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Published: Jul 9, 2024 License: Apache-2.0, BSD-2-Clause Imports: 14 Imported by: 364


TensorFlow in Go

Construct and execute TensorFlow graphs in Go.


WARNING: The API defined in this package is not stable and can change without notice. The same goes for the package path: (github.com/tensorflow/tensorflow/tensorflow/go).


The TensorFlow team is not currently maintaning the Documentation for installing the Go bindings for TensorFlow.

The instructions has been maintained by the third party contributor: @wamuir

Please follow this source by @wamuir for the installation of Golang with Tensorflow.



Package tensorflow is a Go binding to TensorFlow.

The API is subject to change and may break at any time.

TensorFlow (www.tensorflow.org) is an open source software library for numerical computation using data flow graphs. This package provides functionality to build and execute such graphs and depends on TensorFlow being available. For installation instructions see https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/README.md

package main

import (

	tf "github.com/tensorflow/tensorflow/tensorflow/go"

func main() {
	// An example for using the TensorFlow Go API for image recognition
	// using a pre-trained inception model (http://arxiv.org/abs/1512.00567).
	// Sample usage: <program> -dir=/tmp/modeldir -image=/path/to/some/jpeg
	// The pre-trained model takes input in the form of a 4-dimensional
	// tensor with shape [ BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, 3 ],
	// where:
	// - BATCH_SIZE allows for inference of multiple images in one pass through the graph
	// - IMAGE_HEIGHT is the height of the images on which the model was trained
	// - IMAGE_WIDTH is the width of the images on which the model was trained
	// - 3 is the (R, G, B) values of the pixel colors represented as a float.
	// And produces as output a vector with shape [ NUM_LABELS ].
	// output[i] is the model-implied probability of the input image having
	// the i-th label.
	// A separate file contains a list of string labels corresponding to the
	// integer indices of the output.
	// This example:
	// - Loads the serialized representation of the pre-trained model into a Graph
	// - Creates a Session to execute operations on the Graph
	// - Converts an image file to a Tensor to provide as input to a Session run
	// - Executes the Session and prints out the label with the highest probability
	// To convert an image file to a Tensor suitable for input to the Inception model,
	// this example:
	// - Constructs another TensorFlow graph to normalize the image into a
	//   form suitable for the model (for example, resizing the image)
	// - Creates and executes a Session to obtain a Tensor in this normalized form.
	modeldir := flag.String(
		"Directory containing the trained model files. The directory will be"+
			"created and the model downloaded into it if necessary",
	imagefile := flag.String(
		"Path of a JPEG-image to extract labels for",

	// Load the serialized GraphDef from a file.
	modelfile, labelsfile, err := modelFiles(*modeldir)
	if err != nil {

	labels, err := readLabelsFile(labelsfile)
	if err != nil {

	model, err := os.ReadFile(modelfile)
	if err != nil {

	// Construct an in-memory graph from the serialized form.
	graph := tf.NewGraph()
	if err := graph.Import(model, ""); err != nil {

	// Create a session for inference over graph.
	session, err := tf.NewSession(graph, nil)
	if err != nil {
	defer session.Close()

	// Run inference on *imageFile.
	// For multiple images, session.Run() can be called in a loop (and
	// concurrently). Alternatively, images can be batched since the model
	// accepts batches of image data as input.
	tensor, err := makeTensorFromImage(*imagefile)
	if err != nil {
	output, err := session.Run(
			graph.Operation("input").Output(0): tensor,
	if err != nil {
	// output[0].Value() is a vector containing probabilities of
	// labels for each image in the "batch". The batch size was 1.
	// Find the most probable label index.
	probabilities := output[0].Value().([][]float32)[0]
	printBestLabel(probabilities, labels)




This section is empty.


