人類喜歡將所有事物都納入鄙視鏈的範疇,寵物當然也不例外。一般來說,擁有一隻純種寵物可以讓主人佔據鄙視鏈的雲端,進而鄙視那些混血或者流浪寵物。甚至還發展出了專業的鑑定機構,可以頒發《血統證明書》。但是考究各類純種鑑定的常規方法:例如眼睛的大小、顏色、鼻子的特點、身軀長度、尾巴特徵、毛髮等,當然也包括一些比較玄幻的特徵:寵物家族的個性、氣質等等。拋開“黑魔法”不在此討論之外,既然是基於生物外形特徵鑑定,判斷是否純種的需求本質上就是一個圖像識別服務。
Hello TensorFlow
Tensorflow is not a Machine Learning specific library, instead, is a general purpose computation library that represents computations with graphs.
TensorFlow 開源軟件庫(Apache 2.0 許可證),最初由 Google Brain 團隊開發。TensorFlow 提供了一系列算法模型和編程接口,讓我們可以快速構建一個基於機器學習的智能服務。對於開發者來說,目前有四種編程接口可供選擇:
- C++ source code: Tensorflow 核心基於 C++ 編寫,支持從高到低各個層級的操作;
- Python bindings & Python library: 對標 C++ 實現,支持 Python 調用 C++ 函數;
- Java bindings;
- Go binding;
下面是一個簡單的實例:
環境準備
- 安裝 TensorFlow C library,包含一個頭文件 c_api.h 和 libtensorflow.so
wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.5.0.tar.gz
## options
TF_TYPE="cpu" # Change to "gpu" for GPU support
TF_VERSION='1.5.0'
curl -L \
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-${TF_VERSION}.tar.gz" |
-
安裝 Go 語言環境,參考:玩轉編程語言:Golang
-
安裝 Tensorflow Go binding library
go get github.com/tensorflow/tensorflow/tensorflow/go
go get github.com/tensorflow/tensorflow/tensorflow/go/op
- 下載模型(demo model),包含一個標籤文件 label_strings.txt 和 graph.pb
mkdir model
wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip -O model/inception.zip
unzip model/inception.zip -d model
chmod -R 777 model
Tensorflow Model Function
//Loading TensorFlow model
func loadModel() error {
// Load inception model
model, err := ioutil.ReadFile("./model/tensorflow_inception_graph.pb")
if err != nil {
return err
}
graph = tf.NewGraph()
if err := graph.Import(model, ""); err != nil {
return err
}
// Load labels
labelsFile, err := os.Open("./model/imagenet_comp_graph_label_strings.txt")
if err != nil {
return err
}
defer labelsFile.Close()
scanner := bufio.NewScanner(labelsFile)
// Labels are separated by newlines
for scanner.Scan() {
labels = append(labels, scanner.Text())
}
if err := scanner.Err(); err != nil {
return err
}
return nil
}
Classifying Workflow
基於 Tensorflow 模型實現圖像識別的主要流程如下:
- 圖像轉換 (Convert to tensor )
- 圖像標準化( Normalize )
- 圖像分類 ( Classifying )
func recognizeHandler(w http.ResponseWriter, r *http.Request, _ httprouter.Params) {
// Read image
imageFile, header, err := r.FormFile("image")
// Will contain filename and extension
imageName := strings.Split(header.Filename, ".")
if err != nil {
responseError(w, "Could not read image", http.StatusBadRequest)
return
}
defer imageFile.Close()
var imageBuffer bytes.Buffer
// Copy image data to a buffer
io.Copy(&imageBuffer, imageFile)
// ...
tensor, err := makeTensorFromImage(&imageBuffer, imageName[:1][0])
if err != nil {
responseError(w, "Invalid image", http.StatusBadRequest)
return
}
// ...
}
函數 makeTensorFromImage() which runs an image tensor through the normalization graph.
func makeTensorFromImage(imageBuffer *bytes.Buffer, imageFormat string) (*tf.Tensor, error) {
tensor, err := tf.NewTensor(imageBuffer.String())
if err != nil {
return nil, err
}
graph, input, output, err := makeTransformImageGraph(imageFormat)
if err != nil {
return nil, err
}
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},
[]tf.Output{output},
nil)
if err != nil {
return nil, err
}
return normalized[0], nil
}
函數 maketransformimagegraph() 將圖形的像素值調整到 224x224,以符合模型輸入參數要求。
func makeTransformImageGraph(imageFormat string) (graph *tf.Graph, input, output tf.Output, err error) {
const (
H, W = 224, 224
Mean = float32(117)
Scale = float32(1)
)
s := op.NewScope()
input = op.Placeholder(s, tf.String)
// Decode PNG or JPEG
var decode tf.Output
if imageFormat == "png" {
decode = op.DecodePng(s, input, op.DecodePngChannels(3))
} else {
decode = op.DecodeJpeg(s, input, op.DecodeJpegChannels(3))
}
// Div and Sub perform (value-Mean)/Scale for each pixel
output = op.Div(s,
op.Sub(s,
// Resize to 224x224 with bilinear interpolation
op.ResizeBilinear(s,
// Create a batch containing a single image
op.ExpandDims(s,
// Use decoded pixel values
op.Cast(s, decode, 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
}
最後,將格式化的 image tensor 輸入到 Inception model graph 中運算。
session, err := tf.NewSession(graph, nil)
if err != nil {
log.Fatal(err)
}
defer session.Close()
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 {
responseError(w, "Could not run inference", http.StatusInternalServerError)
return
}
Testing
func main() {
if err := loadModel(); err != nil {
log.Fatal(err)
return
}
r := httprouter.New()
r.POST("/recognize", recognizeHandler)
err := http.ListenAndServe(":8080", r)
if err != nil {
log.Println(err)
return
}
}
$ curl localhost:8080/recognize -F 'image=@../data/IMG_3560.png'
{
"filename":"IMG_3000.png",
"labels":[
{"label":"black swan","probability":0.98746836,"Percent":"98.75%"},
{"label":"oystercatcher","probability":0.0040768473,"Percent":"0.41%"},
{"label":"American coot","probability":0.002185003,"Percent":"0.22%"},
{"label":"black stork","probability":0.0011524856,"Percent":"0.12%"},
{"label":"redshank","probability":0.0010183558,"Percent":"0.10%"}]
}
通過上面的案例我們可以發現,這個服務目前可以對於黑天鵝圖像的推算概率值爲 98.75%,非常準確;但是對於另外兩張寵物狗的圖像,最高的推算概率值也僅有 30% 左右,雖然也沒有被識別成貓咪或者狼,但是和理想效果要求可用性還有一段距離(此處暫時忽略物種本身的複雜性)。主要是因爲現在我們使用的還只是一個非常“原始”的模型,如果需要爲小衆領域服務(寵物,也可以是其它事物),需要通過訓練(Training Models)增強優化,或者引入更豐富的標籤,更合適的模型。當然,訓練過程中也會存在樣本質量不佳的情況,錯誤樣本和各種噪音也會影響準確度。
擴展閱讀:《The Machine Learning Master》
- Machine Learning(一):基於 TensorFlow 實現寵物血統智能識別
- Machine Learning (二) : 寵物智能識別之 Using OpenCV with Node.js
- Machine Learning:機器學習項目
- Machine Learning:機器學習算法
- Machine Learning:機器學習書單
- Machine Learning:機器學習技術與知識產權法
- Machine Learning:人工智能媒體報道集
- 數據可視化(三)基於 Graphviz 實現程序化繪圖
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