//更新权重方法
func (fs *FtrlSolver) Update(x util.Pvector, y float64) float64 {
if !fs.Init {
return 0
}
var weights util.Pvector = make(util.Pvector, fs.Featnum)
var gradients []float64 = make([]float64, fs.Featnum)
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
item := x[i]
if util.UtilGreater(fs.Dropout, 0.0) {
rand_prob := util.UniformDistribution()
if rand_prob < fs.Dropout {
continue
}
}
var idx int = item.Index
if idx >= fs.Featnum {
continue
}
//获取w权重值
var val float64 = fs.GetWeight(idx)
//建立w权重数组
weights = append(weights, util.Pair{idx, val})
//每个样本梯度值默认赋值为样本x本身
gradients = append(gradients, item.Value)
//计算仿射函数wT*x的值
wTx += val * item.Value
}
//计算模型预估值
var pred float64 = util.Sigmoid(wTx)
//计算p_t-y_t值，为计算每个样本的梯度做准备
var grad float64 = pred - y
//计算g_i = (p_t-y_t)*x_i
util.VectorMultiplies(gradients, grad)
for k := 0; k < len(weights); k++ {
var i int = weights[k].Index
var w_i float64 = weights[k].Value
var grad_i float64 = gradients[k]
var sigma float64 = (math.Sqrt(fs.N[i]+grad_i*grad_i) - math.Sqrt(fs.N[i])) / fs.Alpha
//z_i=z_i+g_i-sigma_i*w_(t,i)
fs.Z[i] += grad_i - sigma*w_i
//n_i=n_i+g_i*g_i
fs.N[i] += grad_i * grad_i
}
return pred
}
func (fw *FtrlWorker) Update(
x util.Pvector,
y float64,
param_server *FtrlParamServer) float64 {
if !fw.FtrlSolver.Init {
return 0.
}
var weights util.Pvector = make(util.Pvector, fw.FtrlSolver.Featnum)
var gradients []float64 = make([]float64, fw.FtrlSolver.Featnum)
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
item := x[i]
if util.UtilGreater(fw.FtrlSolver.Dropout, 0.0) {
rand_prob := util.UniformDistribution()
if rand_prob < fw.FtrlSolver.Dropout {
continue
}
}
var idx int = item.Index
if idx >= fw.FtrlSolver.Featnum {
continue
}
//获取w权重值
var val float64 = fw.FtrlSolver.GetWeight(idx)
//建立w权重数组
weights = append(weights, util.Pair{idx, val})
//每个样本梯度值默认赋值为样本x本身
gradients = append(gradients, item.Value)
//计算仿射函数wT*x的值
wTx += val * item.Value
}
//计算模型预估值
var pred float64 = util.Sigmoid(wTx)
//计算p_t-y_t值，为计算每个样本的梯度做准备
var grad float64 = pred - y
//计算g_i = (p_t-y_t)*x_i
util.VectorMultiplies(gradients, grad)
for k := 0; k < len(weights); k++ {
var i int = weights[k].Index
var g int = i / ParamGroupSize
if fw.ParamGroupStep[g]%fw.FetchStep == 0 {
param_server.FetchParamGroup(
fw.FtrlSolver.N,
fw.FtrlSolver.Z,
g)
}
var w_i float64 = weights[k].Value
var grad_i float64 = gradients[k]
var sigma float64 = (math.Sqrt(fw.FtrlSolver.N[i]+grad_i*grad_i) - math.Sqrt(fw.FtrlSolver.N[i])) / fw.FtrlSolver.Alpha
fw.FtrlSolver.Z[i] += grad_i - sigma*w_i
fw.FtrlSolver.N[i] += grad_i * grad_i
fw.ZUpdate[i] += grad_i - sigma*w_i
fw.NUpdate[i] += grad_i * grad_i
if fw.ParamGroupStep[g]%fw.PushStep == 0 {
param_server.PushParamGroup(fw.NUpdate, fw.ZUpdate, g)
}
fw.ParamGroupStep[g] += 1
}
return pred
}
func (fft *FastFtrlTrainer) TrainImpl(
model_file string,
train_file string,
line_cnt int,
test_file string) error {
if !fft.Init {
fft.log4fft.Error("[FastFtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
