Design_CNN_by_using_CIFAR_10_+_Data_Augmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Design CNN by using CIFAR-10 + Data Augmentation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMkD3oDOB0tiEfxmw43SVRe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JINU6497/JINUrepo/blob/master/Design_CNN_by_using_CIFAR_10_%2B_Data_Augmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiSJDlgQa4PG"
      },
      "source": [
        "'''1. Module Import'''\n",
        "import numpy as np  # 선형대수 관련 모듈\n",
        "import matplotlib.pyplot as plt  # 결과 산출물을 시각화하는 모듈\n",
        " \n",
        "import torch\n",
        "import torch.nn as nn  # 인공 신경망 모델 설계에 필요한 함수들 모아 놓은 모듈 \n",
        "import torch.nn.functional as F # torch.nn 중에서도 자주 쓰이는 functional 함수들을 따로 F로 지정 \n",
        "from torchvision import transforms, datasets  # Computer vision 분야에서 자주 이용되는 torchvision 내의 함수 임포트"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO5fvbkJ45En",
        "outputId": "acf72ba5-734a-45ce-a419-35f78f0b89c4"
      },
      "source": [
        "'''2. 딥러닝 모델 설계에 활용하는 장비 확인'''\n",
        "if torch.cuda.is_available():\n",
        "  DEVICE=torch.device('cuda')\n",
        "else:\n",
        "  DEVICE=torch.device('cpu')\n",
        " \n",
        "print(\"Using pytorch version:\",torch.__version__,'Device:',DEVICE)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using pytorch version: 1.9.0+cu102 Device: cpu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiem0Cuc48Q7"
      },
      "source": [
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6ZDbdtW49-D",
        "outputId": "55539c4e-c613-44ee-a496-fdb650ef3ce5"
      },
      "source": [
        "'''3. Data Augmentation 적용한 CIFAR-10 데이터 다운로드(Train과 Test set 분리)'''\n",
        "train_dataset=datasets.CIFAR10(root=\"../data/Fashion,MNIST\",train=True,download=True,\n",
        "                               transform=transforms.Compose([transforms.RandomHorizontalFlip(),\n",
        "                                                             transforms.ToTensor(),\n",
        "                                                             transforms.Normalize((0.5,0.5,0.5),\n",
        "                                                             (0.5,0.5,0.5))]))\n",
        "test_dataset=datasets.CIFAR10(root=\"../data/Fashion,MNIST\",train=False,\n",
        "                               transform=transforms.Compose([transforms.RandomHorizontalFlip(),\n",
        "                                                             transforms.ToTensor(),\n",
        "                                                             transforms.Normalize((0.5,0.5,0.5),\n",
        "                                                             (0.5,0.5,0.5))]))\n",
        " \n",
        "train_loader=torch.utils.data.DataLoader(dataset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)\n",
        "test_loader=torch.utils.data.DataLoader(dataset=test_dataset,batch_size=BATCH_SIZE,shuffle=False)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgoaktPURmS8"
      },
      "source": [
        "transforms.Compose()는 불러오는 이미지 데이터 전처리 및 Augmentation을 다양하게 적용할 때 이용하는 method. 즉, Compose의 괄호 안에 있는 처리 과정을 거친 데이터를 불러오는 것을 의미.\n",
        "RandomHorizontalFlip은 50%확률로 좌우 반전하는 것을 의미\n",
