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classifier.go
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classifier.go
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package gocunets
import (
"math"
"github.com/dereklstinson/gocunets/layers/activation"
"github.com/dereklstinson/gocunets/layers/softmax"
)
//Classifier will take the outputs of a neural network and find the error of it. To be passed back to the rest of the network.
type Classifier struct {
sftmx *softmax.Layer
act *activation.Layer
}
func outputerror(desired float32, actual float32) float32 {
return desired - actual
}
//ActMode are the activationmode flags
type ActMode int
//ActivaitonModeFlag passes ActivationMode flags
type ActivaitonModeFlag struct {
}
//SoftMax returns ActivationMode flag for softmax
func (a ActivaitonModeFlag) SoftMax() ActMode {
return ActMode(1)
}
//Tanh returns ActivationMode flag for Tanh
func (a ActivaitonModeFlag) Tanh() ActMode {
return ActMode(2)
}
//Logistic returns ActivationMode flag for Logistic
func (a ActivaitonModeFlag) Logistic() ActMode {
return ActMode(3)
}
//LossMode is the flags for loss mode
type LossMode int
//LossModeFlag will return flags for LossMode
//These will be added over time.
type LossModeFlag struct {
}
//Huber is a loss mode
func (l LossModeFlag) Huber() LossMode {
return LossMode(1)
}
//Binary is a loss mode
func (l LossModeFlag) Binary() LossMode {
return LossMode(2)
}
func crossentropyloss(target, predicted float32) float32 {
if target == 1 {
return -float32(math.Log(float64(predicted)))
}
return -float32(math.Log(float64(1 - predicted)))
}
//yHat = predicted?
//y = target