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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I_6X4afzQna-"
+ },
+ "source": [
+ "# Суть задания\n",
+ "\n",
+ "\n",
+ "Генеративные модели (GAN, VAE)\\\n",
+ "Общая цель:\n",
+ "Показать понимание архитектур, алгоритмов обучения и функций потерь в GAN, VAE, а также получить практический опыт работы с этими моделями на небольших наборах данных (например, MNIST).\n",
+ "\n",
+ "Примерное время выполнения - ??? часов.\n",
+ "\n",
+ "Дедлайн: 1 марта в 23:59 \\\n",
+ "Сдача в течение 12 часов после дедлайна не штрафуется.\n",
+ "\n",
+ "Штрафные коэффициенты оценивания при просрочке:\n",
+ "\n",
+ "-|-\n",
+ "----|---\n",
+ "От 12 часов до 7 дней после дедлайна | 0.8\n",
+ "От 7 до 14 дней | 0.6\n",
+ "От 14 дней до конца модуля | 0.4\n",
+ "\n",
+ "## Оценивание\n",
+ "\n",
+ "Задание:\n",
+ "\n",
+ "Критерии оценивания | Коэффициент\n",
+ "-------------|--------------------\n",
+ "Код есть, но не работает | 0\n",
+ "Код работает, но качество генерации хуже образцов | 0.5\n",
+ "Качество генерации не хуже образцов | 1\n",
+ "\n",
+ "\\\n",
+ "\n",
+ "\n",
+ "Часть работы | Стоимость в баллах\n",
+ "-------------|--------------------\n",
+ "VAE | 5\n",
+ "GAN | 4\n",
+ "Рнтерполяция | 1\n",
+ "Ртого | 10 баллов\n",
+ "\n",
+ "Формула оценивания всей работы:\n",
+ "\n",
+ "О = Задание * 0.6 + ответы на вопросы * 0.3 + Тест на лекции * 0.1\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Критерии оценивания | Коэффициент\n",
+ "-------------|--------------------\n",
+ "Код есть, но не работает | 0\n",
+ "Код работает, но качество генерации хуже образцов | 0.5\n",
+ "Качество генерации не хуже образцов | 1"
+ ],
+ "metadata": {
+ "id": "6qDkKHe94Avw"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WN4c14ZyJFcx"
+ },
+ "source": [
+ "# 0 Подготовка системы"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kcwIhylJ4UrO"
+ },
+ "source": [
+ "## Рмпорты библиотек"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hECP4El_4ULO"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torchvision.datasets as dset\n",
+ "import torchvision.transforms as transforms\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch.nn.functional as F\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GlWsSZB8FKX9"
+ },
+ "source": [
+ "# Задачи"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "22vyj_Oy00Z1"
+ },
+ "source": [
+ "# Задача 1: Реализация и обучение Variational Autoencoder (VAE) (5 баллов)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nDIhmJNxvlJy"
+ },
+ "source": [
+ "## Vanilla VAE (2 балла)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N6voHwOF_gjZ"
+ },
+ "source": [
+ "* Реализуйте VAE для набора данных MNIST. (0.5 задачи)\n",
+ " * Реализуйте архитектуру с энкодером, который выводит параметры\n",
+ "𝜇 и 𝜎 для аппроксимированного распределения\n",
+ "𝑞(𝑧∣𝑥), и декодером для реконструкции\n",
+ "𝑥 из 𝑧.\n",
+ " * Рспользуйте функцию потерь, включающую реконструкционную ошибку (MSE или BCE) Рё KL-дивергенцию.\n",
+ " * Обучите модель и визуализируйте результаты:\n",
+ " * Сравните исходные изображения и реконструкции.\n",
+ " * Сгенерируйте новые изображения, сэмплируя\n",
+ "* Условная генерация (0.2 задачи)\n",
+ " * Дополните модель для предоставления возможности условной генерации по заданному классу\n",
+ "* Улучшение модели (0.2 задачи)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7Bw7atvO07I9"
+ },
+ "source": [
+ "### Реализация энкодера"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PKUTygo55x2t"
+ },
+ "source": [
+ "Рнкодер - часть VAE, сжимающая входные данные РІ параметры вероятностного распределения рќњ‡ Рё рќњЋ. Для начала реализуйте базовую архитектуру:\n",
+ "\n",
+ "Рнициализируйте `torch.nn.Module` Рё определите несколько слоев для дальнейшего использования.\n",
+ "\n",
+ "Пожалуйста, используйте следующую структуру сети:\n",
+ "- скрытый слой\n",
+ " - линейный (полносвязный) слой со входом 784 и hidden_dim выходных измерений\n",
+ "- выходной слой для среднего значения латентного распределения\n",
+ " - линейный (полносвязный) слой hidden_dim x размерность латентного пространства\n",
+ "- выходной слой для дисперсии латентного распределения\n",
+ " - линейный (полносвязный) слой hidden_dim x размерность латентного пространства\n",
+ "\n",
+ "Также реализуйте прямой проход по сети. В качестве функции активации используйте ReLU."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "okoglm755wX1"
+ },
+ "outputs": [],
+ "source": [
+ "class Encoder(nn.Module):\n",
+ " def __init__(self, input_dim=784, hidden_dim=400, latent_dim=20):\n",
+ " super(Encoder, self).__init__()\n",
+ " # YOUR CODE HERE\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # YOUR CODE HERE\n",
+ " return mu, logvar # logvar - логарифм дисперсии. Мы принуждаем модель сразу выучивать его логарифмическое значение. Почему логарифм? Так будет удобнее считать KL divergence."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HMOqJcBA0_L1"
+ },
+ "source": [
+ "### Реализация декодера"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DRLGuXR1mn5M"
+ },
+ "source": [
+ "Декодер - часть VAE, получающая на вход случайных шум размера латентного пространства и восстанавливающий (генерирующий) на его основе данные. Для начала реализуйте базовую архитектуру:\n",
+ "\n",
+ "Рнициализируйте `torch.nn.Module` Рё определите несколько слоев для дальнейшего использования.\n",
+ "\n",
+ "Пожалуйста, используйте следующую структуру сети:\n",
+ "- скрытый слой\n",
+ " - линейный (полносвязный) слой со входом с размерностью латентного пространства и выходом размера hidden_dim\n",
+ "- выходной слой\n",
+ " - линейный (полносвязный) слой со входом hidden_dim x 784 (размерность изначальных данных)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "D2pXLExT52zG"
+ },
+ "outputs": [],
+ "source": [
+ "class Decoder(nn.Module):\n",
+ " def __init__(self, latent_dim=20, hidden_dim=400, output_dim=784):\n",
+ " super(Decoder, self).__init__()\n",
+ " # YOUR CODE HERE\n",
+ "\n",
+ " def forward(self, z):\n",
+ " # YOUR CODE HERE\n",
+ " hidden = # выход 1-го скрытого слоя\n",
+ " x_recon = # выход модели\n",
+ " return x_recon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X2BFPGUi555X"
+ },
+ "source": [
+ "### Собираем вместе\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "AZqb_fSz9JPe"
+ },
+ "outputs": [],
+ "source": [
+ "class VAE(nn.Module):\n",
+ " def __init__(self, input_dim=784, hidden_dim=400, latent_dim=20):\n",
+ " super(VAE, self).__init__()\n",
+ " self.encoder = Encoder(input_dim, hidden_dim, latent_dim)\n",
+ " self.decoder = Decoder(latent_dim, hidden_dim, input_dim)\n",
+ "\n",
+ " def reparameterize(self, mu, logvar):\n",
+ " # YOUR CODE HERE\n",
+ " std = # Вычислите стандартное отклонение c помощью нашего логарифмического: e^(0.5*logvar)\n",
+ " epsilon = # Сэмплируйте шум из стандартного нормального распределения, можно сделать через torch.rand\n",
+ " z = # Reparameterization trick\n",
+ " return z\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # YOUR CODE HERE\n",
+ " ## TODO: Вызовите энкодер для вычисления mu и logvar (логарифм дисперсии). Почему логарифм? Так будет удобнее считать KL divergence.\n",
+ " ## TODO: Вычислите латентный вектор z через репараметризацию\n",
+ " ## TODO: Вызовите декодер для вычисления выхода модели\n",
+ " return x_recon, mu, logvar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7kvc6Eyy16ON"
+ },
+ "source": [
+ "### Реализация функции ошибки"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JDJhXmzZ6D0-"
+ },
+ "outputs": [],
+ "source": [
+ "# Определите функцию потерь: Потери при реконструкции (BCE) + потери при расхождении KL\n",
+ "def loss_function(recon_x, x, mu, logvar):\n",
+ " # Вычислите бинарную потерю перекрестной энтропии между реконструированным выходом и оригинальным входом\n",
+ " BCE = # YOUR CODE HERE\n",
+ " # Вычислите KL-расхождение между q(z|x) и p(z) = N(0, I)\n",
+ " KL = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
+ " return BCE + KL"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "k00HSc8w6LJG"
+ },
+ "source": [
+ "### Реализация обучения"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XwKMZjpy6XE2"
+ },
+ "source": [
+ "#### Загрузка данных"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "GWyvaWwf6ZI4",
+ "outputId": "1152d627-23e0-411d-9043-95fe712bd28e",
