Lab7_2_Layok.ipynb

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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OlegLaiok/Machine_Learning/blob/main/Lab7_2_Layok.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezjWeWD7WsxU"
      },
      "source": [
        " # FashionMNIST\n",
        "\n",
        " ## Задание 7.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AygNFOvoWsxY"
      },
      "source": [
        "<img src=\"https://emiliendupont.github.io/imgs/mnist-chicken/mnist-and-fashion-examples.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNF5C2b_Wsxh"
      },
      "source": [
        "На практике построим нейронную сеть для классификации картиной. В качестве входных данных используются картинки одежды (в градациях серого, то есть цветовых каналов не 3, а 1). По входной картинке нужно предсказать тип одежды."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchvision"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9j24qsgCy_c",
        "outputId": "901595df-e00f-401b-f134-c6f91592aa39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (0.11.1+cu111)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.19.5)\n",
            "Requirement already satisfied: torch==1.10.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (1.10.0+cu111)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision) (7.1.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.10.0->torchvision) (3.10.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IYPMzDudZeN"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os, sys\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEr2x6meSqNj"
      },
      "source": [
        "Скачаем обучающую и контрольную выборки."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCedv50ReIUZ"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor()])\n",
        "\n",
        "trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.FashionMNIST(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = tuple(str(i) for i in range(10))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZTfJ0dBWszP"
      },
      "source": [
        "Каждая картинка имеет класс от 0 до 9, расшифровка меток класса:  \n",
        "\n",
        "|class_id|class_name|\n",
        "|----|----|\n",
        "|0| T-shirt/top|\n",
        "|1| Trouser|\n",
        "|2| Pullover|\n",
        "|3| Dress|\n",
        "|4| Coat|\n",
        "|5| Sandal|\n",
        "|6| Shirt|\n",
        "|7| Sneaker|\n",
        "|8| Bag|\n",
        "|9| Ankle boot| "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkDjjoj2Ws04"
      },
      "source": [
        "<h2 style=\"text-align: center;\"><b>Задание</b></h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_DjMGZPWszt"
      },
      "source": [
        "Посмотрите на датасет.\n",
        "* Сколько картинок в train? В test?\n",
        "* Равномерно ли распределено количество объектов разных классов в train датасете (это можно понять, поработав с полем `train_data` у переменной `trainset`)? в test?\n",
        "* Выведите несколько картинок с помощью `pyplot`\n",
        "\n",
        "Используя материалы семинаров по многослойным  и свёрточным сетям, напишите:\n",
        "1.  Многослойную нейронную сеть MLP (Multi-Layer Perceptron) классифицирующую этот датасет\n",
        "2. Свёрточную сеть, классифицирующую этот датасет\n",
        "3. Сравните их качество (по графикам лосса и по Accuracy Score)\n",
        "\n",
        "Если качество, которое получается у  нейросети, выше 95%, то Вы решили задачу с точностью, которую можно использовать в жизни."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ev5LShUSWs06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1281d69-0df8-4591-e4d8-3007ea6dc446"
      },
      "source": [
        "trainset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset FashionMNIST\n",
              "    Number of datapoints: 60000\n",
              "    Root location: ./data\n",
              "    Split: Train"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-AJoxHnElRw",
        "outputId": "6f8e6a8d-9ba1-46e5-ef11-412100a47aaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset FashionMNIST\n",
              "    Number of datapoints: 10000\n",
              "    Root location: ./data\n",
              "    Split: Test"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import notebook"
      ],
      "metadata": {
        "id": "EVVfrpJAMSmW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(28*28, 500)\n",
        "        self.fc2 = nn.Linear(500, 100)\n",
