/TensorFlow 2.4


Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.

A SparseTensor of rank R is represented by three tensors: sparse_indices, sparse_values, and sparse_shape, where

sparse_indices.shape[1] == sparse_shape.shape[0] == R

An N-minibatch of SparseTensor objects is represented as a SparseTensor having a first sparse_indices column taking values between [0, N), where the minibatch size N == sparse_shape[0].

The input SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The stored SparseTensor objects pointed to by each row of the output sparse_handles will have rank R-1.

The SparseTensor values can then be read out as part of a minibatch by passing the given keys as vector elements to TakeManySparseFromTensorsMap. To ensure the correct SparseTensorsMap is accessed, ensure that the same container and shared_name are passed to that Op. If no shared_name is provided here, instead use the name of the Operation created by calling AddManySparseToTensorsMap as the shared_name passed to TakeManySparseFromTensorsMap. Ensure the Operations are colocated.

sparse_indices A Tensor of type int64. 2-D. The indices of the minibatch SparseTensor. sparse_indices[:, 0] must be ordered values in [0, N).
sparse_values A Tensor. 1-D. The values of the minibatch SparseTensor.
sparse_shape A Tensor of type int64. 1-D. The shape of the minibatch SparseTensor. The minibatch size N == sparse_shape[0].
container An optional string. Defaults to "". The container name for the SparseTensorsMap created by this op.
shared_name An optional string. Defaults to "". The shared name for the SparseTensorsMap created by this op. If blank, the new Operation's unique name is used.
name A name for the operation (optional).
A Tensor of type int64.

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Code samples licensed under the Apache 2.0 License.