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+ "Failed to download (trying next):\n",
+ "HTTP Error 404: Not Found\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 9.91M/9.91M [00:00<00:00, 15.0MB/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+ "\n",
+ "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+ "Failed to download (trying next):\n",
+ "HTTP Error 404: Not Found\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 28.9k/28.9k [00:00<00:00, 507kB/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+ "\n",
+ "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+ "Failed to download (trying next):\n",
+ "HTTP Error 404: Not Found\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 1.65M/1.65M [00:00<00:00, 3.83MB/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+ "\n",
+ "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+ "Failed to download (trying next):\n",
+ "HTTP Error 404: Not Found\n",
+ "\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
+ "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 4.54k/4.54k [00:00<00:00, 10.7MB/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Подготовьте датасет MNIST\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Lambda(lambda x: x.view(-1)) # Разворачиваем изображение в вектор\n",
+ "])\n",
+ "\n",
+ "mnist_train = dset.MNIST(root='./data', train=True, transform=transform, download=True)\n",
+ "train_loader = DataLoader(mnist_train, batch_size=128, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EybPgC8_6b--"
+ },
+ "source": [
+ "#### Обучение"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4jxiDxV46qrF"
+ },
+ "outputs": [],
+ "source": [
+ "hidden_dim = 400 # размерность скрытого слоя\n",
+ "latent_dim = 20 # размерность латентного пространства\n",
+ "\n",
+ "# Рнициализируйте модель VAE, оптимизатор Рё устройство настройки\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "model = VAE(input_dim=784, hidden_dim=hidden_dim, latent_dim=latent_dim).to(device)\n",
+ "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "18XwuMST6V1u",
+ "outputId": "2e7e0b21-e76a-4afe-dcdd-c447a6776c8e",
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 0 [0/60000 (0%)]\tLoss: 549.651123\n",
+ "Epoch: 0 [12800/60000 (21%)]\tLoss: 197.578308\n",
+ "Epoch: 0 [25600/60000 (43%)]\tLoss: 160.793076\n",
+ "Epoch: 0 [38400/60000 (64%)]\tLoss: 145.548248\n",
+ "Epoch: 0 [51200/60000 (85%)]\tLoss: 136.780762\n",
+ "====> Epoch: 0 Average loss: 169.9801\n",
+ "Epoch: 1 [0/60000 (0%)]\tLoss: 137.496185\n",
+ "Epoch: 1 [12800/60000 (21%)]\tLoss: 129.622253\n",
+ "Epoch: 1 [25600/60000 (43%)]\tLoss: 130.465042\n",
+ "Epoch: 1 [38400/60000 (64%)]\tLoss: 128.617569\n",
+ "Epoch: 1 [51200/60000 (85%)]\tLoss: 122.140762\n",
+ "====> Epoch: 1 Average loss: 126.7344\n",
+ "Epoch: 2 [0/60000 (0%)]\tLoss: 125.899216\n",
+ "Epoch: 2 [12800/60000 (21%)]\tLoss: 121.322174\n",
+ "Epoch: 2 [25600/60000 (43%)]\tLoss: 117.357246\n",
+ "Epoch: 2 [38400/60000 (64%)]\tLoss: 117.975067\n",
+ "Epoch: 2 [51200/60000 (85%)]\tLoss: 115.896896\n",
+ "====> Epoch: 2 Average loss: 117.2913\n",
+ "Epoch: 3 [0/60000 (0%)]\tLoss: 110.319069\n",
+ "Epoch: 3 [12800/60000 (21%)]\tLoss: 112.937500\n",
+ "Epoch: 3 [25600/60000 (43%)]\tLoss: 115.407402\n",
+ "Epoch: 3 [38400/60000 (64%)]\tLoss: 113.595947\n",
+ "Epoch: 3 [51200/60000 (85%)]\tLoss: 115.166046\n",
+ "====> Epoch: 3 Average loss: 112.8359\n",
+ "Epoch: 4 [0/60000 (0%)]\tLoss: 114.871292\n",
+ "Epoch: 4 [12800/60000 (21%)]\tLoss: 111.309570\n",
+ "Epoch: 4 [25600/60000 (43%)]\tLoss: 113.547142\n",
+ "Epoch: 4 [38400/60000 (64%)]\tLoss: 108.120529\n",
+ "Epoch: 4 [51200/60000 (85%)]\tLoss: 110.265701\n",
+ "====> Epoch: 4 Average loss: 110.4146\n",
+ "Epoch: 5 [0/60000 (0%)]\tLoss: 108.669205\n",
+ "Epoch: 5 [12800/60000 (21%)]\tLoss: 109.608719\n",
+ "Epoch: 5 [25600/60000 (43%)]\tLoss: 111.186172\n",
+ "Epoch: 5 [38400/60000 (64%)]\tLoss: 110.170494\n",
+ "Epoch: 5 [51200/60000 (85%)]\tLoss: 109.295723\n",
+ "====> Epoch: 5 Average loss: 108.8416\n",
+ "Epoch: 6 [0/60000 (0%)]\tLoss: 105.835258\n",
+ "Epoch: 6 [12800/60000 (21%)]\tLoss: 104.374489\n",
+ "Epoch: 6 [25600/60000 (43%)]\tLoss: 109.559021\n",
+ "Epoch: 6 [38400/60000 (64%)]\tLoss: 106.296310\n",
+ "Epoch: 6 [51200/60000 (85%)]\tLoss: 107.563034\n",
+ "====> Epoch: 6 Average loss: 107.7811\n",
+ "Epoch: 7 [0/60000 (0%)]\tLoss: 106.230667\n",
+ "Epoch: 7 [12800/60000 (21%)]\tLoss: 107.318367\n",
+ "Epoch: 7 [25600/60000 (43%)]\tLoss: 103.941063\n",
+ "Epoch: 7 [38400/60000 (64%)]\tLoss: 108.115807\n",
+ "Epoch: 7 [51200/60000 (85%)]\tLoss: 106.539688\n",
+ "====> Epoch: 7 Average loss: 107.0404\n",
+ "Epoch: 8 [0/60000 (0%)]\tLoss: 109.100060\n",
+ "Epoch: 8 [12800/60000 (21%)]\tLoss: 106.600693\n",
+ "Epoch: 8 [25600/60000 (43%)]\tLoss: 109.035187\n",
+ "Epoch: 8 [38400/60000 (64%)]\tLoss: 105.633469\n",
+ "Epoch: 8 [51200/60000 (85%)]\tLoss: 104.844879\n",
+ "====> Epoch: 8 Average loss: 106.4914\n",
+ "Epoch: 9 [0/60000 (0%)]\tLoss: 107.363815\n",
+ "Epoch: 9 [12800/60000 (21%)]\tLoss: 105.478142\n",
+ "Epoch: 9 [25600/60000 (43%)]\tLoss: 104.234360\n",
+ "Epoch: 9 [38400/60000 (64%)]\tLoss: 105.959175\n",
+ "Epoch: 9 [51200/60000 (85%)]\tLoss: 103.581192\n",
+ "====> Epoch: 9 Average loss: 106.0234\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 10 эпох - очень мало, но подойдёт, чтобы удостовериться, что ваша модель в своей базе работает нормально.\n",
+ "num_epochs = 10 # Для получения более презентабельного результата используйте 50 и больше эпох\n",
+ "model.train()\n",
+ "for epoch in range(num_epochs):\n",
+ " train_loss = 0\n",
+ " for batch_idx, (data, _) in enumerate(train_loader):\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: Переместите входные данные (data) на правильное устройство\n",
+ " ## TODO: Обнулите градиенты\n",
+ " ## TODO: Вычислите выходы энкодера (recon_batch, mu, logvar)\n",
+ " ## TODO: Рассчитайте функцию потерь (loss)\n",
+ " ## TODO: Выполните обратное распространение градиентов с учетом функции потерь\n",
+ " ## TODO: Обновите общий loss, добавив текущий loss\n",
+ " ## TODO: Выполните шаг оптимизатора\n",
+ "\n",
+ " if batch_idx % 100 == 0:\n",
+ " print(f'Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} '\n",
+ " f'({100. * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item() / len(data):.6f}')\n",
+ " print(f'====> Epoch: {epoch} Average loss: {train_loss / len(train_loader.dataset):.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NhmTGz8q9oTQ"
+ },
+ "source": [
+ "### Рнференс (генерация)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hltHYvEu-hgh"
+ },
+ "source": [
+ "#### Восстановление входных данных"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359
+ },
+ "id": "-8LatwOW-Sgv",
+ "outputId": "1c7b5f01-8c15-455f-d61c-03b2cee5a7ec"
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<Figure size 1600x400 with 16 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Определите то же преобразование, что и во время обучения\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Lambda(lambda x: x.view(-1)) # Сплющите изображение (28x28 -> 784)\n",
+ "])\n",
+ "\n",
+ "# Загрузите тестовый набор данных MNIST\n",
+ "mnist_test = dset.MNIST(root='./data', train=False, transform=transform, download=True)\n",
+ "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=16, shuffle=True)\n",
+ "\n",
+ "model.eval() # Переведите модель в режим оценки\n",
+ "\n",
+ "# ---- Визуализация: Реконструкция тестовых изображений ----\n",
+ "# Получите партию тестовых изображений\n",
+ "data_iter = iter(test_loader)\n",
+ "images, _ = next(data_iter)\n",
+ "images = images.to(device)\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " recon_images, mu, logvar = model(images)\n",
+ "\n",
+ "# Функция построения графика исходного и реконструированного изображений\n",
+ "def plot_images(original, reconstructed, n=8):\n",
+ " plt.figure(figsize=(16, 4))\n",
+ " for i in range(n):\n",
+ " # Оригинальные изображения\n",
+ " plt.subplot(2, n, i+1)\n",
+ " plt.imshow(original[i].cpu().view(28, 28), cmap='gray')\n",
+ " plt.title(\"Original\")\n",
+ " plt.axis(\"off\")\n",
+ " # Реконструированные изображения\n",
+ " plt.subplot(2, n, i+n+1)\n",
+ " plt.imshow(reconstructed[i].cpu().view(28, 28), cmap='gray')\n",
+ " plt.title(\"Reconstructed\")\n",
+ " plt.axis(\"off\")\n",
+ " plt.show()\n",
+ "\n",
