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
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.
|