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func TypeOf

func TypeOf(dt DataType, shape []int64) reflect.Type

TypeOf converts from a DataType and Shape to the equivalent Go type.

func Version added in v0.12.0

func Version() string

Version returns a string describing the version of the underlying TensorFlow runtime.


type Consumer added in v1.10.0

type Consumer struct {
	// Op is the Operation that is consuming the output of another operation.
	Op *Operation

	// Index is the index of the input within Op that the output of another
	// operation is connected to.
	Index int

Consumer identifies a specific input of an operation that consumes the output of another operation.

func (Consumer) DataType added in v1.10.0

func (p Consumer) DataType() DataType

DataType returns the type of the input.

func (Consumer) Producer added in v1.10.0

func (p Consumer) Producer() Output

Producer returns the Output that is connected to this Consumer.

type Context added in v1.12.1

type Context struct {
	// contains filtered or unexported fields

Context for executing operations eagerly.

A Context allows operations to be executed immediately. It encapsulates information such as the available devices, resource manager etc. It also allows the user to configure execution using a ConfigProto, as they can configure a Session when executing a Graph.

func NewContext added in v1.12.1

func NewContext(options *ContextOptions) (*Context, error)

NewContext creates a new context for eager execution. options may be nil to use the default options.

func (*Context) ListDevices added in v1.12.1

func (c *Context) ListDevices() ([]Device, error)

ListDevices returns the list of devices associated with a Context.

type ContextOptions added in v1.12.1

type ContextOptions struct {
	// Config is a binary-serialized representation of the
	// tensorflow.ConfigProto protocol message
	// (https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto).
	Config []byte

	// Sets the default execution mode
	Async bool

ContextOptions contains configuration information for a session

type DataType

type DataType C.TF_DataType

DataType holds the type for a scalar value. E.g., one slot in a tensor.

const (
	Float        DataType = C.TF_FLOAT
	Double       DataType = C.TF_DOUBLE
	Int32        DataType = C.TF_INT32
	Uint32       DataType = C.TF_UINT32
	Uint8        DataType = C.TF_UINT8
	Int16        DataType = C.TF_INT16
	Int8         DataType = C.TF_INT8
	String       DataType = C.TF_STRING
	Complex64    DataType = C.TF_COMPLEX64
	Complex      DataType = C.TF_COMPLEX
	Int64        DataType = C.TF_INT64
	Uint64       DataType = C.TF_UINT64
	Bool         DataType = C.TF_BOOL
	Qint8        DataType = C.TF_QINT8
	Quint8       DataType = C.TF_QUINT8
	Qint32       DataType = C.TF_QINT32
	Bfloat16     DataType = C.TF_BFLOAT16
	Qint16       DataType = C.TF_QINT16
	Quint16      DataType = C.TF_QUINT16
	Uint16       DataType = C.TF_UINT16
	Complex128   DataType = C.TF_COMPLEX128
	Half         DataType = C.TF_HALF
	Float8e5m2   DataType = C.TF_FLOAT8_E5M2
	Float8e4m3fn DataType = C.TF_FLOAT8_E4M3FN
	Int4         DataType = C.TF_INT4
	Uint4        DataType = C.TF_UINT4

Types of scalar values in the TensorFlow type system.

type Device added in v1.6.0

type Device struct {
	Name, Type       string
	MemoryLimitBytes int64

Device structure contains information about a device associated with a session, as returned by ListDevices()

func (Device) String added in v1.12.1

func (d Device) String() string

String describes d and implements fmt.Stringer.

type Graph

type Graph struct {
	// contains filtered or unexported fields

Graph represents a computation graph. Graphs may be shared between sessions.

func NewGraph

func NewGraph() *Graph

NewGraph returns a new Graph.

func (*Graph) AddGradients added in v1.12.1

func (g *Graph) AddGradients(prefix string, y []Output, x []Output, dx []Output) ([]Output, error)

AddGradients adds operations to compute the partial derivatives of the sum of tensors in y with respect to tensors in x, i.e., d(y[0] + y[1] + ...) / d x[0], d(y[0] + y[1] + ... ) / d x[1] etc.

prefix, if non-empty, is the name prefix used for all operations added to the graph to compute these gradients.

func (*Graph) AddOperation added in v0.12.0

func (g *Graph) AddOperation(args OpSpec) (*Operation, error)

AddOperation adds an operation to g.

func (*Graph) Import added in v0.12.0

func (g *Graph) Import(def []byte, prefix string) error

Import imports the nodes and edges from a serialized representation of another Graph into g.