+ "plot_images(images, recon_images, n=8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DNfpu8x__BcH"
+ },
+ "source": [
+ "#### Генерация новых изображений"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IPDW7Ank_Ai-"
+ },
+ "outputs": [],
+ "source": [
+ "# ---- Визуализация: Генерация новых изображений из латентного пространства ----\n",
+ "with torch.no_grad():\n",
+ " # Выборка латентных векторов z из стандартного нормального распределения\n",
+ " # YOUR CODE HERE:\n",
+ " z = # 64 образца случайного шума, каждый из которых имеет размерность latent_dim\n",
+ " generated_images = model.decoder(z)\n",
+ "\n",
+ "def plot_generated(images):\n",
+ " fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
+ " for i, ax in enumerate(axes.flat):\n",
+ " img = images[i].view(28,28).numpy()\n",
+ " ax.imshow(img, cmap=\"gray\")\n",
+ " ax.axis(\"off\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "plot_generated(generated_images)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LPldM_K9IEQL"
+ },
+ "source": [
+ "## Conditional VAE (2 балла)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nT2EKsBlwI-7"
+ },
+ "source": [
+ "Как вы могли заметить, получившаяся модель а) генерирует изображения случайных классов из нашего датасета б) не очень высокого качества. Попробуем что-то с этим сделать. Для начала научим нашу модель генерировать изображения заданного класса. Такая генерация называется условной (conditional generation)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Chv9ZEEiFv2I"
+ },
+ "source": [
+ "### Определение условного энкодера"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EuCCu5LTw9LL"
+ },
+ "source": [
+ "Чтобы придать энкодеру возможность работы с метками классов, необходимо добавить (сконкатенировать) эти самые метки с вектором входных данных. Чтобы модель могла эти метки нормально воспринять, их нужно закодировать методом one-hot кодирования (one-hot encoding), позволяющим переводить категориальные данные в численные. При количестве классов 10 каждая метка будет превращаться в вектор такой же длины. Соответственно, вам нужно учесть это дополнение при описании входного слоя модели.\n",
+ "\n",
+ "Архитектуру энкодера и декодера можете использовать аналогичную vanilla VAE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "FKJKC5ECIG41"
+ },
+ "outputs": [],
+ "source": [
+ "class ConditionalEncoder(nn.Module):\n",
+ " def __init__(self, input_dim=784, condition_dim=10, hidden_dim=400, latent_dim=20):\n",
+ " super(ConditionalEncoder, self).__init__()\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: задать скрытый слой модели правильного размера\n",
+ " ## TODO: задать слой для генерации среднего\n",
+ " ## TODO: задать слой для генерации логарифмической дисперсии\n",
+ "\n",
+ " def forward(self, x, c):\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: вычислить вектор входных данных с учётом вектора метки класса\n",
+ " ## TODO: вычислить среднее и logvar\n",
+ " return mu, logvar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Foq4VqpXF2GF"
+ },
+ "source": [
+ "### Определение условного декодера"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F8mDqFjTy1KJ"
+ },
+ "source": [
+ "С декодером необходимо проделать аналогичные преобразования - добавить возможность подавать на вход вместе с шумом вектор метки класса, чтобы получить ту самую условную генерацию."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5q_Ff3EtIKKM"
+ },
+ "outputs": [],
+ "source": [
+ "class ConditionalDecoder(nn.Module):\n",
+ " def __init__(self, latent_dim=20, condition_dim=10, hidden_dim=400, output_dim=784):\n",
+ " super(ConditionalDecoder, self).__init__()\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: задать скрытые слои декодера правильного размера\n",
+ "\n",
+ " def forward(self, z, c):\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: вычислить вектор входных данных (шум + вектора метки класса)\n",
+ " ## TODO: вычислить вектор скрытого слоя\n",
+ " ## TODO: вычислить выходной вектор (реконструированное/сгенерированное изображение)\n",
+ " return x_recon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9U4QrYYdF5uJ"
+ },
+ "source": [
+ "### Собираем Conditional VAE воедино"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dE5nq96oILd2"
+ },
+ "outputs": [],
+ "source": [
+ "class ConditionalVAE(nn.Module):\n",
+ " def __init__(self, input_dim=784, condition_dim=10, hidden_dim=400, latent_dim=50):\n",
+ " super(ConditionalVAE, self).__init__()\n",
+ " self.encoder = ConditionalEncoder(input_dim, condition_dim, hidden_dim, latent_dim)\n",
+ " self.decoder = ConditionalDecoder(latent_dim, condition_dim, hidden_dim, input_dim)\n",
+ "\n",
+ " def reparameterize(self, mu, logvar):\n",
+ " # YOUR CODE HERE\n",
+ " std = # Вычислите стандартное отклонение c помощью нашего логарифмического: e^(0.5*logvar)\n",
+ " epsilon = # Сэмплируйте шум из стандартного нормального распределения, можно сделать через torch.rand\n",
+ " z = # Reparameterization trick\n",
+ " return z\n",
+ "\n",
+ " def forward(self, x):\n",
+ " # YOUR CODE HERE\n",
+ " ## TODO: Вызовите энкодер для вычисления mu и logvar (логарифм дисперсии). Почему логарифм? Так будет удобнее считать KL divergence.\n",
+ " ## TODO: Вычислите латентный вектор z через репараметризацию\n",
+ " ## TODO: Вызовите декодер для вычисления выхода модели\n",
+ " return x_recon, mu, logvar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HOvUUsdl4WNy"
+ },
+ "source": [
+ "Функцию ошибки заново определять не нужно."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bDwCWob5F9tk"
+ },
+ "source": [
+ "### Обучение"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "odzA_NErJb-M"
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 128\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Lambda(lambda x: x.view(-1))\n",
+ "])\n",
+ "\n",
+ "train_dataset = dset.MNIST(root='./data', train=True, download=True, transform=transform)\n",
+ "test_dataset = dset.MNIST(root='./data', train=False, download=True, transform=transform)\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JZzTjd_3JlpI"
+ },
+ "outputs": [],
+ "source": [
+ "hidden_dim = 400 # размерность скрытого слоя\n",
+ "latent_dim = 20 # размерность латентного пространства\n",
+ "\n",
+ "# Рнициализируйте модель VAE, оптимизатор Рё используемое устройство\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "model = ConditionalVAE(input_dim=784, condition_dim=10, hidden_dim=hidden_dim, latent_dim=latent_dim).to(device)\n",
+ "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "background_save": true,
+ "base_uri": "https://localhost:8080/"
+ },
+ "collapsed": true,
+ "id": "TUI0MfTIJqlt",
+ "outputId": "eb053ee5-e3aa-4d56-a1ed-b53947c8875e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 1 [0/60000 (0%)]\tLoss: 549.490356\n",
+ "Epoch: 1 [12800/60000 (21%)]\tLoss: 189.086334\n",
+ "Epoch: 1 [25600/60000 (43%)]\tLoss: 159.345978\n",
+ "Epoch: 1 [38400/60000 (64%)]\tLoss: 143.850876\n",
+ "Epoch: 1 [51200/60000 (85%)]\tLoss: 136.648788\n",
+ "====> Epoch: 1 Average loss: 167.3469\n",
+ "Epoch: 2 [0/60000 (0%)]\tLoss: 132.546448\n",
+ "Epoch: 2 [12800/60000 (21%)]\tLoss: 128.750565\n",
+ "Epoch: 2 [25600/60000 (43%)]\tLoss: 126.024300\n",
+ "Epoch: 2 [38400/60000 (64%)]\tLoss: 117.869049\n",
+ "Epoch: 2 [51200/60000 (85%)]\tLoss: 120.187111\n",
+ "====> Epoch: 2 Average loss: 123.7226\n",
+ "Epoch: 3 [0/60000 (0%)]\tLoss: 115.634392\n",
+ "Epoch: 3 [12800/60000 (21%)]\tLoss: 118.327660\n",
+ "Epoch: 3 [25600/60000 (43%)]\tLoss: 112.278519\n",
+ "Epoch: 3 [38400/60000 (64%)]\tLoss: 115.505165\n",
+ "Epoch: 3 [51200/60000 (85%)]\tLoss: 115.376556\n",
+ "====> Epoch: 3 Average loss: 114.3658\n",
+ "Epoch: 4 [0/60000 (0%)]\tLoss: 110.471176\n",
+ "Epoch: 4 [12800/60000 (21%)]\tLoss: 110.238785\n",
+ "Epoch: 4 [25600/60000 (43%)]\tLoss: 109.829414\n",
+ "Epoch: 4 [38400/60000 (64%)]\tLoss: 109.979599\n",
+ "Epoch: 4 [51200/60000 (85%)]\tLoss: 106.215889\n",
+ "====> Epoch: 4 Average loss: 110.1279\n",
+ "Epoch: 5 [0/60000 (0%)]\tLoss: 112.286789\n",
+ "Epoch: 5 [12800/60000 (21%)]\tLoss: 107.491936\n",
+ "Epoch: 5 [25600/60000 (43%)]\tLoss: 111.115189\n",
+ "Epoch: 5 [38400/60000 (64%)]\tLoss: 101.237656\n",
+ "Epoch: 5 [51200/60000 (85%)]\tLoss: 110.056480\n",
+ "====> Epoch: 5 Average loss: 107.6375\n",
+ "Epoch: 6 [0/60000 (0%)]\tLoss: 106.335884\n",
+ "Epoch: 6 [12800/60000 (21%)]\tLoss: 105.354462\n",
+ "Epoch: 6 [25600/60000 (43%)]\tLoss: 107.972977\n",
