tf.contrib.layers.weighted_sparse_column(sparse_id_column, weight_column_name, dtype=tf.float32)

tf.contrib.layers.weighted_sparse_column(sparse_id_column, weight_column_name, dtype=tf.float32)

See the guide: Layers (contrib) > Feature columns

Creates a _SparseColumn by combining sparse_id_column with a weight column.


sparse_feature = sparse_column_with_hash_bucket(column_name="sparse_col",
weighted_feature = weighted_sparse_column(sparse_id_column=sparse_feature,

This configuration assumes that input dictionary of model contains the following two items: * (key="sparse_col", value=sparse_tensor) where sparse_tensor is a SparseTensor. * (key="weights_col", value=weights_tensor) where weights_tensor is a SparseTensor. Following are assumed to be true: * sparse_tensor.indices = weights_tensor.indices * sparse_tensor.dense_shape = weights_tensor.dense_shape


  • sparse_id_column: A _SparseColumn which is created by sparse_column_with_* functions.
  • weight_column_name: A string defining a sparse column name which represents weight or value of the corresponding sparse id feature.
  • dtype: Type of weights, such as tf.float32 Returns: A _WeightedSparseColumn composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example. Raises:
  • ValueError: if dtype is not convertible to float.

Defined in tensorflow/contrib/layers/python/layers/