myDNN_with_MNIST.ipynb

{
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  "metadata": {
    "colab": {
      "name": "myDNN_with_MNIST.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOWku4Gjsx56OKux7QPURGE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khodid/2020_SAI_MONING2/blob/master/myDNN_with_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDwnKZW3-eGT",
        "colab_type": "text"
      },
      "source": [
        "# 참고 소스:\n",
        "\n",
        "- 모두의 딥러닝 시즌 2 - lab 07-2 mnist introduction\n",
        "https://github.com/deeplearningzerotoall/PyTorch/blob/master/lab-07_2_mnist_introduction.ipynb\n",
        "\n",
        "- 모두의 딥러닝 시즌 2 - 프로젝트 A\n",
        "(내가 작성했던 것.)\n",
        "https://github.com/khodid/2020_SAI_MONING2/blob/master/projectA_DNN.ipynb\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWIepOUNDDiV",
        "colab_type": "text"
      },
      "source": [
        "# 패키지 받아오기.\n",
        "- torch\n",
        "- torch.nn : Neural Network를 위한 모델\n",
        "- torchvision: datasets(Torch에서 제공되는 dataset 받아오기)/transforms( 이미지 파일 가공하기)\n",
        "- matplotlib: 데이터들의 시각적 표현을 위해서.(그래프 그리기, 등등.)\n",
        "- numpy: 수학 함수들 불러오기\n",
        "\n",
        "밑에 DataLoader 패키지를 import해오는 부분도 있긴 한데, 예시에서 따로 중간에 import를 해온 것은, 아무래도 일회성으로 쓰는 것이기 때문에 읽는 사람이 이해하기 편하도록 그 패키지가 사용되는 블럭 근처에 선언을 해준 게 아닌가 싶다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqy-XdCn-dCa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HolPuQhAjRLv",
        "colab_type": "text"
      },
      "source": [
        "GPU를 사용하거나 CPU를 사용하도록 하는 코드...\n",
        "이걸 왜 써줘야 하는 지는 예전에 설명을 들었던 것 같은데 까먹었음.\n",
        "\n",
        "궁금해지면 'cuda'나 'gpu와 cpu' 등등의 키워드로 알아보면 될 듯."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34TlgPSbjUZu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYz9WuTokc9t",
        "colab_type": "text"
      },
      "source": [
        "# 하이퍼 파라미터 설정.\n",
        "- 배치 크기(미니배치 방법을 쓰기 때문)\n",
        "- 학습률(learning rate): 너무 크면 overshoot이나 gradient exploding 일어나고, 작으면 학습이 더뎌서 원하는 결과가 나올 때까지 epoch 수를 늘려야 함.\n",
        "- epoch: 1회의 학습 과정(cost 계산, GD에 따라 weight update) 끝나는 걸 1 epoch라고 했던 것 같다.\n",
        "\n",
        "이 파라미터는 전부 모델을 설계하는 사람이 알아서, 감과 경험을 동원해 다르게 설정해주는 것으로 알고 있다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HafU2jOdkdL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 50\n",
        "learning_rate = 1e-3\n",
        "num_of_epoch = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbEyku1jlgSo",
        "colab_type": "text"
      },
      "source": [
        "# 데이터 불러오기.\n",
        "\n",
        "위의 참고 소스의 데이터 불러오기 파트 따라치기로 구현했음.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnIIejsspwmz",
        "colab_type": "text"
      },
      "source": [
        "root : 데이터를 어디서 받아올 것인지,\n",
        "\n",
        "\n",
        "trasnsform : 데이터를 어떻게 저장할 것인지.\n",
        "\n",
        "[torchvision.transforms 라이브러리 설명](https://pytorch.org/docs/stable/torchvision/transforms.html)\n",
        "- transforms.Compose 함수는 여러 가지 전처리 함수를 묶어주는 역할을 한다.\n",
        "- ToTensor는 이미지 파일을 텐서로 바꾸어주는 역할\n",
        "- Normalizaze 함수는 데이터를 정규분포로 바꾸어줌. \n",
        "이 정규화 전처리의 유용성은 [4월 6일 한땀한땀](https://www.notion.so/20-04-06-8c08ab4a451d4930b47d8f7191422dac#32766507a8db49ce94961f974cd9c439)에서 정리한 바 있음.\n",
        "\n",
