05-이미지_처리능력이_탁월한_CNN/resnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 신경망 깊게 쌓아 컬러 데이터셋에 적용하기\n",
    "Convolutional Neural Network (CNN) 을 쌓아올려 딥한 러닝을 해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, datasets, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_CUDA = torch.cuda.is_available()\n",
    "DEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 하이퍼파라미터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS     = 300\n",
    "BATCH_SIZE = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.CIFAR10('./.data',\n",
    "                   train=True,\n",
    "                   download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.RandomCrop(32, padding=4),\n",
    "                       transforms.RandomHorizontalFlip(),\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.5, 0.5, 0.5),\n",
    "                                            (0.5, 0.5, 0.5))])),\n",
    "    batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.CIFAR10('./.data',\n",
    "                   train=False, \n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.5, 0.5, 0.5),\n",
    "                                            (0.5, 0.5, 0.5))])),\n",
    "    batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ResNet 모델 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicBlock(nn.Module):\n",
    "    def __init__(self, in_planes, planes, stride=1):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,\n",
    "                               stride=stride, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,\n",
    "                               stride=1, padding=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "\n",
    "        self.shortcut = nn.Sequential()\n",
    "        if stride != 1 or in_planes != planes:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_planes, planes,\n",
    "                          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)\n",
    "        out = F.relu(out)\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(ResNet, self).__init__()\n",
    "        self.in_planes = 16\n",
    "\n",
    "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3,\n",
    "                               stride=1, padding=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(16)\n",
    "        self.layer1 = self._make_layer(16, 2, stride=1)\n",
    "        self.layer2 = self._make_layer(32, 2, stride=2)\n",
    "        self.layer3 = self._make_layer(64, 2, stride=2)\n",
    "        self.linear = nn.Linear(64, num_classes)\n",
    "\n",
    "    def _make_layer(self, planes, num_blocks, stride):\n",
    "        strides = [stride] + [1]*(num_blocks-1)\n",
    "        layers = []\n",
    "        for stride in strides:\n",
    "            layers.append(BasicBlock(self.in_planes, planes, stride))\n",
    "            self.in_planes = planes\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 = F.avg_pool2d(out, 8)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet().to(DEVICE)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1,\n",
