TensorFlow 2 version | View source on GitHub |
Deserialize and concatenate SparseTensors
from a serialized minibatch.
tf.io.deserialize_many_sparse(
serialized_sparse, dtype, rank=None, name=None
)
The input serialized_sparse
must be a string matrix of shape [N x 3]
where
N
is the minibatch size and the rows correspond to packed outputs of
serialize_sparse
. The ranks of the original SparseTensor
objects
must all match. When the final SparseTensor
is created, it has rank one
higher than the ranks of the incoming SparseTensor
objects (they have been
concatenated along a new row dimension).
The output SparseTensor
object's shape values for all dimensions but the
first are the max across the input SparseTensor
objects' shape values
for the corresponding dimensions. Its first shape value is N
, the minibatch
size.
The input SparseTensor
objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run sparse.reorder
to restore index ordering.
For example, if the serialized input is a [2, 3]
matrix representing two
original SparseTensor
objects:
index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
and
index = [ 2]
[10]
values = [4, 5]
shape = [30]
then the final deserialized SparseTensor
will be:
index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
Args | |
---|---|
serialized_sparse
|
2-D Tensor of type string of shape [N, 3] .
The serialized and packed SparseTensor objects.
|
dtype
|
The dtype of the serialized SparseTensor objects.
|
rank
|
(optional) Python int, the rank of the SparseTensor objects.
|
name
|
A name prefix for the returned tensors (optional) |
Returns | |
---|---|
A SparseTensor representing the deserialized SparseTensor s,
concatenated along the SparseTensor s' first dimension.
All of the serialized |