From 26c5a01eaff4d1960e675129ea89d56ce0d11e90 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 13 Mar 2017 11:14:53 -0800 Subject: [PATCH] Go: Update generated wrapper functions for TensorFlow ops. Change: 149972913 --- tensorflow/go/op/wrappers.go | 347 +++++------------------------------ 1 file changed, 46 insertions(+), 301 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 7633c410947..e9d7bbe1fa3 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -225,6 +225,29 @@ func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output return op.Output(0), op.Output(1), op.Output(2) } +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// +// and `max` to 'outputs' tensor of same shape as `inputs`. +// +// [min; max] is the clamping range for the 'inputs' data. Op divides this range +// into 255 steps (total of 256 values), then replaces each 'inputs' value with the +// closest of the quantized step values. +// +// This operation has a gradient and thus allows for training `min` and `max` values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output) (outputs tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVars", + Input: []tf.Input{ + inputs, min, max, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm. type QuantizedInstanceNormAttr func(optionalAttr) @@ -334,243 +357,6 @@ func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, inp return op.Output(0), op.Output(1), op.Output(2) } -// DebugNumericSummaryAttr is an optional argument to DebugNumericSummary. -type DebugNumericSummaryAttr func(optionalAttr) - -// DebugNumericSummaryTensorName sets the optional tensor_name attribute to value. -// -// value: Name of the input tensor. -// If not specified, defaults to "" -func DebugNumericSummaryTensorName(value string) DebugNumericSummaryAttr { - return func(m optionalAttr) { - m["tensor_name"] = value - } -} - -// DebugNumericSummaryDebugUrls sets the optional debug_urls attribute to value. -// -// value: List of URLs to debug targets, e.g., -// file:///foo/tfdbg_dump, grpc:://localhost:11011 -// If not specified, defaults to <> -func DebugNumericSummaryDebugUrls(value []string) DebugNumericSummaryAttr { - return func(m optionalAttr) { - m["debug_urls"] = value - } -} - -// Debug Numeric Summary Op. -// -// Provide a basic summary of numeric value types, range and distribution. -// -// Arguments: -// input: Input tensor, non-Reference type, float or double. -// -// Returns A double tensor of shape [12], the elements of which are: -// [0]: is initialized (1.0) or not (0.0). -// [1]: total number of elements -// [2]: -inf count -// [3]: negative element count (excluding -inf) -// [4]: zero element count -// [5]: positive element count (excluding +inf) -// [6]: +inf element count -// [7]: NaN element count -// Output elements [1:8] are all zero, if the tensor is uninitialized. -// [8]: minimum of all non-inf and non-NaN elements. -// If uninitialized or no such element exists: +inf. -// [9]: maximum of all non-inf and non-NaN elements. -// If uninitialized or no such element exists: -inf. -// [10]: mean of all non-inf and non-NaN elements. -// If uninitialized or no such element exists: NaN. -// [11]: variance of all non-inf and non-NaN elements. -// If uninitialized or no such element exists: NaN. -func DebugNumericSummary(scope *Scope, input tf.Output, optional ...DebugNumericSummaryAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DebugNumericSummary", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` -// -// and `max` to 'outputs' tensor of same shape as `inputs`. -// -// [min; max] is the clamping range for the 'inputs' data. Op divides this range -// into 255 steps (total of 256 values), then replaces each 'inputs' value with the -// closest of the quantized step values. -// -// This operation has a gradient and thus allows for training `min` and `max` values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output) (outputs tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", - Input: []tf.Input{ - inputs, min, max, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DebugNanCountAttr is an optional argument to DebugNanCount. -type DebugNanCountAttr func(optionalAttr) - -// DebugNanCountTensorName sets the optional tensor_name attribute to value. -// -// value: Name of the input tensor. -// If not specified, defaults to "" -func DebugNanCountTensorName(value string) DebugNanCountAttr { - return func(m optionalAttr) { - m["tensor_name"] = value - } -} - -// DebugNanCountDebugUrls sets the optional debug_urls attribute to value. -// -// value: List of URLs to debug targets, e.g., -// file:///foo/tfdbg_dump, grpc:://localhost:11011 -// If not specified, defaults to <> -func DebugNanCountDebugUrls(value []string) DebugNanCountAttr { - return func(m optionalAttr) { - m["debug_urls"] = value - } -} - -// Debug NaN Value Counter Op -// -// Counts number of NaNs in the input tensor, for debugging. -// -// Arguments: -// input: Input tensor, non-Reference type. -// -// Returns An integer output tensor that is the number of NaNs in the input. -func DebugNanCount(scope *Scope, input tf.Output, optional ...DebugNanCountAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DebugNanCount", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DebugIdentityAttr