टेंसरफ़्लो:: ऑप्स:: मैट्रिक्ससेटडायगV3

#include <array_ops.h>

नए बैच वाले विकर्ण मानों के साथ बैचयुक्त मैट्रिक्स टेंसर लौटाता है।

सारांश

input और diagonal देखते हुए, यह ऑपरेशन input के समान आकार और मान के साथ एक टेंसर लौटाता है, अंतरतम मैट्रिक्स के निर्दिष्ट विकर्णों को छोड़कर। इन्हें diagonal में मानों द्वारा अधिलेखित कर दिया जाएगा।

input में r+1 आयाम हैं [I, J, ..., L, M, N] । जब k अदिश या k[0] == k[1] , diagonal r आयाम होता है [I, J, ..., L, max_diag_len] । अन्यथा, इसके r+1 आयाम हैं [I, J, ..., L, num_diags, max_diag_len]num_diags विकर्णों की संख्या है, num_diags = k[1] - k[0] + 1max_diag_len श्रेणी में सबसे लंबा विकर्ण है [k[0], k[1]] , max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))

आउटपुट [I, J, ..., L, M, N] आयामों के साथ रैंक k+1 का एक टेंसर है। यदि k अदिश राशि है या k[0] == k[1] :

output[i, j, ..., l, m, n]
  = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]
    input[i, j, ..., l, m, n]              ; otherwise

अन्यथा,

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

offset शून्य है, सिवाय इसके कि जब विकर्ण का संरेखण दाईं ओर हो।

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
जहां diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))

उदाहरण के लिए:

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

# A superdiagonal (per batch).
tf.matrix_set_diag(input, diagonal, k = 1)
  ==> [[[7, 1, 7, 7],  # Output shape: (2, 3, 4)
        [7, 7, 2, 7],
        [7, 7, 7, 3]],
       [[7, 4, 7, 7],
        [7, 7, 5, 7],
        [7, 7, 7, 6]]]

# A band of diagonals.
diagonals = np.array([[[0, 9, 1],  # Diagonal shape: (2, 4, 3)
                       [6, 5, 8],
                       [1, 2, 3],
                       [4, 5, 0]],
                      [[0, 1, 2],
                       [5, 6, 4],
                       [6, 1, 2],
                       [3, 4, 0]]])
tf.matrix_set_diag(input, diagonals, k = (-1, 2))
  ==> [[[1, 6, 9, 7],  # Output shape: (2, 3, 4)
        [4, 2, 5, 1],
        [7, 5, 3, 8]],
       [[6, 5, 1, 7],
        [3, 1, 6, 2],
        [7, 4, 2, 4]]]

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

  

Arguments:

  • scope: A Scope object
  • input: Rank r+1, where r >= 1.
  • diagonal: Rank r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k >= 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].

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: Rank r+1, with output.shape = input.shape.

Constructors and Destructors

MatrixSetDiagV3(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input diagonal, ::tensorflow::Input k)
MatrixSetDiagV3(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input diagonal, ::tensorflow::Input k, const MatrixSetDiagV3::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::MatrixSetDiagV3::Attrs

Optional attribute setters for MatrixSetDiagV3.

Public attributes

operation

Operation operation

आउटपुट

::tensorflow::Output output

सार्वजनिक समारोह

मैट्रिक्ससेटडायगV3

 MatrixSetDiagV3(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input diagonal,
  ::tensorflow::Input k
)

मैट्रिक्ससेटडायगV3

 MatrixSetDiagV3(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input diagonal,
  ::tensorflow::Input k,
  const MatrixSetDiagV3::Attrs & attrs
)

नोड

::tensorflow::Node * node() const 

ऑपरेटर::टेन्सरफ़्लो::इनपुट

 operator::tensorflow::Input() const 

ऑपरेटर::टेन्सरफ़्लो::आउटपुट

 operator::tensorflow::Output() const 

सार्वजनिक स्थैतिक कार्य

संरेखित

Attrs Align(
  StringPiece x
)