code_practices/ResNet18_CIFAR10_Training_with_Input_Mixup.ipynb

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    {
      "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.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": "af99feb0-c22c-40d3-88ef-6e48445accc9"
      },
      "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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            ]
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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)"
      ],
      "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 = nn.CrossEntropyLoss()\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": "9202e802-dba2-473a-b182-135625d69d65"
      },
      "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: 0.09727836367854385\n",
            "Current batch average train loss: 0.018746694549918175\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.23465121261291963\n",
            "Current batch average train loss: 0.016454745084047318\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.333794545157437\n",
            "Current batch average train loss: 0.014404334127902985\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.26906486580878847\n",
            "Current batch average train loss: 0.015901871025562286\n",
            "\n",
            "Total average train accuarcy: 0.2438999810762169\n",
            "Total average train loss: 0.01657938927412033\n",
            "\n",
            "[ Test epoch: 0 ]\n",
            "\n",
            "Total average test accuarcy: 0.2885\n",
            "Total average test loss: 0.019975642216205597\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 67.31932187080383\n",
            "\n",
            "[ Train epoch: 1 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.24919886781709963\n",
            "Current batch average train loss: 0.015324986539781094\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4636160828714663\n",
            "Current batch average train loss: 0.012422642670571804\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.34419466939811794\n",
            "Current batch average train loss: 0.015307758003473282\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.31797102300464986\n",
            "Current batch average train loss: 0.01510600931942463\n",
            "\n",
            "Total average train accuarcy: 0.35010402917211925\n",
            "Total average train loss: 0.014451653120517731\n",
            "\n",
            "[ Test epoch: 1 ]\n",
            "\n",
            "Total average test accuarcy: 0.4511\n",
            "Total average test loss: 0.015768038177490236\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 134.12930130958557\n",
            "\n",
            "[ Train epoch: 2 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.39064543301020394\n",
            "Current batch average train loss: 0.01368321105837822\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4536923300926184\n",
            "Current batch average train loss: 0.013418509624898434\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5843046093906161\n",
            "Current batch average train loss: 0.010685338638722897\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4138342762235759\n",
            "Current batch average train loss: 0.013748647645115852\n",
            "\n",
            "Total average train accuarcy: 0.42018612307862413\n",
            "Total average train loss: 0.01337722238779068\n",
            "\n",
            "[ Test epoch: 2 ]\n",
            "\n",
            "Total average test accuarcy: 0.3748\n",
            "Total average test loss: 0.01846726050376892\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 200.83397006988525\n",
            "\n",
            "[ Train epoch: 3 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6173987355731223\n",
            "Current batch average train loss: 0.00914319884032011\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.5900493613504846\n",
            "Current batch average train loss: 0.009842365980148315\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5708004272097587\n",
            "Current batch average train loss: 0.010575332678854465\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.5454333471487535\n",
            "Current batch average train loss: 0.011715037748217583\n",
            "\n",
            "Total average train accuarcy: 0.4552326009648567\n",
            "Total average train loss: 0.012816074323654175\n",
            "\n",
            "[ Test epoch: 3 ]\n",
            "\n",
            "Total average test accuarcy: 0.542\n",
            "Total average test loss: 0.013031093001365662\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 267.6033661365509\n",
            "\n",
            "[ Train epoch: 4 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5670800999019762\n",
            "Current batch average train loss: 0.011077708564698696\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.33942700380630286\n",
            "Current batch average train loss: 0.01571563258767128\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5527230620729884\n",
            "Current batch average train loss: 0.011261177249252796\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.41191504465987094\n",
            "Current batch average train loss: 0.01367579773068428\n",
            "\n",
            "Total average train accuarcy: 0.4815957606387106\n",
            "Total average train loss: 0.012333299213647843\n",
            "\n",
            "[ Test epoch: 4 ]\n",
            "\n",
            "Total average test accuarcy: 0.533\n",
            "Total average test loss: 0.013741466772556304\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 334.2372772693634\n",
            "\n",
            "[ Train epoch: 5 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.32909885804028555\n",
            "Current batch average train loss: 0.014123043045401573\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.35232902116638143\n",
            "Current batch average train loss: 0.01461983285844326\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.40546838587707035\n",
            "Current batch average train loss: 0.01375819556415081\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.37276580497994516\n",
            "Current batch average train loss: 0.014229300431907177\n",
            "\n",
            "Total average train accuarcy: 0.5138012749180852\n",
            "Total average train loss: 0.011761493802070618\n",
            "\n",
            "[ Test epoch: 5 ]\n",
            "\n",
            "Total average test accuarcy: 0.6636\n",
            "Total average test loss: 0.010204350268840789\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 400.89489698410034\n",
            "\n",
            "[ Train epoch: 6 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.4859315226893304\n",
            "Current batch average train loss: 0.012627273797988892\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4099717220535597\n",
            "Current batch average train loss: 0.013684434816241264\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.7072205058748521\n",
