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)
}
func (lft *LockFreeFtrlTrainer) TrainBatch(
encodemodel string,
instances []string) error {
line_cnt := len(instances)
if line_cnt == 0 {
lft.log.Error("[LockFreeFtrlTrainer-TrainBatch] No model retrained.")
return errors.New("[LockFreeFtrlTrainer-TrainBatch] No model retrained.")
}
var fls solver.FtrlSolver
err := json.Unmarshal([]byte(encodemodel), &fls)
if err != nil {
lft.log.Error("[LockFreeFtrlTrainer-TrainBatch]" + err.Error())
return errors.New("[LockFreeFtrlTrainer-TrainBatch]" + err.Error())
}
lft.Solver = fls
lft.log.Info(fmt.Sprintf("[%s] params={alpha:%.2f, beta:%.2f, l1:%.2f, l2:%.2f, dropout:%.2f, epoch:%d}\n",
lft.JobName,
lft.Solver.Alpha,
lft.Solver.Beta,
lft.Solver.L1,
lft.Solver.L2,
lft.Solver.Dropout,
lft.Epoch))
predict_func := func(x util.Pvector) float64 {
return lft.Solver.Predict(x)
}
var timer util.StopWatch
timer.StartTimer()
for iter := 0; iter < lft.Epoch; iter++ {
var stream_parser StreamParser
stream_parser.Open(instances)
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 := stream_parser.ReadSampleMultiThread()
if flag != nil {
break
}
pred := lft.Solver.Update(x, y)
local_loss += calc_loss(y, pred)
local_count++
if i == 0 && local_count%10000 == 0 {
tmp_cnt := math.Min(float64(local_count*lft.NumThreads), float64(line_cnt))
lft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
lft.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()
defer c.Done()
}
util.UtilParallelRun(worker_func, lft.NumThreads)
stream_parser.Close()
lft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
lft.JobName,
iter,
float64(count*100)/float64(line_cnt),
timer.StopTimer(),
float64(loss)/float64(count)))
eval_loss := evaluate_stream(instances, predict_func, 0)
lft.log.Info(fmt.Sprintf("[%s] validation-loss=[%f]\n", lft.JobName, float64(eval_loss)))
}
return nil
}
func (lft *LockFreeFtrlTrainer) TrainImpl(
model_file string,
train_file string,
line_cnt int,
test_file string) error {
if !lft.Init {
lft.log.Error("[LockFreeFtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
return errors.New("[LockFreeFtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
}
lft.log.Info(fmt.Sprintf("[%s] params={alpha:%.2f, beta:%.2f, l1:%.2f, l2:%.2f, dropout:%.2f, epoch:%d}\n",
lft.JobName,
lft.Solver.Alpha,
lft.Solver.Beta,
lft.Solver.L1,
lft.Solver.L2,
lft.Solver.Dropout,
lft.Epoch))
predict_func := func(x util.Pvector) float64 {
return lft.Solver.Predict(x)
}
var timer util.StopWatch
timer.StartTimer()
for iter := 0; iter < lft.Epoch; iter++ {
var file_parser FileParser
file_parser.OpenFile(train_file)
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()
if flag != nil {
break
}
pred := lft.Solver.Update(x, y)
local_loss += calc_loss(y, pred)
local_count++
if i == 0 && local_count%10000 == 0 {
tmp_cnt := math.Min(float64(local_count*lft.NumThreads), float64(line_cnt))
lft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
lft.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()
defer c.Done()
}
util.UtilParallelRun(worker_func, lft.NumThreads)
file_parser.CloseFile()
lft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
lft.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, 0)
lft.log.Info(fmt.Sprintf("[%s] validation-loss=[%f]\n", lft.JobName, float64(eval_loss)))
}
}
return lft.Solver.SaveModel(model_file)
}
func (ft *FtrlTrainer) TrainImpl(
model_file string,
train_file string,
line_cnt int,
test_file string) error {
if !ft.Init {
ft.log.Error("[FtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
return errors.New("[FtrlTrainer-TrainImpl] Fast ftrl trainer restore error.")
}
ft.log.Info(fmt.Sprintf("[%s] params={alpha:%.2f, beta:%.2f, l1:%.2f, l2:%.2f, dropout:%.2f, epoch:%d}\n",
ft.JobName,
ft.Solver.Alpha,
ft.Solver.Beta,
ft.Solver.L1,
ft.Solver.L2,
ft.Solver.Dropout,
ft.Epoch))
predict_func := func(x util.Pvector) float64 {
return ft.Solver.Predict(x)
}
var timer util.StopWatch
timer.StartTimer()
var last_time float64 = 0
for iter := 0; iter < ft.Epoch; iter++ {
var file_parser FileParser
file_parser.OpenFile(train_file)
cur_cnt := 0
last_cnt := 0
var loss float64 = 0
for {
flag, y, x := file_parser.ReadSample()
if flag != nil {
break
}
pred := ft.Solver.Update(x, y)
loss += calc_loss(y, pred)
cur_cnt++
if cur_cnt-last_cnt > 100000 && timer.StopTimer()-last_time > 0.5 {
ft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
ft.JobName,
iter,
float64(cur_cnt*100)/float64(line_cnt),
timer.StopTimer(),
float64(loss)/float64(cur_cnt)))
last_cnt = cur_cnt
last_time = timer.StopTimer()
}
}
ft.log.Info(fmt.Sprintf("[%s] epoch=%d processed=[%.2f%%] time=[%.2f] train-loss=[%.6f]\n",
ft.JobName,
iter,
float64(cur_cnt*100)/float64(line_cnt),
timer.StopTimer(),
float64(loss)/float64(cur_cnt)))
file_parser.CloseFile()
if test_file != "" {
eval_loss := evaluate_file(test_file, predict_func, 0)
ft.log.Info(fmt.Sprintf("[%s] validation-loss=[%f]\n", float64(eval_loss)))
}
}
return ft.Solver.SaveModel(model_file)
}