Source code for deepctr.models.nfm

# -*- coding:utf-8 -*-
    Weichen Shen,

    [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Dropout

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

[docs]def NFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0, seed=1024, bi_dropout=0, dnn_dropout=0, dnn_activation='relu', task='binary'): """Instantiates the Neural 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 dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param l2_reg_linear: float. L2 regularizer strength applied to linear part. :param l2_reg_dnn: float . L2 regularizer strength applied to DNN :param seed: integer ,to use as random seed. :param biout_dropout: When not ``None``, the probability we will drop out the output of BiInteractionPooling Layer. :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 deep net :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) sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed) fm_input = concat_func(sparse_embedding_list, axis=1) bi_out = BiInteractionPooling()(fm_input) if bi_dropout: bi_out = Dropout(bi_dropout)(bi_out, training=None) dnn_input = combined_dnn_input([bi_out], dense_value_list) dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input) dnn_logit = Dense(1, use_bias=False)(dnn_output) final_logit = add_func([linear_logit, dnn_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model