commit d7df4d25379a75ea16a9580bd230d84f7f5e88f8 Author: pinb Date: Thu Apr 27 02:56:04 2023 +0900 init diff --git a/CNN_Wafer.ipynb b/CNN_Wafer.ipynb new file mode 100644 index 0000000..f7f4853 --- /dev/null +++ b/CNN_Wafer.ipynb @@ -0,0 +1,618 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "1857acd7", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efb5db0b", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class CNN_WDI(nn.Module):\n", + " def __init__(self, class_num=9):\n", + " super(CNN_WDI, self).__init__()\n", + "\n", + " self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=0)\n", + " self.bn1 = nn.BatchNorm2d(16)\n", + " self.pool1 = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)\n", + " self.bn2 = nn.BatchNorm2d(16)\n", + "\n", + " self.conv3 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n", + " self.bn3 = nn.BatchNorm2d(32)\n", + " self.pool2 = nn.MaxPool2d(2, 2)\n", + " self.conv4 = nn.Conv2d(32, 32, kernel_size=3, padding=1)\n", + " self.bn4 = nn.BatchNorm2d(32)\n", + "\n", + " self.conv5 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", + " self.bn5 = nn.BatchNorm2d(64)\n", + " self.pool3 = nn.MaxPool2d(2, 2)\n", + " self.conv6 = nn.Conv2d(64, 64, kernel_size=3, padding=1)\n", + " self.bn6 = nn.BatchNorm2d(64)\n", + "\n", + " self.conv7 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", + " self.bn7 = nn.BatchNorm2d(128)\n", + " self.pool4 = nn.MaxPool2d(2, 2)\n", + " self.conv8 = nn.Conv2d(128, 128, kernel_size=3, padding=1)\n", + " self.bn8 = nn.BatchNorm2d(128)\n", + "\n", + " self.spatial_dropout = nn.Dropout2d(0.2)\n", + " self.pool5 = nn.MaxPool2d(2, 2)\n", + "\n", + " self.fc1 = nn.Linear(4608, 512)\n", + " self.fc2 = nn.Linear(512, class_num)\n", + "\n", + " def forward(self, x):\n", + " x = F.relu(self.bn1(self.conv1(x)))\n", + " x = self.pool1(F.relu(self.bn2(self.conv2(x))))\n", + "\n", + " x = F.relu(self.bn3(self.conv3(x)))\n", + " x = self.pool2(F.relu(self.bn4(self.conv4(x))))\n", + "\n", + " x = F.relu(self.bn5(self.conv5(x)))\n", + " x = self.pool3(F.relu(self.bn6(self.conv6(x))))\n", + "\n", + " x = F.relu(self.bn7(self.conv7(x)))\n", + " x = self.pool4(F.relu(self.bn8(self.conv8(x))))\n", + "\n", + " x = self.spatial_dropout(x)\n", + " x = self.pool5(x)\n", + "\n", + " x = x.view(x.size(0), -1)\n", + " x = F.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + "\n", + " return F.softmax(x, dim=1)\n", + "\n", + "cnn_wdi = CNN_WDI(class_num=9)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1c383602", + "metadata": {}, + "source": [ + "# Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a865c00c", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision import transforms, datasets\n", + "\n", + "# 데이터 전처리\n", + "rotation_angles = list(range(0, 361, 15))\n", + "rotation_transforms = [transforms.RandomRotation(degrees=(angle, angle), expand=False, center=None, fill=None) for angle in rotation_angles]\n", + "\n", + "data_transforms = transforms.Compose([\n", + " transforms.Pad(padding=224, fill=0, padding_mode='constant'),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.RandomVerticalFlip(),\n", + " transforms.RandomApply(rotation_transforms, p=1),\n", + " transforms.CenterCrop((224, 224)),\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "# ImageFolder를 사용하여 데이터셋 불러오기\n", + "train_dataset = datasets.ImageFolder(root='E:/wm_images/train/', transform=data_transforms)\n", + "val_dataset = datasets.ImageFolder(root='E:/wm_images/val/', transform=data_transforms)\n", + "test_dataset = datasets.ImageFolder(root='E:/wm_images/test/', transform=data_transforms)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "36039ab9", + "metadata": {}, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b466f397", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.optim as optim\n", + "\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "cnn_wdi.to(device)\n", + "print(str(device) + ' loaded.')