1 原文

没有仔细看，只是看了一下模型结构。点击【原文】即可下载。

2 模型

对于监督学习问题，类别特征作为输入，一般One-hot，所以需要引入特征交互来做出更精确的预测；但是如果直接以product的方式来显示交互，对于稀疏输入数据集，只能观察到一些交叉特征；

所以FM被提出了，利用隐变量来做内积实现交互，但是FM也存在问题，也就是所有交互特征的权重是一样的；

但是在实际中，应该预测性较低的特征，其对应权重也较低。所以AFM就是基于这个思想，AFM模型的结构如下：

求解公式：


AFM可以用于不同的任务：回归、分类、排序等，一般对于回归问题是平方损失，二分类是Logloss，本文专注于平方损失，且使用SGD算法来最优化模型参数；

防止过拟合： 一般考虑L2和Dropout，文中没有同时使用L2+Dropout，效果不好；所以只用了L2在MLP层，如下所示：

3 python

Embedding Size：K
Batch Size：N
Attention Size ：A
Field Size (这里是field size 不是feature size！！！！）： F

weights[‘attention_w’] 的维度为 K * A，
weights[‘attention_b’] 的维度为 A，
weights[‘attention_h’] 的维度为 A，
weights[‘attention_p’] 的维度为 K * 1

import numpy as np
import tensorflow as tf

from time import time
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics import roc_auc_score

class AFM(BaseEstimator, TransformerMixin):

    def __init__(self, feature_size, field_size,
                 embedding_size=8,attention_size=10,
                 deep_layers=[32, 32], deep_init_size = 50,
                 dropout_deep=[0.5, 0.5, 0.5],
                 deep_layer_activation=tf.nn.relu,
                 epoch=10, batch_size=256,
                 learning_rate=0.001, optimizer="adam",
                 batch_norm=0, batch_norm_decay=0.995,
                 verbose=False, random_seed=2016,
                 loss_type="logloss", eval_metric=roc_auc_score,
                greater_is_better=True,
                 use_inner=True):
        assert loss_type in ["logloss", "mse"], 
            "loss_type can be either 'logloss' for classification task or 'mse' for regression task"

        self.feature_size = feature_size
        self.field_size = field_size
        self.embedding_size = embedding_size
        self.attention_size = attention_size

        self.deep_layers = deep_layers
        self.deep_init_size = deep_init_size
        self.dropout_dep = dropout_deep
        self.deep_layers_activation = deep_layer_activation

        self.epoch = epoch
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.optimizer_type = optimizer

        self.batch_norm = batch_norm
        self.batch_norm_decay = batch_norm_decay

        self.verbose = verbose
        self.random_seed = random_seed
        self.loss_type = loss_type
        self.eval_metric = eval_metric
        self.greater_is_better = greater_is_better
        self.train_result,self.valid_result = [],[]

        self.use_inner = use_inner

        self._init_graph()

    def _init_graph(self):
        self.graph = tf.Graph()
        with self.graph.as_default():
            tf.set_random_seed(self.random_seed)

            self.feat_index = tf.placeholder(tf.int32,
                                             shape=[None,None],
                                             name='feat_index')
            self.feat_value = tf.placeholder(tf.float32,
                                           shape=[None,None],
                                           name='feat_value')

            self.label = tf.placeholder(tf.float32,shape=[None,1],name='label')
            self.dropout_keep_deep = tf.placeholder(tf.float32,shape=[None],name='dropout_deep_deep')
            self.train_phase = tf.placeholder(tf.bool,name='train_phase')

            self.weights = self._initialize_weights()

            # Embeddings
            self.embeddings = tf.nn.embedding_lookup(self.weights['feature_embeddings'],self.feat_index) # N * F * K
            feat_value = tf.reshape(self.feat_value,shape=[-1,self.field_size,1])
            self.embeddings = tf.multiply(self.embeddings,feat_value) # N * F * K


