ResNet_basic_transfer_training.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YenaChoi00/ML-Final-Project/blob/main/ResNet_basic_transfer_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# AS usual, a bit of setup\n",
        "# If you need other libraries, you should import the libraries.\n",
        "\n",
        "import os, sys\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as datasets\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "9ySS6Rn5qwaA"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "# 텐서보드 로그를 저장할 디렉토리 지정\n",
        "log_dir = './logs'\n",
        "# SummaryWriter 객체 생성\n",
        "writer = SummaryWriter(log_dir)"
      ],
      "metadata": {
        "id": "q7sjldIfqx3r"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the device\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(device)"
      ],
      "metadata": {
        "id": "ObcFGJVjqy0N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2def394-a59b-4aa8-c654-b3f84759f4e4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#하이퍼 파라미터 튜닝\n",
        "\n",
        "CFG = {\n",
        "    'IMG_SIZE':32, #이미지 사이즈\n",
        "    'EPOCHS':20, #epoch\n",
        "    'LEARNING_RATE':2e-2, #학습률\n",
        "    'BATCH_SIZE':32, #배치사이즈\n",
        "    'SEED':41, #시드\n",
        "}"
      ],
      "metadata": {
        "id": "YlpVzWKGsj8s"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Loader**"
      ],
      "metadata": {
        "id": "uAtPo97sq6ws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transforms_cifar10 = transforms.Compose([transforms.Resize([CFG['IMG_SIZE'],CFG['IMG_SIZE']]),\n",
        "                                  transforms.ToTensor(),\n",
        "                                  transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "                                  \n",
        "                                 ])\n",
        "\n",
        "#transforms.RandomRotation(30),transforms.RandomHorizontalFlip()\n",
        "\n",
        "# resize, normalize만 들어가게. augmentation은 test에 적용하면 안됨.\n",
        "transforms_cifar10_test = transforms.Compose([transforms.Resize([CFG['IMG_SIZE'],CFG['IMG_SIZE']]),\n",
        "                                  transforms.ToTensor(),\n",
        "                                  transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "                                 ])\n",
        "\n",
        "from torch.utils.data import random_split\n",
        "\n",
        "# Train dataset\n",
        "trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms_cifar10)\n",
        "\n",
        "# Split trainset into train and validation sets\n",
        "train_size = int(0.8 * len(trainset))\n",
        "valid_size = len(trainset) - train_size\n",
        "trainset, validset = random_split(trainset, [train_size, valid_size])\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=CFG['BATCH_SIZE'], shuffle=True, num_workers=2)\n",
        "validloader = torch.utils.data.DataLoader(validset, batch_size=CFG['BATCH_SIZE'], shuffle=False, num_workers=2)\n",
        "\n",
        "# Test dataset\n",
        "testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms_cifar10_test)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=CFG['BATCH_SIZE'], shuffle=False, num_workers=2)\n",
        "\n",
        "# Classes of CIFAR-10 dataset\n",
        "classes = (\"plane\", \"car\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\")"
      ],
      "metadata": {
        "id": "Iow2z1Rwq4nU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cda68d50-5aed-4b1f-e75a-8c58f2c29288"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "train_len=(int(len(datasets.CIFAR10) * 0.75))\n",
        "val_len=int(len(datasets.CIFAR10) * 0.25)\n",
        "\n",
        "train_batches=len(trainloader)\n",
        "vali_batches=len(validloader)\n",
        "\n",
        "print('total train imgs:',train_len,'/ total train bathcnes:', train_batches)\n",
        "print('total valid imgs:',val_len,'/ total valid batches:', vali_batches)\n",
        "'''"
      ],
      "metadata": {
        "id": "BerwMnzOAAm6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "975cc576-4227-4bf8-f11e-b1da0346cebb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\ntrain_len=(int(len(datasets.CIFAR10) * 0.75))\\nval_len=int(len(datasets.CIFAR10) * 0.25)\\n\\ntrain_batches=len(trainloader)\\nvali_batches=len(validloader)\\n\\nprint('total train imgs:',train_len,'/ total train bathcnes:', train_batches)\\nprint('total valid imgs:',val_len,'/ total valid batches:', vali_batches)\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Examples of dataset\n",
        "\n",