    "                      momentum=0.9, weight_decay=0.0005)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "  (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential(\n",
      "        (0): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential(\n",
      "        (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (shortcut): Sequential()\n",
      "    )\n",
      "  )\n",
      "  (linear): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(DEVICE), target.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.cross_entropy(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 테스트하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(DEVICE), target.to(DEVICE)\n",
    "            output = model(data)\n",
    "\n",
    "            # 배치 오차를 합산\n",
    "            test_loss += F.cross_entropy(output, target,\n",
    "                                         reduction='sum').item()\n",
    "\n",
    "            # 가장 높은 값을 가진 인덱스가 바로 예측값\n",
    "            pred = output.max(1, keepdim=True)[1]\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    test_accuracy = 100. * correct / len(test_loader.dataset)\n",
    "    return test_loss, test_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 코드 돌려보기\n",
    "\n",
    "자, 이제 모든 준비가 끝났습니다. 코드를 돌려서 실제로 훈련이 되는지 확인해봅시다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] Test Loss: 1.4414, Accuracy: 46.96%\n",
      "[2] Test Loss: 1.6129, Accuracy: 47.86%\n",
      "[3] Test Loss: 1.5045, Accuracy: 52.38%\n",
      "[4] Test Loss: 1.2362, Accuracy: 60.62%\n",
      "[5] Test Loss: 0.8269, Accuracy: 71.20%\n",
      "[6] Test Loss: 0.8386, Accuracy: 71.09%\n",
      "[7] Test Loss: 1.0024, Accuracy: 68.15%\n",
      "[8] Test Loss: 0.7147, Accuracy: 76.10%\n",
      "[9] Test Loss: 0.8604, Accuracy: 70.36%\n",
      "[10] Test Loss: 0.8569, Accuracy: 71.94%\n",
      "[11] Test Loss: 1.1698, Accuracy: 64.97%\n",
      "[12] Test Loss: 1.5368, Accuracy: 61.46%\n",
      "[13] Test Loss: 1.0091, Accuracy: 68.85%\n",
      "[14] Test Loss: 0.7794, Accuracy: 74.58%\n",
      "[15] Test Loss: 0.7072, Accuracy: 76.37%\n",
      "[16] Test Loss: 0.8953, Accuracy: 70.61%\n",
      "[17] Test Loss: 0.7582, Accuracy: 75.88%\n",
      "[18] Test Loss: 0.7556, Accuracy: 75.05%\n",
      "[19] Test Loss: 0.6844, Accuracy: 76.41%\n",
      "[20] Test Loss: 0.6182, Accuracy: 78.93%\n",
      "[21] Test Loss: 0.6343, Accuracy: 78.74%\n",
      "[22] Test Loss: 0.7613, Accuracy: 75.33%\n",
      "[23] Test Loss: 0.8388, Accuracy: 72.89%\n",
      "[24] Test Loss: 0.8615, Accuracy: 73.75%\n",
      "[25] Test Loss: 0.8367, Accuracy: 73.52%\n",
      "[26] Test Loss: 0.5433, Accuracy: 81.42%\n",
      "[27] Test Loss: 0.7660, Accuracy: 75.04%\n",
      "[28] Test Loss: 0.6100, Accuracy: 80.00%\n",
      "[29] Test Loss: 0.6244, Accuracy: 79.77%\n",
