05-이미지_처리능력이_탁월한_CNN/cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN으로 패션 아이템 구분하기\n",
    "Convolutional Neural Network (CNN) 을 이용하여 패션아이템 구분 성능을 높여보겠습니다."
   ]
  },
  {
   "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"
   ]
  },
  {
   "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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS     = 40\n",
    "BATCH_SIZE = 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('./.data',\n",
    "                   train=True,\n",
    "                   download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('./.data',\n",
    "                   train=False, \n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 뉴럴넷으로 Fashion MNIST 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 하이퍼파라미터 \n",
    "\n",
    "`to()` 함수는 모델의 파라미터들을 지정한 곳으로 보내는 역할을 합니다. 일반적으로 CPU 1개만 사용할 경우 필요는 없지만, GPU를 사용하고자 하는 경우 `to(\"cuda\")`로 지정하여 GPU로 보내야 합니다. 지정하지 않을 경우 계속 CPU에 남아 있게 되며 빠른 훈련의 이점을 누리실 수 없습니다.\n",
    "\n",
    "최적화 알고리즘으로 파이토치에 내장되어 있는 `optim.SGD`를 사용하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model     = Net().to(DEVICE)\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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()\n",
    "\n",
    "        if batch_idx % 200 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 테스트하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 학습이 되는지 확인해봅시다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.316563\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.325833\n",
      "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.639851\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.588295\n",
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.383355\n",
      "[1] Test Loss: 0.1806, Accuracy: 94.80%\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.376632\n",
      "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.576826\n",
      "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.290264\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.384606\n",
      "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.231127\n",
      "[2] Test Loss: 0.1157, Accuracy: 96.63%\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.322572\n",
      "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.243691\n",
      "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.147031\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.457077\n",
      "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.264105\n",
      "[3] Test Loss: 0.0980, Accuracy: 96.95%\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.383000\n",
      "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.298453\n",
      "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.301662\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.128000\n",
      "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.274840\n",
      "[4] Test Loss: 0.0757, Accuracy: 97.55%\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.153898\n",
      "Train Epoch: 5 [12800/60000 (21%)]\tLoss: 0.186248\n",
      "Train Epoch: 5 [25600/60000 (43%)]\tLoss: 0.298463\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss: 0.149131\n",
      "Train Epoch: 5 [51200/60000 (85%)]\tLoss: 0.306070\n",
