tensorflow: github.com/tensorflow/tensorflow/tensorflow/go Index | Examples | Files | Directories

package tensorflow

import "github.com/tensorflow/tensorflow/tensorflow/go"

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 probability that the input image was recognized as
    // 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("dir", "", "Directory containing the trained model files. The directory will be created and the model downloaded into it if necessary")
    imagefile := flag.String("image", "", "Path of a JPEG-image to extract labels for")
    if *modeldir == "" || *imagefile == "" {
    // Load the serialized GraphDef from a file.
    modelfile, labelsfile, err := modelFiles(*modeldir)
    if err != nil {
    model, err := ioutil.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 probably label index.
    probabilities := output[0].Value().([][]float32)[0]
    printBestLabel(probabilities, labelsfile)

func printBestLabel(probabilities []float32, labelsFile string) {
    bestIdx := 0
    for i, p := range probabilities {
        if p > probabilities[bestIdx] {
            bestIdx = i
    // Found the best match. Read the string from labelsFile, which
    // contains one line per label.
    file, err := os.Open(labelsFile)
    if err != nil {
    defer file.Close()
    scanner := bufio.NewScanner(file)
    var labels []string
    for scanner.Scan() {
        labels = append(labels, scanner.Text())
    if err := scanner.Err(); err != nil {
        log.Printf("ERROR: failed to read %s: %v", labelsFile, err)
    fmt.Printf("BEST MATCH: (%2.0f%% likely) %s\n", probabilities[bestIdx]*100.0, labels[bestIdx])

// Convert the image in filename to a Tensor suitable as input to the Inception model.
func makeTensorFromImage(filename string) (*tf.Tensor, error) {
    bytes, err := ioutil.ReadFile(filename)
    if err != nil {
        return nil, err
    // DecodeJpeg uses a scalar String-valued tensor as input.
    tensor, err := tf.NewTensor(string(bytes))
    if err != nil {
        return nil, err
    // Construct a graph to normalize the image
    graph, input, output, err := constructGraphToNormalizeImage()
    if err != nil {
        return nil, err
    // Execute that graph to normalize this one image
    session, err := tf.NewSession(graph, nil)
    if err != nil {
        return nil, err
    defer session.Close()
    normalized, err := session.Run(
        map[tf.Output]*tf.Tensor{input: tensor},
    if err != nil {
        return nil, err
    return normalized[0], nil

// The inception model takes as input the image described by a Tensor in a very
// specific normalized format (a particular image size, shape of the input tensor,
// normalized pixel values etc.).
// This function constructs a graph of TensorFlow operations which takes as
// input a JPEG-encoded string and returns a tensor suitable as input to the
// inception model.
func constructGraphToNormalizeImage() (graph *tf.Graph, input, output tf.Output, err error) {
    // Some constants specific to the pre-trained model at:
    // https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
    // - The model was trained after with images scaled to 224x224 pixels.
    // - The colors, represented as R, G, B in 1-byte each were converted to
    //   float using (value - Mean)/Scale.
    const (
        H, W  = 224, 224
        Mean  = float32(117)
        Scale = float32(1)
    // - input is a String-Tensor, where the string the JPEG-encoded image.
    // - The inception model takes a 4D tensor of shape
    //   [BatchSize, Height, Width, Colors=3], where each pixel is
    //   represented as a triplet of floats
    // - Apply normalization on each pixel and use ExpandDims to make
    //   this single image be a "batch" of size 1 for ResizeBilinear.
    s := op.NewScope()
    input = op.Placeholder(s, tf.String)
    output = op.Div(s,
                        op.DecodeJpeg(s, input, op.DecodeJpegChannels(3)), tf.Float),
                    op.Const(s.SubScope("make_batch"), int32(0))),
                op.Const(s.SubScope("size"), []int32{H, W})),
            op.Const(s.SubScope("mean"), Mean)),
        op.Const(s.SubScope("scale"), Scale))
    graph, err = s.Finalize()
    return graph, input, output, err

func modelFiles(dir string) (modelfile, labelsfile string, err error) {
    const URL = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"
    var (
        model   = filepath.Join(dir, "tensorflow_inception_graph.pb")
        labels  = filepath.Join(dir, "imagenet_comp_graph_label_strings.txt")
        zipfile = filepath.Join(dir, "inception5h.zip")
    if filesExist(model, labels) == nil {
        return model, labels, nil
    log.Println("Did not find model in", dir, "downloading from", URL)
    if err := os.MkdirAll(dir, 0755); err != nil {
        return "", "", err
    if err := download(URL, zipfile); err != nil {
        return "", "", fmt.Errorf("failed to download %v - %v", URL, err)
    if err := unzip(dir, zipfile); err != nil {
        return "", "", fmt.Errorf("failed to extract contents from model archive: %v", err)
    return model, labels, filesExist(model, labels)

func filesExist(files ...string) error {
    for _, f := range files {
        if _, err := os.Stat(f); err != nil {
            return fmt.Errorf("unable to stat %s: %v", f, err)
    return nil

func download(URL, filename string) error {
    resp, err := http.Get(URL)
    if err != nil {
        return err
    defer resp.Body.Close()
    file, err := os.OpenFile(filename, os.O_RDWR|os.O_CREATE, 0644)
    if err != nil {
        return err
    defer file.Close()
    _, err = io.Copy(file, resp.Body)
    return err

func unzip(dir, zipfile string) error {
    r, err := zip.OpenReader(zipfile)
    if err != nil {
        return err
    defer r.Close()
    for _, f := range r.File {
        src, err := f.Open()
        if err != nil {
            return err
        log.Println("Extracting", f.Name)
        dst, err := os.OpenFile(filepath.Join(dir, f.Name), os.O_WRONLY|os.O_CREATE, 0644)
        if err != nil {
            return err
        if _, err := io.Copy(dst, src); err != nil {
            return err
    return nil



