tensor akışı:: işlem:: MatrixDiagV3

#include <array_ops.h>

Verilen toplu çapraz değerlere sahip toplu bir çapraz tensör döndürür.

Özet

İçeriği bir matrisin k[0] -th ila k[1] -th köşegenleri kadar diagonal olan ve geri kalan her şeyin padding ile doldurulduğu bir tensör döndürür. num_rows ve num_cols çıktının en içteki matrisinin boyutunu belirtir. Her ikisi de belirtilmezse, op en içteki matrisin kare olduğunu varsayar ve boyutunu k ve diagonal en içteki boyutundan çıkarır. Bunlardan yalnızca biri belirtilirse, op belirtilmemiş değerin diğer kriterlere göre mümkün olan en küçük değer olduğunu varsayar.

diagonal r boyutlu olsun [I, J, ..., L, M, N] . Yalnızca bir köşegen verildiğinde çıkış tensörünün rütbesi r+1 olup [I, J, ..., L, M, num_rows, num_cols] şeklindedir ( k bir tam sayıdır veya k[0] == k[1] ) . Aksi takdirde, [I, J, ..., L, num_rows, num_cols] şeklinde r rütbesine sahiptir.

diagonal en içteki ikinci boyutunun çift anlamı vardır. k skaler olduğunda veya k[0] == k[1] olduğunda, M parti boyutunun [I, J, ..., M] bir parçasıdır ve çıkış tensörü şöyledir: 

output[i, j, ..., l, m, n]
  = diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper
    padding_value                             ; otherwise

Aksi takdirde, M aynı gruptaki matrisin köşegen sayısı olarak kabul edilir ( M = k[1]-k[0]+1 ) ve çıkış tensörü şöyledir: 

output[i, j, ..., l, m, n]
  = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1]
    padding_value                                     ; otherwise
 burada d = n - m , diag_index = [k] - d ve index_in_diag = n - max(d, 0) + offset .

köşegen hizalamasının sağa olması dışında offset sıfırdır.

offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT}
                                           and `d >= 0`) or
                                         (`align` in {LEFT_RIGHT, RIGHT_RIGHT}
                                           and `d <= 0`)
         0                          ; otherwise
 burada diag_len(d) = min(cols - max(d, 0), rows + min(d, 0)) .

Örneğin: 

# The main diagonal.
diagonal = np.array([[1, 2, 3, 4],            # Input shape: (2, 4)
                     [5, 6, 7, 8]])
tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0],  # Output shape: (2, 4, 4)
                               [0, 2, 0, 0],
                               [0, 0, 3, 0],
                               [0, 0, 0, 4]],
                              [[5, 0, 0, 0],
                               [0, 6, 0, 0],
                               [0, 0, 7, 0],
                               [0, 0, 0, 8]]]

# A superdiagonal (per batch).
diagonal = np.array([[1, 2, 3],  # Input shape: (2, 3)
                     [4, 5, 6]])
tf.matrix_diag(diagonal, k = 1)
  ==> [[[0, 1, 0, 0],  # Output shape: (2, 4, 4)
        [0, 0, 2, 0],
        [0, 0, 0, 3],
        [0, 0, 0, 0]],
       [[0, 4, 0, 0],
        [0, 0, 5, 0],
        [0, 0, 0, 6],
        [0, 0, 0, 0]]]

# A tridiagonal band (per batch).
diagonals = np.array([[[0, 8, 9],  # Input shape: (2, 2, 3)
                       [1, 2, 3],
                       [4, 5, 0]],
                      [[0, 2, 3],
                       [6, 7, 9],
                       [9, 1, 0]]])
tf.matrix_diag(diagonals, k = (-1, 1))
  ==> [[[1, 8, 0],  # Output shape: (2, 3, 3)
        [4, 2, 9],
        [0, 5, 3]],
       [[6, 2, 0],
        [9, 7, 3],
        [0, 1, 9]]]

