SimpleMultiTask¶
简介¶
针对简单的多任务模型,所有任务共享特征和embedding,但是针对每个任务使用单独的Task Tower,任务之间相互独立
配置说明¶
model_config:{
model_class: "SimpleMultiTask"
feature_groups {
group_name: "all"
feature_names: "user_id"
feature_names: "cms_segid"
...
feature_names: "tag_brand_list"
wide_deep: DEEP
}
simple_multi_task {
task_towers {
tower_name: "ctr"
label_name: "clk"
dnn {
hidden_units: [256, 192, 128, 64]
}
num_class: 1
weight: 1.0
loss_type: CLASSIFICATION
metrics_set: {
auc {}
}
}
task_towers {
tower_name: "cvr"
label_name: "buy"
dnn {
hidden_units: [256, 192, 128, 64]
}
num_class: 1
weight: 1.0
loss_type: CLASSIFICATION
metrics_set: {
auc {}
}
}
l2_regularization: 0.0
}
embedding_regularization: 0.0
}
model_class: ‘SimpleMultiTask’, 不需要修改
feature_groups: 配置一个名为’all’的feature_group。
simple_multi_task: 相关的参数
task_towers 根据任务数配置task_towers
tower_name:任务名
label_name: tower对应的label名,若不设置,label_fields需与task_towers一一对齐
dnn deep part的参数配置
hidden_units: dnn每一层的channel数目,即神经元的数目
默认为二分类任务,即num_class默认为1,weight默认为1.0,loss_type默认为CLASSIFICATION,metrics_set为auc
embedding_regularization: 对embedding部分加regularization,防止overfit
SimpleMultiTask模型每个塔的输出名为:”logits_” / “probs_” / “y_” + tower_name 其中,logits/probs/y对应: sigmoid之前的值/概率/回归模型的预测值 SimpleMultiTask模型每个塔的指标为:指标名+ “_” + tower_name