22、谷歌MMOE多任务学习模型（转）

文章发表在KDD 2018 Research Track上，链接为Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts。

一、摘要

多任务学习可被用在许多应用上，如推荐系统。如在电影推荐中，用户可购买和喜欢观看偏好的电影，故可同时预测用户购买量以及对电影的打分。

多任务学习常对任务之间的相关性较敏感，故权衡任务之间的目标以及任务内部关系十分重要。

MMOE模型可用来学习任务之间的关系，本文采用MOE（专家模型）在多个任务之间通过共享专家子网络来进行多任务学习，其中设置一个门结构来训练优化每个任务。

二、引言

• 许多基于DNN的多任务学习存在着对数据分布不平衡、任务相关性等问题，内在的任务差异冲突会损害一些任务的预测。
• 也有一些论文提出新的建模技术来处理多任务学习中的任务差异，但技术常设计为每个模型增加更多模型参数，导致计算开销变大。
• MMOE：学习任务之间的关系，学习特定任务功能，自动分配参数捕获共享任务信息或特定任务信息，避免每次添加新参数。

多任务模型通过学习不同任务的联系和差异，可提高每个任务的学习效率和质量。

（1）多任务学习的的框架广泛采用shared-bottom的结构，不同任务间共用底部的隐层。

这种结构本质上可以减少过拟合的风险，但是效果上可能受到任务差异和数据分布带来的影响。

（2）也有一些其他结构，比如两个任务的参数不共用，但是通过对不同任务的参数增加L2范数的限制；也有一些对每个任务分别学习一套隐层然后学习所有隐层的组合。

和shared-bottom结构相比，这些模型对增加了针对任务的特定参数，在任务差异会影响公共参数的情况下对最终效果有提升。

缺点就是模型增加了参数量所以需要更大的数据量来训练模型，而且模型更复杂并不利于在真实生产环境中实际部署使用。

因此，论文中提出了一个Multi-gate Mixture-of-Experts(MMoE)的多任务学习结构。MMoE模型刻画了任务相关性，基于共享表示来学习特定任务的函数，避免了明显增加参数的缺点。

MMoE模型的结构(下图c)基于广泛使用的Shared-Bottom结构(下图a)和MoE结构，其中图(b)是图(c)的一种特殊情况。

 

 

三、一般的多任务学习模型

1、框架：

 

 

如上图a所示，shared-bottom网络（表示为函数f）位于底部，多个任务共用这一层。往上，K个子任务分别对应一个tower network（表示为 ），每个子任务的输出  。

 

 

2、任务相关性实验

接下来，我们通过一个实验来探讨任务相关性和多任务学习效果的关系。

假设模型中包含两个回归任务，而数据通过采样生成，并且规定输入相同，输出label不同。那么任务的相关性就使用label之间的皮尔逊相关系数来表示，相关系数越大，表示任务之间越相关，数据生成的过程如下：

首先，生成了两个垂直的单位向量u1和u2，并根据两个单位向量生成了模型的系数w1和w2，如上图中的第二步。w1和w2之间的cosine距离即为p，大伙可以根据cosine的计算公式得到。

随后基于正态分布的到输入数据x，而y根据下面的两个式子的到：

注意，这里x和y之间并非线性的关系，因为模型的第二步是多个sin函数，因此label之间的皮尔逊相关系数和参数w1和w2之间的cosine距离并不相等，但是呈现出一个正相关的关系，如下图：

因此，本文中使用参数的cosine距离来近似表示任务之间的相关性。

3、实验结果

基于上述数据生成过程以及任务相关性的表示方法，分别测试任务相关性在0.5、0.9和1时的多任务学习模型的效果，如下图：

可以看到的是，随着任务相关性的提升，模型的loss越小，效果越好，从而印证了前面的猜想。

四、MMOE模型

1、MOE模型

先来看一下Mixture-of-Experts (MoE)模型（文中后面称作 One-gate Mixture-of-Experts (OMoE)），如下图所示：

可以看到，相较于一般的多任务学习框架，共享的底层分为了多个expert，同时设置了一个Gate，使不同的数据可以多样化的使用共享层。此时共享层的输出可以表示为：

其中fi代表第i个expert的输出，  是n个expert network（expert network可认为是一个神经网络），gi代表第第i个expert对应的权重，是基于输入数据得到的，计算公式为g(x) = softmax(Wgx)，其中  。g是组合experts结果的gating network，具体来说g产生n个experts上的概率分布，最终的输出是所有experts的带权加和。显然，MoE可看做基于多个独立模型的集成方法。

