// NewScope creates a Scope initialized with an empty Graph.
func NewScope() *Scope {
return &Scope{graph: tf.NewGraph(), namemap: make(map[string]int), err: new(scopeErr)}
}
func Example() {
// An example for using the TensorFlow Go API for image recognition
// using a pre-trained inception model (http://arxiv.org/abs/1512.00567).
//
// 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 for Graph execution
// - Exectues the graph and prints out the label with the highest probability
const (
// Path to a pre-trained inception model.
// The two files are extracted from a zip archive as so:
/*
curl -L https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip -o /tmp/inception5h.zip
unzip /tmp/inception5h.zip -d /tmp
*/
modelFile = "/tmp/tensorflow_inception_graph.pb"
labelsFile = "/tmp/imagenet_comp_graph_label_strings.txt"
// Image file to "recognize".
testImageFilename = "/tmp/test.jpg"
)
// Load the serialized GraphDef from a file.
model, err := ioutil.ReadFile(modelFile)
if err != nil {
log.Fatal(err)
}
// Construct an in-memory graph from the serialized form.
graph := tf.NewGraph()
if err := graph.Import(model, ""); err != nil {
log.Fatal(err)
}
// Create a session for inference over graph.
session, err := tf.NewSession(graph, nil)
if err != nil {
log.Fatal(err)
}
defer session.Close()
// Run inference on testImageFilename.
// For multiple images, session.Run() can be called in a loop (and
// concurrently). Furthermore, images can be batched together since the
// model accepts batches of image data as input.
tensor, err := makeTensorFromImageForInception(testImageFilename)
if err != nil {
log.Fatal(err)
}
output, err := session.Run(
map[tf.Output]*tf.Tensor{
graph.Operation("input").Output(0): tensor,
},
[]tf.Output{
graph.Operation("output").Output(0),
},
nil)
if err != nil {
log.Fatal(err)
}
// 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 Example() {
// 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 an 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")
flag.Parse()
if *modeldir == "" || *imagefile == "" {
flag.Usage()
return
}
// Load the serialized GraphDef from a file.
modelfile, labelsfile, err := modelFiles(*modeldir)
if err != nil {
log.Fatal(err)
}
model, err := ioutil.ReadFile(modelfile)
if err != nil {
log.Fatal(err)
}
// Construct an in-memory graph from the serialized form.
graph := tf.NewGraph()
if err := graph.Import(model, ""); err != nil {
log.Fatal(err)
}
// Create a session for inference over graph.
session, err := tf.NewSession(graph, nil)
if err != nil {
log.Fatal(err)
}
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 {
log.Fatal(err)
}
output, err := session.Run(
map[tf.Output]*tf.Tensor{
graph.Operation("input").Output(0): tensor,
},
[]tf.Output{
graph.Operation("output").Output(0),
},
nil)
if err != nil {
log.Fatal(err)
}
// 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)
}
// NewScope creates a Scope initialized with an empty Graph.
func NewScope() *Scope {
return &Scope{graph: tf.NewGraph(), namemap: make(map[string]int)}
}