Names of imported nodes will be prefixed with prefix.

func (*Graph) ImportWithOptions added in v1.12.1

func (g *Graph) ImportWithOptions(def []byte, options GraphImportOptions) error

ImportWithOptions imports the nodes and edges from a serialized representation of another Graph into g.

Multiple options can be specified for the newly imported nodes.

func (*Graph) Operation added in v0.12.0

func (g *Graph) Operation(name string) *Operation

Operation returns the Operation named name in the Graph, or nil if no such operation is present.

func (*Graph) Operations added in v1.5.0

func (g *Graph) Operations() []Operation

Operations returns a list of all operations in the graph

func (*Graph) WriteTo added in v0.12.0

func (g *Graph) WriteTo(w io.Writer) (int64, error)

WriteTo writes out a serialized representation of g to w.

Implements the io.WriterTo interface.

type GraphImportOptions added in v1.12.1

type GraphImportOptions struct {
	// Node prefix
	Prefix string

	// Execution device
	Device string
	// contains filtered or unexported fields

The GraphImportOptions struct holds parameters for the ImportWithOptions function.

func (*GraphImportOptions) AddInputMapping

func (o *GraphImportOptions) AddInputMapping(src string, srcIndex int, dst Output)

AddInputMapping adds a mapping between an Output in the imported graph and an Output in the destination graph that it should be replaced with, where src:srcIndex is the name of the Operation and Output index to replace and dst is the output to replace it with.

type Input added in v0.12.0

type Input interface {
	// contains filtered or unexported methods

Input is the interface for specifying inputs to an operation being added to a Graph.

Operations can have multiple inputs, each of which could be either a tensor produced by another operation (an Output object), or a list of tensors produced by other operations (an OutputList). Thus, this interface is implemented by both Output and OutputList.

See OpSpec.Input for more information.

type LibraryHandler

type LibraryHandler struct {
	// contains filtered or unexported fields

func LoadLibrary

func LoadLibrary(path string) (*LibraryHandler, error)

Load library content into current context, useful to load ops implementation into non-monolithic TF build. Returns LibraryHandler or nil and error

type OpSpec added in v0.12.0

type OpSpec struct {
	// Type of the operation (e.g., "Add", "MatMul").
	Type string

	// Name by which the added operation will be referred to in the Graph.
	// If omitted, defaults to Type.
	Name string

	// Inputs to this operation, which in turn must be outputs
	// of other operations already added to the Graph.
	// An operation may have multiple inputs with individual inputs being
	// either a single tensor produced by another operation or a list of
	// tensors produced by multiple operations. For example, the "Concat"
	// operation takes two inputs: (1) the dimension along which to
	// concatenate and (2) a list of tensors to concatenate. Thus, for
	// Concat, len(Input) must be 2, with the first element being an Output
	// and the second being an OutputList.
	Input []Input

	// Map from attribute name to its value that will be attached to this
	// operation.
	Attrs map[string]interface{}

	// Operations that must be executed before executing the operation
	// being added.
	ControlDependencies []*Operation

	// The device on which the operation should be executed.
	// If omitted, an appropriate device will automatically be selected.
	// For example, if set of "/device:GPU:0", then the operation will
	// execute on GPU #0.
	Device string

OpSpec is the specification of an Operation to be added to a Graph (using Graph.AddOperation).

type Operation

type Operation struct {
	// contains filtered or unexported fields

Operation that has been added to the graph.

func (*Operation) Attr added in v1.10.0

func (op *Operation) Attr(name string) (interface{}, error)

Attr returns the value of an attribute on op. It returns an error if the attribute does not exist.