return errors.New("[FastFtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
}
fft.log4fft.Info(fmt.Sprintf(
"[%s] params={alpha:%.2f, beta:%.2f, l1:%.2f, l2:%.2f, dropout:%.2f, epoch:%d}\n",
fft.JobName,
fft.ParamServer.Alpha,
fft.ParamServer.Beta,
fft.ParamServer.L1,
fft.ParamServer.L2,
fft.ParamServer.Dropout,
fft.Epoch))
var solvers []solver.FtrlWorker = make([]solver.FtrlWorker, fft.NumThreads)
for i := 0; i < fft.NumThreads; i++ {
solvers[i].Initialize(&fft.ParamServer, fft.PusStep, fft.FetchStep)
}
predict_func := func(x util.Pvector) float64 {
return fft.ParamServer.Predict(x)
}
var timer util.StopWatch
timer.StartTimer()
for iter := 0; iter < fft.Epoch; iter++ {
var file_parser ParallelFileParser
file_parser.OpenFile(train_file, fft.NumThreads)
count := 0
var loss float64 = 0.
var lock sync.Mutex
worker_func := func(i int, c *sync.WaitGroup) {
local_count := 0
var local_loss float64 = 0
for {
flag, y, x := file_parser.ReadSampleMultiThread(i)
if flag != nil {
break
}
pred := solvers[i].Update(x, y, &fft.ParamServer)
local_loss += calc_loss(y, pred)
local_count++
if i == 0 && local_count%10000 == 0 {
tmp_cnt := math.Min(float64(local_count*fft.NumThreads), float64(line_cnt))
fft.log4fft.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
fft.JobName,
iter,
float64(tmp_cnt*100)/float64(line_cnt),
timer.StopTimer(),
float64(local_loss)/float64(local_count)))
}
}
lock.Lock()
count += local_count
loss += local_loss
lock.Unlock()
solvers[i].PushParam(&fft.ParamServer)
defer c.Done()
}
if iter == 0 && util.UtilGreater(fft.BurnIn, float64(0)) {
burn_in_cnt := int(fft.BurnIn * float64(line_cnt))
var local_loss float64 = 0
for i := 0; i < burn_in_cnt; i++ {
//线程0做预热
flag, y, x := file_parser.ReadSample(0)
if flag != nil {
break
}
pred := fft.ParamServer.Update(x, y)
local_loss += calc_loss(y, pred)
if i%10000 == 0 {
fft.log4fft.Info(fmt.Sprintf("[%s] burn-in processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
fft.JobName,
float64((i+1)*100)/float64(line_cnt),
timer.StopTimer(),
float64(local_loss)/float64(i+1)))
}
}
fft.log4fft.Info(fmt.Sprintf("[%s] burn-in processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
fft.JobName,
float64(burn_in_cnt*100)/float64(line_cnt),
timer.StopTimer(),
float64(local_loss)/float64(burn_in_cnt)))
if util.UtilFloat64Equal(fft.BurnIn, float64(1)) {
continue
}
}
for i := 0; i < fft.NumThreads; i++ {
solvers[i].Reset(&fft.ParamServer)
}
util.UtilParallelRun(worker_func, fft.NumThreads)
file_parser.CloseFile(fft.NumThreads)
// f(w,
// "[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
// fft.JobName,
// iter,
// float64(count*100)/float64(line_cnt),
// timer.StopTimer(),
// float64(loss)/float64(count))
if test_file != "" {
eval_loss := evaluate_file(test_file, predict_func, fft.NumThreads)
fft.log4fft.Info(fmt.Sprintf("[%s] validation-loss=[%f]\n", fft.JobName, float64(eval_loss)))
}
}
return fft.ParamServer.SaveModel(model_file)
}