Struct PoissonNllLoss 

pub struct PoissonNllLoss {
    pub log_input: bool,
    pub full: bool,
    pub eps: f64,
}

Negative Log Likelihood (NLL) loss with a Poisson distribution assumption for the target.

This loss function is used when the target values are assumed to follow a Poisson distribution. The loss is defined as:

target ~ Poisson(input)
L(predictions, target) = predictions - target * log(predictions) + log(target!)

The last term (log(target!)) can be omitted or approximated using Stirling’s formula. The approximation is applied for target > 1, while for target <= 1, zeros are added to the loss.

For more details, see: https://en.wikipedia.org/wiki/Poisson_regression#Maximum_likelihood-based_parameter_estimation

Fields

log_input: bool

If true, the predictions are expected to be in log-space.

full: bool

Whether to compute the full loss, including the Stirling approximation term.

eps: f64

A small value to avoid evaluation of log(0) when log_input is false.

Implementations

impl PoissonNllLoss

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

where B: Backend,

Computes the loss element-wise for the given predictions and targets, then reduces the result to a single loss value.

Arguments

• predictions: The predicted values.
• targets: The target values.
• reduction: The reduction method to apply. Reduction::Auto behaves as Reduction::Mean.

Shapes

• predictions: [...dims]
• targets: [...dims]
• output: [1]

Panics

• Panics if the shapes of predictions and targets do not match.
• Panics if any target value is negative.
• Panics if log_input is false and any prediction value is negative.

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

where B: Backend,

Computes the loss element-wise for the given predictions and targets without reduction.

Arguments

• predictions: The predicted values.
• targets: The target values.

Shapes

• predictions: [...dims]
• targets: [...dims]
• output: [...dims]

Panics

• Panics if the shapes of predictions and targets do not match.
• Panics if any target value is negative.
• Panics if log_input is false and any prediction value is negative.

Trait Implementations

impl<B> AutodiffModule<B> for PoissonNllLoss

where B: AutodiffBackend,

type InnerModule = PoissonNllLoss

Inner module without auto-differentiation.

fn valid(&self) -> <PoissonNllLoss as AutodiffModule<B>>::InnerModule

Get the same module, but on the inner backend without auto-differentiation.

impl Clone for PoissonNllLoss

fn clone(&self) -> PoissonNllLoss

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for PoissonNllLoss

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

Formats the value using the given formatter.

impl Display for PoissonNllLoss

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

Formats the value using the given formatter.

impl<B> Module<B> for PoissonNllLoss

where B: Backend,

type Record = ConstantRecord

Type to save and load the module.

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

where V: ModuleVisitor<B>,

Visit each tensor parameter in the module with a visitor.

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

where M: ModuleMapper<B>,

Map each tensor parameter in the module with a mapper.

fn load_record( self, _record: <PoissonNllLoss as Module<B>>::Record, ) -> PoissonNllLoss

Load the module state from a record.

fn into_record(self) -> <PoissonNllLoss as Module<B>>::Record

Convert the module into a record containing the state.

fn to_device(self, _: &<B as Backend>::Device) -> PoissonNllLoss

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

fn fork(self, _: &<B as Backend>::Device) -> PoissonNllLoss

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

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

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

fn devices(&self) -> Vec<<B as Backend>::Device>

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

fn no_grad(self) -> Self

Each tensor in the module tree will not require grad.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules.

fn save_file<FR, PB>( self, file_path: PB, recorder: &FR, ) -> Result<(), RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Save the module to a file using the provided file recorder.

fn load_file<FR, PB>( self, file_path: PB, recorder: &FR, device: &<B as Backend>::Device, ) -> Result<Self, RecorderError>

where FR: FileRecorder<B>, PB: Into<PathBuf>,

Load the module from a file using the provided file recorder.

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

Quantize the weights of the module.

impl ModuleDisplay for PoissonNllLoss

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 PoissonNllLoss

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.