      "[30] Test Loss: 0.6506, Accuracy: 78.32%\n",
      "[31] Test Loss: 0.6200, Accuracy: 79.06%\n",
      "[32] Test Loss: 0.6625, Accuracy: 78.24%\n",
      "[33] Test Loss: 0.7980, Accuracy: 74.98%\n",
      "[34] Test Loss: 0.6674, Accuracy: 77.96%\n",
      "[35] Test Loss: 0.6683, Accuracy: 77.90%\n",
      "[36] Test Loss: 0.6715, Accuracy: 77.20%\n",
      "[37] Test Loss: 0.6475, Accuracy: 78.53%\n",
      "[38] Test Loss: 0.7152, Accuracy: 77.04%\n",
      "[39] Test Loss: 0.6273, Accuracy: 78.77%\n",
      "[40] Test Loss: 0.6973, Accuracy: 77.39%\n",
      "[41] Test Loss: 0.6422, Accuracy: 78.81%\n",
      "[42] Test Loss: 0.6126, Accuracy: 79.78%\n",
      "[43] Test Loss: 1.0110, Accuracy: 71.57%\n",
      "[44] Test Loss: 0.6521, Accuracy: 79.08%\n",
      "[45] Test Loss: 0.7520, Accuracy: 75.71%\n",
      "[46] Test Loss: 0.7605, Accuracy: 76.57%\n",
      "[47] Test Loss: 0.7896, Accuracy: 74.72%\n",
      "[48] Test Loss: 0.6081, Accuracy: 80.14%\n",
      "[49] Test Loss: 0.6339, Accuracy: 79.06%\n",
      "[50] Test Loss: 0.6187, Accuracy: 80.16%\n",
      "[51] Test Loss: 0.3445, Accuracy: 88.19%\n",
      "[52] Test Loss: 0.3309, Accuracy: 88.69%\n",
      "[53] Test Loss: 0.3238, Accuracy: 88.90%\n",
      "[54] Test Loss: 0.3180, Accuracy: 89.16%\n",
      "[55] Test Loss: 0.3243, Accuracy: 89.10%\n",
      "[56] Test Loss: 0.3208, Accuracy: 89.50%\n",
      "[57] Test Loss: 0.3218, Accuracy: 89.34%\n",
      "[58] Test Loss: 0.3220, Accuracy: 89.25%\n",
      "[59] Test Loss: 0.3182, Accuracy: 89.55%\n",
      "[60] Test Loss: 0.3151, Accuracy: 89.48%\n",
      "[61] Test Loss: 0.3091, Accuracy: 89.61%\n",
      "[62] Test Loss: 0.3248, Accuracy: 89.30%\n",
      "[63] Test Loss: 0.3254, Accuracy: 89.61%\n",
      "[64] Test Loss: 0.3344, Accuracy: 89.14%\n",
      "[65] Test Loss: 0.3521, Accuracy: 88.64%\n",
      "[66] Test Loss: 0.3323, Accuracy: 88.93%\n",
      "[67] Test Loss: 0.3274, Accuracy: 89.43%\n",
      "[68] Test Loss: 0.3278, Accuracy: 89.64%\n",
      "[69] Test Loss: 0.3439, Accuracy: 88.86%\n",
      "[70] Test Loss: 0.3302, Accuracy: 89.44%\n",
      "[71] Test Loss: 0.3370, Accuracy: 89.09%\n",
      "[72] Test Loss: 0.3269, Accuracy: 89.68%\n",
      "[73] Test Loss: 0.3447, Accuracy: 89.06%\n",
      "[74] Test Loss: 0.3705, Accuracy: 87.62%\n",
      "[75] Test Loss: 0.3557, Accuracy: 88.52%\n",
      "[76] Test Loss: 0.3810, Accuracy: 87.94%\n",
      "[77] Test Loss: 0.3673, Accuracy: 88.49%\n",
      "[78] Test Loss: 0.3582, Accuracy: 88.28%\n",
      "[79] Test Loss: 0.3832, Accuracy: 87.99%\n",
      "[80] Test Loss: 0.3742, Accuracy: 87.81%\n",
      "[81] Test Loss: 0.3475, Accuracy: 88.94%\n",
      "[82] Test Loss: 0.3652, Accuracy: 88.56%\n",
      "[83] Test Loss: 0.3416, Accuracy: 89.07%\n",
      "[84] Test Loss: 0.3624, Accuracy: 88.05%\n",
      "[85] Test Loss: 0.3525, Accuracy: 88.37%\n",
