deepctr_torch.models.xdeepfm module

Author:
Wutong Zhang
Reference:
[1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)
class deepctr_torch.models.xdeepfm.xDeepFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 256), cin_layer_size=(256, 128), cin_split_half=True, cin_activation='relu', l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, l2_reg_cin=0, init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task='binary', device='cpu', gpus=None)[source]

Instantiates the xDeepFM 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
  • cin_layer_size – list,list of positive integer or empty list, the feature maps in each hidden layer of Compressed Interaction Network
  • cin_split_half – bool.if set to True, half of the feature maps in each hidden will connect to output unit
  • cin_activation – activation function used on feature maps
  • l2_reg_linear – float. L2 regularizer strength applied to linear part
  • l2_reg_embedding – L2 regularizer strength applied to embedding vector
  • l2_reg_dnn – L2 regularizer strength applied to deep net
  • l2_reg_cin – L2 regularizer strength applied to CIN.
  • 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
  • dnn_use_bn – bool. Whether use BatchNormalization before activation or not 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.