\n", + "\n", + "# 손실 함수 및 최적화 알고리즘 설정\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(cnn_wdi.parameters(), lr=0.001)\n", + "\n", + "# 배치사이즈\n", + "batch_size = 18063360 #112\n", + "\n", + "# 학습 및 평가 실행\n", + "num_epochs = 100 #* 192\n", + "# num_epochs = 50\n", + "\n", + "# Random sample size\n", + "train_max_images = 95\n", + "val_max_images = 25\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cd9ed634", + "metadata": {}, + "source": [ + "# Train Function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8020581f", + "metadata": {}, + "outputs": [], + "source": [ + "# 학습 함수 정의\n", + "def train(model, dataloader, criterion, optimizer, device):\n", + " model.train()\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " for inputs, labels in dataloader:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + "\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_loss = running_loss / len(dataloader.dataset)\n", + " epoch_acc = running_corrects.double() / len(dataloader.dataset)\n", + "\n", + " return epoch_loss, epoch_acc" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2fa4e672", + "metadata": {}, + "source": [ + "# Evaluate Function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "674a1e25", + "metadata": {}, + "outputs": [], + "source": [ + "# 평가 함수 정의\n", + "def evaluate(model, dataloader, criterion, device):\n", + " model.eval()\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in dataloader:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_loss = running_loss / len(dataloader.dataset)\n", + " epoch_acc = running_corrects.double() / len(dataloader.dataset)\n", + "\n", + " return epoch_loss, epoch_acc" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "42148a41", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95074e64", + "metadata": {}, + "outputs": [], + "source": [ + "# Train & Validation의 Loss, Acc 기록 파일\n", + "s_title = 'Epoch,\\tTrain Loss,\\tTrain Acc,\\tVal Loss,\\tVal Acc\\n'\n", + "with open('output.txt', 'a') as file:\n", + " file.write(s_title)\n", + "print(s_title)\n", + "\n", + "for epoch in range(num_epochs + 1):\n", + " # 무작위 샘플 추출\n", + " train_indices = torch.randperm(len(train_dataset))[:train_max_images]\n", + " train_random_subset = torch.utils.data.Subset(train_dataset, train_indices)\n", + " train_loader = torch.utils.data.DataLoader(train_random_subset, batch_size=batch_size, shuffle=True, num_workers=4)\n", + " \n", + " val_indices = torch.randperm(len(val_dataset))[:val_max_images]\n", + " val_random_subset = torch.utils.data.Subset(train_dataset, val_indices)\n", + " val_loader = torch.utils.data.DataLoader(val_random_subset, batch_size=batch_size, shuffle=False, num_workers=4)\n", + "\n", + " # 학습 및 Validation 평가\n", + " train_loss, train_acc = train(cnn_wdi, train_loader, criterion, optimizer, device)\n", + " val_loss, val_acc = evaluate(cnn_wdi, val_loader, criterion, device)\n", + "\n", + " # 로그 기록\n", + " s_output = f'{epoch + 1}/{num_epochs},\\t{train_loss:.4f},\\t{train_acc:.4f},\\t{val_loss:.4f},\\t{val_acc:.4f}\\n'\n", + " with open('output.txt', 'a') as file:\n", + " file.write(s_output)\n", + " print(s_output)\n", + "\n", + " if epoch % 10 == 0:\n", + " # 모델 저장\n", + " torch.save(cnn_wdi.state_dict(), 'CNN_WDI_' + str(epoch) + 'epoch.pth')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "345f1ce5", + "metadata": {}, + "source": [ + "# Confusion Metrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c350bb0d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "import pandas as pd\n", + "\n", + "def plot_metrics(title, class_names, precisions, recalls, f1_scores, acc):\n", + " num_classes = len(class_names)\n", + " index = np.arange(num_classes)\n", + " bar_width = 0.2\n", + "\n", + " plt.figure(figsize=(15, 7))\n", + " plt.bar(index, precisions, bar_width, label='Precision')\n", + " plt.bar(index + bar_width, recalls, bar_width, label='Recall')\n", + " plt.bar(index + 2 * bar_width, f1_scores, bar_width, label='F1-score')\n", + " plt.axhline(y=acc, color='r', linestyle='--', label='Accuracy')\n", + "\n", + " plt.xlabel('Class')\n", + " plt.ylabel('Scores')\n", + " plt.title(title + ': Precision, Recall, F1-score, and Accuracy per Class')\n", + " plt.xticks(index + bar_width, class_names)\n", + " plt.legend(loc='upper right')\n", + " plt.show()\n", + "\n", + "def