hw2_2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 1,
        "id": "kr9vAeEQlRVG"
      },
      "source": [
        "# Домашнее задание 2. Классификация изображений."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 3,
        "id": "BxX49gLclRVJ"
      },
      "source": [
        "В этом задании потребуется обучить классификатор изображений. Будем работать с датасетом, название которого раскрывать не будем. Можете посмотреть самостоятельно на картинки, которые в есть датасете. В нём 200 классов и около 5 тысяч картинок на каждый класс. Классы пронумерованы, как нетрудно догадаться, от 0 до 199. Скачать датасет можно вот [тут](https://yadi.sk/d/BNR41Vu3y0c7qA).\n",
        "\n",
        "Структура датасета простая -- есть директории train/ и val/, в которых лежат обучающие и валидационные данные. В train/ и val/ лежат директориии, соответствующие классам изображений, в которых лежат, собственно, сами изображения.\n",
        " \n",
        "__Задание__. Необходимо выполнить любое из двух заданий\n",
        "\n",
        "1) Добейтесь accuracy **на валидации не менее 0.44**. В этом задании **запрещено** пользоваться предобученными моделями и ресайзом картинок. \n",
        "\n",
        "2) Добейтесь accuracy **на валидации не менее 0.84**. В этом задании делать ресайз и использовать претрейн можно. \n",
        "\n",
        "Напишите краткий отчёт о проделанных экспериментах. Что сработало и что не сработало? Почему вы решили, сделать так, а не иначе? Обязательно указывайте ссылки на чужой код, если вы его используете. Обязательно ссылайтесь на статьи / блогпосты / вопросы на stackoverflow / видосы от ютуберов-машинлернеров / курсы / подсказки от Дяди Васи и прочие дополнительные материалы, если вы их используете. \n",
        "\n",
        "Ваш код обязательно должен проходить все `assert`'ы ниже.\n",
        "\n",
        "Необходимо написать функции `train_one_epoch`, `train` и `predict` по шаблонам ниже (во многом повторяют примеры с семинаров).Обратите особое внимание на функцию `predict`: она должна возвращать список лоссов по всем объектам даталоадера, список предсказанных классов для каждого объекта из даталоалера и список настоящих классов для каждого объекта в даталоадере (и именно в таком порядке).\n",
        "\n",
        "__Использовать внешние данные для обучения строго запрещено в обоих заданиях. Также запрещено обучаться на валидационной выборке__.\n",
        "\n",
        "\n",
        "__Критерии оценки__: Оценка вычисляется по простой формуле: `min(10, 10 * Ваша accuracy / 0.44)` для первого задания и `min(10, 10 * (Ваша accuracy - 0.5) / 0.34)` для второго. Оценка округляется до десятых по арифметическим правилам. Если вы выполнили оба задания, то берется максимум из двух оценок.\n",
        "\n",
        "__Бонус__. Вы получаете 5 бонусных баллов если справляетесь с обоими заданиями на 10 баллов (итого 15 баллов). В противном случае выставляется максимальная из двух оценок и ваш бонус равен нулю.\n",
        "\n",
        "__Советы и указания__:\n",
        " - Наверняка вам потребуется много гуглить о классификации и о том, как заставить её работать. Это нормально, все гуглят. Но не забывайте, что нужно быть готовым за скатанный код отвечать :)\n",
        " - Используйте аугментации. Для этого пользуйтесь модулем `torchvision.transforms` или библиотекой [albumentations](https://github.com/albumentations-team/albumentations)\n",
        " - Можно обучать с нуля или файнтюнить (в зависимости от задания) модели из `torchvision`.\n",
        " - Рекомендуем написать вам сначала класс-датасет (или воспользоваться классом `ImageFolder`), который возвращает картинки и соответствующие им классы, а затем функции для трейна по шаблонам ниже. Однако делать это мы не заставляем. Если вам так неудобно, то можете писать код в удобном стиле. Однако учтите, что чрезмерное изменение нижеперечисленных шаблонов увеличит количество вопросов к вашему коду и повысит вероятность вызова на защиту :)\n",
        " - Валидируйте. Трекайте ошибки как можно раньше, чтобы не тратить время впустую.\n",
        " - Чтобы быстро отладить код, пробуйте обучаться на маленькой части датасета (скажем, 5-10 картинок просто чтобы убедиться что код запускается). Когда вы поняли, что смогли всё отдебажить, переходите обучению по всему датасету\n",
        " - На каждый запуск делайте ровно одно изменение в модели/аугментации/оптимайзере, чтобы понять, что и как влияет на результат.\n",
        " - Фиксируйте random seed.\n",
        " - Начинайте с простых моделей и постепенно переходите к сложным. Обучение лёгких моделей экономит много времени.\n",
        " - Ставьте расписание на learning rate. Уменьшайте его, когда лосс на валидации перестаёт убывать.\n",
        " - Советуем использовать GPU. Если у вас его нет, используйте google colab. Если вам неудобно его использовать на постоянной основе, напишите и отладьте весь код локально на CPU, а затем запустите уже написанный ноутбук в колабе. Авторское решение задания достигает требуемой точности в колабе за 15 минут обучения.\n",
        " \n",
        "Good luck & have fun! :)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 4,
        "id": "LKcSNj4tlRVK"
      },
      "source": [
        "#!pip install numpy torch torchvision pytorch-ignite\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import argmax\n",
        "import torch\n",
        "import torchvision\n",
        "import tqdm\n",