      "[86] Test Loss: 0.3638, Accuracy: 88.35%\n",
      "[87] Test Loss: 0.4042, Accuracy: 87.29%\n",
      "[88] Test Loss: 0.3676, Accuracy: 88.05%\n",
      "[89] Test Loss: 0.3552, Accuracy: 88.21%\n",
      "[90] Test Loss: 0.4019, Accuracy: 87.09%\n",
      "[91] Test Loss: 0.4045, Accuracy: 87.24%\n",
      "[92] Test Loss: 0.3736, Accuracy: 87.78%\n",
      "[93] Test Loss: 0.3753, Accuracy: 88.08%\n",
      "[94] Test Loss: 0.3634, Accuracy: 88.42%\n",
      "[95] Test Loss: 0.3889, Accuracy: 87.89%\n",
      "[96] Test Loss: 0.4017, Accuracy: 87.24%\n",
      "[97] Test Loss: 0.3709, Accuracy: 87.89%\n",
      "[98] Test Loss: 0.3554, Accuracy: 88.50%\n",
      "[99] Test Loss: 0.3531, Accuracy: 88.83%\n",
      "[100] Test Loss: 0.3678, Accuracy: 88.14%\n",
      "[101] Test Loss: 0.2922, Accuracy: 90.22%\n",
      "[102] Test Loss: 0.2874, Accuracy: 90.52%\n",
      "[103] Test Loss: 0.2834, Accuracy: 90.66%\n",
      "[104] Test Loss: 0.2865, Accuracy: 90.62%\n",
      "[105] Test Loss: 0.2843, Accuracy: 90.54%\n",
      "[106] Test Loss: 0.2844, Accuracy: 90.80%\n",
      "[107] Test Loss: 0.2867, Accuracy: 90.74%\n",
      "[108] Test Loss: 0.2829, Accuracy: 90.81%\n",
      "[109] Test Loss: 0.2857, Accuracy: 90.75%\n",
      "[110] Test Loss: 0.2884, Accuracy: 90.90%\n",
      "[111] Test Loss: 0.2855, Accuracy: 90.86%\n",
      "[112] Test Loss: 0.2882, Accuracy: 90.84%\n",
      "[113] Test Loss: 0.2876, Accuracy: 90.79%\n",
      "[114] Test Loss: 0.2861, Accuracy: 90.96%\n",
      "[115] Test Loss: 0.2875, Accuracy: 90.92%\n",
      "[116] Test Loss: 0.2918, Accuracy: 90.85%\n",
      "[117] Test Loss: 0.2879, Accuracy: 91.11%\n",
      "[118] Test Loss: 0.2885, Accuracy: 91.04%\n",
      "[119] Test Loss: 0.2918, Accuracy: 90.86%\n",
      "[120] Test Loss: 0.2913, Accuracy: 90.93%\n",
      "[121] Test Loss: 0.2960, Accuracy: 90.93%\n",
      "[122] Test Loss: 0.2917, Accuracy: 90.91%\n",
      "[123] Test Loss: 0.2940, Accuracy: 90.85%\n",
      "[124] Test Loss: 0.2971, Accuracy: 91.05%\n",
      "[125] Test Loss: 0.2990, Accuracy: 90.86%\n",
      "[126] Test Loss: 0.2970, Accuracy: 90.79%\n",
      "[127] Test Loss: 0.2964, Accuracy: 90.79%\n",
      "[128] Test Loss: 0.2964, Accuracy: 90.94%\n",
      "[129] Test Loss: 0.2952, Accuracy: 90.85%\n",
      "[130] Test Loss: 0.2974, Accuracy: 91.03%\n",
      "[131] Test Loss: 0.2980, Accuracy: 90.74%\n",
      "[132] Test Loss: 0.2979, Accuracy: 91.01%\n",
      "[133] Test Loss: 0.2997, Accuracy: 90.90%\n",
      "[134] Test Loss: 0.2991, Accuracy: 91.11%\n",
      "[135] Test Loss: 0.3004, Accuracy: 91.08%\n",
      "[136] Test Loss: 0.2992, Accuracy: 91.01%\n",
      "[137] Test Loss: 0.3000, Accuracy: 91.06%\n",
      "[138] Test Loss: 0.3035, Accuracy: 90.70%\n",
      "[139] Test Loss: 0.3011, Accuracy: 90.91%\n",
      "[140] Test Loss: 0.3007, Accuracy: 90.91%\n",
      "[141] Test Loss: 0.3042, Accuracy: 90.96%\n",