        "def imshow(img):\n",
        "  img = img /2 + 0.5\n",
        "  npimg = img.numpy()\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "dataiter = iter(trainloader)\n",
        "imgs, labels = next(dataiter)\n",
        "\n",
        "imshow(torchvision.utils.make_grid(imgs))\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "metadata": {
        "id": "CpEJbWGgq8zA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "4bdca63f-1157-459d-ded4-c3bd48648630"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "plane   dog  deer  ship\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Your own CNN**"
      ],
      "metadata": {
        "id": "Jn4S3a_Pq-Lt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import datetime\n",
        "\n",
        "def train(model,optimizer,train_loader,scheduler,device):\n",
        "  model.to(device)\n",
        "  n=len(train_loader)\n",
        "  best_loss=10000\n",
        "\n",
        "  for epoch in range(1,CFG['EPOCHS']+1):  # 에포크 설정\n",
        "    model.train()\n",
        "    running_loss=0.0\n",
        "\n",
        "    for img,label in tqdm(iter(train_loader)):\n",
        "      img,label=img.to(device),label.to(device)\n",
        "      optimizer.zero_grad() # 배치마다 optimzier 초기화\n",
        "\n",
        "      logit=model(img)\n",
        "      loss=criterion(logit,label)\n",
        "\n",
        "      loss.backward()\n",
        "      optimizer.step() # 가중치 최적화\n",
        "      running_loss+=loss.item()\n",
        "\n",
        "    print('[%d] Train loss: %.10f' %(epoch, running_loss/len(train_loader)))\n",
        "\n",
        "    if scheduler is not None:\n",
        "      scheduler.step()\n",
        "\n",
        "    # validation set 평가\n",
        "    model.eval() # evaluatoin 과정에서 사용하지 않아야 하는 layer들을 알아서 off 시키도록 하는 함수\n",
        "    vali_loss=0.0\n",
        "    correct=0\n",
        "\n",
        "    with torch.no_grad(): # 파라미터 업데이트 안하기 때문에 no_grad 사용\n",
        "      for img,label in tqdm(iter(validloader)):\n",
        "        img,label=img.to(device),label.to(device)\n",
        "\n",
        "        logit=model(img)\n",
        "        vali_loss+= criterion(logit,label)\n",
        "        pred=logit.argmax(dim=1,keepdim=True)  # 10개의 class 중 가장 값이 높은 것을 예측 label로 추출\n",
        "        correct += pred.eq(label.view_as(pred)).sum().item()  # 예측값과 실제값이 맞으면 1 아니면 0으로 합산\n",
        "    vali_acc=100 * correct/len(validloader.dataset)\n",
        "\n",
        "    print('Vail set: Loss: {:.4f}, Accuracy: {}/{} ( {:.0f}%)\\n'.format(vali_loss / len(validloader), correct, len(validloader.dataset), 100 * correct / len(testloader.dataset)))\n",
        "    '''\n",
        "    if best_loss > vali_loss:\n",
        "      best_loss=vali_loss\n",
        "\n",
        "      # 현재 날짜와 시간을 문자열로 변환하여 파일 이름에 포함\n",
        "      now = datetime.datetime.now()\n",
        "      current_time = now.strftime(\"%Y%m%d_%H%M%S\")\n",
        "\n",
        "      # 파일 경로와 파일 이름을 조합하여 저장 경로 설정\n",
        "      save_path = '/content/drive/MyDrive/daycon/'\n",
        "      file_name = f\"best_model_{current_time}.pth\"\n",
        "      file_path = os.path.join(save_path, file_name)\n",
        "\n",
        "      # 모델 저장\n",
        "      torch.save(model.state_dict(), file_path)\n",
        "\n",
        "      print('Model Saved as %s' % file_name)\n",
        "'''\n"
      ],
      "metadata": {
        "id": "m2mcR92ZrDy8"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Loss function and optimizer**"
      ],
      "metadata": {
        "id": "doGpF3ZUq_Ks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss function and optimizer\n",
        "'''\n",
        "loss_fun = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n",
        "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n",
        "'''"
      ],
      "metadata": {
        "id": "629PUZJQrB4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "641f5381-e043-487a-b673-983e831c84b1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nloss_fun = nn.CrossEntropyLoss()\\noptimizer = torch.optim.Adam(net.parameters(), lr=0.001)\\nscheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train the network**"
      ],
      "metadata": {
        "id": "sWbVev-XrJ4g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "from tqdm.auto import tqdm\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# 사전 학습 모델 가져오기\n",
        "resnet=models.resnet50(pretrained=True).to(device)\n",
        "\n",
        "for param in resnet.parameters(): # 모든 parameter 고정\n",
        "  param.requires_grad=False\n",
        "\n",
