Struct FeatureDistillationLoss

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pub struct FeatureDistillationLoss {
    pub layer_weights: Vec<f64>,
    pub p_norm: f64,
}

Expand description

Feature-based distillation that matches intermediate layer representations.

Fields§

§layer_weights: Vec<f64>

Weight for each feature layer.

§p_norm: f64

Distance metric (2.0 for L2, 1.0 for L1).

Implementations§

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impl FeatureDistillationLoss

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pub fn new(layer_weights: Vec<f64>, p_norm: f64) -> TrainResult<Self>

Create a new feature distillation loss.

§Arguments

layer_weights - Weights for each intermediate layer
p_norm - Norm to use for distance (1.0 or 2.0)

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pub fn compute_feature_loss( &self, student_features: &[ArrayView<'_, f64, Ix2>], teacher_features: &[ArrayView<'_, f64, Ix2>], ) -> TrainResult<f64>

Compute feature matching loss for intermediate representations.

§Arguments

student_features - Student model’s intermediate features
teacher_features - Teacher model’s intermediate features

§Returns

Weighted sum of feature matching losses

Auto Trait Implementations§

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impl UnwindSafe for FeatureDistillationLoss

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