      "[142] Test Loss: 0.3026, Accuracy: 90.78%\n",
      "[143] Test Loss: 0.3036, Accuracy: 90.85%\n",
      "[144] Test Loss: 0.3075, Accuracy: 90.98%\n",
      "[145] Test Loss: 0.3027, Accuracy: 90.96%\n",
      "[146] Test Loss: 0.3026, Accuracy: 90.98%\n",
      "[147] Test Loss: 0.3022, Accuracy: 90.96%\n",
      "[148] Test Loss: 0.3083, Accuracy: 90.87%\n",
      "[149] Test Loss: 0.3042, Accuracy: 90.93%\n",
      "[150] Test Loss: 0.3062, Accuracy: 90.94%\n",
      "[151] Test Loss: 0.3042, Accuracy: 90.80%\n",
      "[152] Test Loss: 0.3056, Accuracy: 90.83%\n",
      "[153] Test Loss: 0.3046, Accuracy: 90.84%\n",
      "[154] Test Loss: 0.3033, Accuracy: 90.85%\n",
      "[155] Test Loss: 0.3037, Accuracy: 90.89%\n",
      "[156] Test Loss: 0.3018, Accuracy: 90.91%\n",
      "[157] Test Loss: 0.3013, Accuracy: 90.92%\n",
      "[158] Test Loss: 0.3040, Accuracy: 91.02%\n",
      "[159] Test Loss: 0.3038, Accuracy: 91.04%\n",
      "[160] Test Loss: 0.3025, Accuracy: 91.07%\n",
      "[161] Test Loss: 0.3031, Accuracy: 91.08%\n",
      "[162] Test Loss: 0.3049, Accuracy: 91.00%\n",
      "[163] Test Loss: 0.3055, Accuracy: 90.95%\n",
      "[164] Test Loss: 0.3034, Accuracy: 90.98%\n",
      "[165] Test Loss: 0.3027, Accuracy: 90.95%\n",
      "[166] Test Loss: 0.3038, Accuracy: 91.02%\n",
      "[167] Test Loss: 0.3050, Accuracy: 91.06%\n",
      "[168] Test Loss: 0.3032, Accuracy: 91.07%\n",
      "[169] Test Loss: 0.3048, Accuracy: 91.03%\n",
      "[170] Test Loss: 0.3048, Accuracy: 91.10%\n",
      "[171] Test Loss: 0.3019, Accuracy: 91.05%\n",
      "[172] Test Loss: 0.3037, Accuracy: 91.13%\n",
      "[173] Test Loss: 0.3050, Accuracy: 91.09%\n",
      "[174] Test Loss: 0.3034, Accuracy: 90.92%\n",
      "[175] Test Loss: 0.3059, Accuracy: 90.95%\n",
      "[176] Test Loss: 0.3063, Accuracy: 90.90%\n",
      "[177] Test Loss: 0.3052, Accuracy: 90.94%\n",
      "[178] Test Loss: 0.3056, Accuracy: 90.96%\n",
      "[179] Test Loss: 0.3039, Accuracy: 91.10%\n",
      "[180] Test Loss: 0.3041, Accuracy: 90.99%\n",
      "[181] Test Loss: 0.3043, Accuracy: 91.12%\n",
      "[182] Test Loss: 0.3032, Accuracy: 91.05%\n",
      "[183] Test Loss: 0.3030, Accuracy: 91.16%\n",
      "[184] Test Loss: 0.3059, Accuracy: 91.02%\n",
      "[185] Test Loss: 0.3046, Accuracy: 91.03%\n",
      "[186] Test Loss: 0.3062, Accuracy: 90.88%\n",
      "[187] Test Loss: 0.3060, Accuracy: 91.02%\n",
      "[188] Test Loss: 0.3045, Accuracy: 91.04%\n",
      "[189] Test Loss: 0.3058, Accuracy: 91.11%\n",
      "[190] Test Loss: 0.3053, Accuracy: 90.99%\n",
      "[191] Test Loss: 0.3069, Accuracy: 91.07%\n",
      "[192] Test Loss: 0.3077, Accuracy: 91.07%\n",
      "[193] Test Loss: 0.3056, Accuracy: 90.95%\n",
      "[194] Test Loss: 0.3043, Accuracy: 91.04%\n",
      "[195] Test Loss: 0.3056, Accuracy: 91.05%\n",
      "[196] Test Loss: 0.3063, Accuracy: 90.97%\n",
      "[197] Test Loss: 0.3054, Accuracy: 90.95%\n",