        "        self.fc3 = nn.Linear(100, 10)\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 28*28)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "DkMrkdenEzjS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MLP_network = MLP()\n",
        "\n",
        "loss_f = nn.CrossEntropyLoss() \n",
        "optimizer = optim.Adam(MLP_network.parameters(), lr=0.0001)\n",
        "\n",
        "for epoch in notebook.tqdm(range(5)):\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, batch in enumerate(notebook.tqdm(trainloader)):\n",
        "        # так получаем текущий батч\n",
        "        X_batch, y_batch = batch\n",
        "        \n",
        "        # обнуляем веса\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        y_pred = MLP_network(X_batch)\n",
        "        loss = loss_f(y_pred, y_batch)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # выведем текущий loss\n",
        "        running_loss += loss.item()\n",
        "        # выведем качество каждые 2000 батчей\n",
        "        if i % 2000 == 1999:\n",
        "            print('[%d, %5d] loss: %.3f' %\n",
        "                  (epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Обучение закончено')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834,
          "referenced_widgets": [
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        },
        "id": "owiVaQRrKphF",
        "outputId": "8b433c8f-8af9-42de-c093-eb15d324b836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5697137470e9457591eb7ac6ecf45fff",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83c4fef7f10744c78c04b8feefc72fd5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 0.879\n",
            "[1,  4000] loss: 0.547\n",
            "[1,  6000] loss: 0.514\n",
            "[1,  8000] loss: 0.476\n",
            "[1, 10000] loss: 0.471\n",
            "[1, 12000] loss: 0.438\n",
            "[1, 14000] loss: 0.420\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4a63eb72af74396babafd18723cb7cf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2,  2000] loss: 0.402\n",
            "[2,  4000] loss: 0.394\n",
            "[2,  6000] loss: 0.392\n",
            "[2,  8000] loss: 0.368\n",
            "[2, 10000] loss: 0.364\n",
            "[2, 12000] loss: 0.362\n",
            "[2, 14000] loss: 0.365\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1dda258e7594938bac10ef50c7d2716",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3,  2000] loss: 0.340\n",
            "[3,  4000] loss: 0.339\n",
            "[3,  6000] loss: 0.325\n",
            "[3,  8000] loss: 0.329\n",
            "[3, 10000] loss: 0.339\n",
            "[3, 12000] loss: 0.330\n",
            "[3, 14000] loss: 0.329\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5b046a348b141cf8fd2fd912e1673a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4,  2000] loss: 0.313\n",
            "[4,  4000] loss: 0.324\n",
            "[4,  6000] loss: 0.301\n",
            "[4,  8000] loss: 0.295\n",
            "[4, 10000] loss: 0.307\n",
            "[4, 12000] loss: 0.312\n",
            "[4, 14000] loss: 0.304\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4a3459d0ae94be9ab61cc97781e1ae5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5,  2000] loss: 0.294\n",
            "[5,  4000] loss: 0.293\n",
            "[5,  6000] loss: 0.286\n",
            "[5,  8000] loss: 0.280\n",
            "[5, 10000] loss: 0.292\n",
            "[5, 12000] loss: 0.282\n",
            "[5, 14000] loss: 0.287\n",
            "Обучение закончено\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_correct = list(0. for i in range(10))\n",
        "class_total = list(0. for i in range(10))\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        y_pred = MLP_network(images)\n",
        "        _, predicted = torch.max(y_pred, 1)\n",
        "        c = (predicted == labels).squeeze()\n",
        "        for i in range(4):\n",
        "            label = labels[i]\n",
        "            class_correct[label] += c[i].item()\n",
        "            class_total[label] += 1\n",
        "\n",
        "for i in range(10):\n",
        "    print('Accuracy of %5s : %2d %%' % (\n",
        "        classes[i], 100 * class_correct[i] / class_total[i]))\n",
        "total_acc=0\n",
        "for i in range(10):\n",
        "  total_acc+=class_correct[i] / class_total[i]\n",
        "total_acc = (total_acc / 10) * 100\n",
        "print('Total accuracy is ', total_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZCnliCuNewE",
