Lab10_3.ipynb

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   "source": [
    "이번 챕터에서는 앞서 배운 CNN에 층을 더 추가하여 MNIST를 분류해보겠습니다.\n",
    "\n",
    "이번 챕터의 코드는 이전 챕터에서 층이 조금 더 추가되는 것 말고는 동일합니다."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Hufp5V8-QcYn"
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   "source": [
    "import torch\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn.init"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P67KOXpJd-38"
   },
   "source": [
    "만약 GPU를 사용 가능하다면 device 값이 cuda가 되고, 아니라면 cpu가 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KwANhjV7QcYt"
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   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "# for reproducibility\n",
    "torch.manual_seed(777)\n",
    "if device == 'cuda':\n",
    "    torch.cuda.manual_seed_all(777)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rsczZ9RDeCBh"
   },
   "source": [
    "학습에 사용할 파라미터를 설정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "kLAbG1WNQcYu"
   },
   "outputs": [],
   "source": [
    "# parameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 15\n",
    "batch_size = 100"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "gDEyGJWOeE4j"
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   "source": [
    "데이터로더를 사용하여 데이터를 다루기 위해서 데이터셋을 정의해줍니다."
   ]
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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, #False는 test 데이터\n",
    "                         transform=transforms.ToTensor(), #데이터를 PyTorch 텐서로 변환\n",
    "                         download=True) #해당 경로에 MNIST 데이터가 없다면 다운로드"
   ]
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   "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": "1p6e2XPogaOt"
   },
   "source": [
    "이제 클래스로 모델을 설계합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "0_1octbUQcYv"
   },
   "outputs": [],
   "source": [
    "# CNN Model\n",
    "class CNN(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.keep_prob = 0.5\n",
    "        self.layer1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2))\n",
    "       \n",
    "        self.layer2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2))\n",
    "       \n",
    "        self.layer3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=1))\n",
    "\n",
    "        self.fc1 = torch.nn.Linear(4 * 4 * 128, 625, bias=True)\n",
    "        torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
    "        self.layer4 = torch.nn.Sequential(\n",
    "            self.fc1,\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Dropout(p=1 - self.keep_prob))\n",
    "       \n",
    "        self.fc2 = torch.nn.Linear(625, 10, bias=True)\n",
    "        torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.layer1(x)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.layer4(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vK116qCIgcAg"
   },
   "source": [
    "모델을 정의합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "uIZfrNQ2QcYx"
   },
   "outputs": [],
   "source": [
    "# instantiate CNN model\n",
    "model = CNN().to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-c2UpJjJgdYy"
   },
   "source": [
    "비용 함수와 옵티마이저를 정의합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "H7BFVb4PQcYy"
   },
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "criterion = torch.nn.CrossEntropyLoss().to(device) # CrossEntropyLoss()를 사용합니다.\n",
    "optimizer =torch.optim.Adam(model.parameters(),lr=learning_rate) # Adam optimizer를 사용하고 위에서 정의한 learning rate를 사용합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3okQVtDHgfAa"
   },
   "source": [
    "총 배치의 수를 출력해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1_ybCT-Bgfeu",
    "outputId": "8c264fea-6150-4dac-a5c1-8207e68ae240"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n"
     ]
    }
   ],
   "source": [
    "total_batch = len(data_loader)\n",
    "print(total_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L865ZbE8g1hu"
   },
   "source": [
    "총 배치의 수는 600입니다. 그런데 배치 크기를 100으로 했으므로 결국 훈련 데이터는 총 60,000개란 의미입니다. 이제 모델을 훈련시켜보겠습니다. (시간이 꽤 오래 걸립니다.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_VnFZ5xSQcY2",
    "outputId": "55cc60bf-387c-4921-8729-faf37c5fa290"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning started. It takes sometime.\n",
      "[Epoch:    1] cost = 0.188323826\n",
      "[Epoch:    2] cost = 0.0528364964\n",
      "[Epoch:    3] cost = 0.0368160754\n",
      "[Epoch:    4] cost = 0.0286486559\n",
      "[Epoch:    5] cost = 0.0249712206\n",
      "[Epoch:    6] cost = 0.0196859371\n",
      "[Epoch:    7] cost = 0.0160057433\n",
      "[Epoch:    8] cost = 0.0157633461\n",
      "[Epoch:    9] cost = 0.0129499501\n",
      "[Epoch:   10] cost = 0.0109685753\n",
      "[Epoch:   11] cost = 0.0094193425\n",
      "[Epoch:   12] cost = 0.0112330541\n",
      "[Epoch:   13] cost = 0.0087766489\n",
      "[Epoch:   14] cost = 0.00625576405\n",
      "[Epoch:   15] cost = 0.00807551108\n",
      "Learning Finished!\n"
     ]
    }
   ],
   "source": [
    "# train my model\n",
    "print('Learning started. It takes sometime.')\n",
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "\n",
    "    for X, Y in data_loader:\n",
    "        # image is already size of (28x28), no reshape\n",
    "        # label is not one-hot encoded\n",
    "        X = X.to(device)\n",
    "        Y = Y.to(device)\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: {:>4}] cost = {:>.9}'.format(epoch + 1, avg_cost))\n",
    "\n",
    "print('Learning Finished!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RsDx0Zw5g-hP"
   },
   "source": [
    "이제 테스트를 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    "id": "yhD_iGGgQcY4",
    "outputId": "b0eddeee-7230-4cdf-bd03-04dff66f738b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py:60: UserWarning: test_data has been renamed data\n",
      "  warnings.warn(\"test_data has been renamed data\")\n",
      "C:\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py:50: UserWarning: test_labels has been renamed targets\n",
      "  warnings.warn(\"test_labels has been renamed targets\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9835000038146973\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",
    "    X_test = mnist_test.test_data.view(len(mnist_test), 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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WWqQdnKQg_Y3"
   },
   "source": [
    "층을 깊게 쌓는 다고 무조건 정확도가 좋아지는 것이 아니기 때문에 효율적으로 쌓는 것이 중요합니다!"
   ]
  }
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