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

simple_multi_task.png

配置说明

1.内置模型

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

2. 组件化模型

model_config {
  model_name: "SimpleMultiTask"
  model_class: "MultiTaskModel"
  feature_groups {
    group_name: "all"
    feature_names: "user_id"
    feature_names: "cms_segid"
    ...
    wide_deep: DEEP
  }
  backbone {
    blocks {
      name: "identity"
      inputs {
        feature_group_name: "all"
      }
    }
  }
  model_params {
    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: 1e-07
  }
  embedding_regularization: 5e-06
}
  • model_name: 任意自定义字符串,仅有注释作用

  • model_class: ‘MultiTaskModel’, 不需要修改, 通过组件化方式搭建的多目标排序模型都叫这个名字

  • backbone: 通过组件化的方式搭建的主干网络,参考文档

    • blocks: 由多个组件块组成的一个有向无环图(DAG),框架负责按照DAG的拓扑排序执行个组件块关联的代码逻辑,构建TF Graph的一个子图

    • name/inputs: 每个block有一个唯一的名字(name),并且有一个或多个输入(inputs)和输出

  • 其余与内置模型参数相同