        "[torch.utils.data.DataLoader 파라미터 설명](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCXoArkqlkA5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "root = 'MNIST_data/'\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# data 가공방식 설정하기 - 프로젝트 A에서 한 것과 같이 Normalization으로 전처리 시킬 것임.\n",
        "transform   = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean = (0.5,), std=(0.5,))])\n",
        "\n",
        "# data 받아오기\n",
        "train_data  = dset.MNIST(root = root, train = True,   transform = transform,  download = True)\n",
        "test_data   = dset.MNIST(root = root, train = False,  transform = transform, download = True)\n",
        "\n",
        "# data를 실제 학습과 테스트에 적용하기 위해 미니배치로 나누어주기. (매개변수로는 데이터덩어리 / 배치 크기 / 섞을지 말지)\n",
        "train_loader  = DataLoader(train_data, batch_size = batch_size, shuffle = True, drop_last= True)   # 트레이닝할 때는 데이터를 섞어주지 않으면 순서 그 자체를 학습할 수도 있음.\n",
        "test_loader   = DataLoader(test_data, batch_size = batch_size, shuffle = False, drop_last= True)   # 테스트 할 때는 데이터를 섞어주지 않아도 되므로."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ql8qw-c_s_t0",
        "colab_type": "text"
      },
      "source": [
        "# 데이터 샘플 시각화.\n",
        "\n",
        "matplotlib과 numpy 쪽은 생소해서 그냥 태그랑 정렬 방식(열, 행)만 바꾸고 소스를 프로젝트 A에서 그대로 복붙했음."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYo_uQQYq6Q5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "552856d9-f82d-49bc-d647-9988877469a8"
      },
      "source": [
        "labels_map = {0 : '0', 1 : '1', 2 : '2', 3 : '3', 4 : '4', 5 : '5', 6 : '6',\n",
        "              7 : '7', 8 : '8', 9 : '9'}\n",
        "columns = 8\n",
        "rows = 4\n",
        "fig = plt.figure(figsize=(8,8))\n",
        "\n",
        "for i in range(1, columns*rows+1):\n",
        "    data_idx = np.random.randint(len(train_data))\n",
        "    img = train_data[data_idx][0][0,:,:].numpy() # numpy()를 통해 torch Tensor를 numpy array로 변환\n",
        "    label = labels_map[train_data[data_idx][1]] # item()을 통해 torch Tensor를 숫자로 변환\n",
        "    \n",
        "    fig.add_subplot(rows, columns, i)\n",
        "    plt.title(label)\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x576 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnyUzMIEt_vc",
        "colab_type": "text"
      },
      "source": [
        "# 네트워크 설계\n",
        "\n",
        "층층이 얽힌 그래프 구조를 만들어야 할 때인데...\n",
        "\n",
        "기존에 프로젝트 A에선 layer를 2개 쌓아준 걸\n",
        "3개로 늘려서 정의해보도록 하겠음.\n",
        " \n",
        " \n",
        " -> 이렇게 하니깐 밑에 학습 과정중 outputs = model(imgs) 라인에서 자꾸 tuple에는 dim이 없다는 오류가 뜸.\n",
        "\n",
        "Module의  linear 함수에서 이런 현상이 일어나고, \n",
        "\n",
        "AttributeError: 'tuple' object has no attribute 'dim'\n",
        "\n",
        "이 부분은 따라치기를 많이 했음.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZ_fsXEFusC9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3 layer Model 짜려고 했으나 오류가 나서 포기한 부분.\n",
        "\n",
        "# class DNN(nn.Module):\n",
        "#   def __init__(self, num_classes=10):\n",
        "#     super(DNN, self).__init__()\n",
        "#     self.layer1 = nn.Sequential(\n",
        "#         nn.Linear(784, 300),      # 첫 레이어의 output 수 = 두번째 레이어의 input 수는 내 마음대로 설정했음.\n",
        "#         nn.BatchNorm1d(300),\n",
        "#         nn.ReLU()\n",
        "#     )\n",
        "#     self.layer2 = nn.Sequential(\n",
        "#         nn.Linear(300, 64),         # 두번째 - 세번째 레이어 간의 연결도 내 마음대로... \n",
        "#         nn.BatchNorm1d(64),\n",