        "# 직전 layer의 출력값을 마지막 layer의 입력으로 맞춰줌\n",
        "in_features=resnet.fc.in_features\n",
        "\n",
        "classifier=nn.Sequential(\n",
        "    nn.Linear(in_features,1024),\n",
        "    nn.ReLU(),\n",
        "    nn.Dropout(0.4),\n",
        "    nn.Linear(1024,10),\n",
        ")\n",
        "\n",
        "# resnet의 마지막 layer를 내가 정의한 classifier로 바꿈\n",
        "resnet.fc=classifier\n",
        "'''\n",
        "resnet=models.resnet50(pretrained=False).to(device)\n",
        "'''\n",
        "criterion=torch.nn.CrossEntropyLoss()\n",
        "optimizer=torch.optim.SGD(params=resnet.parameters(),lr=CFG['LEARNING_RATE'])\n",
        "scheduler=None\n",
        "\n",
        "resnet.to(device)"
      ],
      "metadata": {
        "id": "rG0TrJButkdX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ff9611f-8a08-4260-c780-acb9849970e1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Sequential(\n",
              "    (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.4, inplace=False)\n",
              "    (3): Linear(in_features=1024, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchsummary import summary\n",
        "summary(resnet,(3,32,32))"
      ],
      "metadata": {
        "id": "_AVLRJ3jt2lH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41973da0-0d45-42d9-aa1e-0bbebfad89ed"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 64, 16, 16]           9,408\n",
            "       BatchNorm2d-2           [-1, 64, 16, 16]             128\n",
            "              ReLU-3           [-1, 64, 16, 16]               0\n",
            "         MaxPool2d-4             [-1, 64, 8, 8]               0\n",
            "            Conv2d-5             [-1, 64, 8, 8]           4,096\n",
            "       BatchNorm2d-6             [-1, 64, 8, 8]             128\n",
            "              ReLU-7             [-1, 64, 8, 8]               0\n",
            "            Conv2d-8             [-1, 64, 8, 8]          36,864\n",
            "       BatchNorm2d-9             [-1, 64, 8, 8]             128\n",
            "             ReLU-10             [-1, 64, 8, 8]               0\n",
            "           Conv2d-11            [-1, 256, 8, 8]          16,384\n",
            "      BatchNorm2d-12            [-1, 256, 8, 8]             512\n",
            "           Conv2d-13            [-1, 256, 8, 8]          16,384\n",
            "      BatchNorm2d-14            [-1, 256, 8, 8]             512\n",
            "             ReLU-15            [-1, 256, 8, 8]               0\n",
            "       Bottleneck-16            [-1, 256, 8, 8]               0\n",
            "           Conv2d-17             [-1, 64, 8, 8]          16,384\n",
            "      BatchNorm2d-18             [-1, 64, 8, 8]             128\n",
            "             ReLU-19             [-1, 64, 8, 8]               0\n",
            "           Conv2d-20             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-21             [-1, 64, 8, 8]             128\n",
            "             ReLU-22             [-1, 64, 8, 8]               0\n",
            "           Conv2d-23            [-1, 256, 8, 8]          16,384\n",
            "      BatchNorm2d-24            [-1, 256, 8, 8]             512\n",
            "             ReLU-25            [-1, 256, 8, 8]               0\n",
            "       Bottleneck-26            [-1, 256, 8, 8]               0\n",
            "           Conv2d-27             [-1, 64, 8, 8]          16,384\n",
            "      BatchNorm2d-28             [-1, 64, 8, 8]             128\n",
            "             ReLU-29             [-1, 64, 8, 8]               0\n",
            "           Conv2d-30             [-1, 64, 8, 8]          36,864\n",
            "      BatchNorm2d-31             [-1, 64, 8, 8]             128\n",
            "             ReLU-32             [-1, 64, 8, 8]               0\n",
            "           Conv2d-33            [-1, 256, 8, 8]          16,384\n",
            "      BatchNorm2d-34            [-1, 256, 8, 8]             512\n",
            "             ReLU-35            [-1, 256, 8, 8]               0\n",
            "       Bottleneck-36            [-1, 256, 8, 8]               0\n",
            "           Conv2d-37            [-1, 128, 8, 8]          32,768\n",
            "      BatchNorm2d-38            [-1, 128, 8, 8]             256\n",
            "             ReLU-39            [-1, 128, 8, 8]               0\n",
            "           Conv2d-40            [-1, 128, 4, 4]         147,456\n",
            "      BatchNorm2d-41            [-1, 128, 4, 4]             256\n",
