T - data type for sparseValues() output@Operator(group="io") public final class DeserializeManySparse<T> extends PrimitiveOp
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 `SerializeSparse`. 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 `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]
operation| Modifier and Type | Method and Description |
|---|---|
static <T> DeserializeManySparse<T> |
create(Scope scope,
Operand<String> serializedSparse,
Class<T> dtype)
Factory method to create a class wrapping a new DeserializeManySparse operation.
|
Output<Long> |
sparseIndices() |
Output<Long> |
sparseShape() |
Output<T> |
sparseValues() |
equals, hashCode, op, toStringpublic static <T> DeserializeManySparse<T> create(Scope scope, Operand<String> serializedSparse, Class<T> dtype)
scope - current scopeserializedSparse - 2-D, The `N` serialized `SparseTensor` objects.
Must have 3 columns.dtype - The `dtype` of the serialized `SparseTensor` objects.Copyright © 2022. All rights reserved.