VGG16得到的混淆矩阵错误

关注 码龄 粉丝数 原力等级 -- 被采纳 被点赞 采纳率 tiaya01 2024-04-18 16:50 采纳率: 85.7% 浏览 1 首页/ 编程语言 / VGG16得到的混淆矩阵错误 python VGG16得到的混淆矩阵错误 这是main代码： import os os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" import json import torch from torchvision import transforms, datasets import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt from prettytable import PrettyTable from model import vgg class ConfusionMatrix(object): def __init__(self, num_classes: int, labels: list): self.matrix = np.zeros((num_classes, num_classes)) self.num_classes = num_classes self.labels = labels def update(self, preds, labels): for p, t in zip(preds, labels): self.matrix[p, t] += 1 def summary(self): # calculate accuracy sum_TP = 0 for i in range(self.num_classes): sum_TP += self.matrix[i, i] acc = sum_TP / np.sum(self.matrix) print("the model accuracy is ", acc) # precision, recall, F1-score, specificity table = PrettyTable() table.field_names = ["label", "Precision", "Recall", "F1-score", "Specificity"] #"Specificity" for i in range(self.num_classes): TP = self.matrix[i, i] FP = np.sum(self.matrix[i, :]) - TP FN = np.sum(self.matrix[:, i]) - TP TN = np.sum(self.matrix) - TP - FP - FN Precision = round(TP / (TP + FP), 3) if TP + FP != 0 else 0. Recall = round(TP / (TP + FN), 3) if TP + FN != 0 else 0. Specificity = round(TN / (TN + FP), 3) if TN + FP != 0 else 0. f1_score = round((2*Precision*Recall)/(Precision+Recall),3) table.add_row([self.labels[i], Precision, Recall, f1_score,Specificity]) print(table) def plot(self): matrix = self.matrix print(matrix) plt.imshow(matrix, cmap=plt.cm.Blues) # # 设置x轴坐标label # plt.xticks(range(self.num_classes), self.labels, rotation=45) # # 设置y轴坐标label # plt.yticks(range(self.num_classes), self.labels) # 设置x轴坐标label为1, 2, 3 plt.xticks(range(self.num_classes), list(range(1, self.num_classes + 1)), rotation=45) # 设置y轴坐标label为1, 2, 3 plt.yticks(range(self.num_classes), list(range(1, self.num_classes + 1))) # 显示colorbar plt.colorbar() plt.xlabel('True Labels') plt.ylabel('Predicted Labels') plt.title('Confusion matrix') # 在图中标注数量/概率信息 thresh = matrix.max() / 2 for x in range(self.num_classes): for y in range(self.num_classes): # 注意这里的matrix[y, x]不是matrix[x, y] info = int(matrix[y, x]) plt.text(x, y, info, verticalalignment='center', horizontalalignment='center', color="white" if info > thresh else "black") plt.tight_layout() plt.show() if __name__ == '__main__': device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) data_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # 数据集路径 data_root = r"C:\Users\yingnuo.DESKTOP-9E5CS2I\Desktop\T1-data" assert os.path.exists(data_root), "data path {} does not exist.".format(data_root) validate_dataset = datasets.ImageFolder(root=os.path.join(data_root, "test"), transform=data_transform) batch_size = 16 validate_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=batch_size, shuffle=False, num_workers=2) net = vgg(model_name="vgg16", num_classes=4) # load pretrain weights model_weight_path = "./weights/best_model.pth" assert os.path.exists(model_weight_path), "cannot find {} file".format(model_weight_path) weight_dict = torch.load(model_weight_path, map_location=device) net.load_state_dict(weight_dict, strict=False) net.to(device) # read class_indict json_label_path = './class_indices.json' assert os.path.exists(json_label_path), "cannot find {} file".format(json_label_path) json_file = open(json_label_path, 'r') class_indict = json.load(json_file) labels = [label for _, label in class_indict.items()] confusion = ConfusionMatrix(num_classes=4, labels=labels) net.eval() with torch.no_grad(): for val_data in tqdm(validate_loader): val_images, val_labels = val_data outputs = net(val_images.to(device)) outputs = torch.softmax(outputs, dim=1) outputs = torch.argmax(outputs, dim=1) confusion.update(outputs.to("cpu").numpy(), val_labels.to("cpu").numpy()) confusion.plot() confusion.summary() 展开全部 收起 写回答 好问题 0 提建议 追加酬金 关注问题 微信扫一扫 点击复制链接 分享 邀请回答 编辑 收藏 删除 结题 收藏 举报 追加酬金 (90%的用户在追加酬金后获得了解决方案) 当前问题酬金 ¥ 0 (可追加 ¥500） ¥ 15¥ 20¥ 50¥ 100¥ 200 支付方式 扫码支付 二维码出错 点击刷新 支付金额 15 元 提供问题酬金的用户不参与问题酬金结算和分配 支付即为同意 《付费问题酬金结算规则》 结题 再想想 删除 再等等