4-2_CIFAR_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 1. Module Import '''\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using PyTorch version: 1.6.0+cu101  Device: cuda\n"
     ]
    }
   ],
   "source": [
    "''' 2. 딥러닝 모델을 설계할 때 활용하는 장비 확인 '''\n",
    "if torch.cuda.is_available():\n",
    "    DEVICE = torch.device('cuda')\n",
    "else:\n",
    "    DEVICE = torch.device('cpu')\n",
    "print('Using PyTorch version:', torch.__version__, ' Device:', DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "''' 3. CIFAR10 데이터 다운로드 (Train set, Test set 분리하기) '''\n",
    "train_dataset = datasets.CIFAR10(root = \"../data/CIFAR_10\",\n",
    "                                  train = True,\n",
    "                                  download = True,\n",
    "                                  transform = transforms.ToTensor())\n",
    "\n",
    "test_dataset = datasets.CIFAR10(root = \"../data/CIFAR_10\",\n",
    "                                train = False,\n",
    "                                transform = transforms.ToTensor())\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n",
    "                                            batch_size = BATCH_SIZE,\n",
    "                                            shuffle = True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset = test_dataset,\n",
    "                                          batch_size = BATCH_SIZE,\n",
    "                                          shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: torch.Size([32, 3, 32, 32]) type: torch.FloatTensor\n",
      "y_train: torch.Size([32]) type: torch.LongTensor\n"
     ]
    }
   ],
   "source": [
    "''' 4. 데이터 확인하기 (1) '''\n",
    "for (X_train, y_train) in train_loader:\n",
    "    print('X_train:', X_train.size(), 'type:', X_train.type())\n",
    "    print('y_train:', y_train.size(), 'type:', y_train.type())\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' 5. 데이터 확인하기 (2) '''\n",
    "pltsize = 1\n",
    "plt.figure(figsize=(10 * pltsize, pltsize))\n",
    "\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(np.transpose(X_train[i], (1, 2, 0)))\n",
    "    plt.title('Class: ' + str(y_train[i].item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 6. Convolutional Neural Network (CNN) 모델 설계하기 '''\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels = 3, out_channels = 8, kernel_size = 3, padding = 1)\n",
    "        self.conv2 = nn.Conv2d(in_channels = 8, out_channels = 16, kernel_size = 3, padding = 1)\n",
    "        self.pool = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "        self.fc1 = nn.Linear(8 * 8 * 16, 64)\n",
    "        self.fc2 = nn.Linear(64, 32)\n",
    "        self.fc3 = nn.Linear(32, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.pool(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        x = x.view(-1, 8 * 8 * 16)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.log_softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=1024, out_features=64, bias=True)\n",
      "  (fc2): Linear(in_features=64, out_features=32, bias=True)\n",
      "  (fc3): Linear(in_features=32, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "''' 7. Optimizer, Objective Function 설정하기 '''\n",
    "model = CNN().to(DEVICE)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 8. CNN 모델 학습을 진행하며 학습 데이터에 대한 모델 성능을 확인하는 함수 정의 '''\n",
    "def train(model, train_loader, optimizer, log_interval):\n",
    "    model.train()\n",
    "    for batch_idx, (image, label) in enumerate(train_loader):\n",
    "        image = image.to(DEVICE)\n",
    "        label = label.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(image)\n",
    "        loss = criterion(output, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print(\"Train Epoch: {} [{}/{} ({:.0f}%)]\\tTrain Loss: {:.6f}\".format(\n",
    "                epoch, batch_idx * len(image), \n",
