Lab07_2.ipynb

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   "cell_type": "markdown",
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   "source": [
    "* 이번 Lab07_2는 코드를 눈으로만 보지말고 직접 쳐보세요!!\n",
    "\n",
    "* 처음 보는 개념이 나오면 PyTorch로 시작하는 딥러닝 입문 책을 활용하세요!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "KmG9KQmeCVQo"
   },
   "outputs": [],
   "source": [
    "# Lab 7 Learning rate and Evaluation\n",
    "import torch\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "MfTc-Ug_CVQu"
   },
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "# for reproducibility\n",
    "random.seed(777)\n",
    "torch.manual_seed(777)\n",
    "if device == 'cuda':\n",
    "    torch.cuda.manual_seed_all(777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "dio3OojNCVQv"
   },
   "outputs": [],
   "source": [
    "# parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100"
   ]
  },
  {
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    "id": "ZwfohfCeCVQv",
    "outputId": "ee81e5e6-7544-4a01-a93b-bac87a0475b1"
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   "outputs": [],
   "source": [
    "# MNIST dataset\n",
    "mnist_train = dsets.MNIST(root='MNIST_data/', #데이터를 다운로드 받을 경로\n",
    "                          train=True, #True는 train 데이터\n",
    "                          transform=transforms.ToTensor(), #데이터를 PyTorch 텐서로 변환\n",
    "                          download=True) #해당 경로에 MNIST 데이터가 없다면 다운로드\n",
    "\n",
    "#test 데이터셋을 불러오세요.\n",
    "mnist_test = dsets.MNIST(root='MNIST_data/', #데이터를 다운로드 받을 경로 : 'MNIST_data/'\n",
    "                         train=False, #False는 test 데이터\n",
    "                         transform=transforms.ToTensor(),\n",
    "                         download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ia7M_81JCVQw"
   },
   "outputs": [],
   "source": [
    "# dataset loader\n",
    "data_loader = torch.utils.data.DataLoader(dataset=mnist_train, #로드할 대상\n",
    "                                          batch_size=batch_size, #배치크기\n",
    "                                          shuffle=True, #Epoch마다 데이터 셋을 섞어서 데이터가 학습되는 순서를 바꿈.\n",
    "                                          drop_last=True) #마지막 배치를 버릴 것인지"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SzBjddHVG3zy"
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   "source": [
    "* drop_last를 하는 이유를 이해하기 위해서 1,000개의 데이터가 있다고 했을 때, 배치 크기가 128이라고 해봅시다. 1,000을 128로 나누면 총 7개가 나오고 나머지로 104개가 남습니다. 이때 104개를 마지막 배치로 한다고 하였을 때 128개를 충족하지 못하였으므로 104개를 그냥 버릴 수도 있습니다. 이때 마지막 배치를 버리려면 drop_last=True를 해주면 됩니다. 이는 다른 미니 배치보다 개수가 적은 마지막 배치를 경사 하강법에 사용하여 마지막 배치가 상대적으로 과대 평가되는 현상을 막아줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "8R_XgLhbCVQx"
   },
   "outputs": [],
   "source": [
    "# MNIST data image of shape 28 * 28 = 784, 0~9까지 10개의 class\n",
    "linear = torch.nn.Linear(784, 10, bias=True).to(device) #GPU를 사용하기 위해 .to(device)를 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ETFb8-88CVQy"
   },
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "criterion = torch.nn.CrossEntropyLoss().to(device)    # Softmax is internally computed.\n",
    "optimizer = torch.optim.SGD(linear.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "spolUux1CVQz",
    "outputId": "bf704c11-1857-4c66-bfaa-bb0b94a801c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.535146713\n",
      "Epoch: 0002 cost = 0.359576792\n",
      "Epoch: 0003 cost = 0.331263006\n",
      "Epoch: 0004 cost = 0.316403985\n",
      "Epoch: 0005 cost = 0.307106525\n",
      "Epoch: 0006 cost = 0.300456434\n",
      "Epoch: 0007 cost = 0.294932842\n",
      "Epoch: 0008 cost = 0.290955842\n",
      "Epoch: 0009 cost = 0.287074119\n",
      "Epoch: 0010 cost = 0.284515619\n",
      "Epoch: 0011 cost = 0.281915396\n",
      "Epoch: 0012 cost = 0.279526889\n",
      "Epoch: 0013 cost = 0.277636290\n",
      "Epoch: 0014 cost = 0.275875181\n",
      "Epoch: 0015 cost = 0.274422675\n",
      "Learning finished\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "    total_batch = len(data_loader)\n",
    "\n",
    "    for X, Y in data_loader:\n",
    "        X = X.view(-1, 28 * 28).to(device) # 배치 크기가 100이므로 X는 (100, 784)의 텐서가 된다.\n",
    "        Y = Y.to(device) # 레이블은 원-핫 인코딩이 된 상태가 아니라 0 ~ 9의 정수.\n",
    "\n",
    "        optimizer.zero_grad() # gradient를 0으로 초기화\n",
    "        hypothesis = linear(X) #모델을 생성해서 hypothesis를 구해보세요.\n",
    "        cost = criterion(hypothesis, Y) #위에서 정의한 비용함수를 사용하세요.\n",
    "        cost.backward()  # 비용 함수를 미분하여 gradient 계산\n",
    "        optimizer.step() # W와 b를 업데이트\n",
    "\n",
    "        avg_cost += cost / total_batch\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))\n",
    "\n",
    "print('Learning finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "yMTVJzFMCVQ0",
    "outputId": "c7a0c57b-08f0-4046-d6bf-98d53ced94b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8883000016212463\n",
      "Label:  8\n",
      "Prediction:  3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py:67: UserWarning: test_data has been renamed data\n",
      "  warnings.warn(\"test_data has been renamed data\")\n",
      "C:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py:57: UserWarning: test_labels has been renamed targets\n",
      "  warnings.warn(\"test_labels has been renamed targets\")\n"
     ]
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the model using test sets\n",
    "with torch.no_grad(): # torch.no_grad()를 하면 gradient 계산을 수행하지 않는다. test 할때는 gradient 계산이 필요가 없다.\n",
    "    X_test = mnist_test.test_data.view(-1, 28 * 28).float().to(device)\n",
    "    Y_test = mnist_test.test_labels.to(device)\n",
    "\n",
    "    prediction = linear(X_test)\n",
    "    correct_prediction = torch.argmax(prediction, 1) == Y_test # 각각의 데이터 마다 가장 큰 값의 인덱스를 취함 => class를 예측하는 것\n",
    "    accuracy = correct_prediction.float().mean()\n",
    "    print('Accuracy:', accuracy.item())\n",
    "\n",
    "    # MNIST 테스트 데이터에서 무작위로 하나를 뽑아서 예측을 해본다\n",
    "    r = random.randint(0, len(mnist_test) - 1)\n",
    "    X_single_data = mnist_test.test_data[r:r + 1].view(-1, 28 * 28).float().to(device)\n",
    "    Y_single_data = mnist_test.test_labels[r:r + 1].to(device)\n",
    "\n",
    "    print('Label: ', Y_single_data.item())\n",
    "    single_prediction = linear(X_single_data)\n",
    "    print('Prediction: ', torch.argmax(single_prediction, 1).item())\n",
    "\n",
    "    plt.imshow(mnist_test.test_data[r:r + 1].view(28, 28), cmap='Greys', interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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