            "             ReLU-42            [-1, 128, 4, 4]               0\n",
            "           Conv2d-43            [-1, 512, 4, 4]          65,536\n",
            "      BatchNorm2d-44            [-1, 512, 4, 4]           1,024\n",
            "           Conv2d-45            [-1, 512, 4, 4]         131,072\n",
            "      BatchNorm2d-46            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-47            [-1, 512, 4, 4]               0\n",
            "       Bottleneck-48            [-1, 512, 4, 4]               0\n",
            "           Conv2d-49            [-1, 128, 4, 4]          65,536\n",
            "      BatchNorm2d-50            [-1, 128, 4, 4]             256\n",
            "             ReLU-51            [-1, 128, 4, 4]               0\n",
            "           Conv2d-52            [-1, 128, 4, 4]         147,456\n",
            "      BatchNorm2d-53            [-1, 128, 4, 4]             256\n",
            "             ReLU-54            [-1, 128, 4, 4]               0\n",
            "           Conv2d-55            [-1, 512, 4, 4]          65,536\n",
            "      BatchNorm2d-56            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-57            [-1, 512, 4, 4]               0\n",
            "       Bottleneck-58            [-1, 512, 4, 4]               0\n",
            "           Conv2d-59            [-1, 128, 4, 4]          65,536\n",
            "      BatchNorm2d-60            [-1, 128, 4, 4]             256\n",
            "             ReLU-61            [-1, 128, 4, 4]               0\n",
            "           Conv2d-62            [-1, 128, 4, 4]         147,456\n",
            "      BatchNorm2d-63            [-1, 128, 4, 4]             256\n",
            "             ReLU-64            [-1, 128, 4, 4]               0\n",
            "           Conv2d-65            [-1, 512, 4, 4]          65,536\n",
            "      BatchNorm2d-66            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-67            [-1, 512, 4, 4]               0\n",
            "       Bottleneck-68            [-1, 512, 4, 4]               0\n",
            "           Conv2d-69            [-1, 128, 4, 4]          65,536\n",
            "      BatchNorm2d-70            [-1, 128, 4, 4]             256\n",
            "             ReLU-71            [-1, 128, 4, 4]               0\n",
            "           Conv2d-72            [-1, 128, 4, 4]         147,456\n",
            "      BatchNorm2d-73            [-1, 128, 4, 4]             256\n",
            "             ReLU-74            [-1, 128, 4, 4]               0\n",
            "           Conv2d-75            [-1, 512, 4, 4]          65,536\n",
            "      BatchNorm2d-76            [-1, 512, 4, 4]           1,024\n",
            "             ReLU-77            [-1, 512, 4, 4]               0\n",
            "       Bottleneck-78            [-1, 512, 4, 4]               0\n",
            "           Conv2d-79            [-1, 256, 4, 4]         131,072\n",
            "      BatchNorm2d-80            [-1, 256, 4, 4]             512\n",
            "             ReLU-81            [-1, 256, 4, 4]               0\n",
            "           Conv2d-82            [-1, 256, 2, 2]         589,824\n",
            "      BatchNorm2d-83            [-1, 256, 2, 2]             512\n",
            "             ReLU-84            [-1, 256, 2, 2]               0\n",
            "           Conv2d-85           [-1, 1024, 2, 2]         262,144\n",
            "      BatchNorm2d-86           [-1, 1024, 2, 2]           2,048\n",
            "           Conv2d-87           [-1, 1024, 2, 2]         524,288\n",
            "      BatchNorm2d-88           [-1, 1024, 2, 2]           2,048\n",
            "             ReLU-89           [-1, 1024, 2, 2]               0\n",
            "       Bottleneck-90           [-1, 1024, 2, 2]               0\n",
            "           Conv2d-91            [-1, 256, 2, 2]         262,144\n",
            "      BatchNorm2d-92            [-1, 256, 2, 2]             512\n",
            "             ReLU-93            [-1, 256, 2, 2]               0\n",
            "           Conv2d-94            [-1, 256, 2, 2]         589,824\n",
            "      BatchNorm2d-95            [-1, 256, 2, 2]             512\n",
            "             ReLU-96            [-1, 256, 2, 2]               0\n",
            "           Conv2d-97           [-1, 1024, 2, 2]         262,144\n",
            "      BatchNorm2d-98           [-1, 1024, 2, 2]           2,048\n",
            "             ReLU-99           [-1, 1024, 2, 2]               0\n",
