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ubuntu 22.04, python=3.9.18, torch=2.1.0, GPU=3090,
利用preprocess_ali_ccp.py脚本处理得到 train set & test set(没有将test set进一步拆分为validation set和test set)
tutorials/Multi_Task.ipynb文件中,将task_types = ["classification", "classification"] 修改为task_types = ["classification", "classification", "classification"](MTLTrainer中第111行,ESMM模型的total_loss = sum(loss_list[1:]),因此训练时一定是要将task_type设置成三个二分类任务(cvr,ctr,ctcvr)),其他超参数设置不变,进行全量模型训练,得到的日志如下
train loss: {'task_0:': 0.0009412071586964892, 'task_1:': 0.15991775316040385, 'task_2:': 5.389307421478335e-05} epoch: 0 validation scores: [1.0, 0.5951526439762982, 1.0]
The text was updated successfully, but these errors were encountered:
原始预处理中,sparse feature中含有 ctcvr_label = cvr_label,删除后模型训练正常。
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环境
ubuntu 22.04, python=3.9.18, torch=2.1.0, GPU=3090,
预处理
利用preprocess_ali_ccp.py脚本处理得到 train set & test set(没有将test set进一步拆分为validation set和test set)
参数设置
tutorials/Multi_Task.ipynb文件中,将task_types = ["classification", "classification"] 修改为task_types = ["classification", "classification", "classification"](MTLTrainer中第111行,ESMM模型的total_loss = sum(loss_list[1:]),因此训练时一定是要将task_type设置成三个二分类任务(cvr,ctr,ctcvr)),其他超参数设置不变,进行全量模型训练,得到的日志如下
输出日志
train loss: {'task_0:': 0.0009412071586964892, 'task_1:': 0.15991775316040385, 'task_2:': 5.389307421478335e-05}
epoch: 0 validation scores: [1.0, 0.5951526439762982, 1.0]
The text was updated successfully, but these errors were encountered: