deepctr_torch.models.nfm module

Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027)
class deepctr_torch.models.nfm.NFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-05, l2_reg_linear=1e-05, l2_reg_dnn=0, init_std=0.0001, seed=1024, bi_dropout=0, dnn_dropout=0, dnn_activation='relu', task='binary', device='cpu', gpus=None)[source]

Instantiates the NFM Network 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.
  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of deep net
  • l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
  • l2_reg_linear – float. L2 regularizer strength applied to linear part.
  • 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.
  • biout_dropout – When not None, the probability we will drop out the output of BiInteractionPooling Layer.
  • dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
  • dnn_activation – Activation function to use in deep net
  • 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.