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goneural

package module

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Versions in this module

v0

v0.0.4

Nov 25, 2021

Changes in this version

+ func GetLayerTotLoss(l LossFunction, expectedX, predictedX *Matrix) float64

+ func ShuffleBoth(X, Y []*Matrix)

+ type Activation interface

+ Calc func(x float64) float64

+ Diff func(x float64) float64

+ type DenseLayer struct

+ Ac Activation

+ Weights Matrix

+ func NewDenseLayer(numInputs, numOutputs int, activation Activation) *DenseLayer

+ func (l *DenseLayer) GetActivation() Activation

+ func (l *DenseLayer) GetNumInputs() int

+ func (l *DenseLayer) GetNumOutputs() int

+ func (l *DenseLayer) PropagateValues(X *Matrix) *Matrix

+ func (l *DenseLayer) TrainGradientDescent(learningRate float64, layerInputs, deltas *Matrix) *Matrix

+ func (l *DenseLayer) TrainMinibatch(learningRate float64, avInputs, avDeltas *Matrix) *Matrix

+ type FeedForwardNetwork struct

+ Inputs int

+ Layers []Layer

+ Loss LossFunction

+ Outputs int

+ func NewFeedForwardNetwork(inputs int, loss LossFunction) *FeedForwardNetwork

+ func (n *FeedForwardNetwork) AddLayer(layer Layer)

+ func (n *FeedForwardNetwork) GetLastLayerDeltas(pred, expec *Matrix) *Matrix

+ func (n *FeedForwardNetwork) Predict(X *Matrix) *Matrix

+ func (n *FeedForwardNetwork) PredictAll(Xs []*Matrix) []*Matrix

+ type Layer interface

+ GetActivation func() Activation

+ GetNumInputs func() int

+ GetNumOutputs func() int

+ PropagateValues func(X *Matrix) *Matrix

+ type LeakyReLUActivation struct

+ SubZeroGradient float64

+ func (a LeakyReLUActivation) Calc(x float64) float64

+ func (a LeakyReLUActivation) Diff(x float64) float64

+ type LinearActivation types.Nil

+ func (a LinearActivation) Calc(x float64) float64

+ func (a LinearActivation) Diff(x float64) float64

+ type LossFunction interface

+ SingleLoss func(expected, predicted float64) float64

+ SingleLossDiff func(expected, predicted float64) float64

+ type Matrix struct

+ Shape []int

+ Values []float64

+ func GetLayerLossDiffs(l LossFunction, expectedX, predictedX *Matrix) *Matrix

+ func New1DMatrix(vals []float64) *Matrix

+ func New2DMatrix(vals [][]float64) *Matrix

+ func NewZerosMatrix(shape ...int) *Matrix

+ func (m *Matrix) Add(m2 *Matrix) *Matrix

+ func (m *Matrix) AddValue1D(i int, v float64)

+ func (m *Matrix) AddValue2D(x, y int, v float64)

+ func (m *Matrix) Copy() *Matrix

+ func (m *Matrix) CopyTo(m2 *Matrix)

+ func (m *Matrix) CopyToSmaller(m2 *Matrix)

+ func (m *Matrix) Get1DLength() int

+ func (m *Matrix) GetValue1D(i int) float64

+ func (m *Matrix) GetValue2D(x, y int) float64

+ func (m *Matrix) Mul(f float64) *Matrix

+ func (m *Matrix) SetValue1D(i int, v float64)

+ func (m *Matrix) SetValue2D(x, y int, v float64)

+ type MinibatchOptimizer struct

+ BatchSize int

+ LearningRate float64

+ func (o *MinibatchOptimizer) FitBatch(n *FeedForwardNetwork, X, Y []*Matrix)

+ func (o *MinibatchOptimizer) GetBatchSize() int

+ type Optimizer interface

+ FitBatch func(n *FeedForwardNetwork, X, Y []*Matrix)

+ GetBatchSize func() int

+ type ReLUActivation types.Nil

+ func (a ReLUActivation) Calc(x float64) float64

+ func (a ReLUActivation) Diff(x float64) float64

+ type SGDLayer interface

+ TrainMinibatch func(learningRate float64, layerInputs, totDeltas *Matrix) *Matrix

+ type SerialTrainer struct

+ Opt Optimizer

+ UseRelativeLearningRate bool

+ func (s *SerialTrainer) Fit(n *FeedForwardNetwork, Xs, Ys []*Matrix, epochs, printRate int)

+ type SigmoidActivation types.Nil

+ func (a SigmoidActivation) Calc(x float64) float64

+ func (a SigmoidActivation) Diff(x float64) float64

+ type SquaredErrorLoss types.Nil

+ func (l SquaredErrorLoss) SingleLoss(expected, predicted float64) float64

+ func (l SquaredErrorLoss) SingleLossDiff(expected, predicted float64) float64

v0.0.3

Oct 22, 2021

v0.0.2

Oct 21, 2021

v0.0.1

Oct 21, 2021