tf.contrib.layers.weighted_sparse_column( sparse_id_column, weight_column_name, dtype=tf.float32 )
Creates a _SparseColumn by combining sparse_id_column with a weight column.
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
_SparseColumnwhich is created by
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.