tf.io.SparseFeature

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Configuration for parsing a sparse input feature from an Example.

View aliases

Main aliases

`tf.SparseFeature`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.SparseFeature, tf.compat.v1.io.SparseFeature, `tf.compat.v2.io.SparseFeature`

tf.io.SparseFeature(
    index_key, value_key, dtype, size, already_sorted=False
)

Note, preferably use VarLenFeature (possibly in combination with a SequenceExample) in order to parse out SparseTensors instead of SparseFeature due to its simplicity.

Closely mimicking the SparseTensor that will be obtained by parsing an Example with a SparseFeature config, a SparseFeature contains a

• value_key: The name of key for a Feature in the Example whose parsed Tensor will be the resulting SparseTensor.values.

• index_key: A list of names - one for each dimension in the resulting SparseTensor whose indices[i][dim] indicating the position of the i-th value in the dim dimension will be equal to the i-th value in the Feature with key named index_key[dim] in the Example.

• size: A list of ints for the resulting SparseTensor.dense_shape.

For example, we can represent the following 2D SparseTensor

SparseTensor(indices=[[3, 1], [20, 0]],
             values=[0.5, -1.0]
             dense_shape=[100, 3])

with an Example input proto

features {
  feature { key: "val" value { float_list { value: [ 0.5, -1.0 ] } } }
  feature { key: "ix0" value { int64_list { value: [ 3, 20 ] } } }
  feature { key: "ix1" value { int64_list { value: [ 1, 0 ] } } }
}

and SparseFeature config with 2 index_keys

SparseFeature(index_key=["ix0", "ix1"],
              value_key="val",
              dtype=tf.float32,
              size=[100, 3])

Fields:

• index_key: A single string name or a list of string names of index features. For each key the underlying feature's type must be int64 and its length must always match that of the value_key feature. To represent SparseTensors with a dense_shape of rank higher than 1 a list of length rank should be used.
• value_key: Name of value feature. The underlying feature's type must be dtype and its length must always match that of all the index_keys' features.
• dtype: Data type of the value_key feature.
• size: A Python int or list thereof specifying the dense shape. Should be a list if and only if index_key is a list. In that case the list must be equal to the length of index_key. Each for each entry i all values in the index_key[i] feature must be in [0, size[i]).
• already_sorted: A Python boolean to specify whether the values in value_key are already sorted by their index position. If so skip sorting. False by default (optional).

Attributes
index_key
value_key
dtype
size
already_sorted