大数据开发中如何绘制损失函数

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-- 绘制损失函数曲线-- -- drwaLossFunction.py-- -- loadFromPickle.py-- -- drwaLossCurve_tensorboard.py-- -- drwaLossCurve_matplotlib.py

文件: drwaLossFunction.py

# 获取训练世代和相应损失值import osimport pickledef createPickleFiles():'''用于生成三个pickle文件,分别用于保存训练世代数、置信度损失和定位损失'''myFolderPath = r'logs'files = os.listdir(myFolderPath)  # 列表类型 元素是文件名 字符串类型    files.sort(key=lambda x:int(x[5:x.index('-')]),         reverse=False)  # 按照训练世代排序loss4loc = list()  # 记录定位损失loss4cof = list()  # 记录置信度损失record4epoch = list()  # 记录训练世代数for item  in files:info = item.split('-')record4epoch.append(int(info[0][5:]))loss4loc.append(float(info[1][3:]))loss4cof.append(float(info[2][4:-4]))with open('绘制损失函数曲线/临时文件-EPOCH.pickle','wb') as file4epoch:pickle.dump(record4epoch,file4epoch)  # 保存世代信息with open('绘制损失函数曲线/临时文件-LOSS4CONFIDENT.pickle','wb') as file4conf:pickle.dump(loss4cof,file4conf)  # 保存置信度损失信息with open('绘制损失函数曲线/临时文件-LOSS4LOCATION.pickle','wb') as file4loc:pickle.dump(loss4loc,file4loc)  # 保存定位损失信息if __name__ == '__main__':createPickleFiles()

文件: loadFromPickle.py

# 从pickle文件中加载世代数和相应损失import pickledef getInfo():'''返回三个列表,分别用于保存世代、置信度损失和定位损失'''info = list()fileNames = ['绘制损失函数曲线/临时文件-EPOCH.pickle','绘制损失函数曲线/临时文件-LOSS4CONFIDENT.pickle','绘制损失函数曲线/临时文件-LOSS4LOCATION.pickle']  # 三个文件的名字for name in fileNames:with open(name, 'rb') as f:item = pickle.load(f)info.append(item)return info        if __name__ == '__main__':info = getInfo()# print(info[0])  # 世代# print(info[1])  # 置信度损失# print(info[2])  # 定位损失

文件: drwaLossCurve_tensorboard.py

from torch.utils.tensorboard import SummaryWriterimport loadFromPickleimport drwaLossFunctiondrwaLossFunction.createPickleFiles()info = loadFromPickle.getInfo()writer = SummaryWriter('绘制损失函数曲线/单独绘制')for epoch, conf, loc in zip(*info):writer.add_scalar('置信度损失', conf, epoch)writer.add_scalar('定位损失', loc, epoch)writer.close()writer = SummaryWriter('绘制损失函数曲线/三者对比')for epoch, conf, loc in zip(*info):writer.add_scalars('损失函数', {         'conf':conf,'loc':loc,'conf + loc': conf + loc}, epoch)writer.close()# '''# tensorboard --logdir=绘制损失函数曲线# '''

文件: drwaLossCurve_matplotlib.py

# 使用matplotlib绘制损失函数曲线 import drwaLossFunctionimport loadFromPickleimport matplotlibimport matplotlib.pyplot as plt matplotlib.rcParams['font.family'] = 'SimHei' # 'SimHei'  # 'STSong'matplotlib.rcParams['font.size'] = 15  # 修改字体大小plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号  drwaLossFunction.createPickleFiles()info = loadFromPickle.getInfo()epoch, conf, loc = infoplt.plot(epoch, loc,'rx-.', label='定位损失')plt.plot(epoch, conf, 'go-', label='置信度损失')plt.xlabel('训练世代: epoch')plt.ylabel('损失值: loss')plt.legend(loc='upper right', frameon=True)plt.show()

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