            "      Bottleneck-100           [-1, 1024, 2, 2]               0\n",
            "          Conv2d-101            [-1, 256, 2, 2]         262,144\n",
            "     BatchNorm2d-102            [-1, 256, 2, 2]             512\n",
            "            ReLU-103            [-1, 256, 2, 2]               0\n",
            "          Conv2d-104            [-1, 256, 2, 2]         589,824\n",
            "     BatchNorm2d-105            [-1, 256, 2, 2]             512\n",
            "            ReLU-106            [-1, 256, 2, 2]               0\n",
            "          Conv2d-107           [-1, 1024, 2, 2]         262,144\n",
            "     BatchNorm2d-108           [-1, 1024, 2, 2]           2,048\n",
            "            ReLU-109           [-1, 1024, 2, 2]               0\n",
            "      Bottleneck-110           [-1, 1024, 2, 2]               0\n",
            "          Conv2d-111            [-1, 256, 2, 2]         262,144\n",
            "     BatchNorm2d-112            [-1, 256, 2, 2]             512\n",
            "            ReLU-113            [-1, 256, 2, 2]               0\n",
            "          Conv2d-114            [-1, 256, 2, 2]         589,824\n",
            "     BatchNorm2d-115            [-1, 256, 2, 2]             512\n",
            "            ReLU-116            [-1, 256, 2, 2]               0\n",
            "          Conv2d-117           [-1, 1024, 2, 2]         262,144\n",
            "     BatchNorm2d-118           [-1, 1024, 2, 2]           2,048\n",
            "            ReLU-119           [-1, 1024, 2, 2]               0\n",
            "      Bottleneck-120           [-1, 1024, 2, 2]               0\n",
            "          Conv2d-121            [-1, 256, 2, 2]         262,144\n",
            "     BatchNorm2d-122            [-1, 256, 2, 2]             512\n",
            "            ReLU-123            [-1, 256, 2, 2]               0\n",
            "          Conv2d-124            [-1, 256, 2, 2]         589,824\n",
            "     BatchNorm2d-125            [-1, 256, 2, 2]             512\n",
            "            ReLU-126            [-1, 256, 2, 2]               0\n",
            "          Conv2d-127           [-1, 1024, 2, 2]         262,144\n",
            "     BatchNorm2d-128           [-1, 1024, 2, 2]           2,048\n",
            "            ReLU-129           [-1, 1024, 2, 2]               0\n",
            "      Bottleneck-130           [-1, 1024, 2, 2]               0\n",
            "          Conv2d-131            [-1, 256, 2, 2]         262,144\n",
            "     BatchNorm2d-132            [-1, 256, 2, 2]             512\n",
            "            ReLU-133            [-1, 256, 2, 2]               0\n",
            "          Conv2d-134            [-1, 256, 2, 2]         589,824\n",
            "     BatchNorm2d-135            [-1, 256, 2, 2]             512\n",
            "            ReLU-136            [-1, 256, 2, 2]               0\n",
            "          Conv2d-137           [-1, 1024, 2, 2]         262,144\n",
            "     BatchNorm2d-138           [-1, 1024, 2, 2]           2,048\n",
            "            ReLU-139           [-1, 1024, 2, 2]               0\n",
            "      Bottleneck-140           [-1, 1024, 2, 2]               0\n",
            "          Conv2d-141            [-1, 512, 2, 2]         524,288\n",
            "     BatchNorm2d-142            [-1, 512, 2, 2]           1,024\n",
            "            ReLU-143            [-1, 512, 2, 2]               0\n",
            "          Conv2d-144            [-1, 512, 1, 1]       2,359,296\n",
            "     BatchNorm2d-145            [-1, 512, 1, 1]           1,024\n",
            "            ReLU-146            [-1, 512, 1, 1]               0\n",
            "          Conv2d-147           [-1, 2048, 1, 1]       1,048,576\n",
            "     BatchNorm2d-148           [-1, 2048, 1, 1]           4,096\n",
            "          Conv2d-149           [-1, 2048, 1, 1]       2,097,152\n",
            "     BatchNorm2d-150           [-1, 2048, 1, 1]           4,096\n",
            "            ReLU-151           [-1, 2048, 1, 1]               0\n",
            "      Bottleneck-152           [-1, 2048, 1, 1]               0\n",
            "          Conv2d-153            [-1, 512, 1, 1]       1,048,576\n",
            "     BatchNorm2d-154            [-1, 512, 1, 1]           1,024\n",
            "            ReLU-155            [-1, 512, 1, 1]               0\n",
            "          Conv2d-156            [-1, 512, 1, 1]       2,359,296\n",
            "     BatchNorm2d-157            [-1, 512, 1, 1]           1,024\n",