      "[198] Test Loss: 0.3051, Accuracy: 91.17%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[199] Test Loss: 0.3071, Accuracy: 91.11%\n",
      "[200] Test Loss: 0.3058, Accuracy: 91.04%\n",
      "[201] Test Loss: 0.3044, Accuracy: 91.02%\n",
      "[202] Test Loss: 0.3075, Accuracy: 91.02%\n",
      "[203] Test Loss: 0.3071, Accuracy: 90.97%\n",
      "[204] Test Loss: 0.3071, Accuracy: 90.99%\n",
      "[205] Test Loss: 0.3064, Accuracy: 91.04%\n",
      "[206] Test Loss: 0.3072, Accuracy: 91.02%\n",
      "[207] Test Loss: 0.3032, Accuracy: 91.03%\n",
      "[208] Test Loss: 0.3071, Accuracy: 91.03%\n",
      "[209] Test Loss: 0.3022, Accuracy: 91.18%\n",
      "[210] Test Loss: 0.3029, Accuracy: 91.14%\n",
      "[211] Test Loss: 0.3062, Accuracy: 91.21%\n",
      "[212] Test Loss: 0.3042, Accuracy: 91.05%\n",
      "[213] Test Loss: 0.3073, Accuracy: 91.00%\n",
      "[214] Test Loss: 0.3039, Accuracy: 91.13%\n",
      "[215] Test Loss: 0.3068, Accuracy: 91.05%\n",
      "[216] Test Loss: 0.3042, Accuracy: 91.05%\n",
      "[217] Test Loss: 0.3063, Accuracy: 91.00%\n",
      "[218] Test Loss: 0.3066, Accuracy: 90.97%\n",
      "[219] Test Loss: 0.3053, Accuracy: 91.07%\n",
      "[220] Test Loss: 0.3068, Accuracy: 91.12%\n",
      "[221] Test Loss: 0.3055, Accuracy: 91.13%\n",
      "[222] Test Loss: 0.3032, Accuracy: 91.12%\n",
      "[223] Test Loss: 0.3070, Accuracy: 91.14%\n",
      "[224] Test Loss: 0.3051, Accuracy: 91.05%\n",
      "[225] Test Loss: 0.3049, Accuracy: 91.17%\n",
      "[226] Test Loss: 0.3053, Accuracy: 91.07%\n",
      "[227] Test Loss: 0.3049, Accuracy: 91.19%\n",
      "[228] Test Loss: 0.3061, Accuracy: 91.01%\n",
      "[229] Test Loss: 0.3071, Accuracy: 90.93%\n",
      "[230] Test Loss: 0.3073, Accuracy: 91.02%\n",
      "[231] Test Loss: 0.3045, Accuracy: 91.11%\n",
      "[232] Test Loss: 0.3078, Accuracy: 91.04%\n",
      "[233] Test Loss: 0.3045, Accuracy: 91.06%\n",
      "[234] Test Loss: 0.3064, Accuracy: 91.13%\n",
      "[235] Test Loss: 0.3054, Accuracy: 91.08%\n",
      "[236] Test Loss: 0.3046, Accuracy: 91.12%\n",
      "[237] Test Loss: 0.3063, Accuracy: 91.12%\n",
      "[238] Test Loss: 0.3055, Accuracy: 91.10%\n",
      "[239] Test Loss: 0.3078, Accuracy: 90.92%\n",
      "[240] Test Loss: 0.3057, Accuracy: 91.12%\n",
      "[241] Test Loss: 0.3049, Accuracy: 91.03%\n",
      "[242] Test Loss: 0.3061, Accuracy: 91.05%\n",
      "[243] Test Loss: 0.3066, Accuracy: 91.00%\n",
      "[244] Test Loss: 0.3073, Accuracy: 91.05%\n",
      "[245] Test Loss: 0.3056, Accuracy: 91.07%\n",
      "[246] Test Loss: 0.3053, Accuracy: 90.93%\n",
      "[247] Test Loss: 0.3034, Accuracy: 91.08%\n",
      "[248] Test Loss: 0.3069, Accuracy: 91.02%\n",
      "[249] Test Loss: 0.3059, Accuracy: 90.99%\n",
      "[250] Test Loss: 0.3061, Accuracy: 91.09%\n",
      "[251] Test Loss: 0.3083, Accuracy: 90.95%\n",
      "[252] Test Loss: 0.3067, Accuracy: 91.09%\n",
