tf.compat.v1.sparse_concat Concatenates a list of SparseTensor along the specified dimension. (deprecated arguments)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.sparse.concat  
tf.compat.v1.sparse_concat(
    axis, sp_inputs, name=None, expand_nonconcat_dim=False, concat_dim=None,
    expand_nonconcat_dims=None
)
 Warning: SOME ARGUMENTS ARE DEPRECATED: (concat_dim). They will be removed in a future version. Instructions for updating: concat_dim is deprecated, use axis instead Concatenation is with respect to the dense versions of each sparse input. It is assumed that each inputs is a SparseTensor whose elements are ordered along increasing dimension number. If expand_nonconcat_dim is False, all inputs' shapes must match, except for the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are allowed to vary among all inputs. The indices, values, and shapes lists must have the same length. If expand_nonconcat_dim is False, then 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. If expand_nonconcat_dim is True, then the output shape along the non-concat dimensions will be expand to be the largest among all inputs, and it is the sum of the inputs sizes along the concat 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 axis = 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          ]
 Another example, if 'axis = 1' and the inputs are sp_inputs[0]: shape = [3, 3]
[0, 2]: "a"
[1, 0]: "b"
[2, 1]: "c"

sp_inputs[1]: shape = [2, 4]
[0, 1]: "d"
[0, 2]: "e"
 if expand_nonconcat_dim = False, this will result in an error. But if expand_nonconcat_dim = True, this will result in: shape = [3, 7]
[0, 2]: "a"
[0, 4]: "d"
[0, 5]: "e"
[1, 0]: "b"
[2, 1]: "c"
 Graphically this is equivalent to doing [    a] concat [  d e  ] = [    a   d e  ]
[b    ]        [       ]   [b            ]
[  c  ]                    [  c          ]

 


 Args
  axis   Dimension to concatenate along. Must be in range [-rank, rank), where rank is the number of dimensions in each input SparseTensor.  
  sp_inputs   List of SparseTensor to concatenate.  
  name   A name prefix for the returned tensors (optional).  
  expand_nonconcat_dim   Whether to allow the expansion in the non-concat dimensions. Defaulted to False.  
  concat_dim   The old (deprecated) name for axis.  
  expand_nonconcat_dims   alias for expand_nonconcat_dim   
 


 Returns   A SparseTensor with the concatenated output.  

 


 Raises
  TypeError   If sp_inputs is not a list of SparseTensor.