            "Current batch average train loss: 0.0078122615814208984\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4435237277726132\n",
            "Current batch average train loss: 0.013115045614540577\n",
            "\n",
            "Total average train accuarcy: 0.5430234982488837\n",
            "Total average train loss: 0.011276201122999192\n",
            "\n",
            "[ Test epoch: 6 ]\n",
            "\n",
            "Total average test accuarcy: 0.6475\n",
            "Total average test loss: 0.010523168969154359\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 467.57990646362305\n",
            "\n",
            "[ Train epoch: 7 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6297623337920402\n",
            "Current batch average train loss: 0.010165085084736347\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6370089383482624\n",
            "Current batch average train loss: 0.009543618187308311\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4830356924365709\n",
            "Current batch average train loss: 0.01263895258307457\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.48627439472570266\n",
            "Current batch average train loss: 0.01262323372066021\n",
            "\n",
            "Total average train accuarcy: 0.5658904739480652\n",
            "Total average train loss: 0.01078166707277298\n",
            "\n",
            "[ Test epoch: 7 ]\n",
            "\n",
            "Total average test accuarcy: 0.7351\n",
            "Total average test loss: 0.008803094333410264\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 534.4943442344666\n",
            "\n",
            "[ Train epoch: 8 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6179928894647404\n",
            "Current batch average train loss: 0.01031770184636116\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4461918547591837\n",
            "Current batch average train loss: 0.013237190432846546\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.437434791532017\n",
            "Current batch average train loss: 0.012683529406785965\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.575830526296256\n",
            "Current batch average train loss: 0.01113214623183012\n",
            "\n",
            "Total average train accuarcy: 0.5788749931425785\n",
            "Total average train loss: 0.01062203551530838\n",
            "\n",
            "[ Test epoch: 8 ]\n",
            "\n",
            "Total average test accuarcy: 0.7095\n",
            "Total average test loss: 0.009076267409324646\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 601.1747407913208\n",
            "\n",
            "[ Train epoch: 9 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6525943929354701\n",
            "Current batch average train loss: 0.009136104956269264\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4819095752220173\n",
            "Current batch average train loss: 0.01225016824901104\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4268182041022082\n",
            "Current batch average train loss: 0.012797015719115734\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.49720414690439\n",
            "Current batch average train loss: 0.012337896041572094\n",
            "\n",
            "Total average train accuarcy: 0.5839467842568282\n",
            "Total average train loss: 0.010511346663236618\n",
            "\n",
            "[ Test epoch: 9 ]\n",
            "\n",
            "Total average test accuarcy: 0.7276\n",
            "Total average test loss: 0.008879742527008056\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 667.8142864704132\n",
            "\n",
            "[ Train epoch: 10 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.7178603226344424\n",
            "Current batch average train loss: 0.007616589777171612\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6975085007000397\n",
            "Current batch average train loss: 0.008920421823859215\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6220718926364055\n",
            "Current batch average train loss: 0.010429859161376953\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4618727088078343\n",
            "Current batch average train loss: 0.012916531413793564\n",
            "\n",
            "Total average train accuarcy: 0.6016285790264628\n",
            "Total average train loss: 0.010106802842617035\n",
            "\n",
            "[ Test epoch: 10 ]\n",
            "\n",
            "Total average test accuarcy: 0.7199\n",
            "Total average test loss: 0.008745429641008377\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 734.5619456768036\n",
            "\n",
            "[ Train epoch: 11 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.4219043275471749\n",
            "Current batch average train loss: 0.013182992115616798\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6511148120217487\n",
            "Current batch average train loss: 0.010063281282782555\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.740341836708163\n",
            "Current batch average train loss: 0.00755461398512125\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8213815048950152\n",
            "Current batch average train loss: 0.004997447598725557\n",
            "\n",
            "Total average train accuarcy: 0.6096975259085268\n",
            "Total average train loss: 0.009987703077197076\n",
            "\n",
            "[ Test epoch: 11 ]\n",
            "\n",
            "Total average test accuarcy: 0.7646\n",
            "Total average test loss: 0.007680280905961991\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 801.3842809200287\n",
            "\n",
            "[ Train epoch: 12 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6617535001141703\n",
            "Current batch average train loss: 0.008984318003058434\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6308431080735917\n",
            "Current batch average train loss: 0.010096214711666107\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.43816233190509773\n",
            "Current batch average train loss: 0.012839829549193382\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6357544708714679\n",
            "Current batch average train loss: 0.009972522035241127\n",
            "\n",
            "Total average train accuarcy: 0.6199243457839402\n",
            "Total average train loss: 0.009749777668714524\n",
            "\n",
            "[ Test epoch: 12 ]\n",
            "\n",
            "Total average test accuarcy: 0.777\n",
            "Total average test loss: 0.007399796807765961\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 868.0868828296661\n",
            "\n",
            "[ Train epoch: 13 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.44815357209391793\n",
            "Current batch average train loss: 0.012733022682368755\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4778757776836638\n",
            "Current batch average train loss: 0.01197742111980915\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.7937129235829692\n",
            "Current batch average train loss: 0.0061811646446585655\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6306109508019635\n",
            "Current batch average train loss: 0.010059140622615814\n",