            # element_wise
            element_wise_product_list = []
            for i in range(self.field_size):
                for j in range(i+1,self.field_size):
                    element_wise_product_list.append(tf.multiply(self.embeddings[:,i,:],self.embeddings[:,j,:])) # None * K

            self.element_wise_product = tf.stack(element_wise_product_list) # (F * F - 1 / 2) * None * K
            self.element_wise_product = tf.transpose(self.element_wise_product,perm=[1,0,2],name='element_wise_product') # None * (F * F - 1 / 2) *  K

            #self.interaction

            # attention part
            num_interactions = int(self.field_size * (self.field_size - 1) / 2)
            # wx+b -> relu(wx+b) -> h*relu(wx+b)
            self.attention_wx_plus_b = tf.reshape(tf.add(tf.matmul(tf.reshape(self.element_wise_product,shape=(-1,self.embedding_size)),
                                                                   self.weights['attention_w']),
                                                         self.weights['attention_b']),
                                                  shape=[-1,num_interactions,self.attention_size]) # N * ( F * F - 1 / 2) * A

            self.attention_exp = tf.exp(tf.reduce_sum(tf.multiply(tf.nn.relu(self.attention_wx_plus_b),
                                                           self.weights['attention_h']),
                                               axis=2,keep_dims=True)) # N * ( F * F - 1 / 2) * 1

            self.attention_exp_sum = tf.reduce_sum(self.attention_exp,axis=1,keep_dims=True) # N * 1 * 1

            self.attention_out = tf.div(self.attention_exp,self.attention_exp_sum,name='attention_out')  # N * ( F * F - 1 / 2) * 1

            self.attention_x_product = tf.reduce_sum(tf.multiply(self.attention_out,self.element_wise_product),axis=1,name='afm') # N * K

            self.attention_part_sum = tf.matmul(self.attention_x_product,self.weights['attention_p']) # N * 1



            # first order term
            self.y_first_order = tf.nn.embedding_lookup(self.weights['feature_bias'], self.feat_index)
            self.y_first_order = tf.reduce_sum(tf.multiply(self.y_first_order, feat_value), 2)

            # bias
            self.y_bias = self.weights['bias'] * tf.ones_like(self.label)


            # out
            self.out = tf.add_n([tf.reduce_sum(self.y_first_order,axis=1,keep_dims=True),
                                 self.attention_part_sum,
                                 self.y_bias],name='out_afm')

            # loss
            if self.loss_type == "logloss":
                self.out = tf.nn.sigmoid(self.out)
                self.loss = tf.losses.log_loss(self.label, self.out)
            elif self.loss_type == "mse":
                self.loss = tf.nn.l2_loss(tf.subtract(self.label, self.out))



            if self.optimizer_type == "adam":
                self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate, beta1=0.9, beta2=0.999,
                                                        epsilon=1e-8).minimize(self.loss)
            elif self.optimizer_type == "adagrad":
                self.optimizer = tf.train.AdagradOptimizer(learning_rate=self.learning_rate,
                                                           initial_accumulator_value=1e-8).minimize(self.loss)
            elif self.optimizer_type == "gd":
                self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
            elif self.optimizer_type == "momentum":
                self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.learning_rate, momentum=0.95).minimize(
                    self.loss)


            #init
            self.saver = tf.train.Saver()
            init = tf.global_variables_initializer()
            self.sess = tf.Session()
            self.sess.run(init)

            # number of params
            total_parameters = 0
            for variable in self.weights.values():
                shape = variable.get_shape()
                variable_parameters = 1
                for dim in shape:
                    variable_parameters *= dim.value
                total_parameters += variable_parameters
            if self.verbose > 0:
                print("#params: %d" % total_parameters)





    def _initialize_weights(self):
        weights = dict()

