deepctr_torch.models.afn module

Author:
Weiyu Cheng, weiyu_cheng@sjtu.edu.cn
Reference:
[1] Cheng, W., Shen, Y. and Huang, L. 2020. Adaptive Factorization Network: Learning Adaptive-Order Feature
Interactions. Proceedings of the AAAI Conference on Artificial Intelligence. 34, 04 (Apr. 2020), 3609-3616.
class deepctr_torch.models.afn.AFN(linear_feature_columns, dnn_feature_columns, ltl_hidden_size=256, afn_dnn_hidden_units=(256, 128), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary', device='cpu', gpus=None)[source]

Instantiates the Adaptive Factorization Network architecture.

In DeepCTR-Torch, we only provide the non-ensembled version of AFN for the consistency of model interfaces. For the ensembled version of AFN+, please refer to https://github.com/WeiyuCheng/DeepCTR-Torch (Pytorch Version) or https://github.com/WeiyuCheng/AFN-AAAI-20 (Tensorflow Version).

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.
  • ltl_hidden_size – integer, the number of logarithmic neurons in AFN
  • afn_dnn_hidden_units – list, list of positive integer or empty list, the layer number and units in each layer of DNN layers in AFN
  • 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
  • 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_activation – Activation function to use in DNN
  • 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.