nn

Overview ¶

Package nn provides neural network layers and building blocks.

Overview ¶

This package contains:

• Layers: Linear, Conv2D, MaxPool2D
• Activations: ReLU, Sigmoid, Tanh
• Loss functions: CrossEntropyLoss, MSELoss
• Utilities: Sequential, Module interface, Parameter
• Initialization: Xavier, Zeros, Ones, Randn

Basic Usage ¶

import (
    "github.com/born-ml/born/nn"
    "github.com/born-ml/born/backend/cpu"
)

func main() {
    backend := cpu.New()

    // Build a simple MLP
    model := nn.NewSequential(
        nn.NewLinear(784, 128, backend),
        nn.NewReLU(),
        nn.NewLinear(128, 10, backend),
    )

    // Forward pass
    output := model.Forward(input)
}

Layers ¶

Linear: Fully connected layer with Xavier initialization

layer := nn.NewLinear(inFeatures, outFeatures, backend)

Conv2D: 2D convolutional layer with im2col algorithm

conv := nn.NewConv2D(inChannels, outChannels, kernelSize, stride, padding, backend)

MaxPool2D: 2D max pooling layer

pool := nn.NewMaxPool2D(kernelSize, stride, backend)

Activations ¶

Common activation functions:

relu := nn.NewReLU()
sigmoid := nn.NewSigmoid()
tanh := nn.NewTanh()

Loss Functions ¶

CrossEntropyLoss: For classification tasks (numerically stable)

criterion := nn.NewCrossEntropyLoss(backend)
loss := criterion.Forward(logits, labels)

MSELoss: For regression tasks

criterion := nn.NewMSELoss(backend)
loss := criterion.Forward(predictions, targets)

Sequential Models ¶

Build models by composing layers:

model := nn.NewSequential(
    nn.NewLinear(784, 256, backend),
    nn.NewReLU(),
    nn.NewLinear(256, 128, backend),
    nn.NewReLU(),
    nn.NewLinear(128, 10, backend),
)

Parameter Management ¶

Access model parameters for optimization:

params := model.Parameters()
for _, param := range params {
    fmt.Println(param.Name(), param.Tensor().Shape())
}