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feat(classifier): add neural network classifier for face reconization
BREAK CHANGE:
- Instance changed to Estimator
- People saved into Storage (storage serialized as a zip file includes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,40 @@ | ||
| package classifier | ||
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| import ( | ||
| "io" | ||
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| "github.com/bububa/facenet/core" | ||
| ) | ||
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| // Classifier represents classifier interface | ||
| type Classifier interface { | ||
| Identity() ClassifierIdentity | ||
| Train(people *core.People, split float64, iterations int, verbosity int) | ||
| BatchTrain(people *core.People, split float64, iterations int, verbosity int, batch int) | ||
| Predict(input []float32) []float64 | ||
| Match(input []float32) (int, float64) | ||
| Write(io.Writer) error | ||
| Read(io.Reader) error | ||
| } | ||
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| // ClassifierIdentity represents classifier type | ||
| type ClassifierIdentity int | ||
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| const ( | ||
| // UnknownClassifier represents unknown classifier which is not defined | ||
| UnknownClassifier ClassifierIdentity = iota | ||
| // NeuralClassifier represents neural deep learning classifier | ||
| NeuralClassifier | ||
| // BayesClassifier represents bayes classifier | ||
| BayesClassifier | ||
| ) | ||
|
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| // NewDefault returns a default Neural classifier | ||
| func NewDefault() Classifier { | ||
| return new(Neural) | ||
| } | ||
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| const ( | ||
| // NeuralMatchThreshold returns neural classifier match threshold | ||
| NeuralMatchThreshold float64 = 0.75 | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,143 @@ | ||
| package classifier | ||
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| import ( | ||
| "encoding/json" | ||
| "io" | ||
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| deep "github.com/patrikeh/go-deep" | ||
| "github.com/patrikeh/go-deep/training" | ||
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| "github.com/bububa/facenet/core" | ||
| ) | ||
|
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| // Neural represents neural classifier | ||
| type Neural struct { | ||
| ml *deep.Neural | ||
| threshold float64 | ||
| } | ||
|
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| // Name return sclassifier name | ||
| func (n *Neural) Identity() ClassifierIdentity { | ||
| return NeuralClassifier | ||
| } | ||
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| // Write implement Classifier interface | ||
| func (n *Neural) Write(w io.Writer) error { | ||
| dump := n.ml.Dump() | ||
| return json.NewEncoder(w).Encode(dump) | ||
| } | ||
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| // Read implement Classifier interface | ||
| func (n *Neural) Read(r io.Reader) error { | ||
| var dump deep.Dump | ||
| if err := json.NewDecoder(r).Decode(&dump); err != nil { | ||
| return err | ||
| } | ||
| n.ml = deep.FromDump(&dump) | ||
| return nil | ||
| } | ||
|
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| // SetThreadshold set Neural match threshold | ||
| func (n *Neural) SetThreadshold(threshold float64) { | ||
| n.threshold = threshold | ||
| } | ||
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| func (n *Neural) peopleToExamples(people *core.People, split float64) (training.Examples, training.Examples) { | ||
| var data training.Examples | ||
| var heldout training.Examples | ||
| classes := len(people.GetList()) | ||
| for idx, person := range people.GetList() { | ||
| var examples training.Examples | ||
| embeddings := person.GetEmbeddings() | ||
| for _, embedding := range embeddings { | ||
| e := training.Example{ | ||
| Response: onehot(classes, idx), | ||
| Input: convInputs(embedding.GetValue()), | ||
| } | ||
| deep.Standardize(e.Input) | ||
| examples = append(examples, e) | ||
| } | ||
| examples.Shuffle() | ||
| t, h := examples.Split(split) | ||
| data = append(data, t...) | ||
| heldout = append(heldout, h...) | ||
| } | ||
| data.Shuffle() | ||
| heldout.Shuffle() | ||
| return data, heldout | ||
| } | ||
|
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| func (n *Neural) initDeep(inputs int, layout []int, std float64, mean float64) { | ||
| n.ml = deep.NewNeural(&deep.Config{ | ||
| Inputs: inputs, | ||
| Layout: layout, | ||
| // Activation: deep.ActivationTanh, | ||
| // Activation: deep.ActivationSigmoid, | ||
| Activation: deep.ActivationReLU, | ||
| //Activation: deep.ActivationSoftmax, | ||
| Mode: deep.ModeMultiClass, | ||
| Weight: deep.NewNormal(std, mean), | ||
| Bias: true, | ||
| }) | ||
| } | ||
|
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| // Train implement Classifier interface | ||
| func (n *Neural) Train(people *core.People, split float64, iterations int, verbosity int) { | ||
| n.initDeep(512, []int{64, 16, len(people.GetList())}, 0.5, 0) | ||
| //trainer := training.NewTrainer(training.NewSGD(0.01, 0.5, 1e-6, true), 1) | ||
| //trainer := training.NewTrainer(training.NewSGD(0.005, 0.5, 1e-6, true), 50) | ||
| //trainer := training.NewBatchTrainer(training.NewSGD(0.005, 0.1, 0, true), 50, 300, 16) | ||
| //trainer := training.NewTrainer(training.NewAdam(0.1, 0, 0, 0), 50) | ||
| // solver := training.NewSGD(0.01, 0.5, 1e-6, true) | ||
| solver := training.NewAdam(0.02, 0.9, 0.999, 1e-8) | ||
| trainer := training.NewTrainer(solver, verbosity) | ||
| data, heldout := n.peopleToExamples(people, split) | ||
| trainer.Train(n.ml, data, heldout, iterations) | ||
| } | ||
|
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| // BatchTrain implement Classifier interface | ||
| func (n *Neural) BatchTrain(people *core.People, split float64, iterations int, verbosity int, batch int) { | ||
| n.initDeep(512, []int{64, 16, len(people.GetList())}, 0.5, 0) | ||
| //solver := training.NewSGD(0.01, 0.5, 1e-6, true) | ||
| solver := training.NewAdam(0.02, 0.9, 0.999, 1e-8) | ||
| trainer := training.NewBatchTrainer(solver, verbosity, batch, 4) | ||
| data, heldout := n.peopleToExamples(people, split) | ||
| trainer.Train(n.ml, data, heldout, iterations) | ||
| } | ||
|
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| // Predict implement Classifier interface | ||
| func (n *Neural) Predict(embedding []float32) []float64 { | ||
| return n.ml.Predict(convInputs(embedding)) | ||
| } | ||
|
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| // Match returns best match result | ||
| func (n *Neural) Match(input []float32) (int, float64) { | ||
| scores := n.Predict(input) | ||
| var index = -1 | ||
| var maxScore float64 | ||
| threshold := n.threshold | ||
| if threshold < 1e-15 { | ||
| threshold = NeuralMatchThreshold | ||
| } | ||
| for idx, score := range scores { | ||
| if score >= threshold && maxScore < score { | ||
| maxScore = score | ||
| index = idx | ||
| } | ||
| } | ||
| return index, maxScore | ||
| } | ||
|
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| func convInputs(embedding []float32) []float64 { | ||
| ret := make([]float64, len(embedding)) | ||
| for i, v := range embedding { | ||
| ret[i] = float64(v) | ||
| } | ||
| return ret | ||
| } | ||
|
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| func onehot(classes int, val int) []float64 { | ||
| res := make([]float64, classes) | ||
| res[val] = 1 | ||
| return res | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| // Package classifier include different classifiers | ||
| package classifier |
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