Source code for deepctr.models.deepfm

# -*- coding:utf-8 -*-
"""
Author:
    Weichen Shen,wcshen1994@163.com

Reference:
    [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)

"""

from itertools import chain

import tensorflow as tf

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


[docs]def DeepFM(linear_feature_columns, dnn_feature_columns, fm_group=[DEFAULT_GROUP_NAME], dnn_hidden_units=(128, 128), 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 DeepFM 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 fm_group: list, group_name of features that will be used to do feature interactions. :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. """ features = build_input_features( linear_feature_columns + dnn_feature_columns) inputs_list = list(features.values()) linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear) group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed, support_group=True) fm_logit = add_func([FM()(concat_func(v, axis=1)) for k, v in group_embedding_dict.items() if k in fm_group]) dnn_input = combined_dnn_input(list(chain.from_iterable( group_embedding_dict.values())), dense_value_list) dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed)(dnn_input) dnn_logit = tf.keras.layers.Dense( 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(dnn_output) final_logit = add_func([linear_logit, fm_logit, dnn_logit]) output = PredictionLayer(task)(final_logit) model = tf.keras.models.Model(inputs=inputs_list, outputs=output) return model