func (*Operation) Device added in v1.10.0

func (op *Operation) Device() string

Device returns a specification of the device on which this operation will be executed, or the empty string if there is no such specification.

func (*Operation) Name added in v0.12.0

func (op *Operation) Name() string

Name returns the name of the operation.

func (*Operation) NumInputs added in v1.10.0

func (op *Operation) NumInputs() int

NumInputs returns the number of inputs of op.

func (*Operation) NumOutputs added in v0.12.0

func (op *Operation) NumOutputs() int

NumOutputs returns the number of outputs of op.

func (*Operation) Output added in v0.12.0

func (op *Operation) Output(i int) Output

Output returns the i-th output of op.

func (*Operation) OutputListSize added in v0.12.0

func (op *Operation) OutputListSize(output string) (int, error)

OutputListSize returns the size of the list of Outputs that is produced by a named output of op.

An Operation has multiple named outputs, each of which produces either a single tensor or a list of tensors. This method returns the size of the list of tensors for a specific output of the operation, identified by its name.

func (*Operation) Type added in v0.12.0

func (op *Operation) Type() string

Type returns the name of the operator used by this operation.

type Output

type Output struct {
	// Op is the Operation that produces this Output.
	Op *Operation

	// Index specifies the index of the output within the Operation.
	Index int

Output represents one of the outputs of an operation in the graph. Has a DataType (and eventually a Shape). May be passed as an input argument to a function for adding operations to a graph, or to a Session's Run() method to fetch that output as a tensor.

func (Output) Consumers added in v1.10.0

func (p Output) Consumers() []Consumer

Consumers returns the inputs that consume this output.

func (Output) DataType added in v1.1.0

func (p Output) DataType() DataType

DataType returns the type of elements in the tensor produced by p.

func (Output) Shape added in v0.12.0

func (p Output) Shape() Shape

Shape returns the (possibly incomplete) shape of the tensor produced p.

type OutputList added in v0.12.0

type OutputList []Output

OutputList represents a list of Outputs that can be provided as input to another operation.

type PartialRun added in v1.1.0

type PartialRun struct {
	// contains filtered or unexported fields

PartialRun enables incremental evaluation of graphs.

PartialRun allows the caller to pause the evaluation of a graph, run arbitrary code that depends on the intermediate computation of the graph, and then resume graph execution. The results of the arbitrary code can be fed into the graph when resuming execution. In contrast, Session.Run executes the graph to compute the requested fetches using the provided feeds and discards all intermediate state (e.g., value of intermediate tensors) when it returns.

For example, consider a graph for unsupervised training of a neural network model. PartialRun can be used to pause execution after the forward pass of the network, let the caller actuate the output (e.g., play a game, actuate a robot etc.), determine the error/loss and then feed this calculated loss when resuming the backward pass of the graph.

var (
	// Create a graph: a + 2 + 3 + b.
	// Skipping error handling for brevity of this example.
	// The 'op' package can be used to make graph construction code
	// with error handling more succinct.
	g        = NewGraph()
	a, _     = Placeholder(g, "a", Int32)
	b, _     = Placeholder(g, "b", Int32)
	two, _   = Const(g, "Two", int32(2))
	three, _ = Const(g, "Three", int32(3))

	plus2, _ = Add(g, "plus2", a, two)       // a + 2
	plus3, _ = Add(g, "plus3", plus2, three) // (a + 2) + 3
	plusB, _ = Add(g, "plusB", plus3, b)     // ((a + 2) + 3) + b

sess, err := NewSession(g, nil)
if err != nil {
defer sess.Close()

// All the feeds, fetches and targets for subsequent PartialRun.Run
// calls must be provided at setup.
pr, err := sess.NewPartialRun(
	[]Output{a, b},
	[]Output{plus2, plusB},
if err != nil {

