View source on GitHub |
Configuration for parsing a sparse input feature from an Example
.
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 SparseTensor
s 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_key
s
SparseFeature(index_key=["ix0", "ix1"], value_key="val", dtype=tf.float32, size=[100, 3])
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 SparseTensor
s 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_key
s' 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 |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/io/SparseFeature