        "outputId": "64243c4e-cfc1-4b85-f959-e24104a2aff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of     0 : 89 %\n",
            "Accuracy of     1 : 96 %\n",
            "Accuracy of     2 : 73 %\n",
            "Accuracy of     3 : 88 %\n",
            "Accuracy of     4 : 83 %\n",
            "Accuracy of     5 : 96 %\n",
            "Accuracy of     6 : 63 %\n",
            "Accuracy of     7 : 95 %\n",
            "Accuracy of     8 : 97 %\n",
            "Accuracy of     9 : 95 %\n",
            "Total accuracy is  88.02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class ConvNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        # вызов конструктора родителя\n",
        "        super(ConvNet, self).__init__()\n",
        "        # необходмо заранее знать, сколько каналов у картинки (сейчас = 1),\n",
        "        # которую будем подавать в сеть, больше ничего\n",
        "        # про входящие картинки знать не нужно\n",
        "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=20, kernel_size=5, padding=1)\n",
        "        # out_channels - это количество фильтров, которые применятся для свёртки\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.conv2 = nn.Conv2d(in_channels=20, out_channels=40, kernel_size=5, padding=1)\n",
        "        #self.conv3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, padding=1)\n",
        "        self.fc1 = nn.Linear(5 * 5 * 40, 500)\n",
        "        self.fc2 = nn.Linear(500, 250)\n",
        "        self.fc3 = nn.Linear(250, 125)\n",
        "        self.fc4 = nn.Linear(125, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        #x = self.pool(F.relu(self.conv3(x)))\n",
        "        #print(x.shape)\n",
        "        x = x.view(-1, 5 * 5 * 40)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = F.relu(self.fc3(x))\n",
        "        x = self.fc4(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "3hACgK1kPtQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# объявляем сеть\n",
        "Conv_net = ConvNet()\n",
        "\n",
        "# выбираем функцию потерь\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "# выбираем алгоритм оптимизации и learning_rate\n",
        "learning_rate = 1e-4\n",
        "optimizer = torch.optim.Adam(Conv_net.parameters(), lr=learning_rate, betas=(0.9,0.99), eps=1e-08, weight_decay=0.001)\n",
        "\n",
        "# итерируемся\n",
        "for epoch in notebook.tqdm(range(10)):\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, batch in enumerate(notebook.tqdm(trainloader)):\n",
        "        # так получаем текущий батч\n",
        "        X_batch, y_batch = batch\n",
        "        \n",
        "        # обнуляем веса\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        y_pred = Conv_net(X_batch)\n",
        "        loss = loss_fn(y_pred, y_batch)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # выведем текущий loss\n",
        "        running_loss += loss.item()\n",
        "        # выведем качество каждые 2000 батчей\n",
        "        if i % 2000 == 1999:\n",
        "            print('[%d, %5d] loss: %.3f' %\n",
        "                  (epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Обучение закончено')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "QugU6_1WQue9",
        "outputId": "8f5e66ef-45b8-4b0a-e925-1a69ef8e2ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "47f07629c4404b52b8a4e7f3560731c8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 1.000\n",
            "[1,  4000] loss: 0.676\n",
            "[1,  6000] loss: 0.596\n",
            "[1,  8000] loss: 0.542\n",
            "[1, 10000] loss: 0.502\n",
            "[1, 12000] loss: 0.485\n",
            "[1, 14000] loss: 0.482\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6dd7406dbc9f46ab98e182bbd07d58e0",
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            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2,  2000] loss: 0.419\n",
            "[2,  4000] loss: 0.399\n",
            "[2,  6000] loss: 0.394\n",
            "[2,  8000] loss: 0.365\n",
            "[2, 10000] loss: 0.378\n",
            "[2, 12000] loss: 0.362\n",
            "[2, 14000] loss: 0.361\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40cd85caf8ee4d40ae29408a4bb2fecc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3,  2000] loss: 0.327\n",
            "[3,  4000] loss: 0.350\n",
            "[3,  6000] loss: 0.336\n",
            "[3,  8000] loss: 0.310\n",
            "[3, 10000] loss: 0.317\n",
            "[3, 12000] loss: 0.314\n",
