pub struct EdgeData {
pub weight: DenseTensor,
pub structure_logits: f64,
pub existence_prob: f64,
pub exists: bool,
pub structure_gradient: Option<f64>,
pub weight_gradient: Option<DenseTensor>,
}Expand description
边数据:统一存储权重和结构参数
Fields§
§weight: DenseTensor权重张量
structure_logits: f64结构 logits(决定边是否存在)
existence_prob: f64边存在概率(由 logits 计算)
exists: bool离散化后的存在性
structure_gradient: Option<f64>结构梯度
weight_gradient: Option<DenseTensor>权重梯度
Implementations§
Source§impl EdgeData
impl EdgeData
Sourcepub fn new(weight: DenseTensor, init_prob: f64) -> Self
pub fn new(weight: DenseTensor, init_prob: f64) -> Self
创建新的边数据
Sourcepub fn logits_to_prob(logits: f64, temperature: f64) -> f64
pub fn logits_to_prob(logits: f64, temperature: f64) -> f64
logits 转概率(带温度)
Sourcepub fn update_logits(&mut self, gradient: f64, learning_rate: f64)
pub fn update_logits(&mut self, gradient: f64, learning_rate: f64)
更新结构 logits
Sourcepub fn update_weight(&mut self, gradient: &DenseTensor, learning_rate: f64)
pub fn update_weight(&mut self, gradient: &DenseTensor, learning_rate: f64)
更新权重
Sourcepub fn discretize(&mut self, temperature: f64, threshold: f64)
pub fn discretize(&mut self, temperature: f64, threshold: f64)
离散化(使用 STE)
Trait Implementations§
Auto Trait Implementations§
impl Freeze for EdgeData
impl RefUnwindSafe for EdgeData
impl Send for EdgeData
impl Sync for EdgeData
impl Unpin for EdgeData
impl UnsafeUnpin for EdgeData
impl UnwindSafe for EdgeData
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