deepctr.models.fgcnn module

Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Liu B, Tang R, Chen Y, et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1904.04447, 2019. (https://arxiv.org/pdf/1904.04447)
deepctr.models.fgcnn.FGCNN(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(7, 7, 7, 7), conv_filters=(14, 16, 18, 20), new_maps=(3, 3, 3, 3), pooling_width=(2, 2, 2, 2), dnn_hidden_units=(128, ), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, dnn_dropout=0, init_std=0.0001, seed=1024, task='binary')[source]

Instantiates the Feature Generation by Convolutional Neural 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.
  • 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.
  • new_maps – list, list of positive integer or empty list, the feature maps of generated features.
  • pooling_width – list, list of positive integer or empty list,the width of pooling layer.
  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of deep net.
  • 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
  • seed – integer ,to use as random seed.
  • task – str, "binary" for binary logloss or "regression" for regression loss
Returns:

A Keras model instance.