后面有些文章将MoE作为一个基本的组成单元，将多个MoE结构堆叠在一个大网络中。比如一个MoE层可以接受上一层MoE层的输出作为输入，其输出作为下一层的输入使用。

2、 MMoE模型

文章提出的模型（简称MMoE）目的就是相对于shared-bottom结构不明显增加模型参数的要求下捕捉任务的不同。其核心思想是将shared-bottom网络中的函数f替换成MoE层

相较于MoE模型，Multi-gate Mixture-of-Experts (MMoE)模型为每一个task设置了一个gate，使不同的任务和不同的数据可以多样化的使用共享层，模型结构如下：

此时每个任务的共享层的输出不同，第k个任务的共享层输出计算公式如下：

输入就是input feature，输出是所有experts上的权重。一方面，因为gating networks通常是轻量级的，而且expert networks是所有任务共用，所以相对于论文中提到的一些baseline方法在计算量和参数量上具有优势。

随后每个任务对应的共享层输出，经过多层全连接神经网络得到每个任务的输出：

从直观上考虑，如果两个任务并不十分相关，那么经过Gate之后，二者得到的权重系数会差别比较大，从而可以利用部分expert网络输出的信息，近似于多个单任务学习模型。如果两个任务紧密相关，那么经过Gate得到的权重分布应该相差不多，类似于一般的多任务学习框架。

相对于所有任务公共一个门控网络(One-gate MoE model，如上图b)，这里MMoE(上图c)中每个任务使用单独的gating networks。每个任务的gating networks通过最终输出权重不同实现对experts的选择性利用。不同任务的gating networks可以学习到不同的组合experts的模式，因此模型考虑到了捕捉到任务的相关性和区别。

网络中export是切分的子网络，实现的时候其实可以看做是三维tensor，形状为：

dim of input feature * number of units per expert * number of experts

更新时是对这个三维tensor进行更新。

gate的形状则为：
dim of input feature * number of experts * number of tasks

然后一点网络中的小小小details，贴在这里可以参考一下，帮助理解：

f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper

g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))

 

五、实验结果

1 人工合成数据集

下图是实验结果，OMoE是单门MoE。可以看到在相关性强的数据上，OMoE和MMoE差别不大，但是在相关性低的数据上，MMoE胜过其他两个方法很多。

2、UCI census-income dataset

 

 

 

3、Large-scale Content Recommendation

 

 六、主要代码

1、导包

import pandas as pd
from keras.utils import to_categorical
from keras import backend as K
from keras.optimizers import Adam
from keras.initializers import VarianceScaling
from keras.layers import Input, Dense
from keras.models import Model
from keras.callbacks import Callback
from sklearn.metrics import roc_auc_score

import numpy as np
import random

import tensorflow as tf
from mmoe import MMoE #模型代码

SEED = 1

# Fix numpy seed for reproducibility
np.random.seed(SEED)

# Fix random seed for reproducibility
random.seed(SEED)

# Fix TensorFlow graph-level seed for reproducibility
tf.set_random_seed(SEED)


#设置tensorflow的session

 

 2、加载数据---1994年income数据

column_names = ['age', 'class_worker', 'det_ind_code', 'det_occ_code', 'education', 'wage_per_hour', 'hs_college',
                'marital_stat', 'major_ind_code', 'major_occ_code', 'race', 'hisp_origin', 'sex', 'union_member',
                'unemp_reason', 'full_or_part_emp', 'capital_gains', 'capital_losses', 'stock_dividends',
                'tax_filer_stat', 'region_prev_res', 'state_prev_res', 'det_hh_fam_stat', 'det_hh_summ',
                'instance_weight', 'mig_chg_msa', 'mig_chg_reg', 'mig_move_reg', 'mig_same', 'mig_prev_sunbelt',
                'num_emp', 'fam_under_18', 'country_father', 'country_mother', 'country_self', 'citizenship',
                'own_or_self', 'vet_question', 'vet_benefits', 'weeks_worked', 'year', 'income_50k']

# Load the dataset in Pandas
train_df = pd.read_csv(
    'data/census-income.data.gz',
    delimiter=',',
    header=None,
    index_col=None,
    names=column_names
)
other_df = pd.read_csv(
    'data/census-income.test.gz',
    delimiter=',',
    header=None,
    index_col=None,
    names=column_names
)

 

切分feature和label

label_columns = ['income_50k', 'marital_stat']

