code_practices/ResNet18_CIFAR10_Training_with_Input_Mixup_and_Label_Smoothing.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/ndb796/Deep-Learning-Paper-Review-and-Practice/blob/master/code_practices/ResNet18_CIFAR10_Training_with_Input_Mixup_and_Label_Smoothing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2SSsQMSknm2"
      },
      "source": [
        "#### <b>ResNet18 모델 아키텍처 정의</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpUcgk5xkgGZ"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import os\n",
        "\n",
        "\n",
        "# ResNet18을 위해 최대한 간단히 수정한 BasicBlock 클래스\n",
        "class BasicBlock(nn.Module):\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(BasicBlock, self).__init__()\n",
        "\n",
        "        # 3x3 필터를 사용 (너비와 높이를 줄일 때는 stride 값 조절)\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n",
        "\n",
        "        # 3x3 필터를 사용 (패딩을 1만큼 주기 때문에 너비와 높이가 동일)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n",
        "\n",
        "        self.shortcut = nn.Sequential() # identity인 경우\n",
        "        if stride != 1: # stride가 1이 아니라면, identity mapping이 아닌 경우\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.bn2(self.conv2(out))\n",
        "        out += self.shortcut(x) # (핵심) skip connection\n",
        "        out = F.relu(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "# ResNet 클래스 정의\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=10):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 64\n",
        "\n",
        "        # 64개의 3x3 필터(filter)를 사용\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
        "        self.linear = nn.Linear(512, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
        "        strides = [stride] + [1] * (num_blocks - 1)\n",
        "        layers = []\n",
        "        for stride in strides:\n",
        "            layers.append(block(self.in_planes, planes, stride))\n",
        "            self.in_planes = planes # 다음 레이어를 위해 채널 수 변경\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out) # 출력: [batch_size, 512, 4, 4]\n",
        "        out = F.avg_pool2d(out, 4) # 출력: [batch_size, 512, 1, 1]\n",
        "        out = out.view(out.size(0), -1) # 출력: [batch_size, 512]\n",
        "        out = self.linear(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "# ResNet18 함수 정의\n",
        "def ResNet18():\n",
        "    return ResNet(BasicBlock, [2, 2, 2, 2])"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCNacrgtktlr"
      },
      "source": [
        "#### <b>데이터셋(Dataset) 다운로드 및 불러오기</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmmQZ8p5kq_C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
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        },
        "outputId": "29d6b9fd-ae96-4e82-a161-02136d5eb569"
      },
      "source": [
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=2)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUXHwcAS9UhB"
      },
      "source": [
        "#### <b>필요한 라이브러리 구현</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWXXtLSn9WUe"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "mixup_alpha = 1.0\n",
        "\n",
        "def mixup_data(x, y):\n",
        "    lam = np.random.beta(mixup_alpha, mixup_alpha)\n",
        "    batch_size = x.size()[0]\n",
        "    index = torch.randperm(batch_size).cuda()\n",
        "    mixed_x = lam * x + (1 - lam) * x[index]\n",
        "    y_a, y_b = y, y[index]\n",
        "    return mixed_x, y_a, y_b, lam\n",
        "\n",
        "\n",
        "def mixup_criterion(criterion, pred, y_a, y_b, lam):\n",
        "    return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)\n",
        "\n",
        "\n",
        "class LabelSmoothingCrossEntropy(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LabelSmoothingCrossEntropy, self).__init__()\n",
        "    def forward(self, y, targets, smoothing=0.1):\n",
        "        confidence = 1. - smoothing\n",
        "        log_probs = F.log_softmax(y, dim=-1) # 예측 확률 계산\n",
        "        true_probs = torch.zeros_like(log_probs)\n",
        "        true_probs.fill_(smoothing / (y.shape[1] - 1))\n",
        "        true_probs.scatter_(1, targets.data.unsqueeze(1), confidence) # 정답 인덱스의 정답 확률을 confidence로 변경\n",
        "        return torch.mean(torch.sum(true_probs * -log_probs, dim=-1)) # negative log likelihood"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dl1-47E7pHD_"
      },
      "source": [
        "#### <b>환경 설정 및 학습(Training) 함수 정의</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhm_eVykk-Z8"
      },
      "source": [
        "device = 'cuda'\n",
        "\n",
        "net = ResNet18()\n",
        "net = net.to(device)\n",
        "\n",
        "learning_rate = 0.1\n",
        "file_name = 'resnet18_cifar10.pth'\n",
        "\n",
        "criterion = LabelSmoothingCrossEntropy()\n",
        "optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0002)\n",
        "\n",
        "\n",
        "def train(epoch):\n",
        "    print('\\n[ Train epoch: %d ]' % epoch)\n",
        "    net.train()\n",
        "    train_loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    for batch_idx, (inputs, targets) in enumerate(train_loader):\n",
        "        inputs, targets = inputs.to(device), targets.to(device)\n",
        "        inputs, targets_a, targets_b, lam = mixup_data(inputs, targets)\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        loss = mixup_criterion(criterion, outputs, targets_a, targets_b, lam)\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        train_loss += loss.item()\n",
        "        _, predicted = outputs.max(1)\n",
        "\n",
        "        total += targets.size(0)\n",
        "        current_correct = (lam * predicted.eq(targets_a).sum().item() + (1 - lam) * predicted.eq(targets_b).sum().item())\n",