# LEFT_RIGHT alignment.
diagonals = np.array([[[8, 9, 0],  # Input shape: (2, 2, 3)
                       [1, 2, 3],
                       [0, 4, 5]],
                      [[2, 3, 0],
                       [6, 7, 9],
                       [0, 9, 1]]])
tf.matrix_diag(diagonals, k = (-1, 1), align="LEFT_RIGHT")
  ==> [[[1, 8, 0],  # Output shape: (2, 3, 3)
        [4, 2, 9],
        [0, 5, 3]],
       [[6, 2, 0],
        [9, 7, 3],
        [0, 1, 9]]]

# Rectangular matrix.
diagonal = np.array([1, 2])  # Input shape: (2)
tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)
  ==> [[0, 0, 0, 0],  # Output shape: (3, 4)
       [1, 0, 0, 0],
       [0, 2, 0, 0]]

# Rectangular matrix with inferred num_cols and padding_value = 9.
tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)
  ==> [[9, 9],  # Output shape: (3, 2)
       [1, 9],
       [9, 2]]

  

  
Arguments:
    
      
  • scope: A Scope object
    • 
      
  • diagonal: Rank r, where r >= 1
    • 
      
  • k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. k can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. k[0] must not be larger than k[1].
    • 
      
  • num_rows: The number of rows of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of diagonal.
    • 
      
  • num_cols: The number of columns of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of diagonal.
    • 
      
  • padding_value: The number to fill the area outside the specified diagonal band with. Default is 0.
    • 
    

  
Optional attributes (see Attrs):
    
      
  • align: Some diagonals are shorter than max_diag_len and need to be padded. align is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. There are four possible alignments: "RIGHT_LEFT" (default), "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is the opposite alignment.
    • 
    

  
Returns:
    
      
  • Output: Has rank r+1 when k is an integer or k[0] == k[1], rank r otherwise. 
    • 
    

                                              




        
Constructors and Destructors

      		





        MatrixDiagV3(const ::tensorflow::Scope & scope, ::tensorflow::Input diagonal, ::tensorflow::Input k, ::tensorflow::Input num_rows, ::tensorflow::Input num_cols, ::tensorflow::Input padding_value)
        

      		





        MatrixDiagV3(const ::tensorflow::Scope & scope, ::tensorflow::Input diagonal, ::tensorflow::Input k, ::tensorflow::Input num_rows, ::tensorflow::Input num_cols, ::tensorflow::Input padding_value, const MatrixDiagV3::Attrs & attrs)
        

      		




                                                          




        
Public attributes

      		





        operation
      

        
      





        output
      

        
      




                                                                              




        
Public functions

      		





        node() const 
      

        

          ::tensorflow::Node *
        

      		





        operator::tensorflow::Input() const 
      

        

          
        

      		





        operator::tensorflow::Output() const 
      

        

          
        

      		




                                      




        
Public static functions

      		





        Align(StringPiece x)
      

        
      




                                      




        
Structs

      		





        tensorflow::ops::MatrixDiagV3::Attrs
      

        
Optional attribute setters for MatrixDiagV3. 

      		




  
Public attributes

  

    
operation

    
Operation operation
 çıktı 
::tensorflow::Output output
 Kamu işlevleri 
 MatrixDiagV3 
 MatrixDiagV3(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input diagonal,
  ::tensorflow::Input k,
  ::tensorflow::Input num_rows,
  ::tensorflow::Input num_cols,
  ::tensorflow::Input padding_value
)
 MatrixDiagV3 
 MatrixDiagV3(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input diagonal,
  ::tensorflow::Input k,
  ::tensorflow::Input num_rows,
  ::tensorflow::Input num_cols,
  ::tensorflow::Input padding_value,
  const MatrixDiagV3::Attrs & attrs
)
 düğüm 
::tensorflow::Node * node() const 
 operatör::tensorflow::Giriş 
 operator::tensorflow::Input() const 
 operatör::tensorflow::Çıktı 
 operator::tensorflow::Output() const 
 Genel statik işlevler 
 Hizala 
Attrs Align(
  StringPiece x
)