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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 341541392
Change-Id: Ib3245dd6b7868bfebbeb1925c603e9d0f9f58f5e
This commit is contained in:
A. Unique TensorFlower 2020-11-09 20:46:51 -08:00 committed by TensorFlower Gardener
parent 852bf4bada
commit f2c163919f
1 changed files with 90 additions and 70 deletions
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160
tensorflow/go/op/wrappers.go
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@ -14585,6 +14585,23 @@ func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (fi
return op.Output(0)
}
// Generate a sharded filename. The filename is printf formatted as
//
// %s-%05d-of-%05d, basename, shard, num_shards.
func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
Type: "ShardedFilename",
Input: []tf.Input{
basename, shard, num_shards,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// Saves the input tensors to disk.
//
// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
@ -15496,59 +15513,6 @@ func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, option
return op.Output(0)
}
// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
type SelfAdjointEigV2Attr func(optionalAttr)
// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value.
//
// value: If `True` then eigenvectors will be computed and returned in `v`.
// Otherwise, only the eigenvalues will be computed.
// If not specified, defaults to true
func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr {
return func(m optionalAttr) {
m["compute_v"] = value
}
}
// Computes the eigen decomposition of one or more square self-adjoint matrices.
//
// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
// are sorted in non-decreasing order.
//
// ```python
// # a is a tensor.
// # e is a tensor of eigenvalues.
// # v is a tensor of eigenvectors.
// e, v = self_adjoint_eig(a)
// e = self_adjoint_eig(a, compute_v=False)
// ```
//
// Arguments:
// input: `Tensor` input of shape `[N, N]`.
//
// Returns:
// e: Eigenvalues. Shape is `[N]`.
// v: Eigenvectors. Shape is `[N, N]`.
func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
Type: "SelfAdjointEigV2",
Input: []tf.Input{
input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0), op.Output(1)
}
// Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
//
// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead.
@ -23478,23 +23442,6 @@ func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y
return op.Output(0)
}
// Generate a sharded filename. The filename is printf formatted as
//
// %s-%05d-of-%05d, basename, shard, num_shards.
func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
Type: "ShardedFilename",
Input: []tf.Input{
basename, shard, num_shards,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// Elementwise computes the bitwise OR of `x` and `y`.
//
// The result will have those bits set, that are set in `x`, `y` or both. The
@ -37373,6 +37320,26 @@ func Erfc(scope *Scope, x tf.Output) (y tf.Output) {
return op.Output(0)
}
// Returns max(x, y) element-wise.
//
// *NOTE*: `RiscMax` does not supports broadcasting.
//
// Given two input tensors, the `tf.risc_max` operation computes the maximum for every element in the tensor.
//
func RiscMax(scope *Scope, x tf.Output, y tf.Output) (max tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
Type: "RiscMax",
Input: []tf.Input{
x, y,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// RandomUniformIntAttr is an optional argument to RandomUniformInt.
type RandomUniformIntAttr func(optionalAttr)
@ -45067,6 +45034,59 @@ func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear t
return scope.AddOperation(opspec)
}
// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
type SelfAdjointEigV2Attr func(optionalAttr)
// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value.
//
// value: If `True` then eigenvectors will be computed and returned in `v`.
// Otherwise, only the eigenvalues will be computed.
// If not specified, defaults to true
func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr {
return func(m optionalAttr) {
m["compute_v"] = value
}
}
// Computes the eigen decomposition of one or more square self-adjoint matrices.
//
// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
// are sorted in non-decreasing order.
//
// ```python
// # a is a tensor.
// # e is a tensor of eigenvalues.
// # v is a tensor of eigenvectors.
// e, v = self_adjoint_eig(a)
// e = self_adjoint_eig(a, compute_v=False)
// ```
//
// Arguments:
// input: `Tensor` input of shape `[N, N]`.
//
// Returns:
// e: Eigenvalues. Shape is `[N]`.
// v: Eigenvectors. Shape is `[N, N]`.
func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
Type: "SelfAdjointEigV2",
Input: []tf.Input{
input,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0), op.Output(1)
}
// Computes softmax cross entropy cost and gradients to backpropagate.
//
// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept