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enuSpace-tensorflow Manual

SparseConcat

tensorflow C++ API

tensorflow::ops::SparseConcat

Concatenates a list of SparseTensor along the specified dimension.

Summary

Concatenation is with respect to the dense versions of these sparse tensors. It is assumed that each input is a SparseTensor whose elements are ordered along increasing dimension number.

All inputs’ shapes must match, except for the concat dimension. The indices, values, and shapes lists must have the same length.

The output shape is identical to the inputs’, except along the concat dimension, where it is the sum of the inputs’ sizes along that dimension.

The output elements will be resorted to preserve the sort order along increasing dimension number.

This op runs in O(M log M) time, where M is the total number of non-empty values across all inputs. This is due to the need for an internal sort in order to concatenate efficiently across an arbitrary dimension.

For example, if concat_dim = 1 and the inputs are

sp_inputs[0]: shape =[2,3]
//[0, 2]: "a"
//[1, 0]: "b"
//[1, 1]: "c"

sp_inputs[1]: shape =[2,4]
//[0, 1]: "d"
//[0, 2]: "e"

then the output will be

shape =[2,7]
//[0, 2]: "a"
//[0, 4]: "d"
//[0, 5]: "e"
//[1, 0]: "b"
//[1, 1]: "c"

Graphically this is equivalent to doing

[    a] concat[ d e  ]=[   a   d e  ]
[b c  ]       [      ] [b c         ]

Arguments:

  • scope: A Scope object
  • indices: 2-D. Indices of each input SparseTensor.
  • values: 1-D. Non-empty values of each SparseTensor.
  • shapes: 1-D. Shapes of each SparseTensor.
  • concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), where rank is the number of dimensions in each input SparseTensor.

Returns:

  • Output output_indices: 2-D. Indices of the concatenated SparseTensor.
  • Output output_values: 1-D. Non-empty values of the concatenated SparseTensor.
  • Output output_shape: 1-D. Shape of the concatenated SparseTensor.

SparseConcat block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_sparse.cpp

Argument:

  • Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
  • InputList indices: connect Input node.
  • InputList values: connect Input node.
  • InputList shapes: connect Input node.
  • int64 concat_dim : intput int64 in value.

Return:

  • Output output_indices: Output object of SparseAdd class object.
  • Output output_values: Output object of SparseAdd class object.
  • Output output_shape: Output object of SparseAdd class object.

Result:

  • std::vector(Tensor) product_result : Returned object of executed result by calling session.

Using Method