Source code for deepctr.models.flen

# -*- coding:utf-8 -*-
    Tingyi Tan,

    [1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(


from itertools import chain

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense

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

[docs]def FLEN(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0, dnn_activation='relu', dnn_use_bn=False, task='binary'): """Instantiates the FLEN 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 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_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()) group_embedding_dict, dense_value_list = input_from_feature_columns( features, dnn_feature_columns, l2_reg_embedding, seed, support_group=True) linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear) fm_mf_out = FieldWiseBiInteraction(seed=seed)( [concat_func(v, axis=1) for k, v in group_embedding_dict.items()]) 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=seed)(dnn_input) dnn_logit = Dense(1, use_bias=False)(concat_func([fm_mf_out, dnn_output])) final_logit = add_func([linear_logit, dnn_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model