        "        correct += current_correct\n",
        "\n",
        "        if batch_idx % 100 == 0:\n",
        "            print('\\nCurrent batch:', str(batch_idx))\n",
        "            print('Current batch average train accuracy:', current_correct / targets.size(0))\n",
        "            print('Current batch average train loss:', loss.item() / targets.size(0))\n",
        "\n",
        "    print('\\nTotal average train accuarcy:', correct / total)\n",
        "    print('Total average train loss:', train_loss / total)\n",
        "\n",
        "\n",
        "def test(epoch):\n",
        "    print('\\n[ Test epoch: %d ]' % epoch)\n",
        "    net.eval()\n",
        "    loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    for batch_idx, (inputs, targets) in enumerate(test_loader):\n",
        "        inputs, targets = inputs.to(device), targets.to(device)\n",
        "        total += targets.size(0)\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        loss += criterion(outputs, targets).item()\n",
        "\n",
        "        _, predicted = outputs.max(1)\n",
        "        correct += predicted.eq(targets).sum().item()\n",
        "\n",
        "    print('\\nTotal average test accuarcy:', correct / total)\n",
        "    print('Total average test loss:', loss / total)\n",
        "\n",
        "    state = {\n",
        "        'net': net.state_dict()\n",
        "    }\n",
        "    if not os.path.isdir('checkpoint'):\n",
        "        os.mkdir('checkpoint')\n",
        "    torch.save(state, './checkpoint/' + file_name)\n",
        "    print('Model Saved!')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mv5z7CEMRrn"
      },
      "source": [
        "#### <b>학습(Training) 진행</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4voLj7TKlaB1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e2f03bb-0de5-46f2-f284-8061098a0150"
      },
      "source": [
        "import time\n",
        "\n",
        "\n",
        "def adjust_learning_rate(optimizer, epoch):\n",
        "    lr = learning_rate\n",
        "    if epoch >= 50:\n",
        "        lr /= 10\n",
        "    if epoch >= 100:\n",
        "        lr /= 10\n",
        "    for param_group in optimizer.param_groups:\n",
        "        param_group['lr'] = lr\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "for epoch in range(0, 150):\n",
        "    adjust_learning_rate(optimizer, epoch)\n",
        "    train(epoch)\n",
        "    test(epoch)\n",
        "    print('\\nTime elapsed:', time.time() - start_time)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[ Train epoch: 0 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.0676012122688769\n",
            "Current batch average train loss: 0.018984787166118622\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.186298824527742\n",
            "Current batch average train loss: 0.01728200539946556\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.3010016673709568\n",
            "Current batch average train loss: 0.0157657228410244\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.36779025048328734\n",
            "Current batch average train loss: 0.015063154511153698\n",
            "\n",
            "Total average train accuarcy: 0.24367330731643452\n",
            "Total average train loss: 0.016990246500968934\n",
            "\n",
            "[ Test epoch: 0 ]\n",
            "\n",
            "Total average test accuarcy: 0.3424\n",
            "Total average test loss: 0.01910388306379318\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 62.836387157440186\n",
            "\n",
            "[ Train epoch: 1 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.42075508090404934\n",
            "Current batch average train loss: 0.01484421081840992\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.3048462339166179\n",
            "Current batch average train loss: 0.015838202089071274\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.3217610598301948\n",
            "Current batch average train loss: 0.01561430562287569\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.359907188789915\n",
            "Current batch average train loss: 0.015489274635910988\n",
            "\n",
            "Total average train accuarcy: 0.3488097154641089\n",
            "Total average train loss: 0.015258726615905762\n",
            "\n",
            "[ Test epoch: 1 ]\n",
            "\n",
            "Total average test accuarcy: 0.3912\n",
            "Total average test loss: 0.01791668162345886\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 126.4758734703064\n",
            "\n",
            "[ Train epoch: 2 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.3902821549679593\n",
            "Current batch average train loss: 0.014834233559668064\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.29278435406418835\n",
            "Current batch average train loss: 0.01611459068953991\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.36840534949114256\n",
            "Current batch average train loss: 0.015621483325958252\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.31427664990221515\n",
            "Current batch average train loss: 0.01590396650135517\n",
            "\n",
            "Total average train accuarcy: 0.404441203702364\n",
            "Total average train loss: 0.014561697549819947\n",
            "\n",
            "[ Test epoch: 2 ]\n",
            "\n",
            "Total average test accuarcy: 0.4588\n",
            "Total average test loss: 0.017025049936771394\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 190.1292600631714\n",
            "\n",
            "[ Train epoch: 3 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.35600330708177275\n",