        "from tqdm.auto import tqdm\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from sklearn.metrics import accuracy_score\n",
        "from torchvision import transforms as T\n",
        "import random\n",
        "import zipfile\n",
        "from google.colab import drive\n",
        "#from ignite.metrics import Accuracy, Loss\n",
        "import sys\n",
        "from sklearn.metrics import accuracy_score\n",
        "from torchvision.datasets import ImageFolder"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dsiqCxQiTva",
        "outputId": "4a51fdd8-16f7-4c5c-fa36-04f6b961118e"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y60ecZo-iWev"
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/MyDrive/dataset.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall('dataset')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPmE3XmdigZt"
      },
      "source": [
        "def set_random_seed(seed):\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RytEDW0ylRVN"
      },
      "source": [
        "### Подготовка данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 5,
        "id": "QEdDQtHdlRVO"
      },
      "source": [
        "train_transform = T.Compose([\n",
        "                #T.Resize(128), \n",
        "                #T.CenterCrop(112), \n",
        "                T.RandomHorizontalFlip(),\n",
        "                T.ToTensor(), \n",
        "                T.Normalize(mean=[0.5, 0.5, 0.5], \n",
        "                            std=[0.5, 0.5, 0.5])])\n",
        "val_transform = T.Compose([\n",
        "                #T.Resize(128), \n",
        "                #T.CenterCrop(112),\n",
        "                T.RandomHorizontalFlip(),\n",
        "                T.ToTensor(), \n",
        "                T.Normalize(mean=[0.5, 0.5, 0.5], \n",
        "                            std=[0.5, 0.5, 0.5])])\n",
        "\n",
        "train_dataset = ImageFolder(\"/content/dataset/dataset/dataset/train\", transform=train_transform)\n",
        "val_dataset = ImageFolder(\"/content/dataset/dataset/dataset/val\", transform=val_transform)\n",
        "\n",
        "train_dataloader = torch.utils.data.DataLoader(train_dataset,batch_size=64,shuffle = True,num_workers=1)\n",
        "val_dataloader = torch.utils.data.DataLoader(val_dataset,batch_size=64,shuffle = False,num_workers=1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 6,
        "id": "mrg4Yj0VlRVP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ebf6ced-d4aa-41d4-911a-c4855163a960"
      },
      "source": [
        "# Just very simple sanity checks\n",
        "assert isinstance(train_dataset[0], tuple)\n",
        "assert len(train_dataset[0]) == 2\n",
        "assert isinstance(train_dataset[1][1], int)\n",
        "print(\"tests passed\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tests passed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RlSlmyjlRVP"
      },
      "source": [
        "### Вспомогательные функции, реализация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 7,
        "id": "yYG2-Cq8lRVQ"
      },
      "source": [
        "def train_one_epoch(model, train_dataloader, criterion, optimizer, scheduler, device=\"cuda:0\"):\n",
        "    model = model.to(device).train()\n",
        "    running_loss = []\n",
        "    accs = []\n",
        "    with tqdm(total=len(train_dataloader), file=sys.stdout) as prbar:\n",
        "      for image, label in train_dataloader:\n",
        "        image = image.to(device)\n",
        "        label = label.to(device)\n",
        "        label_pred = model(image)\n",
        "        loss = criterion(label_pred, label)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        scheduler.step()\n",
        "        running_loss.append(loss.item())\n",
        "        accuracy = (label_pred.argmax(1) == label).float().mean()\n",
        "        accs.append(accuracy.item())\n",
        "        prbar.set_description(\n",
        "                f\"Loss: {round(loss.item(), 4)} \"\n",
        "                f\"Accuracy: {round(accuracy.item() * 100, 4)}\"\n",
        "            )\n",
        "        prbar.update(1)\n",
        "    metrics = {'epoch loss': sum(running_loss)/len(running_loss), \n",
        "               'epoch accuracy': sum(accs)/len(accs)}\n",
        "\n",
        "    print(metrics)\n",
        "    return running_loss, accs\n",
        "\n",
        "\n",
        "def predict(model, val_dataloder, criterion, device=\"cuda:0\"):\n",
        "    model.to(device).eval()\n",
        "    losses = 0\n",
        "    predicted_classes = np.array([])\n",
        "    true_classes = np.array([])\n",
        "    with tqdm(total=len(val_dataloader), file=sys.stdout) as prbar:\n",
        "        for image, label in val_dataloader:\n",
        "          image=image.to(device)\n",
        "          label=label.to(device)\n",
        "          predicted = model(image)\n",
        "          loss = criterion(predicted, label)\n",
        "          losses += loss.item()\n",
        "          predicted_classes=np.append(predicted_classes, predicted.argmax(1).cpu().detach().numpy())\n",
        "          true_classes=np.append(true_classes, label.cpu().detach().numpy())\n",
        "          accuracy = (predicted.argmax(1) == label).float().mean()\n",
        "          prbar.set_description(\n",
        "                f\"Loss: {round(loss.item(), 4)} \"\n",
        "                f\"Accuracy: {round(accuracy.item() * 100, 4)}\"\n",
