code_practices/ResNet18_CIFAR10_Training_with_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_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": "d31be60d-b3ff-4d4b-e8ff-9965bf61e3fb"
      },
      "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",
          "data": {
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          "metadata": {
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          }
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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": [
        "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",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, targets)\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 = predicted.eq(targets).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": "f989f0b3-6901-4655-eaf4-2955ac706d66"
      },
      "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: 0.03125\n",
            "Current batch average train loss: 0.01880522444844246\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.265625\n",
            "Current batch average train loss: 0.016130464151501656\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.2734375\n",
            "Current batch average train loss: 0.01486880425363779\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.3671875\n",
            "Current batch average train loss: 0.013500366359949112\n",
            "\n",
            "Total average train accuarcy: 0.3018\n",
            "Total average train loss: 0.01592603366136551\n",
            "\n",
            "[ Test epoch: 0 ]\n",
            "\n",
            "Total average test accuarcy: 0.377\n",
            "Total average test loss: 0.01908127553462982\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 30.87809419631958\n",
            "\n",
            "[ Train epoch: 1 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.375\n",
            "Current batch average train loss: 0.014214173890650272\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.4921875\n",
            "Current batch average train loss: 0.01341882161796093\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.5\n",
            "Current batch average train loss: 0.012172220274806023\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.53125\n",
            "Current batch average train loss: 0.012011099606752396\n",
            "\n",
            "Total average train accuarcy: 0.48536\n",
            "Total average train loss: 0.012660184478759766\n",
            "\n",
            "[ Test epoch: 1 ]\n",
            "\n",
            "Total average test accuarcy: 0.3633\n",
            "Total average test loss: 0.020856681036949157\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 61.54679274559021\n",
            "\n",
            "[ Train epoch: 2 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.5390625\n",
            "Current batch average train loss: 0.01180250197649002\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.609375\n",
            "Current batch average train loss: 0.011584784835577011\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6953125\n",
            "Current batch average train loss: 0.009848041459918022\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6015625\n",
            "Current batch average train loss: 0.01071525365114212\n",
            "\n",
            "Total average train accuarcy: 0.5941\n",
            "Total average train loss: 0.011073713901042939\n",
            "\n",
            "[ Test epoch: 2 ]\n",
            "\n",
            "Total average test accuarcy: 0.5377\n",
            "Total average test loss: 0.01581684398651123\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 92.15917730331421\n",
            "\n",
            "[ Train epoch: 3 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.59375\n",
            "Current batch average train loss: 0.010738015174865723\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.6328125\n",
            "Current batch average train loss: 0.010080509819090366\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6015625\n",
            "Current batch average train loss: 0.010458030737936497\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6796875\n",
            "Current batch average train loss: 0.009776456281542778\n",
            "\n",
            "Total average train accuarcy: 0.67052\n",
            "Total average train loss: 0.009885173313617707\n",
            "\n",
            "[ Test epoch: 3 ]\n",
            "\n",
            "Total average test accuarcy: 0.6083\n",
            "Total average test loss: 0.013877481889724732\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 122.73742628097534\n",
            "\n",
            "[ Train epoch: 4 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.6953125\n",
            "Current batch average train loss: 0.009270559065043926\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7734375\n",
            "Current batch average train loss: 0.00845109298825264\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.6640625\n",
            "Current batch average train loss: 0.009542472660541534\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.6953125\n",
            "Current batch average train loss: 0.008902426809072495\n",
            "\n",
            "Total average train accuarcy: 0.72096\n",
            "Total average train loss: 0.009049770716428757\n",
            "\n",
            "[ Test epoch: 4 ]\n",
            "\n",
            "Total average test accuarcy: 0.716\n",
            "Total average test loss: 0.011751194977760315\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 153.3404083251953\n",
            "\n",
            "[ Train epoch: 5 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.7421875\n",
            "Current batch average train loss: 0.008570997044444084\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.7265625\n",
            "Current batch average train loss: 0.008392542600631714\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.734375\n",
            "Current batch average train loss: 0.009212355129420757\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.796875\n",
            "Current batch average train loss: 0.008193189278244972\n",
            "\n",
            "Total average train accuarcy: 0.76912\n",
