HuberLossRegressionExample

public static void DocumentationExample()

Language: C#

Documentation: How to use HuberLoss with GPU acceleration

Step 1: Initialize the backend

var backend = new TorchSharpBackend();

await backend.InitializeAsync();

Step 2: Create HuberLoss with GPU support

var huberLoss = new HuberLoss(

delta: 1.0f, // Threshold for switching between loss regions

reduction: LossReduction.Mean, // Average loss across batch

gpuOps: backend as IGpuMathOps // Use GPU-accelerated operations

);

Step 3: Compute loss on GPU

float loss = await huberLoss.ComputeLossAsync(predictions, targets);

Step 4: Compute gradients for backpropagation

var gradients = await huberLoss.ComputeGradientAsync(predictions, targets);

Benefits of GPU-Aware Implementation:

• All computation stays on GPU (no CPU marshaling overhead)

• Smooth transitions between L2 and L1 regions (differentiable)

• Robust to outliers in data

• Suitable for regression tasks with noisy or outlier-prone targets

• Scales well with batch size on GPU

When to use HuberLoss:

• Regression with outliers (more robust than MSE)

• Need smooth gradients (better than L1/MAE)

• Hyperparameter: delta controls outlier sensitivity

• delta = 0.5: More sensitive to outliers (mostly L2)

• delta = 1.0: Balanced (default, good starting point)

• delta = 5.0: More robust (mostly L1 for large errors)