Source code for deepctr.models.difm

# -*- coding:utf-8 -*-
    [1] Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]
    //IJCAI. 2020: 3139-3145.(
import tensorflow as tf
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Lambda, Flatten

from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns, SparseFeat, \
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FM, InteractingLayer
from ..layers.utils import concat_func, add_func, combined_dnn_input

[docs]def DIFM(linear_feature_columns, dnn_feature_columns, att_embedding_size=8, att_head_num=8, att_res=True, dnn_hidden_units=(256, 128, 64), l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task='binary'): """Instantiates the DIFM Network architecture. :param linear_feature_columns: An iterable containing all the features used by linear part of the model. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param att_embedding_size: integer, the embedding size in multi-head self-attention network. :param att_head_num: int. The head number in multi-head self-attention network. :param att_res: bool. Whether or not use standard residual connections before output. :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param seed: integer ,to use as random seed. :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param dnn_activation: Activation function to use in DNN :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ if not len(dnn_hidden_units) > 0: raise ValueError("dnn_hidden_units is null!") features = build_input_features( linear_feature_columns + dnn_feature_columns) sparse_feat_num = len(list(filter(lambda x: isinstance(x, SparseFeat) or isinstance(x, VarLenSparseFeat), dnn_feature_columns))) inputs_list = list(features.values()) sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed) if not len(sparse_embedding_list) > 0: raise ValueError("there are no sparse features") att_input = concat_func(sparse_embedding_list, axis=1) att_out = InteractingLayer(att_embedding_size, att_head_num, att_res, scaling=True)(att_input) att_out = Flatten()(att_out) m_vec = Dense(sparse_feat_num, use_bias=False)(att_out) dnn_input = combined_dnn_input(sparse_embedding_list, []) dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) m_bit = Dense(sparse_feat_num, use_bias=False)(dnn_output) input_aware_factor = add_func([m_vec, m_bit]) # the complete input-aware factor m_x linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear, sparse_feat_refine_weight=input_aware_factor) fm_input = concat_func(sparse_embedding_list, axis=1) refined_fm_input = Lambda(lambda x: x[0] * tf.expand_dims(x[1], axis=-1))( [fm_input, input_aware_factor]) fm_logit = FM()(refined_fm_input) final_logit = add_func([linear_logit, fm_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model