            "Total average train loss: 0.008334182484149933\n",
            "\n",
            "[ Test epoch: 5 ]\n",
            "\n",
            "Total average test accuarcy: 0.7684\n",
            "Total average test loss: 0.010591164660453796\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 183.97001314163208\n",
            "\n",
            "[ Train epoch: 6 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.7578125\n",
            "Current batch average train loss: 0.008239216171205044\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.71875\n",
            "Current batch average train loss: 0.008788174018263817\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.7734375\n",
            "Current batch average train loss: 0.008067253045737743\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006919175386428833\n",
            "\n",
            "Total average train accuarcy: 0.79562\n",
            "Total average train loss: 0.007864603089094162\n",
            "\n",
            "[ Test epoch: 6 ]\n",
            "\n",
            "Total average test accuarcy: 0.8077\n",
            "Total average test loss: 0.009903444147109985\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 214.55477356910706\n",
            "\n",
            "[ Train epoch: 7 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8046875\n",
            "Current batch average train loss: 0.007914724759757519\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.84375\n",
            "Current batch average train loss: 0.007555651478469372\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.006476568058133125\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.828125\n",
            "Current batch average train loss: 0.008031073026359081\n",
            "\n",
            "Total average train accuarcy: 0.8178\n",
            "Total average train loss: 0.007495609472990036\n",
            "\n",
            "[ Test epoch: 7 ]\n",
            "\n",
            "Total average test accuarcy: 0.7268\n",
            "Total average test loss: 0.011810015201568604\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 245.16739797592163\n",
            "\n",
            "[ Train epoch: 8 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8125\n",
            "Current batch average train loss: 0.007592304144054651\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.859375\n",
            "Current batch average train loss: 0.006737860850989819\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.828125\n",
            "Current batch average train loss: 0.007081482093781233\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.7890625\n",
            "Current batch average train loss: 0.007972320541739464\n",
            "\n",
            "Total average train accuarcy: 0.8333\n",
            "Total average train loss: 0.007213992146253585\n",
            "\n",
            "[ Test epoch: 8 ]\n",
            "\n",
            "Total average test accuarcy: 0.8123\n",
            "Total average test loss: 0.009640862274169921\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 275.71158146858215\n",
            "\n",
            "[ Train epoch: 9 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.828125\n",
            "Current batch average train loss: 0.007312214467674494\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.006300357170403004\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.8515625\n",
            "Current batch average train loss: 0.006696900352835655\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006077243480831385\n",
            "\n",
            "Total average train accuarcy: 0.84506\n",
            "Total average train loss: 0.007010109835863114\n",
            "\n",
            "[ Test epoch: 9 ]\n",
            "\n",
            "Total average test accuarcy: 0.8343\n",
            "Total average test loss: 0.009263889455795288\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 306.3091461658478\n",
            "\n",
            "[ Train epoch: 10 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.78125\n",
            "Current batch average train loss: 0.007216745056211948\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006340088788419962\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.875\n",
            "Current batch average train loss: 0.0064750793389976025\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.859375\n",
            "Current batch average train loss: 0.006676472723484039\n",
            "\n",
            "Total average train accuarcy: 0.85762\n",
            "Total average train loss: 0.00677084430217743\n",
            "\n",
            "[ Test epoch: 10 ]\n",
            "\n",
            "Total average test accuarcy: 0.8301\n",
            "Total average test loss: 0.009383398050069809\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 336.88195872306824\n",
            "\n",
            "[ Train epoch: 11 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8671875\n",
            "Current batch average train loss: 0.006649106740951538\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8125\n",
            "Current batch average train loss: 0.007354802452027798\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.8671875\n",
            "Current batch average train loss: 0.006676105782389641\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8515625\n",
            "Current batch average train loss: 0.006729396991431713\n",
            "\n",
            "Total average train accuarcy: 0.8652\n",
            "Total average train loss: 0.006644772020578385\n",
            "\n",
            "[ Test epoch: 11 ]\n",
            "\n",
            "Total average test accuarcy: 0.8329\n",
            "Total average test loss: 0.009322253835201263\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 367.3792939186096\n",
            "\n",
            "[ Train epoch: 12 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006566870491951704\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8203125\n",
            "Current batch average train loss: 0.007361989468336105\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.890625\n",
            "Current batch average train loss: 0.006152966991066933\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.875\n",
            "Current batch average train loss: 0.006392735987901688\n",