is an optional argument to DebugIdentity. -type DebugIdentityAttr func(optionalAttr) - -// DebugIdentityTensorName sets the optional tensor_name attribute to value. -// -// value: Name of the input tensor. -// If not specified, defaults to "" -func DebugIdentityTensorName(value string) DebugIdentityAttr { - return func(m optionalAttr) { - m["tensor_name"] = value - } -} - -// DebugIdentityDebugUrls sets the optional debug_urls attribute to value. -// -// value: List of URLs to debug targets, e.g., -// file:///foo/tfdbg_dump, grpc:://localhost:11011 -// If not specified, defaults to <> -func DebugIdentityDebugUrls(value []string) DebugIdentityAttr { - return func(m optionalAttr) { - m["debug_urls"] = value - } -} - -// Debug Identity Op. -// -// Provides an identity mapping of the non-Ref type input tensor for debugging. -// -// Arguments: -// input: Input tensor, non-Reference type. -// -// Returns Output tensor that equals the input tensor. -func DebugIdentity(scope *Scope, input tf.Output, optional ...DebugIdentityAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DebugIdentity", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CopyAttr is an optional argument to Copy. -type CopyAttr func(optionalAttr) - -// CopyTensorName sets the optional tensor_name attribute to value. -// -// value: The name of the input tensor. -// If not specified, defaults to "" -func CopyTensorName(value string) CopyAttr { - return func(m optionalAttr) { - m["tensor_name"] = value - } -} - -// Copy Op. -// -// Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the -// device on which the tensor is allocated. -// -// Unlike the CopyHost Op, this op does not have HostMemory constraint on its -// input or output. -// -// Arguments: -// input: Input tensor. -// -// Returns Output tensor, deep-copied from input. -func Copy(scope *Scope, input tf.Output, optional ...CopyAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Copy", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize. type QuantizeAndDequantizeAttr func(optionalAttr) @@ -5754,11 +5540,11 @@ func RsqrtGrad(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { // The graph specifications are serialized by protobuf as graph_transfer_info. // The implementation / limitations may differ for each platform // and each available peripheral. -func RemoteFusedGraphExecute(scope *Scope, values []tf.Output, N int64, serialized_graph_transfer_info string) (output []tf.Output) { +func RemoteFusedGraphExecute(scope *Scope, values []tf.Output, N int64, U tf.DataType, serialized_graph_transfer_info string) (output []tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"N": N, "serialized_graph_transfer_info": serialized_graph_transfer_info} + attrs := map[string]interface{}{"N": N, "U": U, "serialized_graph_transfer_info": serialized_graph_transfer_info} opspec := tf.OpSpec{ Type: "RemoteFusedGraphExecute", Input: []tf.Input{ @@ -11561,26 +11347,6 @@ func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.O return scope.AddOperation(opspec) } -// Store the input tensor in the state of the current session. -// -// Arguments: -// value: The tensor to be stored. -// -// Returns The handle for the tensor stored in the session state. -func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "GetSessionHandle", - Input: []tf.Input{ - value, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Decode web-safe base64-encoded strings. // // Input may or may not have padding at the end. See EncodeBase64 for padding. @@ -13188,48 +12954,6 @@ func Substr(scope *Scope, input tf.Output, pos tf.Output, len tf.Output) (output return op.Output(0) } -// CopyHostAttr is an optional argument to CopyHost. -type CopyHostAttr func(optionalAttr) - -// CopyHostTensorName sets the optional tensor_name attribute to value. -// -// value: The name of the input tensor. -// If not specified, defaults to "" -func CopyHostTensorName(value string) CopyHostAttr { - return func(m optionalAttr) { - m["tensor_name"] = value - } -} - -// Copy Host Op. -// -// Performs CPU-to-CPU deep-copying of tensor. -// -// Unlike the Copy Op, this op has HostMemory constraint on its input or output. -// -// Arguments: -// input: Input tensor. -// -// Returns Output tensor, deep-copied from input. -func CopyHost(scope *Scope, input tf.Output, optional ...CopyHostAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CopyHost", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the sum along sparse segments of a tensor divided by the sqrt of N. // // N is the size of the segment being reduced. @@ -20029,6 +19753,27 @@ func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, m return op.Output(0) } +// Store the input tensor in the state of the current session. +// +// Arguments: +// value: The tensor to be stored. +// +// Returns The handle for the tensor stored in the session state, represented +// as a ResourceHandle object. +func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GetSessionHandleV2", + Input: []tf.Input{ + value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Restore a Reader to its initial clean state. // // Arguments: -- GitLab