deepctr.estimator.models.ccpm module¶
- 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)
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deepctr.estimator.models.ccpm.
CCPMEstimator
(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5), conv_filters=(4, 4), dnn_hidden_units=(256, ), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, 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)[source]¶ Instantiates the Convolutional Click Prediction Model architecture.
Parameters: - linear_feature_columns – An iterable containing all the features used by linear part of the model.
- dnn_feature_columns – An iterable containing all the features used by deep part of the model.
- conv_kernel_width – list,list of positive integer or empty list,the width of filter in each conv layer.
- conv_filters – list,list of positive integer or empty list,the number of filters in each conv layer.
- dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN.
- l2_reg_linear – float. L2 regularizer strength applied to linear part
- l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
- l2_reg_dnn – float. L2 regularizer strength applied to DNN
- dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
- init_std – float,to use as the initialize std of embedding vector
- task – str,
"binary"
for binary logloss or"regression"
for regression loss - 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.
- config – tf.RunConfig object to configure the runtime settings.
- linear_optimizer – An instance of tf.Optimizer used to apply gradients to the linear part of the model. Defaults to FTRL optimizer.
- dnn_optimizer – An instance of tf.Optimizer used to apply gradients to the deep part of the model. Defaults to Adagrad optimizer.
- training_chief_hooks – Iterable of tf.train.SessionRunHook objects to run on the chief worker during training.
Returns: A Tensorflow Estimator instance.