      "[5] Test Loss: 0.0685, Accuracy: 97.89%\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.117231\n",
      "Train Epoch: 6 [12800/60000 (21%)]\tLoss: 0.094771\n",
      "Train Epoch: 6 [25600/60000 (43%)]\tLoss: 0.472471\n",
      "Train Epoch: 6 [38400/60000 (64%)]\tLoss: 0.157753\n",
      "Train Epoch: 6 [51200/60000 (85%)]\tLoss: 0.064874\n",
      "[6] Test Loss: 0.0626, Accuracy: 98.08%\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.119682\n",
      "Train Epoch: 7 [12800/60000 (21%)]\tLoss: 0.146671\n",
      "Train Epoch: 7 [25600/60000 (43%)]\tLoss: 0.226841\n",
      "Train Epoch: 7 [38400/60000 (64%)]\tLoss: 0.413555\n",
      "Train Epoch: 7 [51200/60000 (85%)]\tLoss: 0.088882\n",
      "[7] Test Loss: 0.0567, Accuracy: 98.23%\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.094884\n",
      "Train Epoch: 8 [12800/60000 (21%)]\tLoss: 0.176951\n",
      "Train Epoch: 8 [25600/60000 (43%)]\tLoss: 0.209330\n",
      "Train Epoch: 8 [38400/60000 (64%)]\tLoss: 0.277064\n",
      "Train Epoch: 8 [51200/60000 (85%)]\tLoss: 0.143112\n",
      "[8] Test Loss: 0.0551, Accuracy: 98.29%\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.254345\n",
      "Train Epoch: 9 [12800/60000 (21%)]\tLoss: 0.152597\n",
      "Train Epoch: 9 [25600/60000 (43%)]\tLoss: 0.118242\n",
      "Train Epoch: 9 [38400/60000 (64%)]\tLoss: 0.195167\n",
      "Train Epoch: 9 [51200/60000 (85%)]\tLoss: 0.145550\n",
      "[9] Test Loss: 0.0536, Accuracy: 98.25%\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.104786\n",
      "Train Epoch: 10 [12800/60000 (21%)]\tLoss: 0.224526\n",
      "Train Epoch: 10 [25600/60000 (43%)]\tLoss: 0.276109\n",
      "Train Epoch: 10 [38400/60000 (64%)]\tLoss: 0.181941\n",
      "Train Epoch: 10 [51200/60000 (85%)]\tLoss: 0.303008\n",
      "[10] Test Loss: 0.0493, Accuracy: 98.41%\n",
      "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.137279\n",
      "Train Epoch: 11 [12800/60000 (21%)]\tLoss: 0.066506\n",
      "Train Epoch: 11 [25600/60000 (43%)]\tLoss: 0.125332\n",
      "Train Epoch: 11 [38400/60000 (64%)]\tLoss: 0.121720\n",
      "Train Epoch: 11 [51200/60000 (85%)]\tLoss: 0.068749\n",
      "[11] Test Loss: 0.0488, Accuracy: 98.48%\n",
      "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.137636\n",
      "Train Epoch: 12 [12800/60000 (21%)]\tLoss: 0.103103\n",
      "Train Epoch: 12 [25600/60000 (43%)]\tLoss: 0.137571\n",
      "Train Epoch: 12 [38400/60000 (64%)]\tLoss: 0.202968\n",
      "Train Epoch: 12 [51200/60000 (85%)]\tLoss: 0.165324\n",
      "[12] Test Loss: 0.0457, Accuracy: 98.48%\n",
      "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.232136\n",
      "Train Epoch: 13 [12800/60000 (21%)]\tLoss: 0.186459\n",
      "Train Epoch: 13 [25600/60000 (43%)]\tLoss: 0.254867\n",
      "Train Epoch: 13 [38400/60000 (64%)]\tLoss: 0.385012\n",
      "Train Epoch: 13 [51200/60000 (85%)]\tLoss: 0.111347\n",
      "[13] Test Loss: 0.0455, Accuracy: 98.62%\n",
      "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.089892\n",
      "Train Epoch: 14 [12800/60000 (21%)]\tLoss: 0.035754\n",
      "Train Epoch: 14 [25600/60000 (43%)]\tLoss: 0.108269\n",
      "Train Epoch: 14 [38400/60000 (64%)]\tLoss: 0.065970\n",
      "Train Epoch: 14 [51200/60000 (85%)]\tLoss: 0.152800\n",
