# -*- 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 get_linear_logit, input_from_feature_columns
from ..utils import deepctr_model_fn, DNN_SCOPE_NAME, variable_scope
from ...layers.core import DNN
from ...layers.sequence import KMaxPooling
from ...layers.utils import concat_func
[docs]def CCPMEstimator(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', model_dir=None, config=None, linear_optimizer='Ftrl',
dnn_optimizer='Adagrad', training_chief_hooks=None):
"""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
:param model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
:param config: tf.RunConfig object to configure the runtime settings.
:param linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. Defaults to FTRL optimizer.
:param dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. Defaults to Adagrad optimizer.
:param training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to
run on the chief worker during training.
:return: A Tensorflow Estimator instance.
"""
if len(conv_kernel_width) != len(conv_filters):
raise ValueError(
"conv_kernel_width must have same element with conv_filters")
def _model_fn(features, labels, mode, config):
train_flag = (mode == tf.estimator.ModeKeys.TRAIN)
linear_logits = get_linear_logit(features, linear_feature_columns, l2_reg_linear=l2_reg_linear)
with variable_scope(DNN_SCOPE_NAME):
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding=l2_reg_embedding)
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, seed=seed)(flatten_result, training=train_flag)
dnn_logit = tf.keras.layers.Dense(1, use_bias=False)(dnn_out)
logits = linear_logits + dnn_logit
return deepctr_model_fn(features, mode, logits, labels, task, linear_optimizer, dnn_optimizer,
training_chief_hooks=training_chief_hooks
)
return tf.estimator.Estimator(_model_fn, model_dir=model_dir, config=config)