From 37146b89788c2a0796ca6b863bde9c4c0dc4068e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 9 Oct 2018 09:46:04 -0700 Subject: Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 216372144 --- tensorflow/go/op/wrappers.go | 222 +++++++++++++++++++++---------------------- 1 file changed, 111 insertions(+), 111 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index fe99915a6c..eb6df2af46 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -10415,6 +10415,79 @@ func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { return op.Output(0) } +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. +// +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. +// If not specified, defaults to DT_INT64 +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["Targmax"] = value + } +} + +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index +// `((b * height + y) * width + x) * channels + c`. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolWithArgmax", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + // ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2. type ResourceSparseApplyFtrlV2Attr func(optionalAttr) @@ -14202,44 +14275,6 @@ func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Outpu return op.Output(0), op.Output(1) } -// Computes the mean along segments of a tensor. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation) -// for an explanation of segments. -// -// Computes a tensor such that -// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is -// over `j` such that `segment_ids[j] == i` and `N` is the total number of -// values summed. -// -// If the mean is empty for a given segment ID `i`, `output[i] = 0`. -// -//
-// -//
-// -// Arguments: -// -// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SegmentMean", - Input: []tf.Input{ - data, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) @@ -15941,79 +15976,6 @@ func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, label return op.Output(0), op.Output(1) } -// Transforms a serialized tensorflow.TensorProto proto into a Tensor. -// -// Arguments: -// serialized: A scalar string containing a serialized TensorProto proto. -// out_type: The type of the serialized tensor. The provided type must match the -// type of the serialized tensor and no implicit conversion will take place. -// -// Returns A Tensor of type `out_type`. -func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"out_type": out_type} - opspec := tf.OpSpec{ - Type: "ParseTensor", - Input: []tf.Input{ - serialized, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. -type MaxPoolWithArgmaxAttr func(optionalAttr) - -// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. -// If not specified, defaults to DT_INT64 -func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { - return func(m optionalAttr) { - m["Targmax"] = value - } -} - -// Performs max pooling on the input and outputs both max values and indices. -// -// The indices in `argmax` are flattened, so that a maximum value at position -// `[b, y, x, c]` becomes flattened index -// `((b * height + y) * width + x) * channels + c`. -// -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. -// -// Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - // Returns the truth value of NOT x element-wise. func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -21926,6 +21888,44 @@ func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Outp return op.Output(0), op.Output(1), op.Output(2) } +// Computes the mean along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. +// +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMean", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the minimum along segments of a tensor. // // Read -- cgit v1.2.3