      "[14] Test Loss: 0.0445, Accuracy: 98.53%\n",
      "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.052044\n",
      "Train Epoch: 15 [12800/60000 (21%)]\tLoss: 0.062982\n",
      "Train Epoch: 15 [25600/60000 (43%)]\tLoss: 0.266990\n",
      "Train Epoch: 15 [38400/60000 (64%)]\tLoss: 0.116900\n",
      "Train Epoch: 15 [51200/60000 (85%)]\tLoss: 0.109915\n",
      "[15] Test Loss: 0.0434, Accuracy: 98.54%\n",
      "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.062919\n",
      "Train Epoch: 16 [12800/60000 (21%)]\tLoss: 0.044109\n",
      "Train Epoch: 16 [25600/60000 (43%)]\tLoss: 0.189800\n",
      "Train Epoch: 16 [38400/60000 (64%)]\tLoss: 0.047951\n",
      "Train Epoch: 16 [51200/60000 (85%)]\tLoss: 0.044380\n",
      "[16] Test Loss: 0.0405, Accuracy: 98.68%\n",
      "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.205460\n",
      "Train Epoch: 17 [12800/60000 (21%)]\tLoss: 0.140326\n",
      "Train Epoch: 17 [25600/60000 (43%)]\tLoss: 0.075525\n",
      "Train Epoch: 17 [38400/60000 (64%)]\tLoss: 0.096706\n",
      "Train Epoch: 17 [51200/60000 (85%)]\tLoss: 0.173940\n",
      "[17] Test Loss: 0.0399, Accuracy: 98.74%\n",
      "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.151406\n",
      "Train Epoch: 18 [12800/60000 (21%)]\tLoss: 0.118890\n",
      "Train Epoch: 18 [25600/60000 (43%)]\tLoss: 0.097406\n",
      "Train Epoch: 18 [38400/60000 (64%)]\tLoss: 0.044863\n",
      "Train Epoch: 18 [51200/60000 (85%)]\tLoss: 0.287857\n",
      "[18] Test Loss: 0.0396, Accuracy: 98.77%\n",
      "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.159900\n",
      "Train Epoch: 19 [12800/60000 (21%)]\tLoss: 0.043677\n",
      "Train Epoch: 19 [25600/60000 (43%)]\tLoss: 0.093753\n",
      "Train Epoch: 19 [38400/60000 (64%)]\tLoss: 0.233987\n",
      "Train Epoch: 19 [51200/60000 (85%)]\tLoss: 0.151285\n",
      "[19] Test Loss: 0.0394, Accuracy: 98.75%\n",
      "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.118045\n",
      "Train Epoch: 20 [12800/60000 (21%)]\tLoss: 0.021452\n",
      "Train Epoch: 20 [25600/60000 (43%)]\tLoss: 0.076194\n",
      "Train Epoch: 20 [38400/60000 (64%)]\tLoss: 0.047916\n",
      "Train Epoch: 20 [51200/60000 (85%)]\tLoss: 0.078987\n",
      "[20] Test Loss: 0.0382, Accuracy: 98.77%\n",
      "Train Epoch: 21 [0/60000 (0%)]\tLoss: 0.167188\n",
      "Train Epoch: 21 [12800/60000 (21%)]\tLoss: 0.053896\n",
      "Train Epoch: 21 [25600/60000 (43%)]\tLoss: 0.112439\n",
      "Train Epoch: 21 [38400/60000 (64%)]\tLoss: 0.278413\n",
      "Train Epoch: 21 [51200/60000 (85%)]\tLoss: 0.080261\n",
      "[21] Test Loss: 0.0378, Accuracy: 98.79%\n",
      "Train Epoch: 22 [0/60000 (0%)]\tLoss: 0.039508\n",
      "Train Epoch: 22 [12800/60000 (21%)]\tLoss: 0.033131\n",
      "Train Epoch: 22 [25600/60000 (43%)]\tLoss: 0.070003\n",
      "Train Epoch: 22 [38400/60000 (64%)]\tLoss: 0.093605\n",
      "Train Epoch: 22 [51200/60000 (85%)]\tLoss: 0.121742\n",
      "[22] Test Loss: 0.0370, Accuracy: 98.77%\n",
      "Train Epoch: 23 [0/60000 (0%)]\tLoss: 0.096805\n",
      "Train Epoch: 23 [12800/60000 (21%)]\tLoss: 0.063695\n",
      "Train Epoch: 23 [25600/60000 (43%)]\tLoss: 0.097901\n",
