SparseSlice

tensorflow C++ API

tensorflow::ops::SparseSlice

Slice aSparseTensorbased on thestartandsize.

Summary

For example, if the input is

input_tensor = shape =[2,7]
[    a   d e  ]
[b c          ]

Graphically the output tensors are:

sparse_slice([0,0],[2,4])= shape =[2,4]
[    a  ]
[b c    ]

sparse_slice([0,4],[2,3])= shape =[2,3]
[ d e  ]
[      ]

Arguments:

• scope: A Scope object
• indices: 2-D tensor represents the indices of the sparse tensor.
• values: 1-D tensor represents the values of the sparse tensor.
• shape: 1-D. tensor represents the shape of the sparse tensor.
• start: 1-D. tensor represents the start of the slice.
• size: 1-D. tensor represents the size of the slice. output indices: A list of 1-D tensors represents the indices of the output sparse tensors.

Returns:

• Outputoutput_indices
• Outputoutput_values: A list of 1-D tensors represents the values of the output sparse tensors.
• Outputoutput_shape: A list of 1-D tensors represents the shape of the output sparse tensors.

SparseSlice 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.)
• Input indices: connect Input node.
• Input values: connect Input node.
• Input shape: connect Input node.
• Input start: connect Input node.
• Input size: connect Input node.

Return:

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

Result:

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

Using Method