# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[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.
(http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf)
"""
import tensorflow as tf
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=(256,), 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 = tf.keras.layers.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 = tf.keras.layers.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 = tf.keras.layers.Flatten()(pooling_result)
dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn,
dropout_rate=dnn_dropout)(flatten_result)
dnn_logit = tf.keras.layers.Dense(1, use_bias=False)(dnn_out)
final_logit = add_func([dnn_logit, linear_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
return model