deepctr.feature_column module

class deepctr.feature_column.DenseFeat

Bases: deepctr.feature_column.DenseFeat

Dense feature Args:

name: feature name. dimension: dimension of the feature, default = 1. dtype: dtype of the feature, default=”float32”. transform_fn: If not None , a function that can be used to transform values of the feature. the function takes the input Tensor as its argument, and returns the output Tensor. (e.g. lambda x: (x - 3.0) / 4.2).

class deepctr.feature_column.SparseFeat

Bases: deepctr.feature_column.SparseFeat

class deepctr.feature_column.VarLenSparseFeat

Bases: deepctr.feature_column.VarLenSparseFeat

dtype

embedding_dim

embedding_name

embeddings_initializer

group_name

name

trainable

use_hash

vocabulary_path

vocabulary_size

deepctr.feature_column.build_input_features(feature_columns, prefix='')

deepctr.feature_column.get_feature_names(feature_columns)

deepctr.feature_column.get_linear_logit(features, feature_columns, units=1, use_bias=False, seed=1024, prefix='linear', l2_reg=0, sparse_feat_refine_weight=None)

deepctr.feature_column.input_from_feature_columns(features, feature_columns, l2_reg, seed, prefix='', seq_mask_zero=True, support_dense=True, support_group=False)