            "\n",
            "Total average train accuarcy: 0.87582\n",
            "Total average train loss: 0.006481006274223327\n",
            "\n",
            "[ Test epoch: 12 ]\n",
            "\n",
            "Total average test accuarcy: 0.8385\n",
            "Total average test loss: 0.009068747627735138\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 397.94065403938293\n",
            "\n",
            "[ Train epoch: 13 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.875\n",
            "Current batch average train loss: 0.006387793458998203\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006648330017924309\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.9296875\n",
            "Current batch average train loss: 0.005699386820197105\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8359375\n",
            "Current batch average train loss: 0.0072545697912573814\n",
            "\n",
            "Total average train accuarcy: 0.87818\n",
            "Total average train loss: 0.006407373772859573\n",
            "\n",
            "[ Test epoch: 13 ]\n",
            "\n",
            "Total average test accuarcy: 0.817\n",
            "Total average test loss: 0.009673649275302888\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 428.4879503250122\n",
            "\n",
            "[ Train epoch: 14 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.00618361122906208\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.0058154938742518425\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.8515625\n",
            "Current batch average train loss: 0.006597920320928097\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8828125\n",
            "Current batch average train loss: 0.006331254728138447\n",
            "\n",
            "Total average train accuarcy: 0.88822\n",
            "Total average train loss: 0.006268391424417496\n",
            "\n",
            "[ Test epoch: 14 ]\n",
            "\n",
            "Total average test accuarcy: 0.8414\n",
            "Total average test loss: 0.008968575477600098\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 459.0219187736511\n",
            "\n",
            "[ Train epoch: 15 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.84375\n",
            "Current batch average train loss: 0.006379478611052036\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.875\n",
            "Current batch average train loss: 0.006394821219146252\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.005852916743606329\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.9453125\n",
            "Current batch average train loss: 0.005574212409555912\n",
            "\n",
            "Total average train accuarcy: 0.892\n",
            "Total average train loss: 0.0062100018739700315\n",
            "\n",
            "[ Test epoch: 15 ]\n",
            "\n",
            "Total average test accuarcy: 0.85\n",
            "Total average test loss: 0.008839400571584702\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 489.55082082748413\n",
            "\n",
            "[ Train epoch: 16 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.875\n",
            "Current batch average train loss: 0.006326047703623772\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.921875\n",
            "Current batch average train loss: 0.00574929965659976\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.890625\n",
            "Current batch average train loss: 0.00628972752019763\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.859375\n",
            "Current batch average train loss: 0.006859431508928537\n",
            "\n",
            "Total average train accuarcy: 0.89586\n",
            "Total average train loss: 0.006118500521183014\n",
            "\n",
            "[ Test epoch: 16 ]\n",
            "\n",
            "Total average test accuarcy: 0.8577\n",
            "Total average test loss: 0.008715888518095016\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 520.1333639621735\n",
            "\n",
            "[ Train epoch: 17 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8828125\n",
            "Current batch average train loss: 0.006166811101138592\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.890625\n",
            "Current batch average train loss: 0.006153083872050047\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.005958856549113989\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.005819123703986406\n",
            "\n",
            "Total average train accuarcy: 0.89862\n",
            "Total average train loss: 0.00607060520529747\n",
            "\n",
            "[ Test epoch: 17 ]\n",
            "\n",
            "Total average test accuarcy: 0.8281\n",
            "Total average test loss: 0.009357624280452729\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 550.7117433547974\n",
            "\n",
            "[ Train epoch: 18 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.00625478383153677\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.890625\n",
            "Current batch average train loss: 0.006185262929648161\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.84375\n",
            "Current batch average train loss: 0.006879061460494995\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.006366616580635309\n",
            "\n",
            "Total average train accuarcy: 0.90458\n",
            "Total average train loss: 0.005974131344556808\n",
            "\n",
            "[ Test epoch: 18 ]\n",
            "\n",
            "Total average test accuarcy: 0.859\n",
            "Total average test loss: 0.008721886271238327\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 581.2404546737671\n",
            "\n",
            "[ Train epoch: 19 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.890625\n",
            "Current batch average train loss: 0.005829574074596167\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.8828125\n",
            "Current batch average train loss: 0.0061010075733065605\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.8984375\n",
            "Current batch average train loss: 0.00587798748165369\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.90625\n",
            "Current batch average train loss: 0.006146017462015152\n",