            "Current batch average train loss: 0.01519140973687172\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.651738012117742\n",
            "Current batch average train loss: 0.011395292356610298\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.48614546678907455\n",
            "Current batch average train loss: 0.013450506143271923\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4373574659819362\n",
            "Current batch average train loss: 0.014534931629896164\n",
            "\n",
            "Total average train accuarcy: 0.4544059776413456\n",
            "Total average train loss: 0.013882053232192992\n",
            "\n",
            "[ Test epoch: 3 ]\n",
            "\n",
            "Total average test accuarcy: 0.5289\n",
            "Total average test loss: 0.015759695172309875\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 253.71214532852173\n",
            "\n",
            "[ Train epoch: 4 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.3743260139455444\n",
            "Current batch average train loss: 0.014908824115991592\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.43029424569754293\n",
            "Current batch average train loss: 0.014474442228674889\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5277369076206723\n",
            "Current batch average train loss: 0.013226015493273735\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.45027510682559546\n",
            "Current batch average train loss: 0.014361130073666573\n",
            "\n",
            "Total average train accuarcy: 0.48014965412785704\n",
            "Total average train loss: 0.013585320439338685\n",
            "\n",
            "[ Test epoch: 4 ]\n",
            "\n",
            "Total average test accuarcy: 0.5717\n",
            "Total average test loss: 0.015029364848136903\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 317.4326469898224\n",
            "\n",
            "[ Train epoch: 5 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6541191291141639\n",
            "Current batch average train loss: 0.011060086078941822\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.27458753183429485\n",
            "Current batch average train loss: 0.016431845724582672\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4775607403155261\n",
            "Current batch average train loss: 0.013927871361374855\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4183847210635727\n",
            "Current batch average train loss: 0.014572057873010635\n",
            "\n",
            "Total average train accuarcy: 0.4982421365848716\n",
            "Total average train loss: 0.013333018417358398\n",
            "\n",
            "[ Test epoch: 5 ]\n",
            "\n",
            "Total average test accuarcy: 0.6397\n",
            "Total average test loss: 0.013634457075595855\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 381.2106065750122\n",
            "\n",
            "[ Train epoch: 6 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6100146160729806\n",
            "Current batch average train loss: 0.012028664350509644\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7146981906995352\n",
            "Current batch average train loss: 0.009072496555745602\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.41218440899191927\n",
            "Current batch average train loss: 0.014734212309122086\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4227369572161287\n",
            "Current batch average train loss: 0.014469994232058525\n",
            "\n",
            "Total average train accuarcy: 0.5379412829419664\n",
            "Total average train loss: 0.01276566632270813\n",
            "\n",
            "[ Test epoch: 6 ]\n",
            "\n",
            "Total average test accuarcy: 0.5705\n",
            "Total average test loss: 0.015035594379901885\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 444.80924940109253\n",
            "\n",
            "[ Train epoch: 7 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.44348206644349364\n",
            "Current batch average train loss: 0.014346424490213394\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.656289236899926\n",
            "Current batch average train loss: 0.010956836864352226\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5102044046632592\n",
            "Current batch average train loss: 0.013654012233018875\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.5409563826242845\n",
            "Current batch average train loss: 0.013552510179579258\n",
            "\n",
            "Total average train accuarcy: 0.5454506758730714\n",
            "Total average train loss: 0.012680412681102753\n",
            "\n",
            "[ Test epoch: 7 ]\n",
            "\n",
            "Total average test accuarcy: 0.6682\n",
            "Total average test loss: 0.01292356172800064\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 508.39383125305176\n",
            "\n",
            "[ Train epoch: 8 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.453822667564615\n",
            "Current batch average train loss: 0.01406262256205082\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6444730113563673\n",
            "Current batch average train loss: 0.01062305923551321\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.42499670576329573\n",
            "Current batch average train loss: 0.014227716252207756\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4133086772139217\n",
            "Current batch average train loss: 0.014001043513417244\n",
            "\n",
            "Total average train accuarcy: 0.5668680733114774\n",
            "Total average train loss: 0.012341257393360137\n",
            "\n",
            "[ Test epoch: 8 ]\n",
            "\n",
            "Total average test accuarcy: 0.7035\n",
