fluxo tensor:: ops:: MatrixSetDiagV2
#include <array_ops.h>
Retorna um tensor de matriz em lote com novos valores diagonais em lote.
Resumo
Dadas input
e diagonal
, esta operação retorna um tensor com a mesma forma e valores de input
, exceto para as diagonais especificadas das matrizes mais internas. Estes serão substituídos pelos valores na diagonal
.
input
tem r+1
dimensões [I, J, ..., L, M, N]
. Quando k
é escalar ou k[0] == k[1]
, diagonal
tem r
dimensões [I, J, ..., L, max_diag_len]
. Caso contrário, possui dimensões r+1
[I, J, ..., L, num_diags, max_diag_len]
. num_diags
é o número de diagonais, num_diags = k[1] - k[0] + 1
. max_diag_len
é a diagonal mais longa no intervalo [k[0], k[1]]
, max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
A saída é um tensor de classificação k+1
com dimensões [I, J, ..., L, M, N]
. Se k
for escalar ou k[0] == k[1]
:
output[i, j, ..., l, m, n] = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1] output[i, j, ..., l, m, n] ; otherwise
De outra forma,
output[i, j, ..., l, m, n] = diagonal[i, j, ..., l, k[1]-d, n-max(d, 0)] ; if d_lower <= d <= d_upper input[i, j, ..., l, m, n] ; otherwiseonde
d = n - m
Por exemplo:
# 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_diag(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_diag(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([[[1, 2, 3], # Diagonal shape: (2, 2, 3) [4, 5, 0]], [[6, 1, 2], [3, 4, 0]]]) tf.matrix_diag(diagonals, k = (-1, 0)) ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4) [4, 2, 7, 7], [0, 5, 3, 7]], [[6, 7, 7, 7], [3, 1, 7, 7], [7, 4, 2, 7]]]
Arguments:
- scope: A Scope object
- input: Rank
r+1
, wherer >= 1
. - diagonal: Rank
r
whenk
is an integer ork[0] == k[1]
. Otherwise, it has rankr+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 thank[1]
.
Returns:
Output
: Rankr+1
, withoutput.shape = input.shape
.
Constructors and Destructors |
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MatrixSetDiagV2(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input diagonal, ::tensorflow::Input k)
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Public attributes |
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operation
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output
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Public functions |
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node() const
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::tensorflow::Node *
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operator::tensorflow::Input() const
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operator::tensorflow::Output() const
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Public attributes
operation
Operation operation
saída
::tensorflow::Output output
Funções públicas
MatrixSetDiagV2
MatrixSetDiagV2( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input diagonal, ::tensorflow::Input k )
nó
::tensorflow::Node * node() const
operador::tensorflow::Input
operator::tensorflow::Input() const
operador::tensorflow::Saída
operator::tensorflow::Output() const