            "\n",
            "Total average train accuarcy: 0.90386\n",
            "Total average train loss: 0.005972706824541092\n",
            "\n",
            "[ Test epoch: 19 ]\n",
            "\n",
            "Total average test accuarcy: 0.8393\n",
            "Total average test loss: 0.009105736541748047\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 611.7690689563751\n",
            "\n",
            "[ Train epoch: 20 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.921875\n",
            "Current batch average train loss: 0.005658816546201706\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.9140625\n",
            "Current batch average train loss: 0.005889735650271177\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.921875\n",
            "Current batch average train loss: 0.0056322128511965275\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.921875\n",
            "Current batch average train loss: 0.005729041062295437\n",
            "\n",
            "Total average train accuarcy: 0.90952\n",
            "Total average train loss: 0.005886522500514984\n",
            "\n",
            "[ Test epoch: 20 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.921875\n",
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            "\n",
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            "\n",
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            "\n",
            "[ Train epoch: 33 ]\n",
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            "\n",
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            "[ Test epoch: 34 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 35 ]\n",
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            "[ Test epoch: 35 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 36 ]\n",
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            "Current batch average train accuracy: 0.9375\n",
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            "[ Test epoch: 36 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 37 ]\n",
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            "[ Test epoch: 37 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 38 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 39 ]\n",
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            "[ Test epoch: 39 ]\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 40 ]\n",
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            "[ Test epoch: 40 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 41 ]\n",
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            "[ Test epoch: 41 ]\n",
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            "[ Train epoch: 42 ]\n",
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            "Current batch average train accuracy: 0.96875\n",
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            "[ Train epoch: 43 ]\n",
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            "[ Train epoch: 52 ]\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "[ Test epoch: 54 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 55 ]\n",
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            "[ Test epoch: 55 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 56 ]\n",
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            "[ Test epoch: 56 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 1741.0541062355042\n",
            "\n",
            "[ Train epoch: 57 ]\n",
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            "[ Test epoch: 57 ]\n",
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            "Total average test loss: 0.006858893686532974\n",
            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 58 ]\n",
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            "\n",
            "[ Test epoch: 58 ]\n",
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            "Total average test loss: 0.006835968124866486\n",
            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 59 ]\n",
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            "[ Test epoch: 59 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 60 ]\n",
            "\n",
            "Current batch: 0\n",
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            "[ Test epoch: 60 ]\n",
            "\n",
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            "Total average test loss: 0.006847112607955933\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1863.0581457614899\n",
            "\n",
            "[ Train epoch: 61 ]\n",
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            "[ Test epoch: 61 ]\n",
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            "Model Saved!\n",
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            "\n",
            "[ Train epoch: 62 ]\n",
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            "[ Test epoch: 62 ]\n",
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            "[ Train epoch: 63 ]\n",
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            "Current batch average train accuracy: 0.9921875\n",
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            "[ Train epoch: 64 ]\n",
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            "[ Train epoch: 65 ]\n",
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            "[ Train epoch: 66 ]\n",
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            "[ Train epoch: 69 ]\n",
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            "\n",
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            "[ Train epoch: 70 ]\n",
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            "\n",
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            "[ Train epoch: 71 ]\n",
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            "\n",
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            "[ Train epoch: 72 ]\n",
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            "[ Test epoch: 72 ]\n",
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            "\n",
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            "[ Train epoch: 73 ]\n",