        "#         nn.ReLU()\n",
        "#     )\n",
        "#     self.layer3 = nn.Sequential(\n",
        "#         nn.Linear(64, 10)\n",
        "#     )\n",
        "#   def forward(self, x):\n",
        "#     x = x.view(x.size(0), -1)\n",
        "#     x_out = self.layer1(x)\n",
        "#     x_out = self.layer2(x_out),   # 이 쪽에 파라미터를 어떤 걸 줘야하는지 약간 헷갈림. 만약 레이어 늘리기에 실패했다면 이게 원인일 가능성이 높음.\n",
        "#     x_out = self.layer3(x_out)\n",
        "#     return x_out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8FGk7OoDX4U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DNN(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super(DNN, self).__init__()\n",
        "        self.layer1 = nn.Sequential(\n",
        "            ## 코드 시작 ##\n",
        "            nn.Linear(784, 64),    # Linear_1 해당하는 층\n",
        "            torch.nn.BatchNorm1d(64),    # BatchNorm_1 해당하는 층\n",
        "            torch.nn.ReLU()     # ReLU_1 해당하는 층\n",
        "            ## 코드 종료 ##\n",
        "        )\n",
        "        self.layer2 = nn.Sequential(\n",
        "            ## 코드 시작 ##\n",
        "            nn.Linear(64, 10)    # Linear_2 해당하는 층 \n",
        "            ## 코드 종료 ##\n",
        "        )\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x = x.view(x.size(0), -1) # flatten\n",
        "        x_out = self.layer1(x)\n",
        "        x_out = self.layer2(x_out)\n",
        "        return x_out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByroRwpExLZT",
        "colab_type": "text"
      },
      "source": [
        "# Weight 초기화\n",
        "\n",
        "DNN 에서의 Weight 초기화를 이론적으로 다룬 부분은 \n",
        "\n",
        "[한땀한땀 4월 20일](https://www.notion.so/20-04-20-edc672e58455447a989f7f669b7beca2#edb720ded0304d13bdbb3503d6c9dffb)\n",
        "에 있다.\n",
        "\n",
        "요약하면 weight initialization은 gradient vanishing/exploding 현상을 방지하기 위해 필수적인데, 주로 쓰는 건 He나 Xavier의 방법이라는 것이다.\n",
        "\n",
        "코드는 프로젝트 A에서 긁어 왔다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOkF59J_xxh3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def weights_init(m):\n",
        "    if isinstance(m, nn.Linear): # 모델의 모든 MLP 레이어에 대해서\n",
        "        nn.init.xavier_normal_(m.weight) # Weight를 xavier_normal로 초기화\n",
        "        # print(m.weight)         # 프로젝트 A에 있던 line인데, 굳이 print해야 할 이유를 모르겠어서 주석처리 하겠음."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEU4YaeUztGx",
        "colab_type": "text"
      },
      "source": [
        "# 모델 생성\n",
        "\n",
        "이제 지금까지 만든 레이어 구조에 weight 초기화를 넣어 학습 전의 모델 기틀을 만들어 준다.\n",
        "\n",
        "\n",
        ".apply()는 torch.nn.Module 에 있는 함수? 기능? 이라고 한다.\n",
        "\n",
        "[설명](https://pytorch.org/docs/stable/nn.html?highlight=apply#torch.nn.Module.apply) 봐도 잘 모르겠다. 객체지향 얼렁 공부해야 할 듯."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMB56EPm0vAK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "a5961300-0715-4718-9f17-b3474ae4ab3a"
      },
      "source": [
        "torch.manual_seed(7777)\n",
        "model = DNN().to(device)\n",
        "model.apply(weights_init)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DNN(\n",
              "  (layer1): Sequential(\n",
              "    (0): Linear(in_features=784, out_features=64, bias=True)\n",
              "    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Linear(in_features=64, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doEyr9uu3muw",