        "Normalize는 ToTensor()로 전환된 이미지에 대해 또 다른 정규화를 진행하는 것을 의미. 정규화를 진행할 때 평균과 표준편차가 필요한데, RGB순으로 평균을 0.5씩 적용하고, 다음 괄호에서는 표준편차를 0.5씩 적용하는 것"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeNyspGA5U5L",
        "outputId": "b62fb5ff-b644-4479-dc04-872afdb55cd9"
      },
      "source": [
        "'''4. 데이터 확인'''\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"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 94
        },
        "id": "UFvzEC3y5a6E",
        "outputId": "fba614bc-bec8-4295-c75c-6444efa9879e"
      },
      "source": [
        "pltsize=1\n",
        "plt.figure(figsize=(10*pltsize,pltsize))\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()))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x72 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRh42qvz5uwI"
      },
      "source": [
        "'''5. Convolutional Neural Network(CNN) 모델 설계하기'''\n",
        "\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",
        "    \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",
        "\n",
        "    return x"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kymDVj_B9z0y"
      },
      "source": [
        "self.conv1=nn.conv2d(in_channels=3, out_channels=8, kernel_size=3, padding=1): \n",
        "\n",
        "in_channel: 이미지에 직접 Convolution 연산을 진행하는 Filter의 크기는 상관 없지만, 채널 수를 이미지의 채널 수와 동일하게 맞춰야 한다. 동일한 위치에 있으며 앞뒤로 존재하는 RGB 픽셀에 대해 동시에 Convolution을 진행해야 하기 때문. 따라서 이미지의 채널 수와 동일하게 채널 수를 3으로 설정.\n",
        "\n",
        "out_channel은 8이므로 해당 Convolution 연산 결과는 Depth가 8인 Feature map이 생성 될 것.\n",
        "\n",
        "kernel_size는 filter의 크기를 설정해주는 부분.\n",
        "\n",
        "log_softmax()를 이용하여 0부터 9까지 총 10가지 경우의 수 중 하나로 분류하는 일을 확률 값 계산.일반적인 softmax가 아닌 log_softmax()를 이용하는 이유는 MLP모델이 Back propagation을 진행할 때 loss 값에 대한 gradient값을 좀 더 원활하게 계산할 수 있기 때문에. log 함수 그래프의 기울기가 부드럽게 변화하는 것을 생각해보면 직관적으로 이해할 수 있다.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_wTbz99Ha35",
        "outputId": "33b57a19-ad75-4c18-8dbf-3c505100f778"
      },
      "source": [
        "'''6. Optimizer, Objective Fundtion 설정'''\n",
        " \n",
        "model=CNN().to(DEVICE)\n",
        "optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\n",
        "criterion=nn.CrossEntropyLoss()\n",
        " \n",
        "print(model)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjofLjfdIfX8"
      },
      "source": [
        "'''7. CNN모델 학습 진행하면서 Train데이터에 대한 모델의 성능 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(Epoch, batch_idx*len(image),\n",
        "                                                                       len(train_loader.dataset),100.*batch_idx/len(train_loader),loss.item()))"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGrixKBdJFWL"
      },
      "source": [
        "'''8. 학습 과정 속에서 Test 데이터에 대한 모델 성능 확인하는 함수 정의'''\n",
        " \n",
        "def evaluate(model, test_loader):\n",
        "  model.eval()\n",
        "  test_loss=0\n",
        "  correct=0\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",
        " \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)\n",
        "  test_accuracy=100.*correct/len(test_loader.dataset)\n",
        "\n",
        "  return test_loss, test_accuracy"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83v0IHM7LnJN",
        "outputId": "14c6e828-74ec-4832-b07b-81fd91a848e8"
      },
      "source": [
