data

type Batch struct {
	X            []*tf.Tensor
	Y            *tf.Tensor
	ClassWeights *tf.Tensor
}

type Dataset interface {
	Len() int
	NumCategoricalClasses() int
	SetMode(mode GeneratorMode) Dataset
	Shuffle(seed int64)
	Unshuffle() error
	GetColumnNames() []string
	GeneratorChan(batchSize int, preFetch int) chan Batch
	Generate(batchSize int) ([]*tf.Tensor, *tf.Tensor, *tf.Tensor, error)
	Reset() error
	SaveProcessors(saveDir string) error
}

type GeneratorMode string

type ImgFolderDataset struct {
	ClassCounts  map[int]int
	ClassWeights map[int]float32
	Count        int
	// contains filtered or unexported fields
}

type ImgFolderDatasetConfig struct {
	ParentDir           string
	CacheDir            string
	TrainPercent        float32
	ValPercent          float32
	TestPercent         float32
	IgnoreParseErrors   bool
	ConcurrentFileLimit int32
	ClassWeights        map[int]float32
}

type Inference struct {
	// contains filtered or unexported fields
}

type SingleFileDataset struct {
	ClassCounts  map[int]int
	ClassWeights map[int]float32
	Count        int
	// contains filtered or unexported fields
}

type SingleFileDatasetConfig struct {
	FilePath               string
	CacheDir               string
	TrainPercent           float32
	ValPercent             float32
	TestPercent            float32
	IgnoreParseErrors      bool
	SkipHeaders            bool
	RowFilter              func(line []string) bool
	ConcurrentFileLimit    int32
	MaxRowsForProcessorFit int
	ClassWeights           map[int]float32
}

type ValuesDataset struct {
	ClassCounts  map[int]int
	ClassWeights map[int]float32
	Count        int
	// contains filtered or unexported fields
}

type ValuesDatasetConfig struct {
	CacheDir     string
	TrainPercent float32
	ValPercent   float32
	TestPercent  float32
}