mmoe_layer

最新推荐文章于 2025-05-11 19:52:07 发布

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最新推荐文章于 2025-05-11 19:52:07 发布 · 209 阅读

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该博客介绍了如何使用TensorFlow实现MMOE（Multi-gate Mixture-of-Experts）层，这是一种用于多任务学习的模型。MMOE层通过多个专家网络和门控网络来处理不同任务，每个任务可以利用多个专家的输出，并通过门控网络加权求和得到最终结果。文章详细阐述了MMOE层的结构、参数配置、权重初始化、激活函数等，并给出了实例代码演示其工作原理。

#!usr/bin/env python
# coding=utf-8

import numpy as np
import pandas as pd
import datetime
import itertools
import tensorflow as tf
from tensorflow.keras.layers import *
import tensorflow.keras.backend as K
from tensorflow.keras import layers

class MMoELayer(Layer):
    """
    The Multi-gate Mixture-of-Experts layer in MMOE model
      Input shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Output shape
        - A list with **num_tasks** elements, which is a 2D tensor with shape: ``(batch_size, units_experts)`` .
      Arguments
        - **num_tasks**: integer, the number of tasks, equal to the number of outputs.
        - **num_experts**: integer, the number of experts.
        - **units_experts**: integer, the dimension of each output of MMOELayer.
    References
      - [Jiaqi Ma, Zhe Zhao, Xinyang Yi, et al. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts[C]](https://dl.acm.org/doi/10.1145/3219819.3220007)
    """

    def __init__(self, units_experts, num_experts, num_tasks,
                 use_expert_bias=True, use_gate_bias=True, expert_activation='relu', gate_activation='softmax',
                 expert_bias_initializer='zeros', gate_bias_initializer='zeros', expert_bias_regularizer=None,
                 gate_bias_regularizer=None, expert_bias_constraint=None, gate_bias_constraint=None,
                 expert_kernel_initializer='VarianceScaling', gate_kernel_initializer='VarianceScaling',
                 expert_kernel_regularizer=None, gate_kernel_regularizer=None, expert_kernel_constraint=None,
                 gate_kernel_constraint=None, activity_regularizer=None, **kwargs):
        super(MMoELayer, self).__init__(**kwargs)

        self

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