            "Total average test loss: 0.012585211062431335\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 571.9801213741302\n",
            "\n",
            "[ Train epoch: 9 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6202876766149835\n",
            "Current batch average train loss: 0.0116799958050251\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.49345294117109506\n",
            "Current batch average train loss: 0.013450956903398037\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6226716870479085\n",
            "Current batch average train loss: 0.01169953215867281\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4054227933247678\n",
            "Current batch average train loss: 0.014529934152960777\n",
            "\n",
            "Total average train accuarcy: 0.5855362428695453\n",
            "Total average train loss: 0.012043875457048416\n",
            "\n",
            "[ Test epoch: 9 ]\n",
            "\n",
            "Total average test accuarcy: 0.7419\n",
            "Total average test loss: 0.01143278819322586\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 635.600715637207\n",
            "\n",
            "[ Train epoch: 10 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.7812294255908361\n",
            "Current batch average train loss: 0.007991970516741276\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6716836947919662\n",
            "Current batch average train loss: 0.011380096897482872\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4276883781476719\n",
            "Current batch average train loss: 0.013933373615145683\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.739998271030397\n",
            "Current batch average train loss: 0.008741522207856178\n",
            "\n",
            "Total average train accuarcy: 0.6065256208579198\n",
            "Total average train loss: 0.011753629404306411\n",
            "\n",
            "[ Test epoch: 10 ]\n",
            "\n",
            "Total average test accuarcy: 0.7532\n",
            "Total average test loss: 0.011584219324588776\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 699.3861904144287\n",
            "\n",
            "[ Train epoch: 11 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6897751945332701\n",
            "Current batch average train loss: 0.010186695493757725\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.5487248613722999\n",
            "Current batch average train loss: 0.013043037615716457\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5792075494967573\n",
            "Current batch average train loss: 0.012941435910761356\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.7239593076388219\n",
            "Current batch average train loss: 0.009526482783257961\n",
            "\n",
            "Total average train accuarcy: 0.6050979404819599\n",
            "Total average train loss: 0.011696265709400177\n",
            "\n",
            "[ Test epoch: 11 ]\n",
            "\n",
            "Total average test accuarcy: 0.7959\n",
            "Total average test loss: 0.01053959857225418\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 762.982922077179\n",
            "\n",
            "[ Train epoch: 12 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6044318219005342\n",
            "Current batch average train loss: 0.01212506927549839\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6763237907414947\n",
            "Current batch average train loss: 0.011208046227693558\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4405408214719995\n",
            "Current batch average train loss: 0.013735400512814522\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8433425029074073\n",
            "Current batch average train loss: 0.007277579512447119\n",
            "\n",
            "Total average train accuarcy: 0.6191107548083057\n",
            "Total average train loss: 0.011588930262327194\n",
            "\n",
            "[ Test epoch: 12 ]\n",
            "\n",
            "Total average test accuarcy: 0.7975\n",
            "Total average test loss: 0.010426692193746567\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 826.6090087890625\n",
            "\n",
            "[ Train epoch: 13 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5135601080380985\n",
            "Current batch average train loss: 0.013436132110655308\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.5722688307687294\n",
            "Current batch average train loss: 0.012452984228730202\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4377129573657287\n",
            "Current batch average train loss: 0.014244984835386276\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.5418043600837767\n",
            "Current batch average train loss: 0.012835737317800522\n",
            "\n",
            "Total average train accuarcy: 0.6181660612761213\n",
            "Total average train loss: 0.011586068314313889\n",
            "\n",
            "[ Test epoch: 13 ]\n",
            "\n",
            "Total average test accuarcy: 0.8136\n",
            "Total average test loss: 0.010256025969982147\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 890.170102596283\n",
            "\n",
            "[ Train epoch: 14 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.556537800577567\n",
            "Current batch average train loss: 0.013214156031608582\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7747646951120749\n",
            "Current batch average train loss: 0.008476853370666504\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.7944026009545104\n",
            "Current batch average train loss: 0.008182990364730358\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4485436611050573\n",
            "Current batch average train loss: 0.013616448268294334\n",
            "\n",
