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Deserialize and concatenate
SparseTensors from a serialized minibatch.
`tf.deserialize_many_sparse`Compat aliases for migration
See Migration guide for more details.
tf.io.deserialize_many_sparse( serialized_sparse, dtype, rank=None, name=None )
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).
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.
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
index = [ 0]   values = [1, 2, 3] shape = 
index = [ 2]  values = [4, 5] shape = 
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]
| || 2-D |
| || The |
| || (optional) Python int, the rank of the |
| ||A name prefix for the returned tensors (optional)|
| A |
All of the serialized
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.