            "            ReLU-158            [-1, 512, 1, 1]               0\n",
            "          Conv2d-159           [-1, 2048, 1, 1]       1,048,576\n",
            "     BatchNorm2d-160           [-1, 2048, 1, 1]           4,096\n",
            "            ReLU-161           [-1, 2048, 1, 1]               0\n",
            "      Bottleneck-162           [-1, 2048, 1, 1]               0\n",
            "          Conv2d-163            [-1, 512, 1, 1]       1,048,576\n",
            "     BatchNorm2d-164            [-1, 512, 1, 1]           1,024\n",
            "            ReLU-165            [-1, 512, 1, 1]               0\n",
            "          Conv2d-166            [-1, 512, 1, 1]       2,359,296\n",
            "     BatchNorm2d-167            [-1, 512, 1, 1]           1,024\n",
            "            ReLU-168            [-1, 512, 1, 1]               0\n",
            "          Conv2d-169           [-1, 2048, 1, 1]       1,048,576\n",
            "     BatchNorm2d-170           [-1, 2048, 1, 1]           4,096\n",
            "            ReLU-171           [-1, 2048, 1, 1]               0\n",
            "      Bottleneck-172           [-1, 2048, 1, 1]               0\n",
            "AdaptiveAvgPool2d-173           [-1, 2048, 1, 1]               0\n",
            "          Linear-174                 [-1, 1024]       2,098,176\n",
            "            ReLU-175                 [-1, 1024]               0\n",
            "         Dropout-176                 [-1, 1024]               0\n",
            "          Linear-177                   [-1, 10]          10,250\n",
            "================================================================\n",
            "Total params: 25,616,458\n",
            "Trainable params: 2,108,426\n",
            "Non-trainable params: 23,508,032\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 5.89\n",
            "Params size (MB): 97.72\n",
            "Estimated Total Size (MB): 103.62\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 구글 드라이브와 연동\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ejl_dj7FOmEt",
        "outputId": "4f5ab125-a358-4a4b-ae94-3969cfac59ee"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train(resnet, optimizer, trainloader, scheduler, device)"
      ],
      "metadata": {
        "id": "oq2Osl24tWqy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      },
      "execution_count": 30,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/1250 [00:00<?, ?it/s]"
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          "text": [
            "[1] Train loss: 1.7601779551\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
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        {
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          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.5142, Accuracy: 4814/10000 ( 48%)\n",
            "\n"
          ]
        },
        {
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          "text": [
            "[2] Train loss: 1.5620951292\n"
          ]
        },
        {
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              "  0%|          | 0/313 [00:00<?, ?it/s]"
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          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.4565, Accuracy: 4978/10000 ( 50%)\n",
            "\n"
          ]
        },
        {
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              "  0%|          | 0/1250 [00:00<?, ?it/s]"
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          "text": [
            "[3] Train loss: 1.5023041506\n"
          ]
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        {
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              "  0%|          | 0/313 [00:00<?, ?it/s]"
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          "text": [
            "Vail set: Loss: 1.4129, Accuracy: 5106/10000 ( 51%)\n",
            "\n"
          ]
        },
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            "[4] Train loss: 1.4683545475\n"
