deepctr_torch.models.dcnmix module¶

Author:

Reference:

[1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD’17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123)

[2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535)

class deepctr_torch.models.dcnmix.DCNMix(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_units=(128, 128), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_cross=1e-05, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0, low_rank=32, num_experts=4, dnn_activation='relu', dnn_use_bn=False, task='binary', device='cpu', gpus=None)[source]¶

Instantiates the DCN-Mix model.

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.
cross_num – positive integet,cross layer number
dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
l2_reg_cross – float. L2 regularizer strength applied to cross net
l2_reg_dnn – float. L2 regularizer strength applied to DNN
init_std – float,to use as the initialize std of embedding vector
seed – integer ,to use as random seed.
dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
dnn_use_bn – bool. Whether use BatchNormalization before activation or not DNN
dnn_activation – Activation function to use in DNN
low_rank – Positive integer, dimensionality of low-rank sapce.
num_experts – Positive integer, number of experts.
task – str, "binary" for binary logloss or "regression" for regression loss
device – str, "cpu" or "cuda:0"
gpus – list of int or torch.device for multiple gpus. If None, run on device. gpus[0] should be the same gpu with device.

Returns:

A PyTorch model instance.

forward(X)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.