trainer

package

v1.55.0 Latest Latest Go to latest Published: Jan 12, 2024 License: GPL-3.0 Imports: 15 Imported by: 0

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Repository

github.com/ChizhovVadim/CounterGo

Links

Open Source Insights

Documentation ¶

Index ¶

Constants
Variables
func Run(ctx context.Context, datasetProvider IDatasetProvider, ...) error
func ValidationCost(output, target float64) float64
type ActivationFn
type Gradient
- func (g *Gradient) Apply(elem *float64)
- func (g *Gradient) Calculate() float64
- func (g *Gradient) Reset()
- func (g *Gradient) Update(delta float64)
type Gradients
- func NewGradients(rows, cols int) Gradients
- func (g *Gradients) AddMatrix(m *Matrix)
- func (g *Gradients) Apply(m *Matrix)
type IDatasetProvider
type Matrix
- func NewMatrix(rows, cols int) Matrix
- func (m *Matrix) Add(row, col int, delta float64)
- func (m *Matrix) Get(row, col int) float64
- func (m *Matrix) Reset()
type Network
- func (n *Network) Save(file string) error
type Neuron
type ReLu
- func (s *ReLu) Sigma(x float64) float64
- func (s *ReLu) SigmaPrime(x float64) float64
type Sample
type Sigmoid
- func (s *Sigmoid) Sigma(x float64) float64
- func (s *Sigmoid) SigmaPrime(x float64) float64
type ThreadData
type Topology
- func (t *Topology) LayerSize() int
type Trainer
- func NewTrainer(training, validation []Sample, topology []int, threads int, seed int64, ...) *Trainer
- func (t *Trainer) Train(epochs int, binFolderPath string) error

Constants ¶

View Source

const (
	Beta1 = 0.9
	Beta2 = 0.999
)

Variables ¶

View Source

var (
	LearningRate = 0.01
	BatchSize    = 16384
)

Functions ¶

func Run ¶

func Run(
	ctx context.Context,
	datasetProvider IDatasetProvider,
	validationProvider IDatasetProvider,
	threads int,
	epochs int,
	sigmoidScale float64,
	netFolderPath string,
) error

func ValidationCost ¶

func ValidationCost(output, target float64) float64

Types ¶

type ActivationFn ¶

type ActivationFn interface {
	Sigma(x float64) float64
	SigmaPrime(x float64) float64
}

type Gradient ¶

type Gradient struct {
	Value float64
	M1    float64
	M2    float64
}

func (*Gradient) Apply ¶

func (g *Gradient) Apply(elem *float64)

func (*Gradient) Calculate ¶

func (g *Gradient) Calculate() float64

func (*Gradient) Reset ¶

func (g *Gradient) Reset()

func (*Gradient) Update ¶

func (g *Gradient) Update(delta float64)

type Gradients ¶

type Gradients struct {
	Data []Gradient
	Rows int
	Cols int
}

func NewGradients ¶

func NewGradients(rows, cols int) Gradients

func (*Gradients) AddMatrix ¶

func (g *Gradients) AddMatrix(m *Matrix)

func (*Gradients) Apply ¶

func (g *Gradients) Apply(m *Matrix)

type IDatasetProvider ¶

type IDatasetProvider interface {
	Load(ctx context.Context, dataset chan<- domain.DatasetItem) error
}

type Matrix ¶

type Matrix struct {
	Data []float64
	Rows int
	Cols int
}

func NewMatrix ¶

func NewMatrix(rows, cols int) Matrix

func (*Matrix) Add ¶

func (m *Matrix) Add(row, col int, delta float64)

func (*Matrix) Get ¶

func (m *Matrix) Get(row, col int) float64

func (*Matrix) Reset ¶

func (m *Matrix) Reset()

type Network ¶

type Network struct {
	Id       uint32
	Topology Topology
	Weights  []Matrix
	Biases   []Matrix
}

Network is a neural network with 3 layers

func (*Network) Save ¶

func (n *Network) Save(file string) error

Binary specification for the NNUE file: - All the data is stored in little-endian layout - All the matrices are written in column-major - The magic number/version consists of 4 bytes (int32):

66 (which is the ASCII code for B), uint8
90 (which is the ASCII code for Z), uint8
2 The major part of the current version number, uint8
0 The minor part of the current version number, uint8

- 4 bytes (int32) to denote the network ID - 4 bytes (int32) to denote input size - 4 bytes (int32) to denote output size - 4 bytes (int32) number to represent the number of inputs - 4 bytes (int32) for the size of each layer - All weights for a layer, followed by all the biases of the same layer - Other layers follow just like the above point

type Neuron ¶

type Neuron struct {
	A, E, Prime float64
}

type ReLu ¶

type ReLu struct{}

func (*ReLu) Sigma ¶

func (s *ReLu) Sigma(x float64) float64

func (*ReLu) SigmaPrime ¶

func (s *ReLu) SigmaPrime(x float64) float64

type Sample ¶

type Sample struct {
	Input  []int16
	Target float32
}

type Sigmoid ¶

type Sigmoid struct {
	SigmoidScale float64
}

func (*Sigmoid) Sigma ¶

func (s *Sigmoid) Sigma(x float64) float64

func (*Sigmoid) SigmaPrime ¶

func (s *Sigmoid) SigmaPrime(x float64) float64

type ThreadData ¶

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

type Topology ¶

type Topology struct {
	Inputs        uint32
	Outputs       uint32
	HiddenNeurons []uint32
}

func (*Topology) LayerSize ¶

func (t *Topology) LayerSize() int

type Trainer ¶

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

func NewTrainer ¶

func NewTrainer(training, validation []Sample,
	topology []int, threads int, seed int64, sigmoidScale float64) *Trainer

func (*Trainer) Train ¶

func (t *Trainer) Train(epochs int, binFolderPath string) error

Source Files ¶

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