+ "Epoch: 6 [38400/60000 (64%)]\tLoss: 108.399605\n",
+ "Epoch: 6 [51200/60000 (85%)]\tLoss: 104.147400\n",
+ "====> Epoch: 6 Average loss: 106.0147\n",
+ "Epoch: 7 [0/60000 (0%)]\tLoss: 103.258789\n",
+ "Epoch: 7 [12800/60000 (21%)]\tLoss: 104.960678\n",
+ "Epoch: 7 [25600/60000 (43%)]\tLoss: 106.622078\n",
+ "Epoch: 7 [38400/60000 (64%)]\tLoss: 98.644440\n",
+ "Epoch: 7 [51200/60000 (85%)]\tLoss: 109.373184\n",
+ "====> Epoch: 7 Average loss: 104.8324\n",
+ "Epoch: 8 [0/60000 (0%)]\tLoss: 102.690903\n",
+ "Epoch: 8 [12800/60000 (21%)]\tLoss: 103.180710\n",
+ "Epoch: 8 [25600/60000 (43%)]\tLoss: 101.197792\n",
+ "Epoch: 8 [38400/60000 (64%)]\tLoss: 106.390335\n",
+ "Epoch: 8 [51200/60000 (85%)]\tLoss: 102.301697\n",
+ "====> Epoch: 8 Average loss: 103.9785\n",
+ "Epoch: 9 [0/60000 (0%)]\tLoss: 103.510254\n",
+ "Epoch: 9 [12800/60000 (21%)]\tLoss: 102.456383\n",
+ "Epoch: 9 [25600/60000 (43%)]\tLoss: 106.319809\n",
+ "Epoch: 9 [38400/60000 (64%)]\tLoss: 100.008072\n",
+ "Epoch: 9 [51200/60000 (85%)]\tLoss: 103.317307\n",
+ "====> Epoch: 9 Average loss: 103.3319\n",
+ "Epoch: 10 [0/60000 (0%)]\tLoss: 104.410042\n",
+ "Epoch: 10 [12800/60000 (21%)]\tLoss: 99.842224\n",
+ "Epoch: 10 [25600/60000 (43%)]\tLoss: 104.068787\n",
+ "Epoch: 10 [38400/60000 (64%)]\tLoss: 98.981735\n",
+ "Epoch: 10 [51200/60000 (85%)]\tLoss: 101.346489\n",
+ "====> Epoch: 10 Average loss: 102.6839\n",
+ "Epoch: 11 [0/60000 (0%)]\tLoss: 102.592026\n",
+ "Epoch: 11 [12800/60000 (21%)]\tLoss: 104.634995\n",
+ "Epoch: 11 [25600/60000 (43%)]\tLoss: 103.697266\n",
+ "Epoch: 11 [38400/60000 (64%)]\tLoss: 103.478897\n",
+ "Epoch: 11 [51200/60000 (85%)]\tLoss: 104.284462\n",
+ "====> Epoch: 11 Average loss: 102.2355\n",
+ "Epoch: 12 [0/60000 (0%)]\tLoss: 102.575760\n",
+ "Epoch: 12 [12800/60000 (21%)]\tLoss: 96.024429\n",
+ "Epoch: 12 [25600/60000 (43%)]\tLoss: 98.083382\n",
+ "Epoch: 12 [38400/60000 (64%)]\tLoss: 102.088242\n",
+ "Epoch: 12 [51200/60000 (85%)]\tLoss: 106.296516\n",
+ "====> Epoch: 12 Average loss: 101.7813\n",
+ "Epoch: 13 [0/60000 (0%)]\tLoss: 103.312263\n",
+ "Epoch: 13 [12800/60000 (21%)]\tLoss: 100.457390\n",
+ "Epoch: 13 [25600/60000 (43%)]\tLoss: 101.510483\n",
+ "Epoch: 13 [38400/60000 (64%)]\tLoss: 97.863297\n",
+ "Epoch: 13 [51200/60000 (85%)]\tLoss: 102.485535\n",
+ "====> Epoch: 13 Average loss: 101.4451\n",
+ "Epoch: 14 [0/60000 (0%)]\tLoss: 101.044464\n",
+ "Epoch: 14 [12800/60000 (21%)]\tLoss: 101.376137\n",
+ "Epoch: 14 [25600/60000 (43%)]\tLoss: 100.656578\n",
+ "Epoch: 14 [38400/60000 (64%)]\tLoss: 103.119469\n",
+ "Epoch: 14 [51200/60000 (85%)]\tLoss: 102.455353\n",
+ "====> Epoch: 14 Average loss: 101.1392\n",
+ "Epoch: 15 [0/60000 (0%)]\tLoss: 102.862000\n",
+ "Epoch: 15 [12800/60000 (21%)]\tLoss: 102.921196\n",
+ "Epoch: 15 [25600/60000 (43%)]\tLoss: 95.685547\n",
+ "Epoch: 15 [38400/60000 (64%)]\tLoss: 100.956352\n",
+ "Epoch: 15 [51200/60000 (85%)]\tLoss: 102.581177\n",
+ "====> Epoch: 15 Average loss: 100.8457\n",
+ "Epoch: 16 [0/60000 (0%)]\tLoss: 98.365273\n",
+ "Epoch: 16 [12800/60000 (21%)]\tLoss: 103.469986\n",
+ "Epoch: 16 [25600/60000 (43%)]\tLoss: 104.007767\n",
+ "Epoch: 16 [38400/60000 (64%)]\tLoss: 99.572769\n",
+ "Epoch: 16 [51200/60000 (85%)]\tLoss: 99.661942\n",
+ "====> Epoch: 16 Average loss: 100.6236\n",
+ "Epoch: 17 [0/60000 (0%)]\tLoss: 96.908447\n",
+ "Epoch: 17 [12800/60000 (21%)]\tLoss: 100.834389\n",
+ "Epoch: 17 [25600/60000 (43%)]\tLoss: 99.591125\n",
+ "Epoch: 17 [38400/60000 (64%)]\tLoss: 100.976036\n",
+ "Epoch: 17 [51200/60000 (85%)]\tLoss: 98.151016\n",
+ "====> Epoch: 17 Average loss: 100.3354\n",
+ "Epoch: 18 [0/60000 (0%)]\tLoss: 99.493332\n",
+ "Epoch: 18 [12800/60000 (21%)]\tLoss: 103.781921\n",
+ "Epoch: 18 [25600/60000 (43%)]\tLoss: 96.200439\n",
+ "Epoch: 18 [38400/60000 (64%)]\tLoss: 102.918533\n",
+ "Epoch: 18 [51200/60000 (85%)]\tLoss: 96.739044\n",
+ "====> Epoch: 18 Average loss: 100.1708\n",
+ "Epoch: 19 [0/60000 (0%)]\tLoss: 98.771065\n",
+ "Epoch: 19 [12800/60000 (21%)]\tLoss: 98.181480\n",
+ "Epoch: 19 [25600/60000 (43%)]\tLoss: 100.055099\n",
+ "Epoch: 19 [38400/60000 (64%)]\tLoss: 100.742676\n",
+ "Epoch: 19 [51200/60000 (85%)]\tLoss: 103.055489\n",
+ "====> Epoch: 19 Average loss: 99.9700\n",
+ "Epoch: 20 [0/60000 (0%)]\tLoss: 103.092148\n",
+ "Epoch: 20 [12800/60000 (21%)]\tLoss: 99.723030\n",
+ "Epoch: 20 [25600/60000 (43%)]\tLoss: 94.468689\n",
+ "Epoch: 20 [38400/60000 (64%)]\tLoss: 100.579842\n",
+ "Epoch: 20 [51200/60000 (85%)]\tLoss: 100.644325\n",
+ "====> Epoch: 20 Average loss: 99.7914\n",
+ "Epoch: 21 [0/60000 (0%)]\tLoss: 95.181412\n",
+ "Epoch: 21 [12800/60000 (21%)]\tLoss: 95.819107\n",
+ "Epoch: 21 [25600/60000 (43%)]\tLoss: 97.987877\n",
+ "Epoch: 21 [38400/60000 (64%)]\tLoss: 102.896431\n",
+ "Epoch: 21 [51200/60000 (85%)]\tLoss: 100.330048\n",
+ "====> Epoch: 21 Average loss: 99.6599\n",
+ "Epoch: 22 [0/60000 (0%)]\tLoss: 96.186913\n",
+ "Epoch: 22 [12800/60000 (21%)]\tLoss: 97.911819\n",
+ "Epoch: 22 [25600/60000 (43%)]\tLoss: 98.991554\n",
+ "Epoch: 22 [38400/60000 (64%)]\tLoss: 96.568634\n",
+ "Epoch: 22 [51200/60000 (85%)]\tLoss: 101.575287\n",
+ "====> Epoch: 22 Average loss: 99.4778\n",
+ "Epoch: 23 [0/60000 (0%)]\tLoss: 99.582672\n",
+ "Epoch: 23 [12800/60000 (21%)]\tLoss: 100.042053\n",
+ "Epoch: 23 [25600/60000 (43%)]\tLoss: 101.546867\n",
+ "Epoch: 23 [38400/60000 (64%)]\tLoss: 99.168091\n",
+ "Epoch: 23 [51200/60000 (85%)]\tLoss: 100.235687\n",
+ "====> Epoch: 23 Average loss: 99.3934\n",
+ "Epoch: 24 [0/60000 (0%)]\tLoss: 99.877205\n",
+ "Epoch: 24 [12800/60000 (21%)]\tLoss: 98.624710\n",
+ "Epoch: 24 [25600/60000 (43%)]\tLoss: 102.436928\n",
+ "Epoch: 24 [38400/60000 (64%)]\tLoss: 96.003510\n",
+ "Epoch: 24 [51200/60000 (85%)]\tLoss: 99.191185\n",
+ "====> Epoch: 24 Average loss: 99.1867\n",
+ "Epoch: 25 [0/60000 (0%)]\tLoss: 99.175629\n",
+ "Epoch: 25 [12800/60000 (21%)]\tLoss: 100.223289\n",
+ "Epoch: 25 [25600/60000 (43%)]\tLoss: 99.570465\n",
+ "Epoch: 25 [38400/60000 (64%)]\tLoss: 99.922440\n",
+ "Epoch: 25 [51200/60000 (85%)]\tLoss: 98.518082\n",
+ "====> Epoch: 25 Average loss: 99.1030\n",
+ "Epoch: 26 [0/60000 (0%)]\tLoss: 101.370712\n",
+ "Epoch: 26 [12800/60000 (21%)]\tLoss: 96.809921\n",
+ "Epoch: 26 [25600/60000 (43%)]\tLoss: 98.497543\n",
+ "Epoch: 26 [38400/60000 (64%)]\tLoss: 98.620590\n",
+ "Epoch: 26 [51200/60000 (85%)]\tLoss: 100.283875\n",
+ "====> Epoch: 26 Average loss: 98.9861\n",
+ "Epoch: 27 [0/60000 (0%)]\tLoss: 97.437698\n",
+ "Epoch: 27 [12800/60000 (21%)]\tLoss: 98.166641\n",
+ "Epoch: 27 [25600/60000 (43%)]\tLoss: 97.810806\n",
+ "Epoch: 27 [38400/60000 (64%)]\tLoss: 98.904976\n",
+ "Epoch: 27 [51200/60000 (85%)]\tLoss: 97.707474\n",
+ "====> Epoch: 27 Average loss: 98.8606\n",
+ "Epoch: 28 [0/60000 (0%)]\tLoss: 99.675491\n",
+ "Epoch: 28 [12800/60000 (21%)]\tLoss: 101.711945\n",
+ "Epoch: 28 [25600/60000 (43%)]\tLoss: 103.872253\n",
+ "Epoch: 28 [38400/60000 (64%)]\tLoss: 101.492767\n",
+ "Epoch: 28 [51200/60000 (85%)]\tLoss: 100.362648\n",
+ "====> Epoch: 28 Average loss: 98.7638\n",
+ "Epoch: 29 [0/60000 (0%)]\tLoss: 97.974152\n",
+ "Epoch: 29 [12800/60000 (21%)]\tLoss: 101.097153\n",
+ "Epoch: 29 [25600/60000 (43%)]\tLoss: 99.067917\n",
+ "Epoch: 29 [38400/60000 (64%)]\tLoss: 101.208740\n",
+ "Epoch: 29 [51200/60000 (85%)]\tLoss: 97.399773\n",
+ "====> Epoch: 29 Average loss: 98.6572\n",
+ "Epoch: 30 [0/60000 (0%)]\tLoss: 101.200050\n",
+ "Epoch: 30 [12800/60000 (21%)]\tLoss: 102.307014\n",
+ "Epoch: 30 [25600/60000 (43%)]\tLoss: 96.769447\n",
+ "Epoch: 30 [38400/60000 (64%)]\tLoss: 92.945297\n",
+ "Epoch: 30 [51200/60000 (85%)]\tLoss: 94.934479\n",
+ "====> Epoch: 30 Average loss: 98.5586\n",
+ "Epoch: 31 [0/60000 (0%)]\tLoss: 101.355705\n",
+ "Epoch: 31 [12800/60000 (21%)]\tLoss: 95.923454\n",
+ "Epoch: 31 [25600/60000 (43%)]\tLoss: 98.980087\n",
+ "Epoch: 31 [38400/60000 (64%)]\tLoss: 96.976807\n",
+ "Epoch: 31 [51200/60000 (85%)]\tLoss: 99.028816\n",
+ "====> Epoch: 31 Average loss: 98.4676\n",
+ "Epoch: 32 [0/60000 (0%)]\tLoss: 98.884567\n",
+ "Epoch: 32 [12800/60000 (21%)]\tLoss: 93.129021\n",
+ "Epoch: 32 [25600/60000 (43%)]\tLoss: 99.730362\n",
+ "Epoch: 32 [38400/60000 (64%)]\tLoss: 99.400452\n",
+ "Epoch: 32 [51200/60000 (85%)]\tLoss: 100.077438\n",
+ "====> Epoch: 32 Average loss: 98.3082\n",
+ "Epoch: 33 [0/60000 (0%)]\tLoss: 96.834579\n",
+ "Epoch: 33 [12800/60000 (21%)]\tLoss: 97.735565\n",