        "'''9. CNN학습을 실행하면서 Test set의 Reconstruction Error 확인'''\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(Epoch,test_loss, test_accuracy))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Epoch:1[0/50000(0%)]\tTrain Loss:2.302436\n",
            "Train Epoch:1[6400/50000(13%)]\tTrain Loss:2.038776\n",
            "Train Epoch:1[12800/50000(26%)]\tTrain Loss:2.032110\n",
            "Train Epoch:1[19200/50000(38%)]\tTrain Loss:2.069396\n",
            "Train Epoch:1[25600/50000(51%)]\tTrain Loss:1.509839\n",
            "Train Epoch:1[32000/50000(64%)]\tTrain Loss:1.475636\n",
            "Train Epoch:1[38400/50000(77%)]\tTrain Loss:1.396343\n",
            "Train Epoch:1[44800/50000(90%)]\tTrain Loss:1.462968\n",
            "\n",
            "[EPOCH:1], \tTest Loss: 0.0470, \tTest Accuracy:44.62% \n",
            "\n",
            "Train Epoch:2[0/50000(0%)]\tTrain Loss:1.669398\n",
            "Train Epoch:2[6400/50000(13%)]\tTrain Loss:1.646930\n",
            "Train Epoch:2[12800/50000(26%)]\tTrain Loss:1.665701\n",
            "Train Epoch:2[19200/50000(38%)]\tTrain Loss:1.169738\n",
            "Train Epoch:2[25600/50000(51%)]\tTrain Loss:1.163653\n",
            "Train Epoch:2[32000/50000(64%)]\tTrain Loss:1.496906\n",
            "Train Epoch:2[38400/50000(77%)]\tTrain Loss:1.587251\n",
            "Train Epoch:2[44800/50000(90%)]\tTrain Loss:1.451402\n",
            "\n",
            "[EPOCH:2], \tTest Loss: 0.0409, \tTest Accuracy:52.59% \n",
            "\n",
            "Train Epoch:3[0/50000(0%)]\tTrain Loss:1.170394\n",
            "Train Epoch:3[6400/50000(13%)]\tTrain Loss:1.515880\n",
            "Train Epoch:3[12800/50000(26%)]\tTrain Loss:1.327065\n",
            "Train Epoch:3[19200/50000(38%)]\tTrain Loss:1.392961\n",
            "Train Epoch:3[25600/50000(51%)]\tTrain Loss:1.045213\n",
            "Train Epoch:3[32000/50000(64%)]\tTrain Loss:1.166742\n",
            "Train Epoch:3[38400/50000(77%)]\tTrain Loss:1.420329\n",
            "Train Epoch:3[44800/50000(90%)]\tTrain Loss:1.816122\n",
            "\n",
            "[EPOCH:3], \tTest Loss: 0.0372, \tTest Accuracy:57.43% \n",
            "\n",
            "Train Epoch:4[0/50000(0%)]\tTrain Loss:1.233939\n",
            "Train Epoch:4[6400/50000(13%)]\tTrain Loss:0.994905\n",
            "Train Epoch:4[12800/50000(26%)]\tTrain Loss:0.773276\n",
            "Train Epoch:4[19200/50000(38%)]\tTrain Loss:1.083998\n",
            "Train Epoch:4[25600/50000(51%)]\tTrain Loss:1.315623\n",
            "Train Epoch:4[32000/50000(64%)]\tTrain Loss:1.090306\n",
            "Train Epoch:4[38400/50000(77%)]\tTrain Loss:1.333412\n",
            "Train Epoch:4[44800/50000(90%)]\tTrain Loss:0.817209\n",
            "\n",
            "[EPOCH:4], \tTest Loss: 0.0353, \tTest Accuracy:60.27% \n",
            "\n",
            "Train Epoch:5[0/50000(0%)]\tTrain Loss:1.053342\n",
            "Train Epoch:5[6400/50000(13%)]\tTrain Loss:0.875939\n",
            "Train Epoch:5[12800/50000(26%)]\tTrain Loss:1.307567\n",
            "Train Epoch:5[19200/50000(38%)]\tTrain Loss:0.739179\n",
            "Train Epoch:5[25600/50000(51%)]\tTrain Loss:1.361813\n",
            "Train Epoch:5[32000/50000(64%)]\tTrain Loss:0.995901\n",
            "Train Epoch:5[38400/50000(77%)]\tTrain Loss:1.310471\n",
            "Train Epoch:5[44800/50000(90%)]\tTrain Loss:1.147473\n",
            "\n",
            "[EPOCH:5], \tTest Loss: 0.0340, \tTest Accuracy:62.04% \n",