        "            )\n",
        "          prbar.update(1)\n",
        "    print('val loss: ', losses/len(val_dataloader), 'val accuracy: ', accuracy_score(predicted_classes, true_classes))#(predicted_classes.argmax(1) == true_classes).float().mean())\n",
        "    return losses, predicted_classes, true_classes\n",
        "\n",
        "\n",
        "def train(model, train_dataloader, val_dataloader, criterion, optimizer, device=\"cuda:0\", n_epochs=10, scheduler=None):\n",
        "    model.to(device)\n",
        "    all_train_losses = np.array([])\n",
        "    all_val_losses = np.array([])\n",
        "    for epoch in range(n_epochs):\n",
        "        print('Epoch', epoch+1)\n",
        "        train_loss, train_accs = train_one_epoch(model, train_dataloader, \n",
        "                                                    criterion, optimizer, scheduler, device)\n",
        "        np.append(all_train_losses, train_loss)\n",
        "        val_loss, val_preds, labels = predict(model, val_dataloader, criterion, device)\n",
        "        np.append(all_val_losses, val_loss)\n",
        "          #print('Loss: ', all_val_losses.mean(), 'Accuracy: ', round(accuracy.item() * 100, 4))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxR3gfcilRVW"
      },
      "source": [
        "### Обучение модели, запуски экспериментов"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSw9DhE_ZoLA"
      },
      "source": [
        "set_random_seed(150)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbXAi4YCUqr4"
      },
      "source": [
        "model = nn.Sequential(    \n",
        " nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, stride=1),\n",
        " nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        " nn.ReLU(inplace=True),\n",
        " nn.Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        " nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        " nn.ReLU(inplace=True),\n",
        "nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),\n",
        "  nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        "  nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        "  nn.Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        "  nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),\n",
        "  nn.Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        "  nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),\n",
        "  nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        "  nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n",
        "  nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
        "  nn.ReLU(inplace=True),\n",
        " nn.MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False),\n",
        " nn.Flatten(start_dim=1, end_dim=-1),\n",
        " nn.Linear(2048, 200, bias=True)   \n",
        ")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 8,
        "scrolled": true,
        "id": "JXFJ6oS8lRVX"
      },
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=0.00002)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, 0.0001, epochs=6, \n",
        "                                                steps_per_epoch=len(train_dataloader))\n",
        "n_epochs = 5\n",
        "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XD2FOxgzjem-"
      },
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 9,
        "id": "CesoOl6BlRVY"
      },
      "source": [
        "Простой тест на проверку правильности написанного кода"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 10,
        "id": "B_LB2jn6lRVY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "1ea190a7f5d040bd9ab90d9b95115fbd",
            "511a13aae51f44338d36d6aea7c33e73",
            "6808365bdd324cffb1bdbef8493f1ee7",
            "f516eaf9af4042218eed141eee5340cb",
            "cb9724d5207d41faa119662ee871a293",
            "3fb5731c85ec48ae92880454c2256a44",
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            "85531582a3dd475683bd2e0c2feec675",
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            "6c6e72d9a00e4189a9af75f49aae42c6",
            "0b14a51d3fd94096880d8c6fece28ec0"
          ]
        },
        "outputId": "497a4cdb-4224-4f90-ee3b-6ab11dadb603"
      },
      "source": [
        "all_losses, predicted_labels, true_labels = predict(model, val_dataloader, criterion, device)\n",
        "assert len(predicted_labels) == len(val_dataset)\n",
        "accuracy = accuracy_score(predicted_labels, true_labels)\n",
        "print(\"tests passed\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1ea190a7f5d040bd9ab90d9b95115fbd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/157 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  5.298520376727839 val accuracy:  0.005\n",
            "tests passed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 11,
        "id": "tS-LLiXUlRVY"
      },
      "source": [
        "Запустить обучение можно в ячейке ниже."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 12,
        "id": "ECIzZ_RYlRVZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707,
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        },
        "outputId": "1fb4fe44-c966-45ce-ab2e-bf8dc80e5f25"
      },
      "source": [