predict_and_plot_metrics(title, model, dataloader, criterion, device):\n", + " model.eval()\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " all_preds = []\n", + " all_labels = []\n", + " class_names = ['Center', 'Donut', 'Edge-Loc', 'Edge-Ring', 'Loc', 'Near-full', 'none', 'Random', 'Scratch']\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in dataloader:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " epoch_loss = running_loss / len(dataloader.dataset)\n", + " epoch_acc = running_corrects.double() / len(dataloader.dataset)\n", + "\n", + "\n", + " # Calculate classification report\n", + " report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)\n", + "\n", + " # Calculate confusion matrix\n", + " cm = confusion_matrix(all_labels, all_preds)\n", + "\n", + " # Calculate precision, recall, and f1-score per class\n", + " precisions = [report[c]['precision'] for c in class_names]\n", + " recalls = [report[c]['recall'] for c in class_names]\n", + " f1_scores = [report[c]['f1-score'] for c in class_names]\n", + " print('p: ' + str(precisions))\n", + " print('r: ' + str(recalls))\n", + " print('f: ' + str(f1_scores))\n", + "\n", + " # Plot confusion matrix with normalized values (percentage)\n", + " cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " plt.figure(figsize=(12, 12))\n", + " sns.heatmap(cm_normalized, annot=True, fmt='.2%', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n", + " plt.xlabel('Predicted Label')\n", + " plt.ylabel('True Label')\n", + " plt.title('Normalized Confusion Matrix: ' + title)\n", + " plt.show()\n", + "\n", + " # Plot precision, recall, f1-score, and accuracy per class\n", + " plot_metrics(title, class_names, precisions, recalls, f1_scores, epoch_acc.item())\n", + "\n", + " return epoch_loss, epoch_acc, report\n" + ] + }, + { + "cell_type": "markdown", + "id": "dfddbcdc", + "metadata": {}, + "source": [ + "# Evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d57f59cd", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "\n", + "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=112, shuffle=False, num_workers=4)\n", + "\n", + "dir = '.'\n", + "models = [file for file in os.listdir(dir) if file.endswith(('.pth'))]\n", + "\n", + "def extract_number(filename):\n", + " return int(re.search(r'\\d+', filename).group(0))\n", + "\n", + "sorted_models = sorted(models, key=extract_number)\n", + "\n", + "for model in sorted_models:\n", + " model_path = os.path.join(dir, model)\n", + "\n", + " # Load the saved model weights\n", + " cnn_wdi.load_state_dict(torch.load(model_path))\n", + "\n", + " # Call the predict_and_plot_metrics function with the appropriate arguments\n", + " epoch_loss, epoch_acc, report = predict_and_plot_metrics(model, cnn_wdi, test_loader, criterion, device)\n", + " # print(f'Model: {model} Test Loss: {test_loss:.4f} Acc: {test_acc:.4f}')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "25fa475d", + "metadata": {}, + "source": [ + "# Loss Graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe2360b6", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# 파일에서 데이터를 읽어들입니다.\n", + "with open('output.txt', 'r') as file:\n", + " lines = file.readlines()[1:] # 첫 번째 줄은 헤더이므로 건너뜁니다.\n", + "\n", + "# 데이터를 분석하여 리스트에 저장합니다.\n", + "epochs = []\n", + "train_losses = []\n", + "train_accuracies = []\n", + "val_losses = []\n", + "val_accuracies = []\n", + "\n", + "for line in lines:\n", + " if line == '\\n':\n", + " continue\n", + " # epoch, train_loss, train_acc, val_loss, val_acc = line.strip().split(', \\t')\n", + " epoch, train_loss, train_acc, val_loss, val_acc = re.split(r'[,\\s\\t]+', line.strip())\n", + " epochs.append(int(epoch.split('/')[0]))\n", + " train_losses.append(float(train_loss))\n", + " train_accuracies.append(float(train_acc))\n", + " val_losses.append(float(val_loss))\n", + " val_accuracies.append(float(val_acc))\n", + "\n", + "# 선 그래프를 그립니다.