# One-hot encoding categorical columns
categorical_columns = ['class_worker', 'det_ind_code', 'det_occ_code', 'education', 'hs_college', 'major_ind_code',
                       'major_occ_code', 'race', 'hisp_origin', 'sex', 'union_member', 'unemp_reason',
                       'full_or_part_emp', 'tax_filer_stat', 'region_prev_res', 'state_prev_res', 'det_hh_fam_stat',
                       'det_hh_summ', 'mig_chg_msa', 'mig_chg_reg', 'mig_move_reg', 'mig_same', 'mig_prev_sunbelt',
                       'fam_under_18', 'country_father', 'country_mother', 'country_self', 'citizenship',
                       'vet_question']
train_raw_labels = train_df[label_columns]
other_raw_labels = other_df[label_columns]
transformed_train = pd.get_dummies(train_df.drop(label_columns, axis=1), columns=categorical_columns)
transformed_other = pd.get_dummies(other_df.drop(label_columns, axis=1), columns=categorical_columns)

 

打标签

transformed_other['det_hh_fam_stat_ Grandchild <18 ever marr not in subfamily'] = 0

# One-hot encoding categorical labels
train_income = to_categorical((train_raw_labels.income_50k == ' 50000+.').astype(int), num_classes=2)   # > 5000的为1, < 5000为0
train_marital = to_categorical((train_raw_labels.marital_stat == ' Never married').astype(int), num_classes=2)  ## Never married为1, married为0

other_income = to_categorical((other_raw_labels.income_50k == ' 50000+.').astype(int), num_classes=2) 
other_marital = to_categorical((other_raw_labels.marital_stat == ' Never married').astype(int), num_classes=2) 

dict_outputs = {
    'income': train_income.shape[1],
    'marital': train_marital.shape[1]
}  ## dict_outputs = {'income' : 2, 'marital' : 2}

dict_train_labels = { 'income': train_income, 'marital': train_marital } 
dict_other_labels = { 'income': other_income, 'marital': other_marital } 
output_info = [(dict_outputs[key], key) for key in sorted(dict_outputs.keys())]  ## output_info = [(2, 'income'), (2, 'marital')]

 

切分验证集和测试集、训练集

# Split the other dataset into 1:1 validation to test according to the paper
validation_indices = transformed_other.sample(frac=0.5, replace=False, random_state=SEED).index
test_indices = list(set(transformed_other.index) - set(validation_indices))
validation_data = transformed_other.iloc[validation_indices]
validation_label = [dict_other_labels[key][validation_indices] for key in sorted(dict_other_labels.keys())]
test_data = transformed_other.iloc[test_indices]
test_label = [dict_other_labels[key][test_indices] for key in sorted(dict_other_labels.keys())]
train_data = transformed_train
train_label = [dict_train_labels[key] for key in sorted(dict_train_labels.keys())]

num_features = train_data.shape[1]
print('Training data shape = {}'.format(train_data.shape))
print('Validation data shape = {}'.format(validation_data.shape))
print('Test data shape = {}'.format(test_data.shape))


############
# Training data shape = (199523, 499)
# Validation data shape = (49881, 499)
# Test data shape = (49881, 499)

 

3、 模型构建

输入层

input_layer = Input(shape=(num_features,))

MMOE层

mmoe_layers = MMoE(
    units=4,
    num_experts=8,
    num_tasks=2
)(input_layer)

output_layers = []

 

 

MMOE代码类：

from keras import backend as K
from keras import activations, initializers, regularizers, constraints
from keras.engine.topology import Layer, InputSpec


class MMoE(Layer):
    """
    Multi-gate Mixture-of-Experts model.
    """

    def __init__(self,
                 units,
                 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):
        """
         Method for instantiating MMoE layer.

        :param units: Number of hidden units
        :param num_experts: Number of experts
        :param num_tasks: Number of tasks
        :param use_expert_bias: Boolean to indicate the usage of bias in the expert weights
        :param use_gate_bias: Boolean to indicate the usage of bias in the gate weights
        :param expert_activation: Activation function of the expert weights
        :param gate_activation: Activation function of the gate weights
        :param expert_bias_initializer: Initializer for the expert bias
        :param gate_bias_initializer: Initializer for the gate bias
        :param expert_bias_regularizer: Regularizer for the expert bias
        :param gate_bias_regularizer: Regularizer for the gate bias
        :param expert_bias_constraint: Constraint for the expert bias
        :param gate_bias_constraint: Constraint for the gate bias
        :param expert_kernel_initializer: Initializer for the expert weights
        :param gate_kernel_initializer: Initializer for the gate weights
        :param expert_kernel_regularizer: Regularizer for the expert weights
        :param gate_kernel_regularizer: Regularizer for the gate weights
        :param expert_kernel_constraint: Constraint for the expert weights
        :param gate_kernel_constraint: Constraint for the gate weights
        :param activity_regularizer: Regularizer for the activity
        :param kwargs: Additional keyword arguments for the Layer class
        """
        # Hidden nodes parameter
        self.units = units
        self.num_experts = num_experts
        self.num_tasks = num_tasks