            "\n",
            "Train Epoch:6[0/50000(0%)]\tTrain Loss:0.652039\n",
            "Train Epoch:6[6400/50000(13%)]\tTrain Loss:0.971676\n",
            "Train Epoch:6[12800/50000(26%)]\tTrain Loss:1.487852\n",
            "Train Epoch:6[19200/50000(38%)]\tTrain Loss:1.152960\n",
            "Train Epoch:6[25600/50000(51%)]\tTrain Loss:0.835237\n",
            "Train Epoch:6[32000/50000(64%)]\tTrain Loss:1.241039\n",
            "Train Epoch:6[38400/50000(77%)]\tTrain Loss:1.215039\n",
            "Train Epoch:6[44800/50000(90%)]\tTrain Loss:0.914375\n",
            "\n",
            "[EPOCH:6], \tTest Loss: 0.0337, \tTest Accuracy:62.14% \n",
            "\n",
            "Train Epoch:7[0/50000(0%)]\tTrain Loss:0.999674\n",
            "Train Epoch:7[6400/50000(13%)]\tTrain Loss:1.152827\n",
            "Train Epoch:7[12800/50000(26%)]\tTrain Loss:0.817326\n",
            "Train Epoch:7[19200/50000(38%)]\tTrain Loss:0.583567\n",
            "Train Epoch:7[25600/50000(51%)]\tTrain Loss:0.914141\n",
            "Train Epoch:7[32000/50000(64%)]\tTrain Loss:0.720853\n",
            "Train Epoch:7[38400/50000(77%)]\tTrain Loss:1.055162\n",
            "Train Epoch:7[44800/50000(90%)]\tTrain Loss:1.097037\n",
            "\n",
            "[EPOCH:7], \tTest Loss: 0.0330, \tTest Accuracy:62.95% \n",
            "\n",
            "Train Epoch:8[0/50000(0%)]\tTrain Loss:1.295884\n",
            "Train Epoch:8[6400/50000(13%)]\tTrain Loss:0.939546\n",
            "Train Epoch:8[12800/50000(26%)]\tTrain Loss:1.147522\n",
            "Train Epoch:8[19200/50000(38%)]\tTrain Loss:1.171001\n",
            "Train Epoch:8[25600/50000(51%)]\tTrain Loss:0.922185\n",
            "Train Epoch:8[32000/50000(64%)]\tTrain Loss:1.243268\n",
            "Train Epoch:8[38400/50000(77%)]\tTrain Loss:1.032151\n",
            "Train Epoch:8[44800/50000(90%)]\tTrain Loss:0.703423\n",
            "\n",
            "[EPOCH:8], \tTest Loss: 0.0325, \tTest Accuracy:63.92% \n",
            "\n",
            "Train Epoch:9[0/50000(0%)]\tTrain Loss:0.611134\n",
            "Train Epoch:9[6400/50000(13%)]\tTrain Loss:0.904275\n",
            "Train Epoch:9[12800/50000(26%)]\tTrain Loss:0.664014\n",
            "Train Epoch:9[19200/50000(38%)]\tTrain Loss:1.092440\n",
            "Train Epoch:9[25600/50000(51%)]\tTrain Loss:1.095589\n",
            "Train Epoch:9[32000/50000(64%)]\tTrain Loss:1.059850\n",
            "Train Epoch:9[38400/50000(77%)]\tTrain Loss:0.910130\n",
            "Train Epoch:9[44800/50000(90%)]\tTrain Loss:1.123999\n",
            "\n",
            "[EPOCH:9], \tTest Loss: 0.0328, \tTest Accuracy:63.45% \n",
            "\n",
            "Train Epoch:10[0/50000(0%)]\tTrain Loss:1.218185\n",
            "Train Epoch:10[6400/50000(13%)]\tTrain Loss:0.881112\n",
            "Train Epoch:10[12800/50000(26%)]\tTrain Loss:1.150976\n",
            "Train Epoch:10[19200/50000(38%)]\tTrain Loss:0.939947\n",
            "Train Epoch:10[25600/50000(51%)]\tTrain Loss:1.045119\n",
            "Train Epoch:10[32000/50000(64%)]\tTrain Loss:0.920240\n",
            "Train Epoch:10[38400/50000(77%)]\tTrain Loss:1.183149\n",
            "Train Epoch:10[44800/50000(90%)]\tTrain Loss:0.757539\n",
            "\n",
            "[EPOCH:10], \tTest Loss: 0.0317, \tTest Accuracy:64.59% \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}