// Feed 'a=1', fetch 'plus2', and compute (but do not fetch) 'plus3'.
// Imagine this to be the forward pass of unsupervised neural network
// training of a robot.
val, _ := NewTensor(int32(1))
fetches, err := pr.Run(
	map[Output]*Tensor{a: val},
if err != nil {
v1 := fetches[0].Value().(int32)

// Now, feed 'b=4', fetch 'plusB=a+2+3+b'
// Imagine this to be the result of actuating the robot to determine
// the error produced by the current state of the neural network.
val, _ = NewTensor(int32(4))
fetches, err = pr.Run(
	map[Output]*Tensor{b: val},
if err != nil {
v2 := fetches[0].Value().(int32)

fmt.Println(v1, v2)

3 10

func (*PartialRun) Run added in v1.1.0

func (pr *PartialRun) Run(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) ([]*Tensor, error)

Run resumes execution of the graph to compute the requested fetches and targets with the provided feeds.

type SavedModel added in v1.1.0

type SavedModel struct {
	Session    *Session
	Graph      *Graph
	Signatures map[string]Signature

SavedModel represents the contents of loaded SavedModel. TODO(jhseu): Add and document metagraphdef when we pregenerate protobufs.

func LoadSavedModel added in v1.1.0

func LoadSavedModel(exportDir string, tags []string, options *SessionOptions) (*SavedModel, error)

LoadSavedModel creates a new SavedModel from a model previously exported to a directory on disk.

Exported models contain a set of graphs and, optionally, variable values. Tags in the model identify a single graph. LoadSavedModel initializes a session with the identified graph and with variables initialized to from the checkpoints on disk.

The tensorflow package currently does not have the ability to export a model to a directory from Go. This function thus currently targets loading models exported in other languages, such as using tf.saved_model.builder in Python. See: https://www.tensorflow.org/code/tensorflow/python/saved_model/

type Session

type Session struct {
	// contains filtered or unexported fields

Session drives a TensorFlow graph computation.

When a Session is created with a given target, a new Session object is bound to the universe of resources specified by that target. Those resources are available to this session to perform computation described in the GraphDef. After creating the session with a graph, the caller uses the Run() API to perform the computation and potentially fetch outputs as Tensors. A Session allows concurrent calls to Run().

func NewSession

func NewSession(graph *Graph, options *SessionOptions) (*Session, error)

NewSession creates a new execution session with the associated graph. options may be nil to use the default options.

func (*Session) Close

func (s *Session) Close() error

Close a session. This contacts any other processes associated with this session, if applicable. Blocks until all previous calls to Run have returned.

func (*Session) ListDevices added in v1.6.0

func (s *Session) ListDevices() ([]Device, error)

ListDevices returns the list of devices associated with a Session.

func (*Session) NewPartialRun added in v1.1.0

func (s *Session) NewPartialRun(feeds, fetches []Output, targets []*Operation) (*PartialRun, error)

NewPartialRun sets up the graph for incremental evaluation.

All values of feeds, fetches and targets that may be provided to Run calls on the returned PartialRun need to be provided to NewPartialRun.

See documentation for the PartialRun type.

func (*Session) Run

func (s *Session) Run(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) ([]*Tensor, error)

Run the graph with the associated session starting with the supplied feeds to compute the value of the requested fetches. Runs, but does not return Tensors for operations specified in targets.

On success, returns the fetched Tensors in the same order as supplied in the fetches argument. If fetches is set to nil, the returned Tensor fetches is empty.

type SessionOptions

type SessionOptions struct {
	// Target indicates the TensorFlow runtime to connect to.
	// If 'target' is empty or unspecified, the local TensorFlow runtime
	// implementation will be used.  Otherwise, the TensorFlow engine
	// defined by 'target' will be used to perform all computations.
	// "target" can be either a single entry or a comma separated list
	// of entries. Each entry is a resolvable address of one of the
	// following formats:
	//   local
	//   ip:port
	//   host:port
	//   ... other system-specific formats to identify tasks and jobs ...
	// NOTE: at the moment 'local' maps to an in-process service-based
	// runtime.
	// Upon creation, a single session affines itself to one of the
	// remote processes, with possible load balancing choices when the
	// "target" resolves to a list of possible processes.
	// If the session disconnects from the remote process during its
	// lifetime, session calls may fail immediately.
	Target string