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          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3823, Accuracy: 5202/10000 ( 52%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adc8fb4790ab4fff822e3a1a45ce2365"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5] Train loss: 1.4351970059\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7a9d37d602d40a7a926089bd0b35724"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3576, Accuracy: 5336/10000 ( 53%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "656aebcacdfd4464842fb97f8e24b9bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6] Train loss: 1.4147419268\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c5bdf1c1382405db62886e1550b901a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3772, Accuracy: 5274/10000 ( 53%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "771d37fcf5d045b094e4508d8f0b3c7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7] Train loss: 1.3989655664\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c34107724ecb4de1b3107a7da23480f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3331, Accuracy: 5348/10000 ( 53%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39e5fa9e275e408b8a5bac69c4a844ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[8] Train loss: 1.3793619026\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13a175c412bb4f6daf34b2671499eb8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3092, Accuracy: 5504/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a2181338a4a40a4812311d882fbc0b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9] Train loss: 1.3635640103\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f88fa38f8c324d09a23cb374d89f96f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3534, Accuracy: 5364/10000 ( 54%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f8de774ab854eaeb828aee7ec412a63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10] Train loss: 1.3520288908\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ca5a4ef554a4ed69ffca289eba0820d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3247, Accuracy: 5435/10000 ( 54%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2517ab0dce54994b86840edec6998ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[11] Train loss: 1.3430984725\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab2b5e315b4e451fafc7c38e6b6e9a89"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2996, Accuracy: 5481/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31ddfbb0637642cb844c1d576b42526f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12] Train loss: 1.3307904000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "593186dda1e4440990942a5f6922a139"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2944, Accuracy: 5502/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9372c2465179450ea66fcef15606d33a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13] Train loss: 1.3210580757\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a4aa28ff35c44dbb7d60047f509338c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2808, Accuracy: 5563/10000 ( 56%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2bc19b322854282b94a8ec32142b472"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14] Train loss: 1.3065303909\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a1a1a721288458491305157212ee25b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3041, Accuracy: 5512/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fffbd538805431db0f5412c989fa687"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[15] Train loss: 1.2974323257\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09f2c03a252c463ca11497e3bce431e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3105, Accuracy: 5543/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c852ee28c0c4280b77ab23e4344b1f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16] Train loss: 1.2904457392\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d68736f8eb14d819d0f10fa548fd279"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2918, Accuracy: 5487/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51dca87cce864cb5879e95effdd03712"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[17] Train loss: 1.2833822998\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e943e7b95a08429586664c58eb4a7cf0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2847, Accuracy: 5544/10000 ( 55%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f84af04c8b849388eed368649042174"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18] Train loss: 1.2746066782\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9f2c766f79b43559f63b814df8417c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2641, Accuracy: 5613/10000 ( 56%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c5dac04ce2548e8be1915cc8077a644"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[19] Train loss: 1.2666753958\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10612f00621b4fc2b14ebb6e7dd69b4e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.2796, Accuracy: 5569/10000 ( 56%)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c659a1f1a87442c5b2b15290716ac8f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[20] Train loss: 1.2563503621\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67dab71fb7854607b012c2a5115c1d78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vail set: Loss: 1.3099, Accuracy: 5578/10000 ( 56%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Validate the Network**"