        # Weight parameter
        self.expert_kernels = None
        self.gate_kernels = None
        self.expert_kernel_initializer = initializers.get(expert_kernel_initializer)
        self.gate_kernel_initializer = initializers.get(gate_kernel_initializer)
        self.expert_kernel_regularizer = regularizers.get(expert_kernel_regularizer)
        self.gate_kernel_regularizer = regularizers.get(gate_kernel_regularizer)
        self.expert_kernel_constraint = constraints.get(expert_kernel_constraint)
        self.gate_kernel_constraint = constraints.get(gate_kernel_constraint)

        # Activation parameter
        self.expert_activation = activations.get(expert_activation)
        self.gate_activation = activations.get(gate_activation)

        # Bias parameter
        self.expert_bias = None
        self.gate_bias = None
        self.use_expert_bias = use_expert_bias
        self.use_gate_bias = use_gate_bias
        self.expert_bias_initializer = initializers.get(expert_bias_initializer)
        self.gate_bias_initializer = initializers.get(gate_bias_initializer)
        self.expert_bias_regularizer = regularizers.get(expert_bias_regularizer)
        self.gate_bias_regularizer = regularizers.get(gate_bias_regularizer)
        self.expert_bias_constraint = constraints.get(expert_bias_constraint)
        self.gate_bias_constraint = constraints.get(gate_bias_constraint)

        # Activity parameter
        self.activity_regularizer = regularizers.get(activity_regularizer)

        # Keras parameter
        self.input_spec = InputSpec(min_ndim=2)
        self.supports_masking = True

        super(MMoE, self).__init__(**kwargs)

    def build(self, input_shape):
        """
        Method for creating the layer weights.

        :param input_shape: Keras tensor (future input to layer)
                            or list/tuple of Keras tensors to reference
                            for weight shape computations
        """
        assert input_shape is not None and len(input_shape) >= 2

        input_dimension = input_shape[-1]

        # Initialize expert weights (number of input features * number of units per expert * number of experts)
        self.expert_kernels = self.add_weight(
            name='expert_kernel',
            shape=(input_dimension, self.units, self.num_experts),
            initializer=self.expert_kernel_initializer,
            regularizer=self.expert_kernel_regularizer,
            constraint=self.expert_kernel_constraint,
        )

        # Initialize expert bias (number of units per expert * number of experts)
        if self.use_expert_bias:
            self.expert_bias = self.add_weight(
                name='expert_bias',
                shape=(self.units, self.num_experts),
                initializer=self.expert_bias_initializer,
                regularizer=self.expert_bias_regularizer,
                constraint=self.expert_bias_constraint,
            )

        # Initialize gate weights (number of input features * number of experts * number of tasks)
        self.gate_kernels = [self.add_weight(
            name='gate_kernel_task_{}'.format(i),
            shape=(input_dimension, self.num_experts),
            initializer=self.gate_kernel_initializer,
            regularizer=self.gate_kernel_regularizer,
            constraint=self.gate_kernel_constraint
        ) for i in range(self.num_tasks)]

        # Initialize gate bias (number of experts * number of tasks)
        if self.use_gate_bias:
            self.gate_bias = [self.add_weight(
                name='gate_bias_task_{}'.format(i),
                shape=(self.num_experts,),
                initializer=self.gate_bias_initializer,
                regularizer=self.gate_bias_regularizer,
                constraint=self.gate_bias_constraint
            ) for i in range(self.num_tasks)]

        self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dimension})

        super(MMoE, self).build(input_shape)

    def call(self, inputs, **kwargs):
        """
        Method for the forward function of the layer.