	// Config is a binary-serialized representation of the
	// tensorflow.ConfigProto protocol message
	// (https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto).
	Config []byte

SessionOptions contains configuration information for a session.

type Shape added in v1.1.0

type Shape struct {
	// contains filtered or unexported fields

Shape represents the (possibly partially known) shape of a tensor that will be produced by an operation.

The zero-value of a Shape represents a shape with an unknown number of dimensions.

func MakeShape added in v1.1.0

func MakeShape(shape ...int64) Shape

MakeShape returns a Shape with the provided size of each dimension.

A value of -1 implies that the size of the corresponding dimension is not known.

func ScalarShape added in v1.1.0

func ScalarShape() Shape

ScalarShape returns a Shape representing a scalar.

func (Shape) IsFullySpecified added in v1.1.0

func (s Shape) IsFullySpecified() bool

IsFullySpecified returns true iff the size of all the dimensions of s are known.

func (Shape) NumDimensions added in v1.1.0

func (s Shape) NumDimensions() int

NumDimensions returns the number of dimensions represented by s, or -1 if unknown.

func (Shape) Size added in v1.1.0

func (s Shape) Size(dim int) int64

Size returns the size of the dim-th dimension of the shape, or -1 if it is unknown.

REQUIRES: 0 <= dim < s.NumDimensions()

func (Shape) String added in v1.1.0

func (s Shape) String() string

func (Shape) ToSlice added in v1.1.0

func (s Shape) ToSlice() ([]int64, error)

ToSlice returns the (possibly partially known) shape represented by s as a slice, or an error if the number of dimensions is not known.

type Signature

type Signature struct {
	Inputs, Outputs map[string]TensorInfo
	MethodName      string

A Signature defines the signature of a computation supported by a TensorFlow graph.

For example, a model with two loss computations, sharing a single input, might have the following signature_def map.

Note that across the two Signatures "loss_A" and "loss_B", the input key, output key, and method_name are identical, and will be used by system(s) that implement or rely upon this particular loss method. The output tensor names differ, demonstrating how different outputs can exist for the same method.

signature_def {
  key: "loss_A"
  value {
    inputs {
      key: "input"
      value {
        name: "input:0"
        dtype: DT_STRING
        tensor_shape: ...
    outputs {
      key: "loss_output"
      value {
        name: "loss_output_A:0"
        dtype: DT_FLOAT
        tensor_shape: ...
  method_name: "some/package/compute_loss"
signature_def {
  key: "loss_B"
  value {
    inputs {
      key: "input"
      value {
        name: "input:0"
        dtype: DT_STRING
        tensor_shape: ...
    outputs {
      key: "loss_output"
      value {
        name: "loss_output_B:0"
        dtype: DT_FLOAT
        tensor_shape: ...
  method_name: "some/package/compute_loss"

type Tensor

type Tensor struct {
	// contains filtered or unexported fields

Tensor holds a multi-dimensional array of elements of a single data type.

func NewTensor

func NewTensor(value any) (*Tensor, error)

NewTensor converts from a Go value to a Tensor. Valid values are scalars, slices, and arrays. Every element of a slice must have the same length so that the resulting Tensor has a valid shape.

func ReadTensor added in v1.0.0

func ReadTensor(dataType DataType, shape []int64, r io.Reader) (*Tensor, error)

ReadTensor constructs a Tensor with the provided type and shape from the serialized tensor contents in r.