+ "Epoch: 33 [25600/60000 (43%)]\tLoss: 99.062378\n",
+ "Epoch: 33 [38400/60000 (64%)]\tLoss: 95.841354\n",
+ "Epoch: 33 [51200/60000 (85%)]\tLoss: 97.978172\n",
+ "====> Epoch: 33 Average loss: 98.2704\n",
+ "Epoch: 34 [0/60000 (0%)]\tLoss: 98.487007\n",
+ "Epoch: 34 [12800/60000 (21%)]\tLoss: 99.157494\n",
+ "Epoch: 34 [25600/60000 (43%)]\tLoss: 98.812057\n",
+ "Epoch: 34 [38400/60000 (64%)]\tLoss: 99.683121\n",
+ "Epoch: 34 [51200/60000 (85%)]\tLoss: 99.781883\n",
+ "====> Epoch: 34 Average loss: 98.1529\n",
+ "Epoch: 35 [0/60000 (0%)]\tLoss: 99.037643\n",
+ "Epoch: 35 [12800/60000 (21%)]\tLoss: 96.334686\n",
+ "Epoch: 35 [25600/60000 (43%)]\tLoss: 96.098648\n",
+ "Epoch: 35 [38400/60000 (64%)]\tLoss: 98.811630\n",
+ "Epoch: 35 [51200/60000 (85%)]\tLoss: 101.727669\n",
+ "====> Epoch: 35 Average loss: 98.1330\n",
+ "Epoch: 36 [0/60000 (0%)]\tLoss: 100.035233\n",
+ "Epoch: 36 [12800/60000 (21%)]\tLoss: 100.530594\n",
+ "Epoch: 36 [25600/60000 (43%)]\tLoss: 93.991135\n",
+ "Epoch: 36 [38400/60000 (64%)]\tLoss: 98.986557\n",
+ "Epoch: 36 [51200/60000 (85%)]\tLoss: 99.435684\n",
+ "====> Epoch: 36 Average loss: 98.0395\n",
+ "Epoch: 37 [0/60000 (0%)]\tLoss: 98.253258\n",
+ "Epoch: 37 [12800/60000 (21%)]\tLoss: 98.195801\n",
+ "Epoch: 37 [25600/60000 (43%)]\tLoss: 98.870934\n",
+ "Epoch: 37 [38400/60000 (64%)]\tLoss: 100.649261\n",
+ "Epoch: 37 [51200/60000 (85%)]\tLoss: 97.776955\n",
+ "====> Epoch: 37 Average loss: 97.9876\n",
+ "Epoch: 38 [0/60000 (0%)]\tLoss: 98.708733\n",
+ "Epoch: 38 [12800/60000 (21%)]\tLoss: 100.157822\n",
+ "Epoch: 38 [25600/60000 (43%)]\tLoss: 99.773056\n",
+ "Epoch: 38 [38400/60000 (64%)]\tLoss: 96.846664\n",
+ "Epoch: 38 [51200/60000 (85%)]\tLoss: 99.477142\n",
+ "====> Epoch: 38 Average loss: 97.9085\n",
+ "Epoch: 39 [0/60000 (0%)]\tLoss: 95.733894\n",
+ "Epoch: 39 [12800/60000 (21%)]\tLoss: 96.716103\n",
+ "Epoch: 39 [25600/60000 (43%)]\tLoss: 99.004089\n",
+ "Epoch: 39 [38400/60000 (64%)]\tLoss: 96.609360\n",
+ "Epoch: 39 [51200/60000 (85%)]\tLoss: 98.293839\n",
+ "====> Epoch: 39 Average loss: 97.8235\n",
+ "Epoch: 40 [0/60000 (0%)]\tLoss: 98.337112\n",
+ "Epoch: 40 [12800/60000 (21%)]\tLoss: 95.827057\n",
+ "Epoch: 40 [25600/60000 (43%)]\tLoss: 98.477692\n",
+ "Epoch: 40 [38400/60000 (64%)]\tLoss: 97.933937\n",
+ "Epoch: 40 [51200/60000 (85%)]\tLoss: 96.017860\n",
+ "====> Epoch: 40 Average loss: 97.7489\n",
+ "Epoch: 41 [0/60000 (0%)]\tLoss: 98.468063\n",
+ "Epoch: 41 [12800/60000 (21%)]\tLoss: 96.421799\n",
+ "Epoch: 41 [25600/60000 (43%)]\tLoss: 100.093048\n",
+ "Epoch: 41 [38400/60000 (64%)]\tLoss: 98.204666\n",
+ "Epoch: 41 [51200/60000 (85%)]\tLoss: 97.178360\n",
+ "====> Epoch: 41 Average loss: 97.7665\n",
+ "Epoch: 42 [0/60000 (0%)]\tLoss: 100.979088\n",
+ "Epoch: 42 [12800/60000 (21%)]\tLoss: 95.539078\n",
+ "Epoch: 42 [25600/60000 (43%)]\tLoss: 102.939972\n",
+ "Epoch: 42 [38400/60000 (64%)]\tLoss: 98.356186\n",
+ "Epoch: 42 [51200/60000 (85%)]\tLoss: 95.231201\n",
+ "====> Epoch: 42 Average loss: 97.6564\n",
+ "Epoch: 43 [0/60000 (0%)]\tLoss: 98.242126\n",
+ "Epoch: 43 [12800/60000 (21%)]\tLoss: 96.585663\n",
+ "Epoch: 43 [25600/60000 (43%)]\tLoss: 96.047897\n",
+ "Epoch: 43 [38400/60000 (64%)]\tLoss: 97.344894\n",
+ "Epoch: 43 [51200/60000 (85%)]\tLoss: 98.973549\n",
+ "====> Epoch: 43 Average loss: 97.5803\n",
+ "Epoch: 44 [0/60000 (0%)]\tLoss: 100.391434\n",
+ "Epoch: 44 [12800/60000 (21%)]\tLoss: 95.992249\n",
+ "Epoch: 44 [25600/60000 (43%)]\tLoss: 95.245644\n",
+ "Epoch: 44 [38400/60000 (64%)]\tLoss: 95.822922\n",
+ "Epoch: 44 [51200/60000 (85%)]\tLoss: 95.581390\n",
+ "====> Epoch: 44 Average loss: 97.5643\n",
+ "Epoch: 45 [0/60000 (0%)]\tLoss: 99.735504\n",
+ "Epoch: 45 [12800/60000 (21%)]\tLoss: 101.708931\n",
+ "Epoch: 45 [25600/60000 (43%)]\tLoss: 96.375183\n",
+ "Epoch: 45 [38400/60000 (64%)]\tLoss: 96.754112\n",
+ "Epoch: 45 [51200/60000 (85%)]\tLoss: 95.826797\n",
+ "====> Epoch: 45 Average loss: 97.4601\n",
+ "Epoch: 46 [0/60000 (0%)]\tLoss: 98.260147\n",
+ "Epoch: 46 [12800/60000 (21%)]\tLoss: 95.457039\n",
+ "Epoch: 46 [25600/60000 (43%)]\tLoss: 96.806732\n",
+ "Epoch: 46 [38400/60000 (64%)]\tLoss: 99.897049\n",
+ "Epoch: 46 [51200/60000 (85%)]\tLoss: 96.852547\n",
+ "====> Epoch: 46 Average loss: 97.3929\n",
+ "Epoch: 47 [0/60000 (0%)]\tLoss: 97.968903\n",
+ "Epoch: 47 [12800/60000 (21%)]\tLoss: 93.317627\n",
+ "Epoch: 47 [25600/60000 (43%)]\tLoss: 95.633125\n",
+ "Epoch: 47 [38400/60000 (64%)]\tLoss: 98.016617\n",
+ "Epoch: 47 [51200/60000 (85%)]\tLoss: 96.292252\n",
+ "====> Epoch: 47 Average loss: 97.3536\n",
+ "Epoch: 48 [0/60000 (0%)]\tLoss: 98.587616\n",
+ "Epoch: 48 [12800/60000 (21%)]\tLoss: 96.673027\n",
+ "Epoch: 48 [25600/60000 (43%)]\tLoss: 99.054855\n",
+ "Epoch: 48 [38400/60000 (64%)]\tLoss: 95.054367\n",
+ "Epoch: 48 [51200/60000 (85%)]\tLoss: 95.701492\n",
+ "====> Epoch: 48 Average loss: 97.3345\n",
+ "Epoch: 49 [0/60000 (0%)]\tLoss: 100.344147\n",
+ "Epoch: 49 [12800/60000 (21%)]\tLoss: 97.647095\n",
+ "Epoch: 49 [25600/60000 (43%)]\tLoss: 95.118668\n",
+ "Epoch: 49 [38400/60000 (64%)]\tLoss: 93.820007\n",
+ "Epoch: 49 [51200/60000 (85%)]\tLoss: 97.188576\n",
+ "====> Epoch: 49 Average loss: 97.2758\n",
+ "Epoch: 50 [0/60000 (0%)]\tLoss: 95.470245\n",
+ "Epoch: 50 [12800/60000 (21%)]\tLoss: 94.774658\n",
+ "Epoch: 50 [25600/60000 (43%)]\tLoss: 99.117859\n",
+ "Epoch: 50 [38400/60000 (64%)]\tLoss: 91.480293\n",
+ "Epoch: 50 [51200/60000 (85%)]\tLoss: 99.089584\n",
+ "====> Epoch: 50 Average loss: 97.2451\n",
+ "Epoch: 51 [0/60000 (0%)]\tLoss: 96.443871\n",
+ "Epoch: 51 [12800/60000 (21%)]\tLoss: 94.017128\n",
+ "Epoch: 51 [25600/60000 (43%)]\tLoss: 97.887375\n",
+ "Epoch: 51 [38400/60000 (64%)]\tLoss: 92.795815\n",
+ "Epoch: 51 [51200/60000 (85%)]\tLoss: 96.275574\n",
+ "====> Epoch: 51 Average loss: 97.1980\n",
+ "Epoch: 52 [0/60000 (0%)]\tLoss: 94.638329\n",
+ "Epoch: 52 [12800/60000 (21%)]\tLoss: 97.908661\n",
+ "Epoch: 52 [25600/60000 (43%)]\tLoss: 98.689041\n",
+ "Epoch: 52 [38400/60000 (64%)]\tLoss: 98.413147\n",
+ "Epoch: 52 [51200/60000 (85%)]\tLoss: 96.102943\n",
+ "====> Epoch: 52 Average loss: 97.1566\n",
+ "Epoch: 53 [0/60000 (0%)]\tLoss: 96.287872\n",
+ "Epoch: 53 [12800/60000 (21%)]\tLoss: 100.826424\n",
+ "Epoch: 53 [25600/60000 (43%)]\tLoss: 98.507477\n",
+ "Epoch: 53 [38400/60000 (64%)]\tLoss: 95.108139\n",
+ "Epoch: 53 [51200/60000 (85%)]\tLoss: 100.178383\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 10 эпох - очень мало, но подойдёт, чтобы удостовериться, что ваша модель в своей базе работает нормально.\n",
+ "num_epochs = 10 # Для получения более презентабельного результата используйте 50 и больше эпох\n",
+ "model.train()\n",
+ "for epoch in tqdm(range(1, num_epochs + 1)):\n",
+ " train_loss = 0\n",
+ " for batch_idx, (data, labels) in enumerate(train_loader):\n",
+ " ## YOUR CODE HERE\n",
+ " ## TODO: Переместите входные данные (data) на правильное устройство\n",
+ " ## TODO: Перенесите метки классов (labels) на правильное устройство\n",
+ " ## TODO: С помощью функции F.one_hot получите массив векторов - закодированных меток классов для вашего батча\n",
+ " ## TODO: Обнулите градиенты\n",
+ " ## TODO: Вычислите выходы энкодера (recon_batch, mu, logvar)\n",
+ " ## TODO: Рассчитайте функцию потерь (loss)\n",
+ " ## TODO: Выполните обратное распространение градиентов с учетом функции потерь\n",
+ " ## TODO: Обновите общий loss, добавив текущий loss\n",
+ " ## TODO: Выполните шаг оптимизатора\n",
+ "\n",
+ " if batch_idx % 100 == 0:\n",
+ " print(f'Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} '\n",
+ " f'({100. * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item() / len(data):.6f}')\n",
+ " print(f'====> Epoch: {epoch} Average loss: {train_loss / len(train_loader.dataset):.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bi-p2073GA2e"
+ },
+ "source": [
+ "### Восстановление изображений"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jvfTFwzKKCru"
+ },
+ "outputs": [],
+ "source": [
+ "data_iter = iter(test_loader)\n",
+ "images, labels = next(data_iter)\n",
+ "images = images.to(device)\n",
+ "labels = labels.to(device)\n",
+ "\n",
+ "# YOUR CODE HERE:\n",
+ "conditions = # Преобразуйте метки в one-hot кодировку для данного условия.\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " recon_images, mu, logvar = model(images, conditions)\n",
+ "\n",
+ "def plot_images(original, reconstructed, conditions, n=8):\n",
+ " labels = conditions.argmax(dim=1)\n",
