Source code for deepctr.models.ccpm

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

    Weichen Shen,

    [1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746.

import tensorflow as tf
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Lambda

from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import DNN, PredictionLayer
from ..layers.sequence import KMaxPooling
from ..layers.utils import concat_func, add_func

[docs]def CCPM(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5), conv_filters=(4, 4), dnn_hidden_units=(128, 64), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_dnn=0, dnn_dropout=0, seed=1024, task='binary'): """Instantiates the Convolutional Click Prediction Model 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 conv_kernel_width: list,list of positive integer or empty list,the width of filter in each conv layer. :param conv_filters: list,list of positive integer or empty list,the number of filters in each conv 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 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 dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param init_std: float,to use as the initialize std of embedding vector :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ if len(conv_kernel_width) != len(conv_filters): raise ValueError( "conv_kernel_width must have same element with conv_filters") 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, l2_reg=l2_reg_linear) sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed, support_dense=False) n = len(sparse_embedding_list) l = len(conv_filters) conv_input = concat_func(sparse_embedding_list, axis=1) pooling_result = Lambda( lambda x: tf.expand_dims(x, axis=3))(conv_input) for i in range(1, l + 1): filters = conv_filters[i - 1] width = conv_kernel_width[i - 1] k = max(1, int((1 - pow(i / l, l - i)) * n)) if i < l else 3 conv_result = Conv2D(filters=filters, kernel_size=(width, 1), strides=(1, 1), padding='same', activation='tanh', use_bias=True, )(pooling_result) pooling_result = KMaxPooling( k=min(k, int(conv_result.shape[1])), axis=1)(conv_result) flatten_result = Flatten()(pooling_result) dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout)(flatten_result) dnn_logit = Dense(1, use_bias=False)( dnn_out) final_logit = add_func([dnn_logit, linear_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model