Baseline\n",
+    "cm_bl = confusion_matrix(y_true_bl, y_pred_bl)\n",
+    "ConfusionMatrixDisplay(confusion_matrix=cm_bl, display_labels=label_names).plot(ax=axes[0], cmap=\"Blues\", colorbar=False)\n",
+    "axes[0].set_title(\"Baseline\")\n",
+    "axes[0].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Improved\n",
+    "cm_imp = confusion_matrix(y_true_imp, y_pred_imp)\n",
+    "ConfusionMatrixDisplay(confusion_matrix=cm_imp, display_labels=label_names).plot(ax=axes[1], cmap=\"Greens\", colorbar=False)\n",
+    "axes[1].set_title(\"Improved\")\n",
+    "axes[1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5e8a48f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Reliability Diagram\n",
+    "def plot_reliability_diagram(probs, labels, title, n_bins=10):\n",
+    "    confidences = probs.max(axis=1)\n",
+    "    predictions = probs.argmax(axis=1)\n",
+    "    accuracies = (predictions == labels).astype(float)\n",
+    "    \n",
+    "    bin_boundaries = np.linspace(0, 1, n_bins + 1)\n",
+    "    bin_centers = (bin_boundaries[:-1] + bin_boundaries[1:]) / 2\n",
+    "    \n",
+    "    bin_accs = []\n",
+    "    bin_confs = []\n",
+    "    bin_counts = []\n",
+    "    \n",
+    "    for i in range(n_bins):\n",
+    "        in_bin = (confidences > bin_boundaries[i]) & (confidences <= bin_boundaries[i + 1])\n",
+    "        if in_bin.sum() > 0:\n",
+    "            bin_accs.append(accuracies[in_bin].mean())\n",
+    "            bin_confs.append(confidences[in_bin].mean())\n",
+    "            bin_counts.append(in_bin.sum())\n",
+    "        else:\n",
+    "            bin_accs.append(0)\n",
+    "            bin_confs.append(0)\n",
+    "            bin_counts.append(0)\n",
+    "    \n",
+    "    plt.figure(figsize=(6, 5))\n",
+    "    plt.bar(bin_centers, bin_accs, width=0.08, alpha=0.7, label='Accuracy')\n",
+    "    plt.plot([0, 1], [0, 1], 'r--', label='Perfect calibration')\n",
+    "    plt.xlabel('Confidence')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title(title)\n",
+    "    plt.legend()\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.xlim(0, 1)\n",
+    "    plt.ylim(0, 1)\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_reliability_diagram(y_prob_bl, y_true_bl, f\"Baseline (ECE = {ece_baseline:.4f})\")\n",
+    "plot_reliability_diagram(y_prob_imp, y_true_imp, f\"Improved (ECE = {ece_improved:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8171eb36",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Итог\n",
+    "\n",
+    "\n",
+    "| Метрика | Baseline | Improved | Transfer | Δ (Transfer vs Baseline) |\n",
+    "|---------|----------|----------|----------|--------------------------|\n",
+    "| **Top-1 Accuracy** | 0.190 | 0.183 | **0.437** | **+24.67%** |\n",
+    "| **F1-macro** | 0.155 | 0.159 | **0.427** | **+27.22%** |\n",
+    "| **ECE** | 0.018 | 0.032 | 0.152 | +0.134 |\n",
+    "| **Train-Val Gap** | 0.046 | −0.023 | 0.031 | — |\n",
+    "\n",
+    "### Применённые методы улучшения\n",
+    "1. **Dropout (0.2)** — отключает 20% нейронов при обучении\n",
+    "2. **Weight Decay (0.005)** — L2-регуляризация весов (AdamW)\n",
+    "3. **Label Smoothing (0.1 / 0.05)** — сглаживание one-hot меток\n",
+    "4. **BatchNorm** — нормализация активаций после Conv2d\n",
+    "5. **Transfer Learning (ResNet-18)** — предобученная на ImageNet модель\n",
+    "6. **Сильные аугментации** — RandomResizedCrop, ColorJitter, RandAugment\n",
+    "\n",
+    "---\n",
+    "\n",
+    "1. **Какой приём дал наибольшее улучшение?**\n",
+    "   \n",
+    "   **Transfer Learning (ResNet-18)** дал колоссальное улучшение: Top-1 Accuracy выросла с 19% до **43.7%** (+24.67%), F1-macro — с 0.155 до **0.427** (+27.22%). Предобученные веса ImageNet содержат универсальные признаки изображений, что критически важно при малом объёме данных (50 изображений на класс).\n",
+    "\n",
+    "2. **Уменьшился ли разрыв между train и val?**\n",
+    "   \n",
+    "   **Да, для Improved модели.** Train-Val Gap снизился с 0.046 (Baseline) до −0.023 (Improved), что говорит об эффективности Dropout и Weight Decay. У Transfer модели Gap = 0.031, что также ниже Baseline.\n",
+    "\n",
+    "3. **Стала ли модель увереннее (ECE снизился)?**\n",
+    "   \n",
+    "   **Нет.** ECE вырос у всех улучшенных моделей:\n",
+    "   - Improved: 0.032 (Label Smoothing снижает уверенность)\n",
+    "   - Transfer: 0.152 (модель стала более уверенной, но не всегда корректно)\n",
+    "   \n",
+    "   Высокий ECE у Transfer объясняется тем, что ResNet обучен на ImageNet и выдаёт высокие confidence-scores, но Food-101 отличается от ImageNet классов.\n",
+    "\n",
+    "4. **Какое направление проверить дальше?**\n",
+    "   \n",
+    "   - **Увеличить размер изображений** до 128×128 или 224×224\n",
+    "   - **Temperature Scaling** для калибровки ECE\n",
+    "   - **MixUp / CutMix** — смешивание изображений для лучшей робастности\n",
+    "   - **Разморозить больше слоёв** ResNet (layer3 + layer4) при fine-tuning\n",
+    "   - **Увеличить объём данных** (200+ изображений на класс)\n",
+    "\n",
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}