+ " plt.figure(figsize=(16, 4))\n",
+ " for i in range(n):\n",
+ " plt.subplot(2, n, i + 1)\n",
+ " plt.imshow(original[i].cpu().view(28, 28), cmap='gray')\n",
+ " plt.title(f\"Original\\nLabel: {labels[i].item()}\")\n",
+ " plt.axis(\"off\")\n",
+ " plt.subplot(2, n, i + n + 1)\n",
+ " plt.imshow(reconstructed[i].cpu().view(28, 28), cmap='gray')\n",
+ " plt.title(f\"Reconstructed\\nLabel: {labels[i].item()}\")\n",
+ " plt.axis(\"off\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "plot_images(images, recon_images, conditions, n=8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qSLlsxSIGE63"
+ },
+ "source": [
+ "### Сэмплирование новых изображений"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7tTv7Z1LKIpY"
+ },
+ "outputs": [],
+ "source": [
+ "latent_dim = 20\n",
+ "\n",
+ "# ---- Визуализация: Генерация новых изображений из латентного пространства ----\n",
+ "with torch.no_grad():\n",
+ " # Выборка латентных векторов z из стандартного нормального распределения\n",
+ " # YOUR CODE HERE:\n",
+ " z = # 64 образца случайного шума, каждый из которых имеет размерность latent_dim\n",
+ "\n",
+ " random_labels = torch.randint(0, 10, (64,)).to(device)\n",
+ " conditions = # С помощью функции F.one_hot получите массив векторов - закодированных меток классов для вашего батча\n",
+ "\n",
+ " generated_images = model.decoder(z, conditions)\n",
+ "\n",
+ "def plot_generated(images):\n",
+ " fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
+ " for i, ax in enumerate(axes.flat):\n",
+ " img = images[i].view(28,28).numpy()\n",
+ " ax.imshow(img, cmap=\"gray\")\n",
+ " ax.axis(\"off\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "plot_generated(generated_images)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PYeG0GxD_SZY"
+ },
+ "source": [
+ "## Улучшение модели (1 балл)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e4wWUttCembc"
+ },
+ "source": [
+ "Стало лучше (и визуально, и по значениям loss). Но вы всё ещё можете улучшить приёмами, с которыми вы уже знакомы! Попробуйте поменять различные параметры модели и обучения, чтобы получить более качественные результаты\n",
+ "\n",
+ "Например, можете поменять:\n",
+ "- Архитектуру энкодера и декодера (добавить/изменить слои)\n",
+ "- Количество эпох обучения\n",
+ "- Размерность латентного пространства\n",
+ "- Параметры оптимизатора\n",
+ "- ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "swwLDCUXArtw"
+ },
+ "outputs": [],
+ "source": [
+ "# YOUR CODE HERE\n",
+ "# Напишите здесь измененный код для объявления/создания/обучения модели\n",
+ "# (НЕ меняйте код, написанный ранее)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lb8DbEtkuWi6"
+ },
+ "source": [
+ "# Задача 2: Реализация и обучение Generative Adversarial Network GAN (4 балла)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ysqfB67kJATR"
+ },
+ "source": [
+ "GAN — это класс моделей глубокого обучения, состоящий из двух сетей: генератора и дискриминатора, которые обучаются одновременно: генератор создает данные, а дискриминатор оценивает их достоверность\n",
+ "\n",
+ "* **Реализация Vanilla GAN (0.5 задачи)** \n",
+ " - Создайте архитектуру GAN, состоящую из: \n",
+ " - Генератора, который принимает на вход случайный шум (вектор) и генерирует изображение. \n",
+ " - Дискриминатора, который классифицирует изображение как реальное или сгенерированное. \n",
+ " - Рспользуйте функцию потерь, основанную РЅР° бинарной РєСЂРѕСЃСЃ-энтропии (Binary Cross-Entropy Loss). \n",
+ " - Обучите модель, чередуя обновление весов генератора и дискриминатора. \n",
+ " - Визуализируйте результаты: \n",
+ " - Покажите примеры изображений, сгенерированных обученным генератором.\n",
+ "\n",
+ "* **Условная генерация (0.5 задачи)** \n",
+ " - Дополните GAN для реализации условной генерации (Conditional GAN, cGAN), где генератор и дискриминатор также принимают на вход метки классов. \n",
+ " - Проверьте, как модель справляется с генерацией изображений заданного класса."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qdQ6mcjMOD6H"
+ },
+ "source": [
+ "## Vanilla GAN (2 балла)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F0Z1YNN5dsz1"
+ },
+ "source": [
+ "### Реализация генератора"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jRZHIiCVJDNm"
+ },
+ "source": [
+ "Генератор — это часть GAN, которая создает новые данные, принимая на вход случайный шум и преобразуя его в данные, похожие на реальные. Для начала реализуйте базовую архитектуру:\n",
+ "- линейный слой с параметрами:\n",
+ " * размер входа - `noise_dim`\n",
+ " * размер выхода - 256\n",
+ "- функция активации `ReLU()`\n",
+ "- линейный слой с параметрами:\n",
+ " * размер входа - 256\n",
+ " * размер выхода - 512\n",
+ "- функция активации `ReLU()`\n",
+ "- линейный слой с параметрами:\n",
+ " * размер входа - 512\n",
+ " * размер выхода - 1024\n",
+ "- функция активации `ReLU()`\n",
+ "- линейный слой с параметрами:\n",
+ " * размер входа - 1024\n",
+ " * размер выхода - произведение выходных размерностей (размеров изображения) `output_shape`\n",
+ "- функция активации `Tanh()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-eruSevodxcl"
+ },
+ "outputs": [],
+ "source": [
+ "class Generator(nn.Module):\n",
+ " def __init__(self, noise_dim = 100, output_shape = (1, 28, 28)):\n",
+ " super(Generator, self).__init__()\n",
+ " self.fc = nn.Sequential(\n",
+ " # TODO: Линейный слой (noise_dim, 256)\n",
+ " # TODO: Функция активации ReLU()\n",
+ " # TODO: Линейный слой (256, 512)\n",
+ " # TODO: Функция активации ReLU()\n",
+ " # TODO: Линейный слой (512, 1024)\n",
+ " # TODO: Функция активации ReLU()\n",
+ " # TODO: Линейный слой (1024, произведение output_shape)\n",
+ " # TODO: Функция активации Tanh()\n",
+ " )\n",
+ "\n",
+ " def forward(self, z):\n",
+ " img = # TODO: Прогоните пакет векторов через модель и измените его размер до (batch_size, *output_shape)\n",
+ " return img"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7eOvVQYddyZh"
+ },
+ "source": [
+ "### Реализация дискриминатора"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f9RvHRngJGZo"
+ },
+ "source": [
+ "Дискриминатор — это часть GAN, задача которой состоит в классификации входных данных как реальных (из тренировочного набора данных), так и сгенерированных (созданных генератором).\n",
+ "\n",
+ "Он нужен для того, чтобы отличить сгенерированное изображение от настоящего, что затем используется при обучении генератора.\n",
+ "\n",
+ "Рспользуйте следующую архитектуру:\n",
+ "* Выровняйте/расплюшьте/разгладьте входное изображение\n",
+ "* Линейный слой с параметрами:\n",
+ " - размер входа равен произведению размероностей изображения `input_dim`\n",
+ " - размер выхода равен 1024\n",
+ "* Функция активации LeakyReLU(0.2, inplace=True)\n",
+ "* Линейный слой с параметрами:\n",
+ " - размер входа равен 1024\n",
+ " - размер выхода равен 512\n",
+ "* Функция активации LeakyReLU(0.2, inplace=True)\n",
+ "* Линейный слой с параметрами:\n",
+ " - размер входа равен 512\n",
+ " - размер выхода равен 256\n",
+ "* Функция активации LeakyReLU(0.2, inplace=True)\n",
+ "* Линейный слой с параметрами:\n",
+ " - размер входа равен 256\n",
+ " - размер выхода равен 1\n",
+ "* Функция активации Sigmoid()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "swI8tiSQRWpn"
+ },
+ "outputs": [],
+ "source": [
+ "class Discriminator(nn.Module):\n",
+ " def __init__(self, input_shape=(1, 28, 28)):\n",
+ " super(Discriminator, self).__init__()\n",
+ " self.fc = nn.Sequential(\n",
+ " # TODO: Слой сплющивания\n",
+ " # TODO: Линейный слой (призведение input_shape, 1024)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (1024, 512)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (512, 256)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (256, 1)\n",
+ " # TODO: Функция активации Sigmoid()\n",
+ " )\n",
+ "\n",
+ " def forward(self, img):\n",
+ " result = # TODO: Прогоните изображение через дискриминатор\n",
+ " return result\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cYxp1IjkeDDW"
+ },
+ "source": [
+ "### Подготовка к обучению"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RpnE5IFYWDeN"
+ },
+ "source": [
+ "Определите функцию ошибки, создайте моедил генератора и дискриминатора, создайте оптимизаторы для генератора и дискриминатора"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YfN8BpBIVfhg"
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 128\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Lambda(lambda x: x.view(-1))\n",
