func Train(rnnFile, sampleDir string, stepSize float64) {
log.Println("Loading samples...")
samples, err := ReadSamples(sampleDir)
if err != nil {
fmt.Fprintln(os.Stderr, "Failed to read samples:", err)
os.Exit(1)
}
var seqFunc *rnn.Bidirectional
rnnData, err := ioutil.ReadFile(rnnFile)
if err == nil {
log.Println("Loaded network from file.")
seqFunc, err = rnn.DeserializeBidirectional(rnnData)
if err != nil {
fmt.Fprintln(os.Stderr, "Failed to deserialize network:", err)
os.Exit(1)
}
} else {
log.Println("Created network.")
seqFunc = createNetwork(samples)
}
crossLen := int(CrossRatio * float64(samples.Len()))
log.Println("Using", samples.Len()-crossLen, "training and",
crossLen, "validation samples...")
// Always shuffle the samples in the same way.
rand.Seed(123)
sgd.ShuffleSampleSet(samples)
validation := samples.Subset(0, crossLen)
training := samples.Subset(crossLen, samples.Len())
gradienter := &sgd.Adam{
Gradienter: &ctc.RGradienter{
Learner: seqFunc,
SeqFunc: seqFunc,
MaxConcurrency: MaxConcurrency,
MaxSubBatch: MaxSubBatch,
},
}
var epoch int
toggleRegularization(seqFunc, true)
sgd.SGDInteractive(gradienter, training, stepSize, BatchSize, func() bool {
toggleRegularization(seqFunc, false)
cost := ctc.TotalCost(seqFunc, training, CostBatchSize, MaxConcurrency)
crossCost := ctc.TotalCost(seqFunc, validation, CostBatchSize, MaxConcurrency)
toggleRegularization(seqFunc, true)
log.Printf("Epoch %d: cost=%e cross=%e", epoch, cost, crossCost)
epoch++
return true
})
toggleRegularization(seqFunc, false)
data, err := seqFunc.Serialize()
if err != nil {
fmt.Fprintln(os.Stderr, "Failed to serialize:", err)
os.Exit(1)
}
if err := ioutil.WriteFile(rnnFile, data, 0755); err != nil {
fmt.Fprintln(os.Stderr, "Failed to save:", err)
os.Exit(1)
}
}
