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Iris inference using tfliego. more information about example: https://github.com/nbortolotti/tflitego_examples/ & and about the library: https://github.com/nbortolotti/tflitego
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package main | |
import ( | |
"fmt" | |
"log" | |
"github.com/nbortolotti/tflitego" | |
) | |
// topSpecie provide the name of the specia infered by the model | |
func topSpecie(results []float32) string { | |
pos := 0 | |
var max = results[0] | |
for i := range results { | |
if results[i] > max { | |
max = results[i] | |
pos = i | |
} | |
} | |
specie := "" | |
switch position := pos; { | |
case position == 0: | |
specie = "Setosa" | |
case position == 1: | |
specie = "Versicolor" | |
case position == 2: | |
specie = "Virginica" | |
} | |
return specie | |
} | |
func main() { | |
model, err := tflitego.NewTFLiteModelFromFile("iris_lite.tflite") | |
defer model.Delete() | |
if err != nil { | |
if model == nil { | |
log.Fatal("cannot load model") | |
} | |
} | |
options, err := tflitego.NewInterpreterOptions() | |
defer options.Delete() | |
if err != nil { | |
options.SetNumThread(4) | |
} | |
interpreter, err := tflitego.NewInterpreter(model, options) | |
defer interpreter.Delete() | |
if err != nil { | |
if interpreter == nil { | |
log.Println("cannot create interpreter") | |
return | |
} | |
} | |
status := interpreter.AllocateTensors() | |
if status != tflitego.TfLiteOk { | |
log.Println("allocate Tensors failed") | |
return | |
} | |
// here is possible change the new specie to test other inference responses. | |
newspecie := []float32{7.9, 3.8, 6.4, 2.0} | |
input, err := interpreter.GetInputTensor(0) | |
input.SetFloat32(newspecie) | |
status = interpreter.Invoke() | |
if status != tflitego.TfLiteOk { | |
log.Println("invoke interpreter failed") | |
return | |
} | |
output := interpreter.GetOutputTensor(0) | |
out := output.OperateFloat32() | |
//here is optional use the function to decode the specie. | |
fmt.Println(topSpecie(out)) | |
} |
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