tf.contrib.layers.weighted_sparse_column(
sparse_id_column,
weight_column_name,
dtype=tf.dtypes.float32
)
Defined in tensorflow/contrib/layers/python/layers/feature_column.py
.
Creates a _SparseColumn by combining sparse_id_column with a weight column.
Example:
sparse_feature = sparse_column_with_hash_bucket(column_name="sparse_col", hash_bucket_size=1000) weighted_feature = weighted_sparse_column(sparse_id_column=sparse_feature, weight_column_name="weights_col")
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
Args:
sparse_id_column
: A_SparseColumn
which is created bysparse_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 astf.float32
. Only floating and integer weights are supported.
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.