        "colab_type": "text"
      },
      "source": [
        "# 학습시킬 준비 - Cost function과 Optimizer \n",
        "\n",
        "cost 계산하는 함수(?)와 optimizer를 설정해준다.\n",
        "\n",
        "이쪽도 코드 베끼기...\n",
        "\n",
        "나중에 개인 프로젝트를 한다면 여기서 다른 cost function을 쓰거나 optimizer를 쓰는 시도를 할 수도 있을 것 같다.\n",
        "\n",
        "Optimizer 관련 내용은 [4/20 스터디](https://www.notion.so/20-04-20-edc672e58455447a989f7f669b7beca2#933466115ddb4f98aabdb794f481e04f)에서 다룬 바 있다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wV3_8_y4DY9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFMCElu94v4q",
        "colab_type": "text"
      },
      "source": [
        "# 학습시키기\n",
        "\n",
        "프로젝트 A에서 참고 자료로 주는 것은 [이 문서](https://pytorch.org/docs/stable/optim.html#taking-an-optimization-step)다.\n",
        "\n",
        "많이 공부해서 익숙하지만, \n",
        "\n",
        "**예측 -> cost 계산 -> back propagation(gradient 계산) ->gradient 업데이트**\n",
        "\n",
        "순으로 써주면 되는 것이다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3dXC-4r5RLl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "7315f16d-51a2-4314-84f7-9c2871933de6"
      },
      "source": [
        "for epoch in range(num_of_epoch):\n",
        "    for i, (imgs, labels) in enumerate(train_loader):\n",
        "        imgs, labels = imgs.to(device), labels.to(device)\n",
        "        ## 코드 시작 ##\n",
        "        outputs = model(imgs)  # 위의 설명 1. 을 참고하여 None을 채우세요.\n",
        "        loss = criterion(outputs, labels)    # 위의 설명 2. 를 참고하여 None을 채우세요.\n",
        "        \n",
        "        optimizer.zero_grad()            # Clear gradients: 위의 설명 3. 을 참고하여 None을 채우세요.\n",
        "        loss.backward()\n",
        "        optimizer.step()            # Gradients 계산: 위의 설명 4. 를 참고하여 None을 채우세요.\n",
        "                                    # Parameters 업데이트: 위의 설명 5. 를 참고하여 None을 채우세요.\n",
        "        ## 코드 종료 ##\n",
        "        \n",
        "        _, argmax = torch.max(outputs, 1)\n",
        "        accuracy = (labels == argmax).float().mean()\n",
        "        \n",
        "        if (i+1) % 300 == 0:\n",
        "            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'.format(\n",
        "                epoch+1, num_of_epoch, i+1, len(train_loader), loss.item(), accuracy.item() * 100))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [1/5], Step [300/1200], Loss: 0.3344, Accuracy: 92.00%\n",
            "Epoch [1/5], Step [600/1200], Loss: 0.1647, Accuracy: 98.00%\n",
            "Epoch [1/5], Step [900/1200], Loss: 0.3787, Accuracy: 88.00%\n",
            "Epoch [1/5], Step [1200/1200], Loss: 0.1752, Accuracy: 96.00%\n",
            "Epoch [2/5], Step [300/1200], Loss: 0.1928, Accuracy: 92.00%\n",
            "Epoch [2/5], Step [600/1200], Loss: 0.1483, Accuracy: 96.00%\n",
            "Epoch [2/5], Step [900/1200], Loss: 0.1171, Accuracy: 96.00%\n",
            "Epoch [2/5], Step [1200/1200], Loss: 0.0887, Accuracy: 98.00%\n",
            "Epoch [3/5], Step [300/1200], Loss: 0.1259, Accuracy: 94.00%\n",
            "Epoch [3/5], Step [600/1200], Loss: 0.1142, Accuracy: 96.00%\n",
            "Epoch [3/5], Step [900/1200], Loss: 0.0276, Accuracy: 100.00%\n",
            "Epoch [3/5], Step [1200/1200], Loss: 0.1057, Accuracy: 96.00%\n",
            "Epoch [4/5], Step [300/1200], Loss: 0.1771, Accuracy: 98.00%\n",
