Lab09.ipynb

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    "이번 Lab에서는 다층 퍼셉트론과 ReLU 함수, Dropout, Xavier Initialization를 이용해 MNIST 데이터를 분류해보겠습니다."
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   "cell_type": "code",
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   "source": [
    "# Lab 10 MNIST and softmax\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "feWzO9_lGpNB"
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   "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": "markdown",
   "metadata": {
    "id": "wOXgKMppHNQ1"
   },
   "source": [
    "필요한 파라미터들을 세팅해줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "id": "6f-1LaocGpNC"
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   "source": [
    "# parameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "drop_prob = 0.3"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "PsQ-6HqYH0X2"
   },
   "source": [
    "torchvision.datasets.dsets.MNIST를 사용하여 MNIST 데이터셋을 불러옵니다."
   ]
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   "source": [
    "# MNIST dataset\n",
    "mnist_train = dsets.MNIST(root='MNIST_data/',\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",
    "mnist_test = dsets.MNIST(root='MNIST_data/',\n",
    "                         train=False, #True는 train 데이터\n",
    "                          transform=transforms.ToTensor(), #데이터를 PyTorch 텐서로 변환\n",
    "                          download=True) #해당 경로에 MNIST 데이터가 없다면 다운로드"
   ]
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   "source": [
    "이렇게 데이터를 다운로드했다면 데이터로더(DataLoader)를 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "# dataset loader\n",
    "data_loader = torch.utils.data.DataLoader(dataset=mnist_train, # mnist_train 데이터를 로드할 대상으로 정해줍니다.\n",
    "                                          batch_size=batch_size, # 위에서 정의한 배치크기를 사용합니다.\n",
    "                                          shuffle=True, # Epoch마다 데이터 셋을 섞어서 데이터가 학습되는 순서를 바꾸겠습니다.\n",
    "                                          drop_last=True) #마지막 배치를 버리겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nNA-ZUcQI3eZ"
   },
   "source": [
    "이제 신경망을 구현해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "UsMDG5f_GpNE"
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   "source": [
    "# nn layers\n",
    "# MNIST data image of shape 28 * 28 = 784, 0~9까지 10개의 class\n",
    "linear1 = nn.Linear(784, 512, bias=True) # input layer의 크기를 생각해보세요.\n",
    "linear2 = nn.Linear(512, 512, bias=True)\n",
    "linear3 = nn.Linear(512, 512, bias=True)\n",
    "linear4 = nn.Linear(512, 512, bias=True)\n",
    "linear5 = nn.Linear(512, 10, bias=True) # output layer의 크기를 생각해보세요.\n",
    "relu = nn.ReLU() # ReLU를 사용해보겠습니다.\n",
    "dropout = nn.Dropout(p=drop_prob) # Dropout을 사용해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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    {
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      "text/plain": [
       "Parameter containing:\n",
       "tensor([[-0.0565,  0.0423, -0.0155,  ...,  0.1012,  0.0459, -0.0191],\n",
       "        [ 0.0772,  0.0452, -0.0638,  ...,  0.0476, -0.0638,  0.0528],\n",
       "        [ 0.0311, -0.1023, -0.0701,  ...,  0.0412, -0.1004,  0.0738],\n",
       "        ...,\n",
       "        [ 0.0334,  0.0187, -0.1021,  ...,  0.0280, -0.0583, -0.1018],\n",
       "        [-0.0506, -0.0939, -0.0467,  ..., -0.0554, -0.0325,  0.0640],\n",
       "        [-0.0183, -0.0123,  0.1025,  ..., -0.0214,  0.0220, -0.0741]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가중치 초기화중 xavier 초기화를 사용해보겠습니다.\n",
    "nn.init.xavier_uniform_(linear1.weight)\n",
    "nn.init.xavier_uniform_(linear2.weight)\n",
    "nn.init.xavier_uniform_(linear3.weight)\n",
    "nn.init.xavier_uniform_(linear4.weight)\n",