        :param inputs: Input tensor
        :param kwargs: Additional keyword arguments for the base method
        :return: A tensor
        """
        gate_outputs = []
        final_outputs = []

        # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper， expert_outputs = {batch_size, units per experts, numbers of experts}
        expert_outputs = K.tf.tensordot(a=inputs, b=self.expert_kernels, axes=1)
        # Add the bias term to the expert weights if necessary
        if self.use_expert_bias:
            expert_outputs = K.bias_add(x=expert_outputs, bias=self.expert_bias)
        expert_outputs = self.expert_activation(expert_outputs)

        # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper, gate_output = { batch_size , 1}
        for index, gate_kernel in enumerate(self.gate_kernels):
            gate_output = K.dot(x=inputs, y=gate_kernel)
            # Add the bias term to the gate weights if necessary
            if self.use_gate_bias:
                gate_output = K.bias_add(x=gate_output, bias=self.gate_bias[index])
            gate_output = self.gate_activation(gate_output)
            gate_outputs.append(gate_output)

        # f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
        for gate_output in gate_outputs:
            expanded_gate_output = K.expand_dims(gate_output, axis=1)
            weighted_expert_output = expert_outputs * K.repeat_elements(expanded_gate_output, self.units, axis=1)
            final_outputs.append(K.sum(weighted_expert_output, axis=2))

        return final_outputs

    def compute_output_shape(self, input_shape):
        """
        Method for computing the output shape of the MMoE layer.

        :param input_shape: Shape tuple (tuple of integers)
        :return: List of input shape tuple where the size of the list is equal to the number of tasks
        """
        assert input_shape is not None and len(input_shape) >= 2

        output_shape = list(input_shape)
        output_shape[-1] = self.units
        output_shape = tuple(output_shape)

        return [output_shape for _ in range(self.num_tasks)]

    def get_config(self):
        """
        Method for returning the configuration of the MMoE layer.

        :return: Config dictionary
        """
        config = {
            'units': self.units,
            'num_experts': self.num_experts,
            'num_tasks': self.num_tasks,
            'use_expert_bias': self.use_expert_bias,
            'use_gate_bias': self.use_gate_bias,
            'expert_activation': activations.serialize(self.expert_activation),
            'gate_activation': activations.serialize(self.gate_activation),
            'expert_bias_initializer': initializers.serialize(self.expert_bias_initializer),
            'gate_bias_initializer': initializers.serialize(self.gate_bias_initializer),
            'expert_bias_regularizer': regularizers.serialize(self.expert_bias_regularizer),
            'gate_bias_regularizer': regularizers.serialize(self.gate_bias_regularizer),
            'expert_bias_constraint': constraints.serialize(self.expert_bias_constraint),
            'gate_bias_constraint': constraints.serialize(self.gate_bias_constraint),
            'expert_kernel_initializer': initializers.serialize(self.expert_kernel_initializer),
            'gate_kernel_initializer': initializers.serialize(self.gate_kernel_initializer),
            'expert_kernel_regularizer': regularizers.serialize(self.expert_kernel_regularizer),
            'gate_kernel_regularizer': regularizers.serialize(self.gate_kernel_regularizer),
            'expert_kernel_constraint': constraints.serialize(self.expert_kernel_constraint),
            'gate_kernel_constraint': constraints.serialize(self.gate_kernel_constraint),
            'activity_regularizer': regularizers.serialize(self.activity_regularizer)
        }
        base_config = super(MMoE, self).get_config()

        return dict(list(base_config.items()) + list(config.items()))

 

输出层(tower layer)

# Build tower layer from MMoE layer
for index, task_layer in enumerate(mmoe_layers):
    tower_layer = Dense(
        units=8,
        activation='relu',
        kernel_initializer=VarianceScaling())(task_layer)
    output_layer = Dense(
        units=output_info[index][0],
        name=output_info[index][1],
        activation='softmax',
        kernel_initializer=VarianceScaling())(tower_layer)
    output_layers.append(output_layer)

 

4、模型训练

model = Model(inputs=[input_layer], outputs=output_layers)
adam_optimizer = Adam()
model.compile(
    loss={'income':'binary_crossentropy'},
    optimizer=adam_optimizer,
    metrics=['accuracy']
)
# Print out model architecture summary
model.summary()

# Train the model
model.fit(
    x=train_data,
    y=train_label,
    validation_data=(validation_data, validation_label),
    callbacks=[
        ROCCallback(
            training_data=(train_data, train_label),
            validation_data=(validation_data, validation_label),
            test_data=(test_data, test_label)
        )
    ],
    epochs=100
)

 

 

 

 

 

 

 

参考文献：

https://zhuanlan.zhihu.com/p/55752344

https://zhuanlan.zhihu.com/p/96796043

多任务学习模型详解：Multi-gate Mixture-of-Experts（MMoE ，Google，KDD2018）

MMOE论文笔记（论文中有维度讲解）