            "Epoch [4/5], Step [600/1200], Loss: 0.0849, Accuracy: 98.00%\n",
            "Epoch [4/5], Step [900/1200], Loss: 0.0713, Accuracy: 100.00%\n",
            "Epoch [4/5], Step [1200/1200], Loss: 0.0772, Accuracy: 98.00%\n",
            "Epoch [5/5], Step [300/1200], Loss: 0.1019, Accuracy: 98.00%\n",
            "Epoch [5/5], Step [600/1200], Loss: 0.0266, Accuracy: 100.00%\n",
            "Epoch [5/5], Step [900/1200], Loss: 0.0933, Accuracy: 96.00%\n",
            "Epoch [5/5], Step [1200/1200], Loss: 0.0409, Accuracy: 98.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbAq7CT9Fv-U",
        "colab_type": "text"
      },
      "source": [
        "코드를 자꾸 수정하면서 계속 돌리니면 학습이 쌓여서 Accuracy가 95% 이상으로 처음부터 높게 나오는 것 같다...\n",
        "\n",
        "실제 모델 설계할 때도 주의해야 할 점인 듯하다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFHxTrkqFpQn",
        "colab_type": "text"
      },
      "source": [
        "# Test하기 \n",
        "\n",
        "이쪽도 솔직히 아직은 직접 만들 엄두는 안 나서...\n",
        "따라치기 정도만 해야겠다.\n",
        "\n",
        "Gradient 업데이트를 하면 안 되기 때문에 torch\\_no\\_grad를 써주고\n",
        "\n",
        "training 과정에서 예측을 하던 부분과\n",
        "\n",
        "Training의 진척도를 출력하는 부분을 똑같이 가져오면 되는 듯 하다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcC-DBpHGRTg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e714986-d6af-4498-8a86-999c103a8ff8"
      },
      "source": [
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  for i, (imgs, labels) in enumerate(test_loader):\n",
        "    imgs, labels = imgs.to(device), labels.to(device)\n",
        "    outputs = model(imgs)\n",
        "\n",
        "    _, argmax = torch.max(outputs, 1)\n",
        "    total += imgs.size(0)\n",
        "    correct += (labels == argmax).sum().item()\n",
        "\n",
        "  print(\"{}개 이미지에 대한 테스트 정확도: {:.3f}%\".format(total, correct/total*100))"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000개 이미지에 대한 테스트 정확도: 97.360%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yhokw7SuHpAq",
        "colab_type": "text"
      },
      "source": [
        "Overfitting 된 것일지 걱정을 좀 했는데 생각보다 좋은 모델을 만든 것 같다.\n",
        "\n",
        "이제 이걸 시각적으로 표현하는 것만 남았는데...\n",
        "솔직히 이건 따라쳐도 이해할 자신은 없다..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sawPOvcHkq5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "8a491a0c-3e7e-4785-8269-98d64e0db0b0"
      },
      "source": [
        "columns = 8\n",
        "rows = 4\n",
        "fig = plt.figure(figsize=(8,8))\n",
        "\n",
        "model.eval()\n",
        "for i in range(1, columns*rows+1):\n",
        "    data_idx = np.random.randint(len(test_data))\n",
        "    input_img = test_data[data_idx][0].unsqueeze(dim=0).to(device) \n",
        "  \n",
        "    output = model(input_img)\n",
        "    _, argmax = torch.max(output, 1)\n",
        "    pred = labels_map[argmax.item()]\n",
        "    label = labels_map[test_data[data_idx][1]]\n",
        "    \n",
        "    fig.add_subplot(rows, columns, i)\n",
        "    if pred == label:\n",
        "        plt.title(pred + '(O)')\n",
        "    else:\n",
        "        plt.title(pred + '(X)' + ' / ' + label)\n",
        "    plot_img = test_data[data_idx][0][0,:,:]\n",
        "    plt.imshow(plot_img, cmap='gray')\n",
        "    plt.axis('off')\n",
        "model.train()\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}