tf.contrib.layers.weighted_sparse_column

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

Defined in tensorflow/contrib/layers/python/layers/feature_column.py.

See the guide: Layers (contrib) > Feature columns

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 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. 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.