      ],
      "metadata": {
        "id": "nAk_7j4ut6r9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(model,test_loader,device):\n",
        "  model.eval()  #모델을 평가 모드로 설정\n",
        "  model_pred=[]   # 예측값 저장\n",
        "\n",
        "  with torch.no_grad(): # 기울기 계산을 비활성화 (모델의 파라미터 업데이트를 방지하여 예측 과정에서 메모리를 절약하고 속도를 향상)\n",
        "    for img in tqdm(iter(test_loader)): # 테스트 데이터로 구성된 데이터로더인 test_loader에서 이미지를 반복적으로 가져옴\n",
        "      img=img.to(device)\n",
        "\n",
        "      pred_logit=model(img)\n",
        "      pred_logit=pred_logit.argmax(dim=1,keepdim=True).squeeze(1) # 예측 로짓의 가장 큰 값을 가진 클래스를 선택하여 예측된 라벨로 변환\n",
        "      model_pred.extend(pred_logit.tolist())    # 예측된 라벨을 model_pred 리스트에 추가\n",
        "\n",
        "  return model_pred # 모든 예측된 라벨이 저장된 model_pred 리스트를 반환"
      ],
      "metadata": {
        "id": "GwLnTnvOtZgq"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime\n",
        "\n",
        "# 현재 날짜와 시간을 문자열로 변환하여 파일 이름에 포함\n",
        "now = datetime.datetime.now()\n",
        "current_time = now.strftime(\"%Y%m%d_%H%M%S\")\n",
        "\n",
        "# 파일 경로와 파일 이름을 조합하여 저장 경로 설정\n",
        "save_path = '/content/drive/MyDrive/daycon/'\n",
        "file_name = f\"best_model_{current_time}.pth\"\n",
        "file_path = os.path.join(save_path, file_name)\n",
        "\n",
        "checkpoint=torch.load(file_path)\n",
        "Predictor=resnet.to(device)\n",
        "Predictor.load_state_dict(checkpoint)\n",
        "\n",
        "preds=predict(Predictor,testloader,device)\n",
        "preds[0:5]    # 테스트 데이터의 첫 5개 샘플에 대한 예측값"
      ],
      "metadata": {
        "id": "VcZbaDi8tc-_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "95e52b11-0f9b-41bb-ad8d-580dbdb4b489"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-cfd871164080>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mcheckpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001b[0m\n\u001b[1;32m    789\u001b[0m         \u001b[0mpickle_load_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    792\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m             \u001b[0;31m# The zipfile reader is going to advance the current file position.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'w'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/daycon/best_model_20230602_204810.pth'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Test the network**"
      ],
      "metadata": {
        "id": "CJsid5nyrINS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the trained model with overall test dataset\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "class_labels = [] # 각 이미지의 실제 클래스 레이블을 저장\n",
        "class_preds = [] #모델의 예측 결과인 클래스 확률을 저장\n",
        "\n",
        "for data in testloader:\n",
        "    # Load the data\n",
        "    inputs_test, labels_test = data\n",
        "    inputs_test = inputs_test.to(device)\n",
        "    labels_test = labels_test.to(device)\n",
        "\n",
        "    # Estimate the output using the trained network\n",
        "    outputs_test = resnet(inputs_test)\n",
        "    _, predicted = torch.max(outputs_test.data, 1)\n",
        "    \n",
        "    # Calculate the accuracy\n",
        "    total += labels_test.size(0)\n",
        "    correct += (predicted == labels_test).sum()\n",
        "\n",
        "\n",
        "# Final accuracy\n",
        "print('Accuracy of the network on the 10,000 test images: %d %%' % (100 * correct / total))\n",
        "\n",
        "## [SimpleNet / Training 5 epochs] Accuracy of the network on the 10,000 test images: 9 %\n",
        "## [VGG11 / Training 5 epochs] Accuracy of the network on the 10,000 test images: 11 %"
      ],
      "metadata": {
        "id": "-YVz8yJtrQhI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "57d03c9b-279a-4eb2-8349-c7d5026b9a8f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-f5f7f65b9ac7>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mclass_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#모델의 예측 결과인 클래스 확률을 저장\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtestloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m# Load the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0minputs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'testloader' is not defined"
          ]
        }
      ]
    }
  ]
}