      "Train Epoch: 23 [38400/60000 (64%)]\tLoss: 0.084583\n",
      "Train Epoch: 23 [51200/60000 (85%)]\tLoss: 0.128350\n",
      "[23] Test Loss: 0.0362, Accuracy: 98.87%\n",
      "Train Epoch: 24 [0/60000 (0%)]\tLoss: 0.422443\n",
      "Train Epoch: 24 [12800/60000 (21%)]\tLoss: 0.110544\n",
      "Train Epoch: 24 [25600/60000 (43%)]\tLoss: 0.289081\n",
      "Train Epoch: 24 [38400/60000 (64%)]\tLoss: 0.083205\n",
      "Train Epoch: 24 [51200/60000 (85%)]\tLoss: 0.071597\n",
      "[24] Test Loss: 0.0362, Accuracy: 98.87%\n",
      "Train Epoch: 25 [0/60000 (0%)]\tLoss: 0.047579\n",
      "Train Epoch: 25 [12800/60000 (21%)]\tLoss: 0.078012\n",
      "Train Epoch: 25 [25600/60000 (43%)]\tLoss: 0.078962\n",
      "Train Epoch: 25 [38400/60000 (64%)]\tLoss: 0.203147\n",
      "Train Epoch: 25 [51200/60000 (85%)]\tLoss: 0.092343\n",
      "[25] Test Loss: 0.0355, Accuracy: 98.90%\n",
      "Train Epoch: 26 [0/60000 (0%)]\tLoss: 0.101361\n",
      "Train Epoch: 26 [12800/60000 (21%)]\tLoss: 0.031745\n",
      "Train Epoch: 26 [25600/60000 (43%)]\tLoss: 0.044232\n",
      "Train Epoch: 26 [38400/60000 (64%)]\tLoss: 0.050185\n",
      "Train Epoch: 26 [51200/60000 (85%)]\tLoss: 0.071562\n",
      "[26] Test Loss: 0.0373, Accuracy: 98.88%\n",
      "Train Epoch: 27 [0/60000 (0%)]\tLoss: 0.105135\n",
      "Train Epoch: 27 [12800/60000 (21%)]\tLoss: 0.082961\n",
      "Train Epoch: 27 [25600/60000 (43%)]\tLoss: 0.150725\n",
      "Train Epoch: 27 [38400/60000 (64%)]\tLoss: 0.029253\n",
      "Train Epoch: 27 [51200/60000 (85%)]\tLoss: 0.081586\n",
      "[27] Test Loss: 0.0356, Accuracy: 98.83%\n",
      "Train Epoch: 28 [0/60000 (0%)]\tLoss: 0.043699\n",
      "Train Epoch: 28 [12800/60000 (21%)]\tLoss: 0.179697\n",
      "Train Epoch: 28 [25600/60000 (43%)]\tLoss: 0.124703\n",
      "Train Epoch: 28 [38400/60000 (64%)]\tLoss: 0.148912\n",
      "Train Epoch: 28 [51200/60000 (85%)]\tLoss: 0.115277\n",
      "[28] Test Loss: 0.0341, Accuracy: 98.93%\n",
      "Train Epoch: 29 [0/60000 (0%)]\tLoss: 0.104593\n",
      "Train Epoch: 29 [12800/60000 (21%)]\tLoss: 0.051618\n",
      "Train Epoch: 29 [25600/60000 (43%)]\tLoss: 0.152967\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 29 [38400/60000 (64%)]\tLoss: 0.106250\n",
      "Train Epoch: 29 [51200/60000 (85%)]\tLoss: 0.231409\n",
      "[29] Test Loss: 0.0358, Accuracy: 98.82%\n",
      "Train Epoch: 30 [0/60000 (0%)]\tLoss: 0.065754\n",
      "Train Epoch: 30 [12800/60000 (21%)]\tLoss: 0.254962\n",
      "Train Epoch: 30 [25600/60000 (43%)]\tLoss: 0.196665\n",
      "Train Epoch: 30 [38400/60000 (64%)]\tLoss: 0.124789\n",
      "Train Epoch: 30 [51200/60000 (85%)]\tLoss: 0.041916\n",
      "[30] Test Loss: 0.0354, Accuracy: 98.84%\n",
      "Train Epoch: 31 [0/60000 (0%)]\tLoss: 0.048826\n",
      "Train Epoch: 31 [12800/60000 (21%)]\tLoss: 0.146526\n",
      "Train Epoch: 31 [25600/60000 (43%)]\tLoss: 0.047718\n",
      "Train Epoch: 31 [38400/60000 (64%)]\tLoss: 0.099325\n",
      "Train Epoch: 31 [51200/60000 (85%)]\tLoss: 0.118444\n",
      "[31] Test Loss: 0.0346, Accuracy: 98.87%\n",
      "Train Epoch: 32 [0/60000 (0%)]\tLoss: 0.054388\n",
      "Train Epoch: 32 [12800/60000 (21%)]\tLoss: 0.068653\n",
      "Train Epoch: 32 [25600/60000 (43%)]\tLoss: 0.140763\n",
