Source code for deepctr.models.afm

# -*- coding:utf-8 -*-

    Weichen Shen,

    [1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

from tensorflow.python.keras.models import Model
from ..feature_column import build_input_features, get_linear_logit, DEFAULT_GROUP_NAME, input_from_feature_columns
from ..layers.core import PredictionLayer
from ..layers.interaction import AFMLayer, FM
from ..layers.utils import concat_func, add_func

[docs]def AFM(linear_feature_columns, dnn_feature_columns, fm_group=DEFAULT_GROUP_NAME, use_attention=True, attention_factor=8, l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_att=1e-5, afm_dropout=0, seed=1024, task='binary'): """Instantiates the Attentional Factorization Machine 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 fm_group: list, group_name of features that will be used to do feature interactions. :param use_attention: bool,whether use attention or not,if set to ``False``.it is the same as **standard Factorization Machine** :param attention_factor: positive integer,units in attention net :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_att: float. L2 regularizer strength applied to attention net :param afm_dropout: float in [0,1), Fraction of the attention net output units to dropout. :param seed: integer ,to use as random seed. :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ features = build_input_features( linear_feature_columns + dnn_feature_columns) inputs_list = list(features.values()) group_embedding_dict, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed, support_dense=False, support_group=True) linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear) if use_attention: fm_logit = add_func([AFMLayer(attention_factor, l2_reg_att, afm_dropout, seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group]) else: fm_logit = add_func([FM()(concat_func(v, axis=1)) for k, v in group_embedding_dict.items() if k in fm_group]) final_logit = add_func([linear_logit, fm_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model