\n", + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(epochs, train_losses, label='Train Loss')\n", + "plt.plot(epochs, train_accuracies, label='Train Acc')\n", + "plt.plot(epochs, val_losses, label='Val Loss')\n", + "plt.plot(epochs, val_accuracies, label='Val Acc')\n", + "\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Values')\n", + "plt.title('Training and Validation Loss and Accuracy')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "66b5fef7", + "metadata": {}, + "source": [ + "# Print Selecting Test Model Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65a5a7c4", + "metadata": {}, + "outputs": [], + "source": [ + "def output(model, dataloader, criterion, device):\n", + " model.eval()\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " all_preds = []\n", + " all_labels = []\n", + " class_names = ['Center', 'Donut', 'Edge-Loc', 'Edge-Ring', 'Loc', 'Near-full', 'none', 'Random', 'Scratch']\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in dataloader:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " epoch_loss = running_loss / len(dataloader.dataset)\n", + " epoch_acc = running_corrects.double() / len(dataloader.dataset)\n", + "\n", + "\n", + " # Calculate classification report\n", + " report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)\n", + "\n", + " # Calculate precision, recall, and f1-score per class\n", + " precisions = [report[c]['precision'] for c in class_names]\n", + " recalls = [report[c]['recall'] for c in class_names]\n", + " f1_scores = [report[c]['f1-score'] for c in class_names]\n", + " accuracy = report['accuracy']\n", + "\n", + " precs = sum(precisions) / len(precisions)\n", + " recs = sum(recalls) / len(recalls)\n", + " f1s = sum(f1_scores) / len(f1_scores)\n", + " print('precisions: ' + str(precs))\n", + " print('recalls: ' + str(recs))\n", + " print('f1_scores: ' + str(f1s))\n", + " print('accuracy ' + str(accuracy))\n", + "\n", + "\n", + "selected_model = 'CNN_WDI_20epoch.pth'\n", + "cnn_wdi.load_state_dict(torch.load(selected_model))\n", + "output(cnn_wdi, test_loader, criterion, device)" + ] + }, + { + "cell_type": "markdown", + "id": "4a2753c3", + "metadata": {}, + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a9f9a39d", + "metadata": {}, + "source": [ + "# 원본 데이터셋 학습 결과\n", + "\n", + "### 배치 사이즈\n", + "* batch_size = 18063360\n", + "\n", + "### 학습 및 평가 실행\n", + "* num_epochs = 100\n", + "\n", + "### Random sample size\n", + "* train_max_images = 95\n", + "* val_max_images = 25\n", + "\n", + "##### 설정으로 학습 진행 시,\n", + "\n", + "``` plaintext\n", + "Epoch,\tTrain Loss,\tTrain Acc,\tVal Loss,\tVal Acc\n", + "\n", + "1/100,\t1.5930,\t0.7789,\t1.8920,\t0.4800\n", + "\n", + "2/100,\t1.5193,\t0.8526,\t1.8520,\t0.5200\n", + "\n", + "3/100,\t1.4562,\t0.9158,\t1.9320,\t0.4400\n", + "\n", + "4/100,\t1.5088,\t0.8632,\t1.7720,\t0.6000\n", + "\n", + "5/100,\t1.5088,\t0.8632,\t1.8920,\t0.4800\n", + "\n", + "6/100,\t1.4983,\t0.8737,\t1.8520,\t0.5200\n", + "\n", + "7/100,\t1.4983,\t0.8737,\t1.9720,\t0.4000\n", + "\n", + "8/100,\t1.5720,\t0.8000,\t2.0120,\t0.3600\n", + "...\n", + "```\n", + "\n", + ": 데이터 부족으로 학습이 제대로 이루어지지 않음." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..6d618ec --- /dev/null +++ b/readme.md @@ -0,0 +1,21 @@ +# 지능화 캡스톤 프로젝트 #1 - WDI-CNN +### *(Wafer Map 데이터를 9종류의 Class로 분류하는 CNN 모델 만들기)* + + +----- + +### 논문 +![반도체 제조공정의 불균형 데이터셋에 대한 웨이퍼 불량 식별을 위한 심층 컨볼루션 신경망](https://file.notion.so/f/s/f80d2b5c-ac36-435b-8cef-19f3f5675940/(%EC%B5%9C%EC%A2%85)%EC%B0%B8%EA%B3%A0%EC%9E%90%EB%A3%8C_%EB%85%BC%EB%AC%B8_Wafer_Map_%EB%B6%88%EB%9F%89%EA%B2%80%EC%B6%9C_%EB%B2%88%EC%97%AD.pdf?id=805060e2-1f8d-4cc0-aba3-26e7b83ed5e9&table=block&spaceId=5c35ea55-42f1-4c42-b112-94f6eb8e2c2e&expirationTimestamp=1682615407281&signature=fK1nI4Wihr3P4g6i0RxbQ8insN8Gcr27vY5_DI0tctk&downloadName=%28%EC%B5%9C%EC%A2%85%29%EC%B0%B8%EA%B3%A0%EC%9E%90%EB%A3%8C_%EB%85%BC%EB%AC%B8_Wafer+Map+%EB%B6%88%EB%9F%89%EA%B2%80%EC%B6%9C_%EB%B2%88%EC%97%AD.pdf) + +### Dataset +[Kaggle - WDI Data](https://www.kaggle.com/qingyi/wm811k-wafer-map/code) + +----- + +### 수행방법 + +* 위 논문을 참고하여 CNN 모델을 구현하고, WDI Dataset을 학습하여 9개의 클래스(Center, Donut, Edge-Loc, Edge-Ring, Loc, Near-full, none, Random, Scratch)를 분류한다. + +### 평가방법 + +* 모델의 성능지표(Precision, Recall, Accuracy, F1-Score)를 혼동행렬(Confusion Metrix)로 구현한다. \ No newline at end of file