func (trainer *MaxEntClassifierTrainer) Train(set data.Dataset) Model {
// 检查训练数据是否是分类问题
if !set.GetOptions().IsSupervisedLearning {
log.Fatal("训练数据不是分类问题数据")
}
// 建立新的优化器
optimizer := optimizer.NewOptimizer(trainer.options.Optimizer)
// 建立特征权重向量
featureDimension := set.GetOptions().FeatureDimension
numLabels := set.GetOptions().NumLabels
var weights *util.Matrix
if set.GetOptions().FeatureIsSparse {
weights = util.NewSparseMatrix(numLabels)
} else {
weights = util.NewMatrix(numLabels, featureDimension)
}
// 得到优化的特征权重向量
optimizer.OptimizeWeights(weights, MaxEntComputeInstanceDerivative, set)
classifier := new(MaxEntClassifier)
classifier.Weights = weights
classifier.NumLabels = numLabels
classifier.FeatureDimension = featureDimension
classifier.FeatureDictionary = set.GetFeatureDictionary()
classifier.LabelDictionary = set.GetLabelDictionary()
return classifier
}
func (rbm *RBM) Train(set data.Dataset) {
featureDimension := set.GetOptions().FeatureDimension
visibleDim := featureDimension
hiddenDim := rbm.options.NumHiddenUnits + 1
log.Printf("#visible = %d, #hidden = %d", featureDimension-1, hiddenDim-1)
// 随机化 weights
rbm.lock.Lock()
rbm.lock.weights = util.NewMatrix(hiddenDim, visibleDim)
oldWeights := util.NewMatrix(hiddenDim, visibleDim)
batchDerivative := util.NewMatrix(hiddenDim, visibleDim)
for i := 0; i < hiddenDim; i++ {
for j := 0; j < visibleDim; j++ {
value := (rand.Float64()*2 - 1) * 0.01
rbm.lock.weights.Set(i, j, value)
}
}
rbm.lock.Unlock()
// 启动工作协程
ch := make(chan *data.Instance, rbm.options.Worker)
out := make(chan *util.Matrix, rbm.options.Worker)
for iWorker := 0; iWorker < rbm.options.Worker; iWorker++ {
go rbm.derivativeWorker(ch, out, visibleDim, hiddenDim)
}
iteration := 0
delta := 1.0
for (rbm.options.MaxIter == 0 || iteration < rbm.options.MaxIter) &&
(rbm.options.Delta == 0 || delta > rbm.options.Delta) {
iteration++
go rbm.feeder(set, ch)
iBatch := 0
batchDerivative.Clear()
numInstances := set.NumInstances()
for it := 0; it < numInstances; it++ {
// 乱序读入
derivative := <-out
batchDerivative.Increment(derivative, rbm.options.LearningRate)
iBatch++
if iBatch == rbm.options.BatchSize || it == numInstances-1 {
rbm.lock.Lock()
rbm.lock.weights.Increment(batchDerivative, 1.0)
rbm.lock.Unlock()
iBatch = 0
batchDerivative.Clear()
}
}
// 统计delta和|weight|
rbm.lock.RLock()
weightsNorm := rbm.lock.weights.Norm()
batchDerivative.DeepCopy(rbm.lock.weights)
batchDerivative.Increment(oldWeights, -1.0)
derivativeNorm := batchDerivative.Norm()
delta = derivativeNorm / weightsNorm
log.Printf("iter = %d, delta = %f, |weight| = %f",
iteration, delta, weightsNorm)
oldWeights.DeepCopy(rbm.lock.weights)
rbm.lock.RUnlock()
}
}