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            "\n",
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            "[ Train epoch: 74 ]\n",
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            "[ Test epoch: 74 ]\n",
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            "\n",
            "Time elapsed: 2290.5148119926453\n",
            "\n",
            "[ Train epoch: 75 ]\n",
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            "[ Test epoch: 75 ]\n",
            "\n",
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            "Total average test loss: 0.0068595117926597594\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2321.0254633426666\n",
            "\n",
            "[ Train epoch: 76 ]\n",
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            "[ Test epoch: 76 ]\n",
            "\n",
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            "Total average test loss: 0.006883870875835418\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2351.5347197055817\n",
            "\n",
            "[ Train epoch: 77 ]\n",
            "\n",
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            "\n",
            "[ Test epoch: 77 ]\n",
            "\n",
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            "Total average test loss: 0.006884250354766846\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2382.077537059784\n",
            "\n",
            "[ Train epoch: 78 ]\n",
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            "\n",
            "[ Test epoch: 78 ]\n",
            "\n",
            "Total average test accuarcy: 0.9442\n",
            "Total average test loss: 0.006864481592178345\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2412.6715528964996\n",
            "\n",
            "[ Train epoch: 79 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
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            "\n",
            "[ Test epoch: 79 ]\n",
            "\n",
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            "Total average test loss: 0.006850521302223206\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2443.1637201309204\n",
            "\n",
            "[ Train epoch: 80 ]\n",
            "\n",
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            "\n",
            "[ Test epoch: 80 ]\n",
            "\n",
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            "Total average test loss: 0.00685507373213768\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2473.710039615631\n",
            "\n",
            "[ Train epoch: 81 ]\n",
            "\n",
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            "\n",
            "[ Test epoch: 81 ]\n",
            "\n",
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            "Total average test loss: 0.006848423379659653\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2504.264497280121\n",
            "\n",
            "[ Train epoch: 82 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
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            "\n",
            "[ Test epoch: 82 ]\n",
            "\n",
            "Total average test accuarcy: 0.9439\n",
            "Total average test loss: 0.00686281201839447\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2534.797729730606\n",
            "\n",
            "[ Train epoch: 83 ]\n",
            "\n",
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            "[ Test epoch: 83 ]\n",
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            "Total average test loss: 0.006879106193780899\n",
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            "[ Train epoch: 86 ]\n",
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            "[ Train epoch: 87 ]\n",
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            "[ Train epoch: 88 ]\n",
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            "\n",
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            "[ Train epoch: 89 ]\n",
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            "\n",
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            "[ Train epoch: 90 ]\n",
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            "Current batch average train accuracy: 0.9921875\n",
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            "[ Test epoch: 90 ]\n",
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            "\n",
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            "[ Train epoch: 91 ]\n",
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            "\n",
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            "[ Train epoch: 92 ]\n",
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            "\n",
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            "[ Train epoch: 93 ]\n",
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            "[ Test epoch: 93 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 94 ]\n",
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            "[ Test epoch: 94 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 95 ]\n",
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            "[ Test epoch: 95 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 96 ]\n",
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            "[ Test epoch: 96 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 2962.328411102295\n",
            "\n",
            "[ Train epoch: 97 ]\n",
            "\n",
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            "[ Test epoch: 97 ]\n",
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            "Total average test loss: 0.006948935943841934\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2992.825804233551\n",
            "\n",
            "[ Train epoch: 98 ]\n",
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            "[ Test epoch: 98 ]\n",
            "\n",
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            "Total average test loss: 0.006894077169895172\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 3023.3549194335938\n",
            "\n",
            "[ Train epoch: 99 ]\n",
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            "\n",
