Struct MultiMarginLoss 

pub struct MultiMarginLoss {
    pub margin: f64,
    pub p: i32,
}

Multi-class classification margin (hinge) loss between input x and target class indices y, following torch.nn.MultiMarginLoss.

For each sample the loss is

L = (1 / C) * sum over i != y of max(0, margin - x[y] + x[i]) ^ p

where C is the number of classes and y is the target class index.

Fields

margin: f64

The margin between the correct class and the others.

p: i32

The norm degree for the loss (1 or 2).

Implementations

impl MultiMarginLoss

pub fn forward( &self, input: Tensor<2>, target: Tensor<1, Int>, reduction: Reduction, ) -> Tensor<1>

Compute the loss for each sample, then reduce to a single value.

Reduction::Auto behaves as Reduction::Mean.

Shapes

• input: [batch_size, num_classes]
• target: [batch_size] (class indices in 0..num_classes)
• output: [1]

pub fn forward_no_reduction( &self, input: Tensor<2>, target: Tensor<1, Int>, ) -> Tensor<1>

Compute the loss for each sample, without reducing.

Shapes

• input: [batch_size, num_classes]
• target: [batch_size] (class indices in 0..num_classes)
• output: [batch_size]

Trait Implementations

impl Clone for MultiMarginLoss

fn clone(&self) -> MultiMarginLoss

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MultiMarginLoss

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Display for MultiMarginLoss

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Module for MultiMarginLoss

fn visit<V>(&self, _visitor: &mut V)

where V: ModuleVisitor,

Visit each parameter and module-owned control value with a visitor.

fn map<M>(self, _mapper: &mut M) -> MultiMarginLoss

where M: ModuleMapper,

Map each parameter and module-owned control value with a mapper.

fn to_device(self, _: &Device) -> MultiMarginLoss

Move the module and all of its sub-modules to the given device.

fn fork(self, _: &Device) -> MultiMarginLoss

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device>) -> Vec<Device>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn materialize(self) -> MultiMarginLoss

Fold parameter reparameterizations into their effective values and remove them.

fn valid(&self) -> MultiMarginLoss

Returns a validation snapshot without autodiff or active training flags.

fn train(self) -> MultiMarginLoss

Enables autodiff and restores configured training state throughout the module.

fn devices(&self) -> Vec<Device>

Return all the devices found in the underneath module tree without duplicates.

fn set_require_grad(self, require_grad: bool) -> Self

Set whether every floating-point tensor parameter in the module tree requires gradients.

fn set_require_grad_group(self, group: ParamGroup, require_grad: bool) -> Self

Set whether matched floating-point tensor parameters require gradients.

fn no_grad(self) -> Self

Disable gradients for every floating-point tensor parameter in the module tree.

fn freeze(self) -> Self

Freeze the whole module tree.

fn unfreeze(self) -> Self

Unfreeze the whole module tree.

fn freeze_group(self, group: ParamGroup) -> Self

Freeze every module-owned value in the given group, leaving the rest of the module untouched.

fn unfreeze_group(self, group: ParamGroup) -> Self

Unfreeze every module-owned value in the given group, leaving the rest of the module untouched.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules, without initializing the ones not initialized yet.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

fn quantize_weights_group( self, quantizer: &mut Quantizer, group: ParamGroup, ) -> Self

Quantize the weights of the given parameter group.

fn apply_reparameterization<R>(self, reparameterizer: R) -> Self

where Self: Sized, R: Reparameterizer,

Attach reparameterizations using the given Reparameterizer.

fn apply_lora(self, lora: Lora) -> Self

where Self: Sized,

Attach LoRA adapters to the module’s 2-D weights, freezing all base tensor parameters.

fn apply_qlora(self, qlora: QLora) -> Self

where Self: Sized,

Apply QLoRA to the module: quantize the (frozen) base tensor parameters and attach trainable LoRA adapters to 2-D weights.

fn into_record(self) -> ModuleRecord

where Self: Sized,

Collect this module’s tensor parameters into a ModuleRecord.

fn into_record_group(self, group: ParamGroup) -> ModuleRecord

where Self: Sized,

Collect the tensor parameters group names into a ModuleRecord.

fn try_load_record(self, record: ModuleRecord) -> Result<Self, RecordError>

where Self: Sized,

Apply a ModuleRecord to this module, returning the loaded module.

fn load_record(self, record: ModuleRecord) -> Self

where Self: Sized,

Apply a ModuleRecord to this module, consuming and returning it.

fn save_file<P>(self, path: P) -> Result<(), RecordError>

where P: AsRef<Path>, Self: Sized,

Save this module’s parameters to a burnpack file on disk.

fn load_file<P>(self, path: P) -> Self

where P: AsRef<Path>, Self: Sized,

Load this module’s parameters from a burnpack file on disk, returning the loaded module.

fn try_load_file<P>(self, path: P) -> Result<Self, RecordError>

where P: AsRef<Path>, Self: Sized,

Fallible variant of load_file.

impl ModuleDisplay for MultiMarginLoss

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, content: Content) -> Option<Content>

Custom attributes for the module.

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

impl ModuleDisplayDefault for MultiMarginLoss

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.