Package Files

attrs.go context.go doc.go graph.go lib.go operation.go saved_model.go session.go shape.go signature.go status.go tensor.go tensor_handle.go version.go

func TypeOf Uses

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

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

func Version Uses

func Version() string

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

type Consumer Uses

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 Uses

func (p Consumer) DataType() DataType

DataType returns the type of the input.

func (Consumer) Producer Uses

func (p Consumer) Producer() Output

Producer returns the Output that is connected to this Consumer.

type Context Uses

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 Uses

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 Uses

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

ListDevices returns the list of devices associated with a Context.

type ContextOptions Uses

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 Uses

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

Types of scalar values in the TensorFlow type system.

type Device Uses

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 Uses

func (d Device) String() string

String describes d and implements fmt.Stringer.

type Graph Uses

type Graph struct {
    // contains filtered or unexported fields

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

func NewGraph Uses

func NewGraph() *Graph

NewGraph returns a new Graph.

func (*Graph) AddGradients Uses

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 Uses

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

AddOperation adds an operation to g.

func (*Graph) Import Uses

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 Uses

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 Uses

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 Uses

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

Operations returns a list of all operations in the graph

func (*Graph) WriteTo Uses

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 Uses

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 Uses

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 Uses

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 Uses

type LibraryHandler struct {
    // contains filtered or unexported fields

func LoadLibrary Uses

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 Uses

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 Uses

type Operation struct {
    // contains filtered or unexported fields

Operation that has been added to the graph.

func (*Operation) Attr Uses

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 Uses

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 Uses

func (op *Operation) Name() string

Name returns the name of the operation.

func (*Operation) NumInputs Uses

func (op *Operation) NumInputs() int

NumInputs returns the number of inputs of op.

func (*Operation) NumOutputs Uses

func (op *Operation) NumOutputs() int

NumOutputs returns the number of outputs of op.

func (*Operation) Output Uses

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

Output returns the i-th output of op.

func (*Operation) OutputListSize Uses

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 Uses

func (op *Operation) Type() string

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

type Output Uses

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 Uses

func (p Output) Consumers() []Consumer

Consumers returns the inputs that consume this output.

func (Output) DataType Uses

func (p Output) DataType() DataType

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

func (Output) Shape Uses

func (p Output) Shape() Shape

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

type OutputList Uses

type OutputList []Output

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

type PartialRun Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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

ListDevices returns the list of devices associated with a Session.

func (*Session) NewPartialRun Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

func ScalarShape() Shape

ScalarShape returns a Shape representing a scalar.

func (Shape) IsFullySpecified Uses

func (s Shape) IsFullySpecified() bool

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

func (Shape) NumDimensions Uses

func (s Shape) NumDimensions() int

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

func (Shape) Size Uses

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 Uses

func (s Shape) String() string

func (Shape) ToSlice Uses

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 Uses

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 Uses

type Tensor struct {
    // contains filtered or unexported fields

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

func NewTensor Uses

func NewTensor(value interface{}) (*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 Uses

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 Uses

func (t *Tensor) DataType() DataType

DataType returns the scalar datatype of the Tensor.

func (*Tensor) Reshape Uses

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 Uses

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

Shape returns the shape of the Tensor.

func (*Tensor) Value Uses

func (t *Tensor) Value() interface{}

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 Uses

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 Uses

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 Uses

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

NewTensorHandle creates a new tensor handle from a tensor.

func (*TensorHandle) BackingDeviceName Uses

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

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

WARNING: The implementation currently returns the same as DeviceName(). After TensoFlow 1.13's C library is released, this implementation will be updated to return what the documentation says!

func (*TensorHandle) CopyToDevice Uses

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 Uses

func (th *TensorHandle) DataType() DataType

DataType returns the TensorHandle's datatype.

func (*TensorHandle) DeviceName Uses

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 Uses

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

Shape returns the shape of the Tensor referenced by th.

func (*TensorHandle) ToTensor Uses

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 Uses

type TensorInfo struct {
    Name  string
    DType DataType
    Shape Shape

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


genopCommand genop generates a Go source file with functions for TensorFlow ops.
genop/internalPackage internal generates Go source code with functions for TensorFlow operations.
opPackage op defines functions for adding TensorFlow operations to a Graph.

Package tensorflow imports 14 packages (graph) and is imported by 90 packages. Updated 2021-01-19. Refresh now. Tools for package owners.