    "nn.init.xavier_uniform_(linear5.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "nfeNRitqGpNG"
   },
   "outputs": [],
   "source": [
    "# nn.Sequential을 통해 각 층, 렐루 함수, 드롭아웃을 연결해줍니다.\n",
    "model = nn.Sequential(linear1, relu, dropout,\n",
    "                            linear2, relu, dropout,\n",
    "                            linear3, relu, dropout,\n",
    "                            linear4, relu, dropout,\n",
    "                            linear5).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mbWF_B4DMSSx"
   },
   "source": [
    "비용 함수와 옵타마이저를 선언합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "nNyBJNc1GpNH"
   },
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "criterion = nn.CrossEntropyLoss().to(device) # CrossEntropyLoss()를 사용합니다.\n",
    "optimizer = torch.optim.SGD(model.parameters(),lr=0.1) # Adam optimizer를 사용하고 위에서 정의한 learning rate를 사용합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m3v95JayNQXb"
   },
   "source": [
    "이제 모델을 훈련시켜보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
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    },
    "id": "Qrzwot99GpNI",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.530777395\n",
      "Epoch: 0002 cost = 0.205043882\n",
      "Epoch: 0003 cost = 0.150083452\n",
      "Epoch: 0004 cost = 0.122937955\n",
      "Epoch: 0005 cost = 0.104875974\n",
      "Epoch: 0006 cost = 0.090239838\n",
      "Epoch: 0007 cost = 0.081477597\n",
      "Epoch: 0008 cost = 0.069910489\n",
      "Epoch: 0009 cost = 0.064866886\n",
      "Epoch: 0010 cost = 0.061304715\n",
      "Epoch: 0011 cost = 0.053514495\n",
      "Epoch: 0012 cost = 0.051698085\n",
      "Epoch: 0013 cost = 0.046536002\n",
      "Epoch: 0014 cost = 0.043594833\n",
      "Epoch: 0015 cost = 0.042433254\n",
      "Learning finished\n"
     ]
    }
   ],
   "source": [
    "total_batch = len(data_loader)\n",
    "model.train()    # set the model to train mode (dropout=True)\n",
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "\n",
    "    for X, Y in data_loader:\n",
    "        X = X.view(-1,28*28).to(device) # view를 이용해 X를 (100, 784)의 텐서로 변환해주세요.\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 = model(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": "markdown",
   "metadata": {
    "id": "UxuDZQ4ONOWD"
   },
   "source": [
    "마지막으로 모델을 테스트 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZKBvytevGpNJ",
    "outputId": "8b8afb3c-2216-449b-fb41-537704ed3f66"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.98089998960495\n",
      "Label:  5\n",
      "Prediction:  5\n"
     ]
    }
   ],
   "source": [
    "# Test model and check accuracy\n",
    "with torch.no_grad():\n",
    "    model.eval()    # set the model to evaluation mode (dropout=False)\n",
    "\n",
    "    # Test the model using test sets\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 = model(X_test)\n",
    "    correct_prediction = torch.argmax(prediction, 1) == Y_test\n",
    "    accuracy = correct_prediction.float().mean()\n",
    "    print('Accuracy:', accuracy.item())\n",
    "\n",
    "    # Get one and predict\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 = model(X_single_data)\n",
    "    print('Prediction: ', torch.argmax(single_prediction, 1).item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VU6KcLkRRDy8"
   },
   "source": [
    "Q1. 신경망에서 레이어가 깊어질수록 어떤 문제가 일어나는가?\n",
    "\n",
    "=> 그래디언트 소멸 문제 발생"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IqKKBz2tRD7b"
   },
   "source": [
    "Q2. 위 문제를 해결할 수 있는 방법에는 어떤 것이 있는가?\n",
    "\n",
    "=> ReLu 활성화 함수 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V0ZYclPlRUO_"
   },
   "source": [
    "Q3. 모델을 학습시킨 후 평가를 할 때도 Dropout을 사용하는가? (O/X)\n",
    "\n",
    "=> X"
   ]
  }
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