code_practices/ResNet18_CIFAR10_Basic_Training.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ndb796/Deep-Learning-Paper-Review-and-Practice/blob/master/code_practices/ResNet18_CIFAR10_Basic_Training.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": "96dda201-7fc9-4560-ebf6-1b0bf71e9006"
      },
      "source": [
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=2)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
          }
        },
        {
          "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": "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",
        "        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": 3,
      "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": "b23e1aa6-e9ec-415e-dafb-0083ebff0dbc"
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[ Train epoch: 0 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.09375\n",
            "Current batch average train loss: 0.018886184319853783\n",
            "\n",
            "Current batch: 100\n",
            "Current batch average train accuracy: 0.2734375\n",
            "Current batch average train loss: 0.014718282967805862\n",
            "\n",
            "Current batch: 200\n",
            "Current batch average train accuracy: 0.3671875\n",
            "Current batch average train loss: 0.014242153614759445\n",
            "\n",
            "Current batch: 300\n",
            "Current batch average train accuracy: 0.359375\n",
            "Current batch average train loss: 0.013957763090729713\n",
            "\n",
            "Total average train accuarcy: 0.33596\n",
            "Total average train loss: 0.014389348886013032\n",
            "\n",
            "[ Test epoch: 0 ]\n",
            "\n",
            "Total average test accuarcy: 0.3378\n",
            "Total average test loss: 0.020276899695396422\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 64.44241976737976\n",
            "\n",
            "[ Train epoch: 1 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.390625\n",
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            "[ Test epoch: 1 ]\n",
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            "[ Train epoch: 2 ]\n",
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            "Current batch average train accuracy: 0.546875\n",
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            "[ Train epoch: 3 ]\n",
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            "Current batch average train accuracy: 0.7265625\n",
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            "[ Train epoch: 4 ]\n",
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            "Current batch average train accuracy: 0.6875\n",
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            "[ Train epoch: 5 ]\n",
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            "\n",
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            "[ Train epoch: 6 ]\n",
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            "\n",
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            "[ Train epoch: 7 ]\n",
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            "Current batch average train accuracy: 0.8828125\n",
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            "\n",
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            "[ Train epoch: 8 ]\n",
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            "[ Test epoch: 8 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 9 ]\n",
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            "Current batch average train accuracy: 0.8828125\n",
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            "[ Test epoch: 9 ]\n",
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            "\n",
            "Time elapsed: 654.1568324565887\n",
            "\n",
            "[ Train epoch: 10 ]\n",
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            "Current batch average train accuracy: 0.8359375\n",
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            "[ Test epoch: 10 ]\n",
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            "\n",
            "Time elapsed: 719.7141706943512\n",
            "\n",
            "[ Train epoch: 11 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.890625\n",
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            "[ Test epoch: 11 ]\n",
            "\n",
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            "Total average test loss: 0.007266859716176986\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 785.2830169200897\n",
            "\n",
            "[ Train epoch: 12 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.8671875\n",
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            "\n",
            "[ Test epoch: 12 ]\n",
            "\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 850.6815376281738\n",
            "\n",
            "[ Train epoch: 13 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.8203125\n",
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            "\n",
            "[ Test epoch: 13 ]\n",
            "\n",
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            "Total average test loss: 0.00512024128139019\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 915.9439511299133\n",
            "\n",
            "[ Train epoch: 14 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.90625\n",
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            "\n",
            "[ Test epoch: 14 ]\n",
            "\n",
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            "Total average test loss: 0.005981730431318283\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 981.3776617050171\n",
            "\n",
            "[ Train epoch: 15 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.859375\n",
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            "\n",
            "[ Test epoch: 15 ]\n",
            "\n",
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            "Total average test loss: 0.005358910104632378\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1046.6485683918\n",
            "\n",
            "[ Train epoch: 16 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.8515625\n",
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            "\n",
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            "\n",
            "[ Test epoch: 16 ]\n",
            "\n",
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            "Total average test loss: 0.005160576257109642\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1111.816444158554\n",
            "\n",
            "[ Train epoch: 17 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.890625\n",
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            "\n",
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            "\n",
            "[ Test epoch: 17 ]\n",
            "\n",
            "Total average test accuarcy: 0.8025\n",
            "Total average test loss: 0.005894530060887337\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1176.9801044464111\n",
            "\n",
            "[ Train epoch: 18 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.9296875\n",
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            "\n",
            "[ Test epoch: 18 ]\n",
            "\n",
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            "Total average test loss: 0.007160867366194725\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1242.2385549545288\n",
            "\n",
            "[ Train epoch: 19 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.890625\n",
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            "\n",
            "[ Test epoch: 19 ]\n",
            "\n",
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            "Total average test loss: 0.004390507641434669\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1307.2592754364014\n",
            "\n",
            "[ Train epoch: 20 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.9140625\n",
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            "\n",
            "[ Test epoch: 20 ]\n",
            "\n",
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            "Total average test loss: 0.005180907422304154\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 1372.4084281921387\n",
            "\n",
            "[ Train epoch: 21 ]\n",
            "\n",
            "Current batch: 0\n",
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            "\n",