            "[ Test epoch: 99 ]\n",
            "\n",
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            "Total average test loss: 0.006928317749500275\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 3054.0938065052032\n",
            "\n",
            "[ Train epoch: 100 ]\n",
            "\n",
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            "\n",
            "[ Test epoch: 100 ]\n",
            "\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 3084.854060649872\n",
            "\n",
            "[ Train epoch: 101 ]\n",
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            "[ Test epoch: 101 ]\n",
            "\n",
            "Total average test accuarcy: 0.9443\n",
            "Total average test loss: 0.006868261885643006\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 3115.456723213196\n",
            "\n",
            "[ Train epoch: 102 ]\n",
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            "\n",
            "[ Test epoch: 102 ]\n",
            "\n",
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            "Total average test loss: 0.006872145926952362\n",
            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 103 ]\n",
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            "\n",
            "[ Test epoch: 103 ]\n",
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            "Total average test loss: 0.006866956532001495\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 3176.6386439800262\n",
            "\n",
            "[ Train epoch: 104 ]\n",
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            "[ Test epoch: 104 ]\n",
            "\n",
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            "\n",
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            "Model Saved!\n",
            "\n",
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            "[ Train epoch: 120 ]\n",
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            "[ Test epoch: 120 ]\n",
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            "Model Saved!\n",
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            "[ Train epoch: 121 ]\n",
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            "[ Test epoch: 121 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 122 ]\n",
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            "[ Test epoch: 122 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 123 ]\n",
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            "[ Test epoch: 123 ]\n",
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            "Model Saved!\n",
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            "\n",
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            "Model Saved!\n",
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            "[ Train epoch: 125 ]\n",
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            "Model Saved!\n",
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            "[ Test epoch: 126 ]\n",
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            "[ Train epoch: 140 ]\n",
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            "\n",
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            "[ Train epoch: 141 ]\n",
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            "[ Test epoch: 141 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 142 ]\n",
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            "[ Test epoch: 142 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 4367.873571395874\n",
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            "[ Train epoch: 143 ]\n",
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            "[ Test epoch: 143 ]\n",
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            "Model Saved!\n",
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            "Time elapsed: 4398.38790845871\n",
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            "[ Train epoch: 144 ]\n",
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            "[ Test epoch: 144 ]\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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            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004263461101800203\n",
            "\n",
            "Total average train accuarcy: 0.99996\n",
            "Total average train loss: 0.004272807295322418\n",
            "\n",
            "[ Test epoch: 147 ]\n",
            "\n",
            "Total average test accuarcy: 0.9466\n",
            "Total average test loss: 0.0068439317524433135\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 4520.554056882858\n",
            "\n",
            "[ Train epoch: 148 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004266662057489157\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.0042663682252168655\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004262769129127264\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004267863929271698\n",
            "\n",
            "Total average train accuarcy: 0.99998\n",
            "Total average train loss: 0.004272111052274704\n",
            "\n",
            "[ Test epoch: 148 ]\n",
            "\n",
            "Total average test accuarcy: 0.9469\n",
            "Total average test loss: 0.006846047413349152\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 4551.130266427994\n",
            "\n",
            "[ Train epoch: 149 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004268049728125334\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004275647923350334\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004265225026756525\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 0.004263889044523239\n",
            "\n",
            "Total average train accuarcy: 0.99996\n",
            "Total average train loss: 0.004272464656829834\n",
            "\n",
            "[ Test epoch: 149 ]\n",
            "\n",
            "Total average test accuarcy: 0.9464\n",
            "Total average test loss: 0.006847339761257172\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 4581.654356956482\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}