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nncore

package

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Versions in this module

v1

v1.7.1

Sep 7, 2026

v1.7.0

Sep 1, 2026

Changes in this version

type Dense

+ RNG *RNG

type DenseConfig

+ Dropout float64

+ L2Regularization float64

+ RNG *RNG

+ type RNG struct

+ func NewRand(seed *uint64) *RNG

+ func (r *RNG) Randn(n, m int) *mat.Dense

+ func (r *RNG) Spawn() *RNG

v1.6.1

Aug 14, 2026

v1.6.0

Aug 1, 2026

Changes in this version

+ const EluActivation

+ const ModeMultiClass

+ const ModeMultiLabel

+ const ModeRegression

+ const ReLUActivation

+ const SigmoidActivation

+ const TanhActivation

+ func ApplyDropoutMask(a *mat.Dense, mask []bool, p float64)

+ func BinaryCrossEntropy(y_hat, y *mat.Dense, parameters map[string]*mat.Dense, lambd float64) float64

+ func CrossEntropy(y_hat, y *mat.Dense, parameters map[string]*mat.Dense, lambd float64) float64

+ func DropoutMask(a *mat.Dense, p float64) []bool

+ func Elu(a *mat.Dense) *mat.Dense

+ func EluDerivative(a *mat.Dense) *mat.Dense

+ func Linear(a *mat.Dense) *mat.Dense

+ func MeanSquaredError(yHat, y *mat.Dense, parameters map[string]*mat.Dense, lambd float64) float64

+ func ReLU(a *mat.Dense) *mat.Dense

+ func ReLUDerivative(a *mat.Dense) *mat.Dense

+ func Sigmoid(a *mat.Dense) *mat.Dense

+ func SigmoidDerivative(a *mat.Dense) *mat.Dense

+ func SigmoidScalar(x float64) float64

+ func Softmax(a *mat.Dense) *mat.Dense

+ func Tanh(a *mat.Dense) *mat.Dense

+ func TanhDerivative(a *mat.Dense) *mat.Dense

+ type Activation struct

+ Derivative ActivationFunction

+ Function ActivationFunction

+ Name ActivationType

+ func NewActivation(actType ActivationType) Activation

+ type ActivationFunction func(*mat.Dense) *mat.Dense

+ type ActivationType string

+ type Dense struct

+ Activation Activation

+ Dropout float64

+ Iter float64

+ L2Regularization float64

+ Optimizer Optimizer

+ OutputActivation OutputActivation

+ Parameters map[string]*mat.Dense

+ func NewDense(config DenseConfig) *Dense

+ func (dn *Dense) BackwardPropagation(Z, A map[string]*mat.Dense, D map[string][]bool, y *mat.Dense) (*mat.Dense, map[string]*mat.Dense, map[string]*mat.Dense)

+ func (dn *Dense) ForwardPropagation(x *mat.Dense, training bool) (*mat.Dense, map[string]*mat.Dense, map[string]*mat.Dense, map[string][]bool)

+ func (dn *Dense) MarshalParameters() ([]byte, error)

+ func (dn *Dense) TrainableParameters(dW, db map[string]*mat.Dense) []*Parameter

+ func (dn *Dense) UnmarshalParameters(data []byte) error

+ func (dn *Dense) UpdateParameters(dW, db map[string]*mat.Dense, learningRate float64)

+ type DenseConfig struct

+ Activation Activation

+ NNStructure []int

+ Optimizer OptimizerType

+ OutputActivation OutputActivation

+ type LossFunction func(*mat.Dense, *mat.Dense, map[string]*mat.Dense, float64) float64

+ type Mode struct

+ LossFunction LossFunction

+ OutputActivation OutputActivation

+ func NewMode(mode ModeType) Mode

+ type ModeType string

+ type Optimizer interface

+ MarshalState func() ([]byte, error)

+ Name func() OptimizerType

+ Step func(parameters []*Parameter, learningRate, t float64)

+ UnmarshalState func([]byte) error

+ func NewOptimizer(optType OptimizerType) Optimizer

+ type OptimizerType string

+ const AdamOptimizer

+ const GradientDescentOptimizer

+ type OutputActivation struct

+ Function OutputActivationFunction

+ Mode ModeType

+ type OutputActivationFunction func(*mat.Dense) *mat.Dense

+ type Parameter struct

+ Gradient *mat.Dense

+ Update func(*mat.Dense)

+ Value *mat.Dense