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model

package

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Versions in this module

v0

v0.1.11

May 13, 2023

v0.1.6

Jun 2, 2023

v0.1.5

Jun 2, 2023

Changes in this version

type Sequential

+ func (s *Sequential) DisposeVMs()

v0.1.4

Jun 2, 2023

v0.1.3

May 13, 2023

v0.1.2

May 13, 2023

v0.1.1

May 13, 2023

v0.1.0

May 13, 2023

Changes in this version

+ var AllMetrics = Metrics

+ var CrossEntropy = &CrossEntropyLoss

+ var MSE = &MSELoss

+ var PseudoCrossEntropy = &PseudoCrossEntropyLoss

+ var PseudoHuber = &PseudoHuberLoss

+ func AsBatch(size int) func(*Input)

+ func AsType(dtype t.Dtype) func(*Input)

+ func NameAsBatch(name string) string

+ func WithBatchSize(size int) func(Model)

+ func WithGraphLogger(log *golog.Logger) func(Model)

+ func WithLogger(logger *log.Logger) func(Model)

+ func WithLoss(loss Loss) func(Model)

+ func WithMetrics(metrics ...Metric) func(Model)

+ func WithOptimizer(optimizer g.Solver) func(Model)

+ func WithTracker(tracker *track.Tracker) func(Model)

+ func WithoutTracker() func(Model)

+ type CloneOpt func(*Input)

+ type CrossEntropyLoss struct

+ func (c *CrossEntropyLoss) CloneTo(graph *g.ExprGraph, opts ...CloneOpt) Loss

+ func (c *CrossEntropyLoss) Compute(yHat, y *g.Node) (loss *g.Node, err error)

+ func (c *CrossEntropyLoss) Inputs() Inputs

+ type Input struct

+ func NewInput(name string, shape t.Shape, opts ...InputOpt) *Input

+ func (i *Input) AsBatch(size int) *Input

+ func (i *Input) AsLayer() l.Layer

+ func (i *Input) Check(value g.Value) error

+ func (i *Input) Clone(opts ...CloneOpt) *Input

+ func (i *Input) CloneTo(graph *g.ExprGraph, opts ...CloneOpt) *Input

+ func (i *Input) Compile(graph *g.ExprGraph, opts ...InputOpt) *g.Node

+ func (i *Input) DType() t.Dtype

+ func (i *Input) EnsureBatch() *Input

+ func (i *Input) Input() *Input

+ func (i *Input) Inputs() Inputs

+ func (i *Input) Name() string

+ func (i *Input) Node() *g.Node

+ func (i *Input) OneOfMany() (err error)

+ func (i *Input) Set(value g.Value) error

+ func (i *Input) Shape() t.Shape

+ func (i *Input) Squeeze() t.Shape

+ func (i *Input) Validate() error

+ type InputLayer struct

+ func (i *InputLayer) Clone() l.Layer

+ func (i *InputLayer) Compile(graph *g.ExprGraph, opts ...l.CompileOpt)

+ func (i *InputLayer) Fwd(x *g.Node) (*g.Node, error)

+ func (i *InputLayer) Graph() *g.ExprGraph

+ func (i *InputLayer) Learnables() g.Nodes

+ func (i *InputLayer) Node() *g.Node

+ type InputOpt func(*Input)

+ type InputOr interface

+ Input func() *Input

+ Inputs func() Inputs

+ type Inputs []*Input

+ func (i Inputs) Clone() Inputs

+ func (i Inputs) Compile(graph *g.ExprGraph, opts ...InputOpt) g.Nodes

+ func (i Inputs) Contains(name string) bool

+ func (i Inputs) Get(name string) (*Input, error)

+ func (i Inputs) Input() *Input

+ func (i Inputs) Inputs() Inputs

+ func (i Inputs) Set(values Values) error

+ type Loss interface

+ CloneTo func(graph *g.ExprGraph, opts ...CloneOpt) Loss

+ Compute func(yHat, y *g.Node) (loss *g.Node, err error)

+ Inputs func() Inputs

+ type MSELoss struct

+ func (m *MSELoss) CloneTo(graph *g.ExprGraph, opts ...CloneOpt) Loss

+ func (m *MSELoss) Compute(yHat, y *g.Node) (loss *g.Node, err error)

+ func (m *MSELoss) Inputs() Inputs

+ type Metric string

+ const TrainBatchLossMetric

+ const TrainLossMetric

+ type Metrics []Metric

+ func (m Metrics) Contains(metric Metric) bool

+ type Model interface

+ Compile func(x InputOr, y *Input, opts ...Opt) error

+ Fit func(x ValueOr, y g.Value) error

+ FitBatch func(x ValueOr, y g.Value) error

+ Graphs func() map[string]*g.ExprGraph

+ Learnables func() g.Nodes

+ Predict func(x g.Value) (prediction g.Value, err error)

+ PredictBatch func(x g.Value) (prediction g.Value, err error)

+ ResizeBatch func(n int) error

+ Visualize func(name string)

+ X func() InputOr

+ Y func() *Input

+ type Opt func(Model)

+ type Opts struct

+ func NewOpts() *Opts

+ func (o *Opts) Add(opts ...Opt)

+ func (o *Opts) Values() []Opt

+ type PseudoCrossEntropyLoss struct

+ func (c *PseudoCrossEntropyLoss) CloneTo(graph *g.ExprGraph, opts ...CloneOpt) Loss

+ func (c *PseudoCrossEntropyLoss) Compute(yHat, y *g.Node) (loss *g.Node, err error)

+ func (c *PseudoCrossEntropyLoss) Inputs() Inputs

+ type PseudoHuberLoss struct

+ Delta float32

+ func NewPseudoHuberLoss(delta float32) *PseudoHuberLoss

+ func (h *PseudoHuberLoss) CloneTo(graph *g.ExprGraph, opts ...CloneOpt) Loss

+ func (h *PseudoHuberLoss) Compute(yHat, y *g.Node) (loss *g.Node, err error)

+ func (h *PseudoHuberLoss) Inputs() Inputs

+ type Sequential struct

+ Chain *layer.Chain

+ Tracker *track.Tracker

+ func NewSequential(name string) (*Sequential, error)

+ func (s *Sequential) AddLayer(layer layer.Config)

+ func (s *Sequential) AddLayers(layers ...layer.Config)

+ func (s *Sequential) CloneLearnablesTo(to *Sequential) error

+ func (s *Sequential) Compile(x InputOr, y *Input, opts ...Opt) error

+ func (s *Sequential) Fit(x ValueOr, y g.Value) error

+ func (s *Sequential) FitBatch(x ValueOr, y g.Value) (err error)

+ func (s *Sequential) Fwd(x *Input)

+ func (s *Sequential) Graphs() map[string]*g.ExprGraph

+ func (s *Sequential) Learnables() g.Nodes

+ func (s *Sequential) Predict(x g.Value) (prediction g.Value, err error)

+ func (s *Sequential) PredictBatch(x g.Value) (prediction g.Value, err error)

+ func (s *Sequential) ResizeBatch(n int) (err error)

+ func (s *Sequential) SetLearnables(desired g.Nodes) error

+ func (s *Sequential) Visualize(name string)

+ func (s *Sequential) X() InputOr

+ func (s *Sequential) Y() *Input

+ type ValueOr interface

+ type Values []g.Value

+ func ValuesFrom(v ValueOr) Values