    "                len(train_loader.dataset), 100. * batch_idx / len(train_loader), \n",
    "                loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 9. 학습되는 과정 속에서 검증 데이터에 대한 모델 성능을 확인하는 함수 정의 '''\n",
    "def evaluate(model, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for image, label in test_loader:\n",
    "            image = image.to(DEVICE)\n",
    "            label = label.to(DEVICE)\n",
    "            output = model(image)\n",
    "            test_loss += criterion(output, label).item()\n",
    "            prediction = output.max(1, keepdim = True)[1]\n",
    "            correct += prediction.eq(label.view_as(prediction)).sum().item()\n",
    "    \n",
    "    test_loss /= (len(test_loader.dataset) / BATCH_SIZE)\n",
    "    test_accuracy = 100. * correct / len(test_loader.dataset)\n",
    "    return test_loss, test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\justin\\101\\lib\\site-packages\\ipykernel_launcher.py:26: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/50000 (0%)]\tTrain Loss: 2.305517\n",
      "Train Epoch: 1 [6400/50000 (13%)]\tTrain Loss: 1.908107\n",
      "Train Epoch: 1 [12800/50000 (26%)]\tTrain Loss: 1.693745\n",
      "Train Epoch: 1 [19200/50000 (38%)]\tTrain Loss: 1.713397\n",
      "Train Epoch: 1 [25600/50000 (51%)]\tTrain Loss: 1.586022\n",
      "Train Epoch: 1 [32000/50000 (64%)]\tTrain Loss: 1.581453\n",
      "Train Epoch: 1 [38400/50000 (77%)]\tTrain Loss: 1.910545\n",
      "Train Epoch: 1 [44800/50000 (90%)]\tTrain Loss: 1.792278\n",
      "\n",
      "[EPOCH: 1], \tTest Loss: 0.0472, \tTest Accuracy: 45.02 % \n",
      "\n",
      "Train Epoch: 2 [0/50000 (0%)]\tTrain Loss: 1.455860\n",
      "Train Epoch: 2 [6400/50000 (13%)]\tTrain Loss: 1.605248\n",
      "Train Epoch: 2 [12800/50000 (26%)]\tTrain Loss: 1.577104\n",
      "Train Epoch: 2 [19200/50000 (38%)]\tTrain Loss: 1.809724\n",
      "Train Epoch: 2 [25600/50000 (51%)]\tTrain Loss: 1.520014\n",
      "Train Epoch: 2 [32000/50000 (64%)]\tTrain Loss: 1.443045\n",
      "Train Epoch: 2 [38400/50000 (77%)]\tTrain Loss: 1.241889\n",
      "Train Epoch: 2 [44800/50000 (90%)]\tTrain Loss: 1.137973\n",
      "\n",
      "[EPOCH: 2], \tTest Loss: 0.0414, \tTest Accuracy: 51.91 % \n",
      "\n",
      "Train Epoch: 3 [0/50000 (0%)]\tTrain Loss: 1.196696\n",
      "Train Epoch: 3 [6400/50000 (13%)]\tTrain Loss: 1.453618\n",
      "Train Epoch: 3 [12800/50000 (26%)]\tTrain Loss: 1.258015\n",
      "Train Epoch: 3 [19200/50000 (38%)]\tTrain Loss: 1.022565\n",
      "Train Epoch: 3 [25600/50000 (51%)]\tTrain Loss: 1.333595\n",
      "Train Epoch: 3 [32000/50000 (64%)]\tTrain Loss: 1.336248\n",
      "Train Epoch: 3 [38400/50000 (77%)]\tTrain Loss: 1.380931\n",
      "Train Epoch: 3 [44800/50000 (90%)]\tTrain Loss: 1.426755\n",
      "\n",
      "[EPOCH: 3], \tTest Loss: 0.0402, \tTest Accuracy: 53.18 % \n",
      "\n",
      "Train Epoch: 4 [0/50000 (0%)]\tTrain Loss: 1.122832\n",
      "Train Epoch: 4 [6400/50000 (13%)]\tTrain Loss: 1.230753\n",
      "Train Epoch: 4 [12800/50000 (26%)]\tTrain Loss: 1.214417\n",
      "Train Epoch: 4 [19200/50000 (38%)]\tTrain Loss: 1.143114\n",
      "Train Epoch: 4 [25600/50000 (51%)]\tTrain Loss: 1.294414\n",
      "Train Epoch: 4 [32000/50000 (64%)]\tTrain Loss: 1.173503\n",
      "Train Epoch: 4 [38400/50000 (77%)]\tTrain Loss: 1.112951\n",
      "Train Epoch: 4 [44800/50000 (90%)]\tTrain Loss: 1.135897\n",
      "\n",
      "[EPOCH: 4], \tTest Loss: 0.0364, \tTest Accuracy: 58.33 % \n",
      "\n",
      "Train Epoch: 5 [0/50000 (0%)]\tTrain Loss: 0.937898\n",
      "Train Epoch: 5 [6400/50000 (13%)]\tTrain Loss: 0.844177\n",
      "Train Epoch: 5 [12800/50000 (26%)]\tTrain Loss: 1.193916\n",
      "Train Epoch: 5 [19200/50000 (38%)]\tTrain Loss: 1.454867\n",
      "Train Epoch: 5 [25600/50000 (51%)]\tTrain Loss: 1.367757\n",
      "Train Epoch: 5 [32000/50000 (64%)]\tTrain Loss: 1.200525\n",