            "[3, 14000] loss: 0.309\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55077987836741a1b7e7446ae1a0ca53",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4,  2000] loss: 0.285\n",
            "[4,  4000] loss: 0.295\n",
            "[4,  6000] loss: 0.296\n",
            "[4,  8000] loss: 0.281\n",
            "[4, 10000] loss: 0.303\n",
            "[4, 12000] loss: 0.284\n",
            "[4, 14000] loss: 0.292\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68624e3e1b9b404992b0182b0d070bc0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5,  2000] loss: 0.280\n",
            "[5,  4000] loss: 0.279\n",
            "[5,  6000] loss: 0.264\n",
            "[5,  8000] loss: 0.265\n",
            "[5, 10000] loss: 0.273\n",
            "[5, 12000] loss: 0.279\n",
            "[5, 14000] loss: 0.270\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56250ae300844bf3862cb54eaa91a3c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6,  2000] loss: 0.260\n",
            "[6,  4000] loss: 0.259\n",
            "[6,  6000] loss: 0.257\n",
            "[6,  8000] loss: 0.247\n",
            "[6, 10000] loss: 0.251\n",
            "[6, 12000] loss: 0.259\n",
            "[6, 14000] loss: 0.250\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71b60cc087a84cedb505c216bbeb94a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7,  2000] loss: 0.241\n",
            "[7,  4000] loss: 0.239\n",
            "[7,  6000] loss: 0.239\n",
            "[7,  8000] loss: 0.243\n",
            "[7, 10000] loss: 0.240\n",
            "[7, 12000] loss: 0.241\n",
            "[7, 14000] loss: 0.249\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38f18a65327643398a4453d8ebaaadf7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[8,  2000] loss: 0.245\n",
            "[8,  4000] loss: 0.227\n",
            "[8,  6000] loss: 0.236\n",
            "[8,  8000] loss: 0.225\n",
            "[8, 10000] loss: 0.231\n",
            "[8, 12000] loss: 0.227\n",
            "[8, 14000] loss: 0.232\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e991bbf57e824e6da486617ee8947bac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9,  2000] loss: 0.210\n",
            "[9,  4000] loss: 0.219\n",
            "[9,  6000] loss: 0.219\n",
            "[9,  8000] loss: 0.229\n",
            "[9, 10000] loss: 0.228\n",
            "[9, 12000] loss: 0.236\n",
            "[9, 14000] loss: 0.220\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "293af85bdd684c4a874d30c19e9a4bb8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/15000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10,  2000] loss: 0.198\n",
            "[10,  4000] loss: 0.214\n",
            "[10,  6000] loss: 0.217\n",
            "[10,  8000] loss: 0.222\n",
            "[10, 10000] loss: 0.209\n",
            "[10, 12000] loss: 0.224\n",
            "[10, 14000] loss: 0.216\n",
            "Обучение закончено\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_correct = list(0. for i in range(10))\n",
        "class_total = list(0. for i in range(10))\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        y_pred = Conv_net(images)\n",
        "        _, predicted = torch.max(y_pred, 1)\n",
        "        c = (predicted == labels).squeeze()\n",
        "        for i in range(4):\n",
        "            label = labels[i]\n",
        "            class_correct[label] += c[i].item()\n",
        "            class_total[label] += 1\n",
        "\n",
        "for i in range(10):\n",
        "    print('Accuracy of %5s : %2d %%' % (\n",
        "        classes[i], 100 * class_correct[i] / class_total[i]))\n",
        "total_acc=0\n",
        "for i in range(10):\n",
        "  total_acc+=class_correct[i] / class_total[i]\n",
        "total_acc = (total_acc / 10) * 100\n",
        "print('Total accuracy is ', total_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1R9Zz9ISDFa",
        "outputId": "ec2a08f7-a162-4e6d-ea75-b0218cee3804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of     0 : 91 %\n",
            "Accuracy of     1 : 97 %\n",
            "Accuracy of     2 : 90 %\n",
            "Accuracy of     3 : 93 %\n",
            "Accuracy of     4 : 83 %\n",
            "Accuracy of     5 : 93 %\n",
            "Accuracy of     6 : 61 %\n",
            "Accuracy of     7 : 98 %\n",
            "Accuracy of     8 : 96 %\n",
            "Accuracy of     9 : 95 %\n",
            "Total accuracy is  90.17000000000002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IE30PZCoR_px"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}