Struct LpLoss 

pub struct LpLoss {
    pub p: f64,
}

Computes the Lp Loss between predictions and targets.

This loss function computes the element-wise p-th power of absolute errors, then reduces them via mean or sum.

Mathematical Definition

For predictions ŷ and targets y, the element-wise loss is:

Lᵢ = |ŷᵢ - yᵢ|ᵖ

With mean reduction (default), the final loss is:

L = (1/n) × Σᵢ |ŷᵢ - yᵢ|ᵖ

Notes

• This implementation computes |error|^p, not the Lp norm (Σ|error|^p)^(1/p).
• The p = 1 case uses an optimized abs() operation.
• The p = 2 case uses an optimized computation error * error instead of powf.

Example

use burn_nn::loss::{LpLossConfig, Reduction};
use burn::tensor::Tensor;

// Create L2 loss
let l2_loss = LpLossConfig::l2();

let predictions: Tensor<2> = /* model output */;
let targets: Tensor<2> = /* ground truth */;

// Compute loss with mean reduction (MSE)
let mse = l2_loss.forward(predictions.clone(), targets.clone(), Reduction::Mean);

// Compute loss with sum reduction (SSE)
let sse = l2_loss.forward(predictions.clone(), targets.clone(), Reduction::Sum);

// Compute loss with no reduction
let unreduced_l2_loss = l2_loss.forward_no_reduction(predictions, targets);

Fields

p: f64

The order of the norm (e.g., 1 for L1, 2 for L2). Equivalently, the exponent p for computing |error|^p.

Implementations

impl LpLoss

pub fn forward<const D: usize>( &self, predictions: Tensor<D>, targets: Tensor<D>, reduction: Reduction, ) -> Tensor<1>

Computes the element-wise loss |error|^p with reduction.

Arguments

• predictions - The model’s predicted values.
• targets - The ground truth target values.
• reduction - Specifies how to reduce the element-wise losses:
  • Reduction::Mean or Reduction::Auto: Returns the mean of all element-wise losses.
  • Reduction::Sum: Returns the sum of all element-wise losses.

Returns

A scalar tensor containing the reduced loss value.

Shapes

• predictions: [...dims] - Any shape
• targets: [...dims] - Must match predictions shape
• output: [1] - Scalar loss value

pub fn forward_no_reduction<const D: usize>( &self, predictions: Tensor<D>, targets: Tensor<D>, ) -> Tensor<D>

Computes the element-wise loss |error|^p without reduction.

Arguments

• predictions - The model’s predicted values.
• targets - The ground truth target values.

Returns

A tensor of the same shape as the inputs, containing |prediction - target|^p for each element.

Shapes

• predictions: [...dims] - Any shape
• targets: [...dims] - Must match predictions shape
• output: [...dims] - Same shape as inputs

pub fn forward_reduce_dims<const D: usize>( &self, predictions: Tensor<D>, targets: Tensor<D>, dims: &[impl AsIndex], ) -> Tensor<D>

Computes the element-wise loss |error|^p with reduction over specified dimensions.

Calculates element-wise |predictions - targets|^p, then takes the mean over the specified dimensions. Useful for per-sample or per-channel losses (e.g., when working with images).

Dimensions can be provided in any order.

Arguments

• predictions - The model’s predicted values.
• targets - The ground truth target values.
• dims - Dimensions to reduce over. Negative dimensions are supported and count from the end.

Returns

A tensor with the specified dimensions reduced to size 1.

Example

// Image tensor: [batch, C, H, W]
let l2_loss = LpLossConfig::l2();

// Per-image MSE for PSNR: reduce over C, H, W → [batch, 1, 1, 1]
let mse_per_image = l2_loss.forward_reduce_dims(predictions, targets, &[1, 2, 3]);

Trait Implementations

impl Clone for LpLoss

fn clone(&self) -> LpLoss

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LpLoss

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

Formats the value using the given formatter.

impl Display for LpLoss

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

Formats the value using the given formatter.

impl Module for LpLoss

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) -> LpLoss

where M: ModuleMapper,

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

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

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

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

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) -> LpLoss

Fold parameter reparameterizations into their effective values and remove them.

fn valid(&self) -> LpLoss

Returns a validation snapshot without autodiff or active training flags.

fn train(self) -> LpLoss

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 LpLoss

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

Formats the module with provided display settings.

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.

impl ModuleDisplayDefault for LpLoss

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.