            "Total average train accuarcy: 0.6250200433594874\n",
            "Total average train loss: 0.011451179267168045\n",
            "\n",
            "[ Test epoch: 14 ]\n",
            "\n",
            "Total average test accuarcy: 0.8196\n",
            "Total average test loss: 0.010186611074209213\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 953.6591124534607\n",
            "\n",
            "[ Train epoch: 15 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.4779435653465455\n",
            "Current batch average train loss: 0.013480901718139648\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4024124869592999\n",
            "Current batch average train loss: 0.014257237315177917\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4454725764401206\n",
            "Current batch average train loss: 0.013589052483439445\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6178511478450565\n",
            "Current batch average train loss: 0.012091102078557014\n",
            "\n",
            "Total average train accuarcy: 0.6348835677626511\n",
            "Total average train loss: 0.011316432839632034\n",
            "\n",
            "[ Test epoch: 15 ]\n",
            "\n",
            "Total average test accuarcy: 0.8118\n",
            "Total average test loss: 0.01048214430809021\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1017.1852705478668\n",
            "\n",
            "[ Train epoch: 16 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8782670503155052\n",
            "Current batch average train loss: 0.007246112916618586\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7570957050068167\n",
            "Current batch average train loss: 0.009035983122885227\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5331402437794686\n",
            "Current batch average train loss: 0.013293350115418434\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8191666251228351\n",
            "Current batch average train loss: 0.008490336127579212\n",
            "\n",
            "Total average train accuarcy: 0.6441521631329292\n",
            "Total average train loss: 0.01110726565361023\n",
            "\n",
            "[ Test epoch: 16 ]\n",
            "\n",
            "Total average test accuarcy: 0.8436\n",
            "Total average test loss: 0.009574643445014954\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1080.7829141616821\n",
            "\n",
            "[ Train epoch: 17 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6277725021142657\n",
            "Current batch average train loss: 0.01189335249364376\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7352641015136211\n",
            "Current batch average train loss: 0.01036761049181223\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5072434320523215\n",
            "Current batch average train loss: 0.012880425900220871\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6397296441982145\n",
            "Current batch average train loss: 0.011010199785232544\n",
            "\n",
            "Total average train accuarcy: 0.6403807738943403\n",
            "Total average train loss: 0.011196920902729035\n",
            "\n",
            "[ Test epoch: 17 ]\n",
            "\n",
            "Total average test accuarcy: 0.8129\n",
            "Total average test loss: 0.010469671201705933\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1144.415091753006\n",
            "\n",
            "[ Train epoch: 18 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5198035609470434\n",
            "Current batch average train loss: 0.012776591815054417\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8766944716390558\n",
            "Current batch average train loss: 0.00726250559091568\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.7821719444985346\n",
            "Current batch average train loss: 0.00861426629126072\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8642162901825544\n",
            "Current batch average train loss: 0.00748040433973074\n",
            "\n",
            "Total average train accuarcy: 0.6464762579683138\n",
            "Total average train loss: 0.011121668900251389\n",
            "\n",
            "[ Test epoch: 18 ]\n",
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            "\n",
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            "\n",
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            "\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 35 ]\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 36 ]\n",
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            "\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 55 ]\n",
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            "Model Saved!\n",
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            "\n",
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            "\n",
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            "Model Saved!\n",
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            "\n",
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            "Model Saved!\n",
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            "\n",
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            "Model Saved!\n",
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            "Model Saved!\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 95 ]\n",
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            "\n",
            "[ Train epoch: 96 ]\n",
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            "Model Saved!\n",
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            "[ Test epoch: 137 ]\n",
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          ],
          "name": "stdout"
        }
      ]
    }
  ]
}