See also WriteContentsTo.

func (*Tensor) DataType

func (t *Tensor) DataType() DataType

DataType returns the scalar datatype of the Tensor.

func (*Tensor) Reshape

func (t *Tensor) Reshape(newShape []int64) error

Reshape updates tensor's shape in place if this is possible or returns an error otherwise.

func (*Tensor) Shape

func (t *Tensor) Shape() []int64

Shape returns the shape of the Tensor.

func (*Tensor) Value

func (t *Tensor) Value() any

Value converts the Tensor to a Go value. For now, not all Tensor types are supported, and this function may panic if it encounters an unsupported DataType.

The type of the output depends on the Tensor type and dimensions. For example: Tensor(int64, 0): int64 Tensor(float64, 3): [][][]float64

func (*Tensor) WriteContentsTo added in v1.0.0

func (t *Tensor) WriteContentsTo(w io.Writer) (int64, error)

WriteContentsTo writes the serialized contents of t to w.

Returns the number of bytes written. See ReadTensor for reconstructing a Tensor from the serialized form.

WARNING: WriteContentsTo is not comprehensive and will fail if t.DataType() is non-numeric (e.g., String). See https://github.com/tensorflow/tensorflow/issues/6003.

type TensorHandle added in v1.12.1

type TensorHandle struct {
	// contains filtered or unexported fields

TensorHandle is a handle to a tensor on a device.

A Tensor referenced by a TensorHandle may be on any device, whereas a Tensor always resides in the host CPU's memory.

A Tensor referenced by a TensorHandle may not have been computed yet. For example, a TensorHandle might reference the output of an operation that has not finished executing. Because of this, various methods, such as Shape() may block until the tensor has been instantiated.

This allows multiple operations to be performed on tensors on a device (e.g. a GPU) without sending these values back to the host CPU in between every operation.

func NewTensorHandle added in v1.12.1

func NewTensorHandle(t *Tensor) (*TensorHandle, error)

NewTensorHandle creates a new tensor handle from a tensor.

func (*TensorHandle) BackingDeviceName added in v1.12.1

func (th *TensorHandle) BackingDeviceName() (string, error)

BackingDeviceName returns the name of the device in whose memory tensor handle th resides. This function will block till the operation that produces th has completed.

func (*TensorHandle) CopyToDevice added in v1.12.1

func (th *TensorHandle) CopyToDevice(c *Context, deviceName string) (*TensorHandle, error)

CopyToDevice creates a new TensorHandle with the same contents as this TensorHandle but placed in the memory of the device 'deviceName'. If source and destination are the same device, then this creates a new handle that shares the underlying buffer. Otherwise, it currently requires at least one of the source or destination devices to be CPU (i.e., for the source or destination tensor to be placed in host memory).

func (*TensorHandle) DataType added in v1.12.1

func (th *TensorHandle) DataType() DataType

DataType returns the TensorHandle's datatype.

func (*TensorHandle) DeviceName added in v1.12.1

func (th *TensorHandle) DeviceName() (string, error)

DeviceName returns the name of the device of the operation that produced the TensorHandle. If the handle was produced by a copy, it returns the destination device of the copy. Note that returned device name is not always the device holding the tensor handle's memory. If you want the latter, use BackingDeviceName. This function will block till the operation that produces th has completed.

func (*TensorHandle) Shape added in v1.12.1

func (th *TensorHandle) Shape() ([]int64, error)

Shape returns the shape of the Tensor referenced by th.

func (*TensorHandle) ToTensor added in v1.12.1

func (th *TensorHandle) ToTensor() (*Tensor, error)

ToTensor returns the Tensor referenced by th. It may block if this tensor is not yet computed.

type TensorInfo

type TensorInfo struct {
	Name  string
	DType DataType
	Shape Shape

A TensorInfo contains the information about a Tensor necessary for feeding or retrieval.


Path Synopsis
Command genop generates a Go source file with functions for TensorFlow ops.
Command genop generates a Go source file with functions for TensorFlow ops.
Package internal generates Go source code with functions for TensorFlow operations.
Package internal generates Go source code with functions for TensorFlow operations.
Package op defines functions for adding TensorFlow operations to a Graph.
Package op defines functions for adding TensorFlow operations to a Graph.

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