      "Train Epoch: 32 [38400/60000 (64%)]\tLoss: 0.143319\n",
      "Train Epoch: 32 [51200/60000 (85%)]\tLoss: 0.044462\n",
      "[32] Test Loss: 0.0332, Accuracy: 98.95%\n",
      "Train Epoch: 33 [0/60000 (0%)]\tLoss: 0.165036\n",
      "Train Epoch: 33 [12800/60000 (21%)]\tLoss: 0.073200\n",
      "Train Epoch: 33 [25600/60000 (43%)]\tLoss: 0.126343\n",
      "Train Epoch: 33 [38400/60000 (64%)]\tLoss: 0.167853\n",
      "Train Epoch: 33 [51200/60000 (85%)]\tLoss: 0.274132\n",
      "[33] Test Loss: 0.0356, Accuracy: 98.82%\n",
      "Train Epoch: 34 [0/60000 (0%)]\tLoss: 0.065629\n",
      "Train Epoch: 34 [12800/60000 (21%)]\tLoss: 0.079576\n",
      "Train Epoch: 34 [25600/60000 (43%)]\tLoss: 0.259723\n",
      "Train Epoch: 34 [38400/60000 (64%)]\tLoss: 0.093253\n",
      "Train Epoch: 34 [51200/60000 (85%)]\tLoss: 0.182953\n",
      "[34] Test Loss: 0.0329, Accuracy: 99.04%\n",
      "Train Epoch: 35 [0/60000 (0%)]\tLoss: 0.051614\n",
      "Train Epoch: 35 [12800/60000 (21%)]\tLoss: 0.032125\n",
      "Train Epoch: 35 [25600/60000 (43%)]\tLoss: 0.136953\n",
      "Train Epoch: 35 [38400/60000 (64%)]\tLoss: 0.154008\n",
      "Train Epoch: 35 [51200/60000 (85%)]\tLoss: 0.260217\n",
      "[35] Test Loss: 0.0324, Accuracy: 98.92%\n",
      "Train Epoch: 36 [0/60000 (0%)]\tLoss: 0.113501\n",
      "Train Epoch: 36 [12800/60000 (21%)]\tLoss: 0.149662\n",
      "Train Epoch: 36 [25600/60000 (43%)]\tLoss: 0.101856\n",
      "Train Epoch: 36 [38400/60000 (64%)]\tLoss: 0.053185\n",
      "Train Epoch: 36 [51200/60000 (85%)]\tLoss: 0.059485\n",
      "[36] Test Loss: 0.0339, Accuracy: 98.89%\n",
      "Train Epoch: 37 [0/60000 (0%)]\tLoss: 0.102327\n",
      "Train Epoch: 37 [12800/60000 (21%)]\tLoss: 0.254774\n",
      "Train Epoch: 37 [25600/60000 (43%)]\tLoss: 0.211139\n",
      "Train Epoch: 37 [38400/60000 (64%)]\tLoss: 0.068077\n",
      "Train Epoch: 37 [51200/60000 (85%)]\tLoss: 0.140684\n",
      "[37] Test Loss: 0.0363, Accuracy: 98.82%\n",
      "Train Epoch: 38 [0/60000 (0%)]\tLoss: 0.139026\n",
      "Train Epoch: 38 [12800/60000 (21%)]\tLoss: 0.069054\n",
      "Train Epoch: 38 [25600/60000 (43%)]\tLoss: 0.093474\n",
      "Train Epoch: 38 [38400/60000 (64%)]\tLoss: 0.176398\n",
      "Train Epoch: 38 [51200/60000 (85%)]\tLoss: 0.171736\n",
      "[38] Test Loss: 0.0345, Accuracy: 99.01%\n",
      "Train Epoch: 39 [0/60000 (0%)]\tLoss: 0.022420\n",
      "Train Epoch: 39 [12800/60000 (21%)]\tLoss: 0.075206\n",
      "Train Epoch: 39 [25600/60000 (43%)]\tLoss: 0.050408\n",
      "Train Epoch: 39 [38400/60000 (64%)]\tLoss: 0.205187\n",
      "Train Epoch: 39 [51200/60000 (85%)]\tLoss: 0.429174\n",
      "[39] Test Loss: 0.0330, Accuracy: 98.94%\n",
      "Train Epoch: 40 [0/60000 (0%)]\tLoss: 0.038683\n",
      "Train Epoch: 40 [12800/60000 (21%)]\tLoss: 0.089646\n",
      "Train Epoch: 40 [25600/60000 (43%)]\tLoss: 0.033504\n",
      "Train Epoch: 40 [38400/60000 (64%)]\tLoss: 0.258388\n",
      "Train Epoch: 40 [51200/60000 (85%)]\tLoss: 0.245377\n",
      "[40] Test Loss: 0.0321, Accuracy: 99.01%\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, EPOCHS + 1):\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))"
   ]
  }
 ],
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