        #embeddings
        weights['feature_embeddings'] = tf.Variable(
            tf.random_normal([self.feature_size,self.embedding_size],0.0,0.01),
            name='feature_embeddings')
        weights['feature_bias'] = tf.Variable(tf.random_normal([self.feature_size,1],0.0,1.0),name='feature_bias')
        weights['bias'] = tf.Variable(tf.constant(0.1),name='bias')

        # attention part
        glorot = np.sqrt(2.0 / (self.attention_size + self.embedding_size))

        weights['attention_w'] = tf.Variable(np.random.normal(loc=0,scale=glorot,size=(self.embedding_size,self.attention_size)),
                                             dtype=tf.float32,name='attention_w')

        weights['attention_b'] = tf.Variable(np.random.normal(loc=0,scale=glorot,size=(self.attention_size,)),
                                             dtype=tf.float32,name='attention_b')

        weights['attention_h'] = tf.Variable(np.random.normal(loc=0,scale=1,size=(self.attention_size,)),
                                             dtype=tf.float32,name='attention_h')


        weights['attention_p'] = tf.Variable(np.ones((self.embedding_size,1)),dtype=np.float32)

        return weights


    def get_batch(self,Xi,Xv,y,batch_size,index):
        start = index * batch_size
        end = (index + 1) * batch_size
        end = end if end < len(y) else len(y)
        return Xi[start:end],Xv[start:end],[[y_] for y_ in y[start:end]]

    # shuffle three lists simutaneously
    def shuffle_in_unison_scary(self, a, b, c):
        rng_state = np.random.get_state()
        np.random.shuffle(a)
        np.random.set_state(rng_state)
        np.random.shuffle(b)
        np.random.set_state(rng_state)
        np.random.shuffle(c)

    def predict(self, Xi, Xv,y):
        """
        :param Xi: list of list of feature indices of each sample in the dataset
        :param Xv: list of list of feature values of each sample in the dataset
        :return: predicted probability of each sample
        """
        # dummy y
        feed_dict = {self.feat_index: Xi,
                     self.feat_value: Xv,
                     self.label: y,
                     self.dropout_keep_deep: [1.0] * len(self.dropout_dep),
                     self.train_phase: True}

        loss = self.sess.run([self.loss], feed_dict=feed_dict)

        return loss


    def fit_on_batch(self,Xi,Xv,y):
        feed_dict = {self.feat_index:Xi,
                     self.feat_value:Xv,
                     self.label:y,
                     self.dropout_keep_deep:self.dropout_dep,
                     self.train_phase:True}

        loss,opt = self.sess.run([self.loss,self.optimizer],feed_dict=feed_dict)

        return loss

    def fit(self, Xi_train, Xv_train, y_train,
            Xi_valid=None, Xv_valid=None, y_valid=None,
            early_stopping=False, refit=False):

        has_valid = Xv_valid is not None
        for epoch in range(self.epoch):
            t1 = time()
            self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
            total_batch = int(len(y_train) / self.batch_size)
            for i in range(total_batch):
                Xi_batch, Xv_batch, y_batch = self.get_batch(Xi_train, Xv_train, y_train, self.batch_size, i)
                self.fit_on_batch(Xi_batch, Xv_batch, y_batch)

            if has_valid:
                y_valid = np.array(y_valid).reshape((-1,1))
                loss = self.predict(Xi_valid, Xv_valid, y_valid)
                print("epoch",epoch,"loss",loss)

参考

1、原文 https://www.ijcai.org/proceedings/2017/0435.pdf

2、代码 https://github.com/liulin7576/DL_CTR/tree/master/AFM

3、代码 https://github.com/princewen/tensorflow_practice/tree/master/recommendation/Basic-AFM-Demo

最后

以上就是魁梧手机为你收集整理的《Attentional Factorization Machines》AFM模型及python实现1 原文2 模型3 python参考的全部内容，希望文章能够帮你解决《Attentional Factorization Machines》AFM模型及python实现1 原文2 模型3 python参考所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。