Source code for deepctr.models.fibinet

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

    [1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

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

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

[docs]def FiBiNET(linear_feature_columns, dnn_feature_columns, bilinear_type='interaction', reduction_ratio=3, dnn_hidden_units=(128, 128), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary'): """Instantiates the Feature Importance and Bilinear feature Interaction 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 bilinear_type: str,bilinear function type used in Bilinear Interaction Layer,can be ``'all'`` , ``'each'`` or ``'interaction'`` :param reduction_ratio: integer in [1,inf), reduction ratio used in SENET Layer :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 wide 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 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) senet_embedding_list = SENETLayer( reduction_ratio, seed)(sparse_embedding_list) senet_bilinear_out = BilinearInteraction( bilinear_type=bilinear_type, seed=seed)(senet_embedding_list) bilinear_out = BilinearInteraction( bilinear_type=bilinear_type, seed=seed)(sparse_embedding_list) dnn_input = combined_dnn_input( [Flatten()(concat_func([senet_bilinear_out, bilinear_out]))], dense_value_list) dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed)(dnn_input) dnn_logit = Dense( 1, use_bias=False, activation=None)(dnn_out) final_logit = add_func([linear_logit, dnn_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model