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            "\n",
            "[ Test epoch: 21 ]\n",
            "\n",
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            "Total average test loss: 0.005904838621616363\n",
            "Model Saved!\n",
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            "[ Train epoch: 22 ]\n",
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            "Current batch average train accuracy: 0.921875\n",
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            "\n",
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            "[ Train epoch: 31 ]\n",
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            "[ Test epoch: 31 ]\n",
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            "\n",
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            "\n",
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            "\n",
            "[ Train epoch: 33 ]\n",
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            "[ Test epoch: 33 ]\n",
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            "\n",
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            "[ Train epoch: 34 ]\n",
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            "Current batch average train accuracy: 0.9375\n",
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            "[ Test epoch: 34 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 2283.878258705139\n",
            "\n",
            "[ Train epoch: 35 ]\n",
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            "[ Test epoch: 35 ]\n",
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            "\n",
            "Time elapsed: 2349.0507106781006\n",
            "\n",
            "[ Train epoch: 36 ]\n",
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            "[ Test epoch: 36 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 2413.7954647541046\n",
            "\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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            "[ Test epoch: 38 ]\n",
            "\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 2544.104204416275\n",
            "\n",
            "[ Train epoch: 39 ]\n",
            "\n",
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            "Current batch average train accuracy: 0.9140625\n",
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            "\n",
            "[ Test epoch: 39 ]\n",
            "\n",
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            "Total average test loss: 0.006029051429033279\n",
            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 40 ]\n",
            "\n",
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            "\n",
            "[ Test epoch: 40 ]\n",
            "\n",
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            "Total average test loss: 0.004685957215726375\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2673.739304304123\n",
            "\n",
            "[ Train epoch: 41 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.953125\n",
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            "[ Test epoch: 41 ]\n",
            "\n",
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            "Total average test loss: 0.004522820676863193\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 2738.607307434082\n",
            "\n",
            "[ Train epoch: 42 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 0.921875\n",
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            "\n",
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            "[ Train epoch: 43 ]\n",
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            "Current batch average train accuracy: 0.9296875\n",
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            "\n",
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            "[ Train epoch: 55 ]\n",
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            "\n",
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            "[ Train epoch: 56 ]\n",
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            "[ Test epoch: 56 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 57 ]\n",
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            "\n",
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            "\n",
            "[ Train epoch: 58 ]\n",
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            "[ Test epoch: 58 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 3842.086311340332\n",
            "\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",
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            "[ Test epoch: 60 ]\n",
            "\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 3971.995265007019\n",
            "\n",
            "[ Train epoch: 61 ]\n",
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            "[ Test epoch: 61 ]\n",
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            "Model Saved!\n",
            "\n",
            "Time elapsed: 4037.0197701454163\n",
            "\n",
            "[ Train epoch: 62 ]\n",
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            "[ Test epoch: 62 ]\n",
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            "Model Saved!\n",
            "\n",
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            "\n",
            "[ Train epoch: 63 ]\n",
            "\n",
            "Current batch: 0\n",
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            "Current batch average train loss: 5.109801350045018e-06\n",
            "\n",
            "Total average train accuarcy: 0.99988\n",
            "Total average train loss: 1.1145122374291531e-05\n",
            "\n",
            "[ Test epoch: 146 ]\n",
            "\n",
            "Total average test accuarcy: 0.9464\n",
            "Total average test loss: 0.002263487147912383\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 9551.685986042023\n",
            "\n",
            "[ Train epoch: 147 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 1.790146779967472e-05\n",
            "\n",
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            "Current batch average train loss: 2.0026860511279665e-05\n",
            "\n",
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            "Current batch average train loss: 1.0119942089659162e-05\n",
            "\n",
            "Current batch: 300\n",
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            "Current batch average train loss: 7.098458354448667e-06\n",
            "\n",
            "Total average train accuarcy: 0.99994\n",
            "Total average train loss: 1.0573746743611992e-05\n",
            "\n",
            "[ Test epoch: 147 ]\n",
            "\n",
            "Total average test accuarcy: 0.9465\n",
            "Total average test loss: 0.0022913724932819603\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 9616.85193657875\n",
            "\n",
            "[ Train epoch: 148 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 2.9258960694278358e-06\n",
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            "Current batch average train loss: 3.624374357968918e-06\n",
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            "Current batch average train loss: 5.877669991605217e-06\n",
            "\n",
            "Total average train accuarcy: 0.9999\n",
            "Total average train loss: 1.0791704915463924e-05\n",
            "\n",
            "[ Test epoch: 148 ]\n",
            "\n",
            "Total average test accuarcy: 0.946\n",
            "Total average test loss: 0.0022807286474853753\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 9681.692086696625\n",
            "\n",
            "[ Train epoch: 149 ]\n",
            "\n",
            "Current batch: 0\n",
            "Current batch average train accuracy: 1.0\n",
            "Current batch average train loss: 3.941001523344312e-06\n",
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            "Current batch average train loss: 5.54010875930544e-06\n",
            "\n",
            "Total average train accuarcy: 0.99994\n",
            "Total average train loss: 1.0787482253508642e-05\n",
            "\n",
            "[ Test epoch: 149 ]\n",
            "\n",
            "Total average test accuarcy: 0.9467\n",
            "Total average test loss: 0.002270778361707926\n",
            "Model Saved!\n",
            "\n",
            "Time elapsed: 9746.708998680115\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}