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feature_transformer.go
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feature_transformer.go
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package tabnet
import (
"github.com/dcu/godl"
"gorgonia.org/gorgonia"
)
// FeatureTransformerOpts contains options for feature transformer layer
type FeatureTransformerOpts struct {
Shared []*godl.LinearModule
VirtualBatchSize int
IndependentBlocks int
InputDimension int
OutputDimension int
WithBias bool
Momentum float64
WeightsInit gorgonia.InitWFn
}
func (o *FeatureTransformerOpts) setDefaults() {
if o.InputDimension == 0 {
panic("input dimension can't be nil")
}
if o.OutputDimension == 0 {
panic("output dimension can't be nil")
}
if o.Momentum == 0 {
o.Momentum = 0.01
}
}
type FeatureTransformerModule struct {
model *godl.Model
layer godl.LayerType
opts FeatureTransformerOpts
shared *GLUBlockModule
independent *GLUBlockModule
}
func (m *FeatureTransformerModule) Forward(inputs ...*godl.Node) godl.Nodes {
if err := m.model.CheckArity(m.layer, inputs, 1); err != nil {
panic(err)
}
x := inputs[0]
res := m.shared.Forward(x)
return m.independent.Forward(res[0])
}
// FeatureTransformer implements a feature transformer layer
func FeatureTransformer(nn *godl.Model, opts FeatureTransformerOpts) *FeatureTransformerModule {
lt := godl.AddLayer("FeatureTransformer")
opts.setDefaults()
shared := GLUBlock(nn, GLUBlockOpts{
InputDimension: opts.InputDimension,
OutputDimension: opts.OutputDimension,
VirtualBatchSize: opts.VirtualBatchSize,
Size: len(opts.Shared),
Shared: opts.Shared,
WithBias: opts.WithBias,
Momentum: opts.Momentum,
WeightsInit: opts.WeightsInit,
})
independent := GLUBlock(nn, GLUBlockOpts{
InputDimension: opts.InputDimension,
OutputDimension: opts.OutputDimension,
VirtualBatchSize: opts.VirtualBatchSize,
Size: opts.IndependentBlocks,
Shared: nil,
WithBias: opts.WithBias,
Momentum: opts.Momentum,
WeightsInit: opts.WeightsInit,
})
return &FeatureTransformerModule{
model: nn,
layer: lt,
opts: opts,
shared: shared,
independent: independent,
}
}