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Update ops-related pbtxt files.

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Change: 123329408
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A. Unique TensorFlower 2016-05-26 09:03:01 -08:00 committed by TensorFlower Gardener
parent cafe948be4
commit a2009115df
2 changed files with 59 additions and 4 deletions
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25
tensorflow/core/ops/compat/ops_history.v0.pbtxt
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@ -3175,6 +3175,31 @@ op {
}
}
}
op {
name: "BatchCholeskyGrad"
input_arg {
name: "l"
type_attr: "T"
}
input_arg {
name: "grad"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
}
op {
name: "BatchFFT"
input_arg {

38
tensorflow/core/ops/ops.pbtxt
View File

@ -1397,6 +1397,36 @@ op {
summary: "Calculates the Cholesky decomposition of a batch of square matrices."
description: "The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions\nform square matrices, with the same constraints as the single matrix Cholesky\ndecomposition above. The output is a tensor of the same shape as the input\ncontaining the Cholesky decompositions for all input submatrices `[..., :, :]`."
}
op {
name: "BatchCholeskyGrad"
input_arg {
name: "l"
description: "Output of batch Cholesky algorithm l = batch_cholesky(A). Shape is `[..., M, M]`.\nAlgorithm depends only on lower triangular part of the innermost matrices of\nthis tensor."
type_attr: "T"
}
input_arg {
name: "grad"
description: "df/dl where f is some scalar function. Shape is `[..., M, M]\'.\nAlgorithm depends only on lower triangular part of the innermost matrices of\nthis tensor."
type_attr: "T"
}
output_arg {
name: "output"
description: "Symmetrized version of df/dA . Shape is `[..., M, M]\'"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
summary: "Calculates the reverse mode backpropagated gradient of the Cholesky algorithm."
description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
}
op {
name: "BatchFFT"
input_arg {
@ -2482,17 +2512,17 @@ op {
name: "CholeskyGrad"
input_arg {
name: "l"
description: "Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`. Algorithm depends only on lower triangular part of this matrix."
description: "Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`.\nAlgorithm depends only on lower triangular part of this matrix."
type_attr: "T"
}
input_arg {
name: "grad"
description: "df/dl where f is some scalar function. Shape is `[M, M]\'. Algorithm depends only on lower triangular part of this matrix."
description: "df/dl where f is some scalar function. Shape is `[M, M]\'.\nAlgorithm depends only on lower triangular part of this matrix."
type_attr: "T"
}
output_arg {
name: "output"
description: "Symmetrized version of df/dA . Shape is `[M, M]\'"
description: "Symmetrized version of df/dA . Shape is `[M, M]\'."
type_attr: "T"
}
attr {
@ -2506,7 +2536,7 @@ op {
}
}
summary: "Calculates the reverse mode backpropagated gradient of the Cholesky algorithm."
description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by Iain Murray http://arxiv.org/abs/1602.07527."
description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
}
op {
name: "Complex"