tf.contrib.layers.weighted_sparse_column

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Creates a _SparseColumn by combining sparse_id_column with a weight column.

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

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

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.

A _WeightedSparseColumn composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example.

ValueError if dtype is not convertible to float.