            "\n",
            "Total average train accuarcy: 0.6335418792071775\n",
            "Total average train loss: 0.009478528788685799\n",
            "\n",
            "[ Test epoch: 13 ]\n",
            "\n",
            "Total average test accuarcy: 0.7944\n",
            "Total average test loss: 0.006542516574263572\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 934.8241033554077\n",
            "\n",
            "[ Train epoch: 14 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5875568833773117\n",
            "Current batch average train loss: 0.011232482269406319\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8639689000154441\n",
            "Current batch average train loss: 0.004735847003757954\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5827944272753317\n",
            "Current batch average train loss: 0.011070692911744118\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.44645285577917937\n",
            "Current batch average train loss: 0.012984460219740868\n",
            "\n",
            "Total average train accuarcy: 0.62095292169662\n",
            "Total average train loss: 0.00972314892232418\n",
            "\n",
            "[ Test epoch: 14 ]\n",
            "\n",
            "Total average test accuarcy: 0.8258\n",
            "Total average test loss: 0.006346612909436226\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1001.5583038330078\n",
            "\n",
            "[ Train epoch: 15 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5835633962138485\n",
            "Current batch average train loss: 0.010527408681809902\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.5700519452897154\n",
            "Current batch average train loss: 0.011226771399378777\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.48238395188915095\n",
            "Current batch average train loss: 0.01207771897315979\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.45311754394325443\n",
            "Current batch average train loss: 0.011893685907125473\n",
            "\n",
            "Total average train accuarcy: 0.6289542669964973\n",
            "Total average train loss: 0.009591986538767815\n",
            "\n",
            "[ Test epoch: 15 ]\n",
            "\n",
            "Total average test accuarcy: 0.8286\n",
            "Total average test loss: 0.006145492041110992\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1068.3338332176208\n",
            "\n",
            "[ Train epoch: 16 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.62411356423882\n",
            "Current batch average train loss: 0.009960032999515533\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6589189026023043\n",
            "Current batch average train loss: 0.009505560621619225\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6410360826937959\n",
            "Current batch average train loss: 0.010046809911727905\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.5568481805690952\n",
            "Current batch average train loss: 0.010873585008084774\n",
            "\n",
            "Total average train accuarcy: 0.6363022077353924\n",
            "Total average train loss: 0.00947659525990486\n",
            "\n",
            "[ Test epoch: 16 ]\n",
            "\n",
            "Total average test accuarcy: 0.5892\n",
            "Total average test loss: 0.012428106665611267\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1135.0796415805817\n",
            "\n",
            "[ Train epoch: 17 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6846911468397897\n",
            "Current batch average train loss: 0.009270016103982925\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6429751959752706\n",
            "Current batch average train loss: 0.010512473061680794\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.4676635350936079\n",
            "Current batch average train loss: 0.0124819315969944\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.7679416398782779\n",
            "Current batch average train loss: 0.007116319146007299\n",
            "\n",
            "Total average train accuarcy: 0.6420090803206528\n",
            "Total average train loss: 0.009280086843967437\n",
            "\n",
            "[ Test epoch: 17 ]\n",
            "\n",
            "Total average test accuarcy: 0.7824\n",
            "Total average test loss: 0.007538222736120224\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1201.8748149871826\n",
            "\n",
            "[ Train epoch: 18 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.43165684080955713\n",
            "Current batch average train loss: 0.012152612209320068\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8488443348484067\n",
            "Current batch average train loss: 0.0050377147272229195\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.9159492446880936\n",
            "Current batch average train loss: 0.00324699142947793\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.4581778256837796\n",
            "Current batch average train loss: 0.012029880657792091\n",
            "\n",
            "Total average train accuarcy: 0.647494602942034\n",
            "Total average train loss: 0.009199973105192185\n",
            "\n",
            "[ Test epoch: 18 ]\n",
            "\n",
            "Total average test accuarcy: 0.8311\n",
            "Total average test loss: 0.0059277821749448775\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1268.725900888443\n",
            "\n",
            "[ Train epoch: 19 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8346932541687989\n",
            "Current batch average train loss: 0.005113417748361826\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4506254012606849\n",
            "Current batch average train loss: 0.012803751043975353\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.44799732320628044\n",
            "Current batch average train loss: 0.012697161175310612\n",
            "\n",
            "Current batch: 300\n",
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            "\n",
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            "Model Saved!\n",
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            "\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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            "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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            "Model Saved!\n",
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            "\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 57 ]\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",
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            "\n",
            "[ Train epoch: 96 ]\n",
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            "Model Saved!\n",
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            "\n",
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            "[ Test epoch: 138 ]\n",
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            "[ Train epoch: 139 ]\n",
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          ],
          "name": "stdout"
        }
      ]
    }
  ]
}