      "[253] Test Loss: 0.3065, Accuracy: 91.12%\n",
      "[254] Test Loss: 0.3029, Accuracy: 91.10%\n",
      "[255] Test Loss: 0.3056, Accuracy: 91.08%\n",
      "[256] Test Loss: 0.3069, Accuracy: 91.10%\n",
      "[257] Test Loss: 0.3051, Accuracy: 91.19%\n",
      "[258] Test Loss: 0.3050, Accuracy: 91.05%\n",
      "[259] Test Loss: 0.3058, Accuracy: 91.15%\n",
      "[260] Test Loss: 0.3068, Accuracy: 90.99%\n",
      "[261] Test Loss: 0.3039, Accuracy: 91.09%\n",
      "[262] Test Loss: 0.3055, Accuracy: 90.99%\n",
      "[263] Test Loss: 0.3052, Accuracy: 91.06%\n",
      "[264] Test Loss: 0.3059, Accuracy: 91.08%\n",
      "[265] Test Loss: 0.3067, Accuracy: 91.17%\n",
      "[266] Test Loss: 0.3064, Accuracy: 91.19%\n",
      "[267] Test Loss: 0.3054, Accuracy: 91.13%\n",
      "[268] Test Loss: 0.3047, Accuracy: 90.97%\n",
      "[269] Test Loss: 0.3039, Accuracy: 91.13%\n",
      "[270] Test Loss: 0.3066, Accuracy: 91.07%\n",
      "[271] Test Loss: 0.3072, Accuracy: 91.04%\n",
      "[272] Test Loss: 0.3089, Accuracy: 90.98%\n",
      "[273] Test Loss: 0.3063, Accuracy: 91.12%\n",
      "[274] Test Loss: 0.3081, Accuracy: 91.01%\n",
      "[275] Test Loss: 0.3066, Accuracy: 91.10%\n",
      "[276] Test Loss: 0.3044, Accuracy: 91.00%\n",
      "[277] Test Loss: 0.3062, Accuracy: 91.02%\n",
      "[278] Test Loss: 0.3065, Accuracy: 91.11%\n",
      "[279] Test Loss: 0.3029, Accuracy: 91.04%\n",
      "[280] Test Loss: 0.3058, Accuracy: 91.07%\n",
      "[281] Test Loss: 0.3061, Accuracy: 91.11%\n",
      "[282] Test Loss: 0.3076, Accuracy: 91.11%\n",
      "[283] Test Loss: 0.3059, Accuracy: 91.11%\n",
      "[284] Test Loss: 0.3054, Accuracy: 91.10%\n",
      "[285] Test Loss: 0.3042, Accuracy: 91.19%\n",
      "[286] Test Loss: 0.3067, Accuracy: 90.89%\n",
      "[287] Test Loss: 0.3051, Accuracy: 91.12%\n",
      "[288] Test Loss: 0.3044, Accuracy: 91.16%\n",
      "[289] Test Loss: 0.3050, Accuracy: 91.00%\n",
      "[290] Test Loss: 0.3039, Accuracy: 91.12%\n",
      "[291] Test Loss: 0.3044, Accuracy: 91.11%\n",
      "[292] Test Loss: 0.3045, Accuracy: 91.08%\n",
      "[293] Test Loss: 0.3058, Accuracy: 90.95%\n",
      "[294] Test Loss: 0.3050, Accuracy: 91.15%\n",
      "[295] Test Loss: 0.3055, Accuracy: 91.07%\n",
      "[296] Test Loss: 0.3086, Accuracy: 91.03%\n",
      "[297] Test Loss: 0.3060, Accuracy: 91.10%\n",
      "[298] Test Loss: 0.3066, Accuracy: 91.07%\n",
      "[299] Test Loss: 0.3060, Accuracy: 91.03%\n",
      "[300] Test Loss: 0.3084, Accuracy: 91.12%\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, EPOCHS + 1):\n",
    "    scheduler.step()\n",
    "    train(model, train_loader, optimizer, epoch)\n",
    "    test_loss, test_accuracy = evaluate(model, test_loader)\n",
    "    \n",
    "    print('[{}] Test Loss: {:.4f}, Accuracy: {:.2f}%'.format(\n",
    "          epoch, test_loss, test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}