Ejemplo n.º 1
0
Archivo: gd.go Proyecto: hycxa/mlf
func (opt *gdOptimizer) OptimizeWeights(
	weights *util.Matrix, derivative_func ComputeInstanceDerivativeFunc, set data.Dataset) {
	// 偏导数向量
	derivative := weights.Populate()

	// 学习率计算器
	learningRate := NewLearningRate(opt.options)

	// 优化循环
	iterator := set.CreateIterator()
	step := 0
	var learning_rate float64
	convergingSteps := 0
	oldWeights := weights.Populate()
	weightsDelta := weights.Populate()
	instanceDerivative := weights.Populate()
	log.Print("开始梯度递降优化")
	for {
		if opt.options.MaxIterations > 0 && step >= opt.options.MaxIterations {
			break
		}
		step++

		// 每次遍历样本前对偏导数向量清零
		derivative.Clear()

		// 遍历所有样本,计算偏导数向量并累加
		iterator.Start()
		instancesProcessed := 0
		for !iterator.End() {
			instance := iterator.GetInstance()
			derivative_func(weights, instance, instanceDerivative)
			derivative.Increment(instanceDerivative, 1.0/float64(set.NumInstances()))
			iterator.Next()
			instancesProcessed++

			if opt.options.GDBatchSize > 0 && instancesProcessed >= opt.options.GDBatchSize {
				// 添加正则化项
				derivative.Increment(ComputeRegularization(weights, opt.options),
					float64(instancesProcessed)/(float64(set.NumInstances())*float64(set.NumInstances())))

				// 计算特征权重的增量
				delta := opt.GetDeltaX(weights, derivative)

				// 根据学习率更新权重
				learning_rate = learningRate.ComputeLearningRate(delta)
				weights.Increment(delta, learning_rate)

				// 重置
				derivative.Clear()
				instancesProcessed = 0
			}
		}

		if instancesProcessed > 0 {
			// 处理剩余的样本
			derivative.Increment(ComputeRegularization(weights, opt.options),
				float64(instancesProcessed)/(float64(set.NumInstances())*float64(set.NumInstances())))
			delta := opt.GetDeltaX(weights, derivative)
			learning_rate = learningRate.ComputeLearningRate(delta)
			weights.Increment(delta, learning_rate)
		}

		weightsDelta.WeightedSum(weights, oldWeights, 1, -1)
		oldWeights.DeepCopy(weights)
		weightsNorm := weights.Norm()
		weightsDeltaNorm := weightsDelta.Norm()
		log.Printf("#%d |w|=%1.3g |dw|/|w|=%1.3g lr=%1.3g", step, weightsNorm, weightsDeltaNorm/weightsNorm, learning_rate)

		// 判断是否溢出
		if math.IsNaN(weightsNorm) {
			log.Fatal("优化失败:不收敛")
		}

		// 判断是否收敛
		if weightsDelta.Norm()/weights.Norm() < opt.options.ConvergingDeltaWeight {
			convergingSteps++
			if convergingSteps > opt.options.ConvergingSteps {
				log.Printf("收敛")
				break
			}
		}
	}
}
Ejemplo n.º 2
0
Archivo: lbfgs.go Proyecto: sguzwf/mlf
func (opt *lbfgsOptimizer) OptimizeWeights(
	weights *util.Matrix, derivative_func ComputeInstanceDerivativeFunc, set data.Dataset) {

	// 学习率计算器
	learningRate := NewLearningRate(opt.options)

	// 偏导数向量
	derivative := weights.Populate()

	// 优化循环
	step := 0
	convergingSteps := 0
	oldWeights := weights.Populate()
	weightsDelta := weights.Populate()

	// 为各个工作协程开辟临时资源
	numLbfgsThreads := *lbfgs_threads
	if numLbfgsThreads == 0 {
		numLbfgsThreads = runtime.NumCPU()
	}
	workerSet := make([]data.Dataset, numLbfgsThreads)
	workerDerivative := make([]*util.Matrix, numLbfgsThreads)
	workerInstanceDerivative := make([]*util.Matrix, numLbfgsThreads)
	for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
		workerBuckets := []data.SkipBucket{
			{true, iWorker},
			{false, 1},
			{true, numLbfgsThreads - 1 - iWorker},
		}
		workerSet[iWorker] = data.NewSkipDataset(set, workerBuckets)
		workerDerivative[iWorker] = weights.Populate()
		workerInstanceDerivative[iWorker] = weights.Populate()
	}

	log.Print("开始L-BFGS优化")
	for {
		if opt.options.MaxIterations > 0 && step >= opt.options.MaxIterations {
			break
		}
		step++

		// 开始工作协程
		workerChannel := make(chan int, numLbfgsThreads)
		for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
			go func(iw int) {
				workerDerivative[iw].Clear()
				iterator := workerSet[iw].CreateIterator()
				iterator.Start()
				for !iterator.End() {
					instance := iterator.GetInstance()
					derivative_func(
						weights, instance, workerInstanceDerivative[iw])
					//					log.Print(workerInstanceDerivative[iw].GetValues(0))
					workerDerivative[iw].Increment(
						workerInstanceDerivative[iw], float64(1)/float64(set.NumInstances()))
					iterator.Next()
				}
				workerChannel <- iw
			}(iWorker)
		}

		derivative.Clear()

		// 等待工作协程结束
		for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
			<-workerChannel
		}
		for iWorker := 0; iWorker < numLbfgsThreads; iWorker++ {
			derivative.Increment(workerDerivative[iWorker], 1)
		}

		// 添加正则化项
		derivative.Increment(ComputeRegularization(weights, opt.options), 1.0/float64(set.NumInstances()))

		// 计算特征权重的增量
		delta := opt.GetDeltaX(weights, derivative)

		// 根据学习率更新权重
		learning_rate := learningRate.ComputeLearningRate(delta)
		weights.Increment(delta, learning_rate)

		weightsDelta.WeightedSum(weights, oldWeights, 1, -1)
		oldWeights.DeepCopy(weights)
		weightsNorm := weights.Norm()
		weightsDeltaNorm := weightsDelta.Norm()
		log.Printf("#%d |dw|/|w|=%f |w|=%f lr=%1.3g", step, weightsDeltaNorm/weightsNorm, weightsNorm, learning_rate)

		// 判断是否溢出
		if math.IsNaN(weightsNorm) {
			log.Fatal("优化失败:不收敛")
		}

		// 判断是否收敛
		if weightsDeltaNorm/weightsNorm < opt.options.ConvergingDeltaWeight {
			convergingSteps++
			if convergingSteps > opt.options.ConvergingSteps {
				log.Printf("收敛")
				break
			}
		} else {
			convergingSteps = 0
		}
	}
}