      "Train Epoch: 5 [38400/50000 (77%)]\tTrain Loss: 1.121082\n",
      "Train Epoch: 5 [44800/50000 (90%)]\tTrain Loss: 1.100181\n",
      "\n",
      "[EPOCH: 5], \tTest Loss: 0.0350, \tTest Accuracy: 60.89 % \n",
      "\n",
      "Train Epoch: 6 [0/50000 (0%)]\tTrain Loss: 1.163504\n",
      "Train Epoch: 6 [6400/50000 (13%)]\tTrain Loss: 0.834767\n",
      "Train Epoch: 6 [12800/50000 (26%)]\tTrain Loss: 1.071186\n",
      "Train Epoch: 6 [19200/50000 (38%)]\tTrain Loss: 1.239095\n",
      "Train Epoch: 6 [25600/50000 (51%)]\tTrain Loss: 0.870492\n",
      "Train Epoch: 6 [32000/50000 (64%)]\tTrain Loss: 0.990509\n",
      "Train Epoch: 6 [38400/50000 (77%)]\tTrain Loss: 1.053377\n",
      "Train Epoch: 6 [44800/50000 (90%)]\tTrain Loss: 1.352071\n",
      "\n",
      "[EPOCH: 6], \tTest Loss: 0.0354, \tTest Accuracy: 59.35 % \n",
      "\n",
      "Train Epoch: 7 [0/50000 (0%)]\tTrain Loss: 1.358243\n",
      "Train Epoch: 7 [6400/50000 (13%)]\tTrain Loss: 0.939281\n",
      "Train Epoch: 7 [12800/50000 (26%)]\tTrain Loss: 1.139706\n",
      "Train Epoch: 7 [19200/50000 (38%)]\tTrain Loss: 0.932403\n",
      "Train Epoch: 7 [25600/50000 (51%)]\tTrain Loss: 0.725873\n",
      "Train Epoch: 7 [32000/50000 (64%)]\tTrain Loss: 0.980499\n",
      "Train Epoch: 7 [38400/50000 (77%)]\tTrain Loss: 1.031030\n",
      "Train Epoch: 7 [44800/50000 (90%)]\tTrain Loss: 0.840739\n",
      "\n",
      "[EPOCH: 7], \tTest Loss: 0.0329, \tTest Accuracy: 63.02 % \n",
      "\n",
      "Train Epoch: 8 [0/50000 (0%)]\tTrain Loss: 0.979106\n",
      "Train Epoch: 8 [6400/50000 (13%)]\tTrain Loss: 0.952561\n",
      "Train Epoch: 8 [12800/50000 (26%)]\tTrain Loss: 0.853497\n",
      "Train Epoch: 8 [19200/50000 (38%)]\tTrain Loss: 1.134573\n",
      "Train Epoch: 8 [25600/50000 (51%)]\tTrain Loss: 0.819062\n",
      "Train Epoch: 8 [32000/50000 (64%)]\tTrain Loss: 1.178903\n",
      "Train Epoch: 8 [38400/50000 (77%)]\tTrain Loss: 0.807230\n",
      "Train Epoch: 8 [44800/50000 (90%)]\tTrain Loss: 1.407131\n",
      "\n",
      "[EPOCH: 8], \tTest Loss: 0.0323, \tTest Accuracy: 63.94 % \n",
      "\n",
      "Train Epoch: 9 [0/50000 (0%)]\tTrain Loss: 1.263657\n",
      "Train Epoch: 9 [6400/50000 (13%)]\tTrain Loss: 0.848667\n",
      "Train Epoch: 9 [12800/50000 (26%)]\tTrain Loss: 0.617771\n",
      "Train Epoch: 9 [19200/50000 (38%)]\tTrain Loss: 0.958646\n",
      "Train Epoch: 9 [25600/50000 (51%)]\tTrain Loss: 0.689571\n",
      "Train Epoch: 9 [32000/50000 (64%)]\tTrain Loss: 0.802199\n",
      "Train Epoch: 9 [38400/50000 (77%)]\tTrain Loss: 1.189155\n",
      "Train Epoch: 9 [44800/50000 (90%)]\tTrain Loss: 0.821852\n",
      "\n",
      "[EPOCH: 9], \tTest Loss: 0.0320, \tTest Accuracy: 63.71 % \n",
      "\n",
      "Train Epoch: 10 [0/50000 (0%)]\tTrain Loss: 0.869421\n",
      "Train Epoch: 10 [6400/50000 (13%)]\tTrain Loss: 0.926571\n",
      "Train Epoch: 10 [12800/50000 (26%)]\tTrain Loss: 0.952819\n",
      "Train Epoch: 10 [19200/50000 (38%)]\tTrain Loss: 1.060941\n",
      "Train Epoch: 10 [25600/50000 (51%)]\tTrain Loss: 0.879187\n",
      "Train Epoch: 10 [32000/50000 (64%)]\tTrain Loss: 0.829416\n",
      "Train Epoch: 10 [38400/50000 (77%)]\tTrain Loss: 0.788162\n",
      "Train Epoch: 10 [44800/50000 (90%)]\tTrain Loss: 1.083434\n",
      "\n",
      "[EPOCH: 10], \tTest Loss: 0.0314, \tTest Accuracy: 65.40 % \n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' 10. CNN 학습 실행하며 Train, Test set의 Loss 및 Test set Accuracy 확인하기 '''\n",
    "for epoch in range(1, EPOCHS + 1):\n",
    "    train(model, train_loader, optimizer, log_interval = 200)\n",
    "    test_loss, test_accuracy = evaluate(model, test_loader)\n",
    "    print(\"\\n[EPOCH: {}], \\tTest Loss: {:.4f}, \\tTest Accuracy: {:.2f} % \\n\".format(\n",
    "        epoch, test_loss, test_accuracy))"
   ]
  }
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