+ "])\n",
+ "\n",
+ "train_dataset = dset.MNIST(root='./data', train=True, download=True, transform=transform)\n",
+ "test_dataset = dset.MNIST(root='./data', train=False, download=True, transform=transform)\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
+ "\n",
+ "criterion = # TODO: Рсопльзуйте BCE функцию ошибки\n",
+ "\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "generator = Generator().to(device)\n",
+ "discriminator = Discriminator().to(device)\n",
+ "\n",
+ "optimizer_g = # Определите оптимизатор\n",
+ "optimizer_d = # Определите оптимизатор"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z4XsjzsxeGD7"
+ },
+ "source": [
+ "### Обучение"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lwJYfHuPWwIa"
+ },
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm\n",
+ "\n",
+ "epochs = # Определите количество эпох\n",
+ "for epoch in tqdm(range(epochs)):\n",
+ " for i, (real_images, _) in enumerate(train_loader):\n",
+ " real_images = real_images.to(device)\n",
+ " batch_size = real_images.size(0)\n",
+ "\n",
+ " # Создайте метки для реальных и фейковых изображений\n",
+ " real_labels = # TODO: Создайте тензор из единиц с размерностью (batch_size, 1)\n",
+ " fake_labels = # TODO: Создайте тензор из нулей с размерностью (batch_size, 1)\n",
+ "\n",
+ " # Обучение дискриминатора\n",
+ " # TODO: Обнулите градиенты дискриминатора\n",
+ " outputs = # TODO: Прогоните реальные изображения через дискриминатор\n",
+ " d_loss_real = # TODO: Вычислите функцию потерь для реальных изображений\n",
+ " # TODO: Выполните обратное распространение ошибки\n",
+ "\n",
+ " noise = # TODO: Создайте случайный шум размером (batch_size, 100)\n",
+ " fake_images = # TODO: Создайте фейковые изображения с помощью генератора\n",
+ " outputs = # TODO: Прогоните фейковые изображения через дискриминатор\n",
+ " d_loss_fake = # TODO: Вычислите функцию потерь для фейковых изображений\n",
+ " # TODO: Выполните обратное распространение ошибки для дискриминатора\n",
+ "\n",
+ " # TODO: Выполните шаг оптимизатора дискриминатора\n",
+ "\n",
+ " # Обучение генератора\n",
+ " # TODO: Обнулите градиенты генератора\n",
+ " outputs = # TODO: Прогоните фейковые изображения через дискриминатор\n",
+ " g_loss = # TODO: Вычислите функцию потерь для генератора\n",
+ " # TODO: Выполните обратное распространение ошибки для генератора\n",
+ "\n",
+ " # TODO: Выполните шаг оптимизатора генератора\n",
+ "\n",
+ " # TODO: Выведите результаты текущей эпохи: сумму D Loss и значение G Loss\n",
+ " print(f'Epoch [{epoch}/{epochs}], D Loss: ..., G Loss: ...')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DYDEIbLoeMft"
+ },
+ "source": [
+ "### Сэмплирование изображений"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 807
+ },
+ "id": "EjYy14pqeLtp",
+ "outputId": "98dff482-a3ec-4da6-e209-c9329fdcfc0c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 800x800 with 64 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# YOUR CODE HERE:\n",
+ "z = # 64 образца случайного шума, каждый из которых имеет размерность noise_dim\n",
+ "generated_images = generator(noise).cpu().detach()\n",
+ "\n",
+ "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
+ "for i, ax in enumerate(axes.flat):\n",
+ " img = generated_images[i].view(28,28).numpy()\n",
+ " ax.imshow(img, cmap=\"gray\")\n",
+ " ax.axis(\"off\")\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kwM-kBNv1EBF"
+ },
+ "source": [
+ "## Conditional GAN (2 балла)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OKgD0qhJYqMG"
+ },
+ "source": [
+ "Аналогично с Conditional VAE попробуем улучшить качество генерации и получить возможность управлять ей при помощи создания Conditional GAN."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tU6Gcyl3ZCrX"
+ },
+ "source": [
+ "### Условный генератор"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_0nCZ7HHZFBH"
+ },
+ "source": [
+ "Чтобы придать генератору возможность работы с метками классов, необходимо добавить (сконкатенировать) эти самые метки с вектором шума. Как и раньше метки кодируются при помощи one-hot encoding. Соответственно, вам нужно учесть это дополнение при описании входного слоя модели.\n",
+ "\n",
+ "Архитектуры генератора и дискриминатора можете использовать аналогичные Vanilla GAN."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "V4xyKWS3ZVyr"
+ },
+ "outputs": [],
+ "source": [
+ "class ConditionalGenerator(nn.Module):\n",
+ " def __init__(self, noise_dim=100, num_classes=10, output_shape=(1, 28, 28)):\n",
+ " super(ConditionalGenerator, self).__init__()\n",
+ " self.output_shape = output_shape\n",
+ " self.img_size = # TODO: Вычислите размер выходного изображения (произведение размерностей output_shape)\n",
+ "\n",
+ " self.fc = nn.Sequential(\n",
+ " # TODO: Линейный слой (noise_dim + num_classes, 256)\n",
+ " # TODO: Функция активации ReLU(True)\n",
+ " # TODO: Линейный слой (256, 512)\n",
+ " # TODO: Функция активации ReLU(True)\n",
+ " # TODO: Линейный слой (512, 1024)\n",
+ " # TODO: Функция активации ReLU(True)\n",
+ " # TODO: Линейный слой (1024, self.img_size)\n",
+ " # TODO: Функция активации Tanh()\n",
+ " )\n",
+ "\n",
+ " def forward(self, z, c):\n",
+ " # TODO: Объедините шум z и условный вектор c\n",
+ " x = # TODO: Прогоните объединённый вектор через fc и преобразуйте результат в форму изображения\n",
+ " return x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "42_REu8oZlcu"
+ },
+ "source": [
+ "### Условный дискриминатор"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b1ghoN5IZ7_U"
+ },
+ "source": [
+ "С дискриминатором необходимо проделать аналогичные преобразования - добавить возможность подавать на вход вместе с изображением вектор метки класса."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6S5cqAftaH-o"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "\n",
+ "class ConditionalDiscriminator(nn.Module):\n",
+ " def __init__(self, input_shape=(1, 28, 28), num_classes=10):\n",
+ " super(ConditionalDiscriminator, self).__init__()\n",
+ " self.input_shape = input_shape\n",
+ " self.num_classes = num_classes\n",
+ "\n",
+ " self.img_dim = # TODO: Вычислите размерность изображения (произведение размерностей input_shape)\n",
+ "\n",
+ " input_dim = # TODO: Определите размер входного вектора (сумма img_dim и num_classes)\n",
+ "\n",
+ " self.fc = nn.Sequential(\n",
+ " # TODO: Линейный слой (input_dim, 1024)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (1024, 512)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (512, 256)\n",
+ " # TODO: Функция активации LeakyReLU(0.2, inplace=True)\n",
+ " # TODO: Линейный слой (256, 1)\n",
+ " # TODO: Функция активации Sigmoid()\n",
+ " )\n",
+ "\n",
+ " def forward(self, img, c):\n",
+ " img_flat = # TODO: Разгладьте изображение в вектор (размерность: batch_size x img_dim)\n",
+ " x = # TODO: Объедините векторы изображения и метки классов и используйте модель\n",
+ " return x\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "46gZDDf5aoKV"
+ },
+ "source": [
+ "### Подготовка к обучению"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "j8suYX3BaqIP"
+ },
+ "outputs": [],
+ "source": [
+ "import torchvision.transforms as transforms\n",
+ "\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "batch_size = 128\n",
+ "lr = 0.0004\n",
+ "noise_dim = 100\n",
+ "num_classes = 10\n",
+ "\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize((0.5,), (0.5,))\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "d8JAaBRxarAP"
+ },
+ "outputs": [],
+ "source": [
+ "import torchvision\n",
+ "import torch.optim as optim\n",
+ "\n",
+ "train_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
+ "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
+ "\n",
+ "generator = ConditionalGenerator(noise_dim=noise_dim, num_classes=num_classes, output_shape=(1, 28, 28)).to(device)\n",
+ "discriminator = ConditionalDiscriminator(input_shape=(1, 28, 28), num_classes=num_classes).to(device)\n",
+ "\n",
+ "criterion = # Определите функцию ошибки BCE\n",
+ "optimizer_G = # Определите оптимизатор для генератора\n",
+ "optimizer_D = # Определите оптимизатор для генератора"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NWKFCWkha7DG"
+ },
+ "source": [
+ "### Обучение"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ChRirmcJa8_g"
+ },
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm\n",
+ "\n",
+ "epochs = # Определите количество эпох\n",