        "train(model, train_dataloader, val_dataloader, criterion, optimizer, n_epochs=6, scheduler=scheduler)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a692559d72e43408eee0aa614b2e6b1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1563 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 4.716284544286404, 'epoch accuracy': 0.07226687460012796}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c72eb02b5ab244ce9ac3489467192581",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/157 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  3.994051199809761 val accuracy:  0.1509\n",
            "Epoch 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bfb06708fa9b49369f13e2ad01a7f056",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1563 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 3.6054662697145896, 'epoch accuracy': 0.20808341330774152}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44c1de24fc5a43dfa57d342d2788012b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  3.4017402146272597 val accuracy:  0.2378\n",
            "Epoch 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e27b67196ed0402db21190d7c91b1f79",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 2.9945772096886514, 'epoch accuracy': 0.31212012156110047}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "836ef8a224394102b4d6d459b9b62dc6",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  2.90371891419599 val accuracy:  0.3276\n",
            "Epoch 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9e45c72cf3344fc8732786766efbfe6",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 2.5517657014397725, 'epoch accuracy': 0.4013915547024952}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1aca75cc9e674a79a5fd76a06aab83dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/157 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  2.585325675025867 val accuracy:  0.3924\n",
            "Epoch 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c16893d209a4525b431d07d2b6aed78",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1563 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 2.1798278786628122, 'epoch accuracy': 0.4834552943058221}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3bb724799954d7fa4ba7c9558aa8211",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  2.3463264525316325 val accuracy:  0.4438\n",
            "Epoch 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ffd8233a8724053a1b4310602cf3019",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'epoch loss': 1.935528080126298, 'epoch accuracy': 0.5453254958413307}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21f1e51d4b7b401d9e509639545fce9e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  2.2833502239482417 val accuracy:  0.4582\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImVW8_EXlRVZ"
      },
      "source": [
        "### Проверка полученной accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 13,
        "id": "FmR-elhJlRVZ"
      },
      "source": [
        "После всех экспериментов которые вы проделали, выберите лучшую из своих моделей, реализуйте и запустите функцию `evaluate`. Эта функция должна брать на вход модель и даталоадер с валидационными данными и возврашать accuracy, посчитанную на этом датасете."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": 14,
        "id": "3TGH0EFalRVb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
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            "22b873d2f5944a6bb1eaa19fc7eaef8d",
            "882bbbe6fe964bf082673f2302bcda21",
            "af04de21e90540e380f54fd3dc84539c",
            "1518cd07d985444daf1add723822a0eb",
            "a0af01b1e2c4449fb66016c0bffb53f5",
            "679afd53a3de47b096f883f4db125ee0"
          ]
        },
        "outputId": "3c29877d-37cd-47be-a123-e17215af7745"
      },
      "source": [
        "all_losses, predicted_labels, true_labels = predict(model, val_dataloader, criterion, device)\n",
        "assert len(predicted_labels) == len(val_dataset)\n",
        "accuracy = accuracy_score(true_labels, predicted_labels)\n",
        "print(\"Оценка за это задание составит {} баллов\".format(min(10, 10 * accuracy / 0.44)))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ad09c196f064815a504046450472d7f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/157 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val loss:  2.279574896879257 val accuracy:  0.4593\n",
            "Оценка за это задание составит 10 баллов\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "cell_id": 15,
        "id": "pT8vfPSolRVb"
      },
      "source": [
        "### Отчёт об экспериментах \n",
        "\n",
        "В этот раз постараюсь написать более сжатый и локаничный отчет.\n",
        "В рамках этого задания пришлось перепробовать много разных штук. \n",
        "1. Сначала я пробовал обучать модель без шедулера. Качество оставляло желать лучшего и пробивало максимум 0.05.\n",
        "2. Потом добавил шаг шедулера на конец каждой эпохи. Качество это сильно не изменило, тогда углубившись в тему я переметил его и начал делать по нему шаг в конце каждого батча. Это значительно лучше изменило ситуацию, но все-таки недостаточно\n",
        "3. В конце концов, методом проб и ошибок я понял, что в первую очередь на качество будут влиять аугументации. Там пришлось немного поэксперементировать и вуаля, осталось доработать какие-то незначительные вещи вроде подбора гиперпараметров, чтобы пробить 0.44\n",
        "\n",
        "Что ж 0.8 пробить пока не получилось, если будет свободное время, я еще попробую, но большие ограничения накладывает использование ГПУ, посмотрим, что из этого получится\n"
      ]
    }
  ]
}