loss

package

v0.1.0 Latest Latest Go to latest Published: Aug 4, 2025 License: Apache-2.0 Imports: 6 Imported by: 0

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github.com/zerfoo/zerfoo

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Documentation ¶

Overview ¶

Package loss provides various loss functions for neural networks.

Index ¶

type CrossEntropyLoss
- func NewCrossEntropyLoss[T tensor.Numeric](engine compute.Engine[T]) *CrossEntropyLoss[T]
type Loss
type MSE
- func NewMSE[T tensor.Numeric](engine compute.Engine[T], ops numeric.Arithmetic[T]) *MSE[T]
- func (m *MSE[T]) Backward(predictions, targets *tensor.Tensor[T]) *tensor.Tensor[T]
- func (m *MSE[T]) Forward(predictions, targets *tensor.Tensor[T]) *tensor.Tensor[T]

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

This section is empty.

Types ¶

type CrossEntropyLoss ¶

type CrossEntropyLoss[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

CrossEntropyLoss computes the cross-entropy loss.

func NewCrossEntropyLoss ¶

func NewCrossEntropyLoss[T tensor.Numeric](engine compute.Engine[T]) *CrossEntropyLoss[T]

NewCrossEntropyLoss creates a new CrossEntropyLoss layer.

func (*CrossEntropyLoss[T]) Backward ¶

func (cel *CrossEntropyLoss[T]) Backward(ctx context.Context, dOut *tensor.Tensor[T], inputs ...*tensor.Tensor[T]) ([]*tensor.Tensor[T], error)

Backward computes the gradients for CrossEntropyLoss. dOut is typically a scalar (1.0) for loss functions.

func (*CrossEntropyLoss[T]) Forward ¶

func (cel *CrossEntropyLoss[T]) Forward(ctx context.Context, inputs ...*tensor.Tensor[T]) (*tensor.Tensor[T], error)

Forward computes the cross-entropy loss. Inputs: predictions (logits), targets (int labels).

func (*CrossEntropyLoss[T]) OutputShape ¶

func (cel *CrossEntropyLoss[T]) OutputShape(inputShapes ...[]int) ([]int, error)

OutputShape returns the output shape of the loss (a scalar).

func (*CrossEntropyLoss[T]) Parameters ¶

func (cel *CrossEntropyLoss[T]) Parameters() []graph.Parameter[T]

Parameters returns an empty slice as CrossEntropyLoss has no trainable parameters.

type Loss ¶

type Loss[T tensor.Numeric] interface {
	// Forward computes the loss and its gradient.
	Forward(predictions, targets *tensor.Tensor[T]) (T, *tensor.Tensor[T])
}

Loss defines the interface for loss functions.

type MSE ¶

type MSE[T tensor.Numeric] struct {
	// contains filtered or unexported fields
}

MSE calculates the mean squared error between predictions and targets.

func NewMSE ¶

func NewMSE[T tensor.Numeric](engine compute.Engine[T], ops numeric.Arithmetic[T]) *MSE[T]

NewMSE creates a new MSE loss function.

func (*MSE[T]) Backward ¶

func (m *MSE[T]) Backward(predictions, targets *tensor.Tensor[T]) *tensor.Tensor[T]

Backward computes the initial gradient of the loss.

func (*MSE[T]) Forward ¶

func (m *MSE[T]) Forward(predictions, targets *tensor.Tensor[T]) *tensor.Tensor[T]

Forward computes the loss value.

Source Files ¶

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