[人工智能]分类评估标准混淆矩阵（图像分类篇）

混淆矩阵是评判模型结果的一种指标，属于模型评估的一部分，一般用作评判分类器的优劣

• 以二分类为例子
• 一级指标FP FN
  
• 二级指标 四个

• 三级指标
  

举例子（三分类来分析加深上面的公式的理解）

• 所有的类别即使Accuracy准确率
  
• 单一类别的Precision精确率
• 单一类别的Recall召回率
  
• 单一类别的Specificity特异度
  

同样上面的单一类别可以计算各自的三级指标

在自己写论文中，分类模型评价的一节里，你可以采用类似下面的图来证明你的模型怎么样。

代码实战

import os
import json

import torch
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdm

import torch
import numpy as np
import matplotlib.pyplot as plt
from prettytable import PrettyTable
from model_v2 import MobileNetV2

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

    #预测值和真实值输入进来，累加到我们的混淆矩阵当中
    def update(self,preds,labels):
        for p,t in zip(preds,labels):
            self.matrix[p,t]+=1

    def summary(self):
        # Acccuracy
        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,Specificity
        table = PrettyTable()
        table.field_names=["","Precision","Recall","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)#对小数的部分只取它的3位
            Recall = round(TP/(TP+FN), 3)
            Specificity = round(TN/(TN+FP), 3)
            table.add_row([self.labels[i], Precision, Recall, Specificity])
        print(table)

    def plot(self):
        matrix=self.matrix
        print(matrix)
        plt.imshow(matrix,cmap=plt.cm.Blues)

        #设置X轴的坐标label
        plt.xticks(range(self.num_classes),self.labels,rotation=45)
        #设置y轴的坐标label
        plt.yticks(range(self.num_classes),self.labels)
        #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])])

    data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
    #image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
    image_path=data_root+"/data_set/flower_data/"

    #validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
                                         #transform=data_transform["val"])
    validate_dataset = datasets.ImageFolder(root=image_path+"val",transform=data_transform)

    train_num = len(validate_dataset)

    batch_size = 16
    validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                                  batch_size=batch_size, shuffle=False,
                                                  num_workers=2)
    # create model
    net = MobileNetV2(num_classes=5)#===================================

    # load pretrain weights
    model_weight_path = "./Mobilenetv2.pth"#==================================
    net.load_state_dict(torch.load(model_weight_path,map_location=device))
    net.to(device)

    #read class_indict
    try:
        json_label_path = './class_indices.json'
        assert os.path.exists(json_label_path), "cannot find {} file".format(json_label_path)
        json_file=open('./class_indices.json','r')
        class_indict=json.load(json_file)
    except Exception as e:
        print(e)
        exit(-1)

    labels=[label for _,label in class_indict.items()]
    confusion=ConfusionMatrix(num_classes=5,labels=labels)
    net.eval()#启动验证模式
    with torch.no_grad():#启动上下文管理器
        for val_data in 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())#按照视频说的会报错
    confusion.plot()
    confusion.summary()

• 结果
  

参考链接：https://blog.csdn.net/qq_37541097?spm=1001.2014.3001.5509
推荐博文：https://blog.csdn.net/Orange_Spotty_Cat/article/details/80520839