+ "for epoch in tqdm(range(epochs)):\n",
+ " for i, (real_images, labels) in enumerate(train_loader):\n",
+ " real_images = real_images.to(device)\n",
+ " batch_size = real_images.size(0)\n",
+ "\n",
+ " # Создайте метки для реальных и фейковых изображений\n",
+ " real_labels = # TODO: Создайте тензор из единиц с размерностью (batch_size, 1)\n",
+ " fake_labels = # TODO: Создайте тензор из нулей с размерностью (batch_size, 1)\n",
+ "\n",
+ " # Обучение дискриминатора\n",
+ " # TODO: Обнулите градиенты дискриминатора\n",
+ " outputs = # TODO: Прогоните реальные изображения и метки через дискриминатор\n",
+ " d_loss_real = # TODO: Вычислите функцию потерь для реальных изображений\n",
+ " # TODO: Выполните обратное распространение ошибки\n",
+ "\n",
+ " noise = # TODO: Создайте случайный шум размером (batch_size, 100)\n",
+ " fake_images = # TODO: Создайте фейковые изображения с помощью генератора\n",
+ " outputs = # TODO: Прогоните фейковые изображения через дискриминатор\n",
+ " d_loss_fake = # TODO: Вычислите функцию потерь для фейковых изображений\n",
+ " # TODO: Выполните обратное распространение ошибки для дискриминатора\n",
+ "\n",
+ " # TODO: Выполните шаг оптимизатора дискриминатора\n",
+ "\n",
+ " # Обучение генератора\n",
+ " # TODO: Обнулите градиенты генератора\n",
+ " outputs = # TODO: Прогоните фейковые изображения через дискриминатор\n",
+ " g_loss = # TODO: Вычислите функцию потерь для генератора\n",
+ " # TODO: Выполните обратное распространение ошибки для генератора\n",
+ "\n",
+ " # TODO: Выполните шаг оптимизатора генератора\n",
+ "\n",
+ " # TODO: Выведите результаты текущей эпохи: сумму D Loss и значение G Loss\n",
+ " print(f'Epoch [{epoch}/{epochs}], D Loss: ..., G Loss: ...')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "63SQ5oqYdeIy"
+ },
+ "source": [
+ "### Сэмплирование изображений"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3ogkNBG9dv8s"
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " sample_noise = # 64 образца случайного шума, каждый из которых имеет размерность noise_dim\n",
+ " sample_labels = torch.arange(0, 64, device=device) % num_classes\n",
+ " sample_labels_onehot = # С помощью функции F.one_hot получите массив векторов - закодированных меток классов для вашего батча\n",
+ " fake_samples = generator(sample_noise, sample_labels_onehot)\n",
+ "\n",
+ " fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
+ " for i, ax in enumerate(axes.flat):\n",
+ " img = fake_samples[i].cpu().view(28, 28).numpy()\n",
+ " ax.imshow(img, cmap=\"gray\", interpolation=\"nearest\")\n",
+ " ax.set_title(f\"Label: {sample_labels[i].item()}\", fontsize=8)\n",
+ " ax.axis(\"off\")\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NDL1cp3v9bup"
+ },
+ "source": [
+ "# Примеры генераций, которые должны у вас получиться"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uM76afNYBtNA"
+ },
+ "source": [
+ "## Vanilla VAE"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XW_C_Wi0CCYx"
+ },
+ "source": [
+ "Восстановление"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lOLfzhreCOca"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_sIRVcf9CDnI"
+ },
+ "source": [
+ "Генерация"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a0pmyOc6Cs0o"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a2UbvJl19nnJ"
+ },
+ "source": [
+ "## CVAE"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vr5CDEHOCKNd"
+ },
+ "source": [
+ "Восстановление"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IGNS-okeDT1J"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RZeV7jUNCNAq"
+ },
+ "source": [
+ "Генерация"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "psLRtVx4DV_h"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FoltkEuwBqax"
+ },
+ "source": [
+ "## Улучшенный CVAE"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XnlNgykqCKnK"
+ },
+ "source": [
+ "Восстановление"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VD9qtzs-Dwtw"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X20BPiF-CNSy"
+ },
+ "source": [
+ "Генерация"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vwGSpVa1DzmI"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l3KHyByiFN1I"
+ },
+ "source": [
+ "## Vanilla GAN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "PlYtYsAy-hJS"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BvNjnuuiFWko"
+ },
+ "source": [
+ "## CGAN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "h4Jdjc4H-smZ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dGsQ_Cr6ClD0"
+ },
+ "source": [
+ "# Задача 3: Задачи со звёздочкой\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Анализ латентного пространства и интерполяция (для VAE и GAN) (1 балл)"
+ ],
+ "metadata": {
+ "id": "vggOGFdK_UOn"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uUGR2iVcC-R4"
+ },
+ "source": [
+ "Проведите интерполяцию между двумя изображениями: выберите две точки в латентном пространстве и сгенерируйте промежуточные изображения. Задание выполняется на моделях с условной генерацией.\n",
+ "\n",
+ "Оцените, насколько плавно и семантически осмысленно происходит переход между различными классами."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Примеры результатов визуализации интерполяции"
+ ],
+ "metadata": {
+ "id": "_jzjvI4MaLSS"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### VAE"
+ ],
+ "metadata": {
+ "id": "NwY8MHmfaSwy"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "aGT86iJaa8vy"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### GAN"
+ ],
+ "metadata": {
+ "id": "F8kzbUNXaQp6"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "vN75y_dCa0eB"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Контрольные вопросы"
+ ],
+ "metadata": {
+ "id": "ULd_XsM8-P9I"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IzhqqDZUFn_O"
+ },
+ "source": [
+ "### Общие вопросы:\n",
+ "\n",
+ "1. Какая модель дает самые четкие изображения? Почему?\n",
+ "\n",
+ "2. Рефлексия: Какая архитектура модели показалась вам наиболее интересной? Почему?\n",
+ " \n",
+ "1. Что такое вариационный автокодировщик (VAE)? \n",
+ "2. В чем заключается принцип работы генеративно-состязательной сети (GAN)? \n",
+ "3. Какие основные компоненты входят в архитектуру VAE и GAN? \n",
+ "4. Для чего используется латентное пространство в VAE? \n",
+ "5. Что такое репараметризация (reparameterization trick) в VAE и зачем она нужна? \n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Р’РѕРїСЂРѕСЃС‹ РїРѕ Variational Autoencoder (VAE): \n",
+ "8. Какова роль KL-дивергенции в функции потерь VAE? \n",
+ "7. Какая функция ошибки используется для реконструкции изображений в VAE? \n",
+ "8. Как в Conditional VAE и Conditional GAN происходит передача метки класса? \n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Р’РѕРїСЂРѕСЃС‹ РїРѕ Generative Adversarial Network (GAN): \n",
+ "11. Какие задачи выполняют генератор и дискриминатор в GAN?\n",
+ "10. Как осуществляется обучение генератора и дискриминатора? \n",
+ "11. В чем различие между Vanilla GAN и Conditional GAN? \n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Вопросы по реализации и обучению моделей: \n",
+ "14. Как производится загрузка набора данных MNIST в PyTorch? \n",
+ "18. Почему в обучении моделей используется метод обратного распространения ошибки?\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KvZjZifD1vba"
+ },
+ "source": [
+ "# Формат сдачи\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FwlTPfyK9XTZ"
+ },
+ "source": [
+ "Для сдачи работы без очной защиты нужно будет предоставить следующие артефакты:\n",
+ " 1. Ссылка на ноутбук в формте .ipynb в гит-репозитории, где будут представлены реализации моделей и визуализации результатов работы моделей.\n",
+ "\n",
+ "Ссылки на все артефакты должны быть загружены в LMS до дедлайна. Кнопка \"отправлено\" должна быть нажата, черновики не рассматриваются.\n",
+ "\n",
+ "При очной сдаче необходимо будет ответить на вопросы из секции \"Контрольные вопросы\". Вам будут заданы 2-4 (на усмотрение принимающего) случайных вопроса."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [
+ "nDIhmJNxvlJy",
+ "LPldM_K9IEQL",
+ "PYeG0GxD_SZY",
+ "kwM-kBNv1EBF",
+ "NDL1cp3v9bup",
+ "uM76afNYBtNA",
+ "a2UbvJl19nnJ",
+ "FoltkEuwBqax",
+ "l3KHyByiFN1I",
+ "BvNjnuuiFWko",
+ "vggOGFdK_UOn",
+ "NwY8MHmfaSwy",
+ "F8kzbUNXaQp6"
+ ],
+ "gpuType": "T4",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file