tensorflow:: ops:: DeserializeSparse

Deserialize SparseTensor objects.

Summary

The input serialized_sparse must have the shape [?, ?, ..., ?, 3] where the last dimension stores serialized SparseTensor objects and the other N dimensions (N >= 0) correspond to a batch. The ranks of the original SparseTensor objects must all match. When the final SparseTensor is created, its rank is the rank of the incoming SparseTensor objects plus N; the sparse tensors have been concatenated along new dimensions, one for each batch.

The output SparseTensor object's shape values for the original dimensions are the max across the input SparseTensor objects' shape values for the corresponding dimensions. The new dimensions match the size of the batch.

The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run SparseReorder to restore index ordering.

For example, if the serialized input is a [2 x 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:

• scope: A Scope object
• serialized_sparse: The serialized SparseTensor objects. The last dimension must have 3 columns.
• dtype: The dtype of the serialized SparseTensor objects.

Returns:

• Output sparse_indices
• Output sparse_values
• Output sparse_shape

Public attributes

Operation operation

::tensorflow::Output sparse_indices

::tensorflow::Output sparse_shape

::tensorflow::Output sparse_values

Public functions

 DeserializeSparse(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input serialized_sparse,
  DataType dtype
)