Module

github.com/BrandonKS05/goneural

Import the goneural package (library code lives in the goneural subdirectory):

import "github.com/BrandonKS05/goneural/goneural"

Install:

go get github.com/BrandonKS05/goneural/goneural@latest

Features

• Configurable layers and activations: sigmoid, tanh, ReLU, leaky ReLU, ELU, SELU (self-normalizing), softplus, identity, and softmax (output layer only, paired with cross-entropy)
• Losses: mean squared error, mean absolute error, Huber, log-cosh, categorical cross-entropy
• Forward pass (Predict), training with backpropagation
• Optimizers: SGD, MBGD (mini-batch), GD (full batch), MomentumSGD/NesterovSGD, Adam, AdamW (decoupled weight decay), Nadam, AMSGrad, Adamax (decayed infinity norm), RAdam (variance-rectified, warmup-free), AdaBelief (steps on gradient surprise rather than magnitude), LAMB (per-layer trust ratios), Adafactor (rank-one factored second moments, rows + cols state per weight matrix instead of rows * cols), Lion (sign momentum), RMSProp, AdaGrad, AdaDelta, and ConcurrentMBGD (mini-batch with per-sample gradients computed in parallel goroutines)
• Optimizer wrappers: Lookahead slow/fast weight averaging and WithWeightDecay decoupled L2, each composable with any optimizer
• Learning-rate schedules: step, exponential and polynomial decay, cosine annealing with warm restarts, and a WithWarmup ramp, composable with any optimizer via WithSchedule/WithScheduleFunc
• Regularization: inverted dropout on hidden layers (HiddenDropout) and gradient clipping by global norm (ClipByGlobalNorm; the momentum optimizer wires it in through MaxGradNorm)
• Weight initialization: Xavier/Glorot (InitXavier) and He (InitHe)
• Metrics and helpers: Accuracy, ConfusionMatrix with Precision/Recall/F1Score, ArgMax, OneHot, LogSumExp, SoftmaxWithTemperature, data-set Split/KFold, plus a Trainer with optional early stopping
• Experimental: ComplexStepGD/ComplexStepSGD, an optimizer that estimates gradients via complex-step differentiation (perturbing weights by an imaginary step) instead of backprop; supports MSE loss with sigmoid/identity activations only. Also used in the test suite as an independent oracle to verify backprop's analytic gradients.
• Optional genetic operators: copy, crossover, Gaussian mutation
• Serialize and deserialize networks to disk (weights, biases, layer metadata)
• matrix subpackage: dense float64 matrices with the usual elementwise and product ops, row/column extraction and sums, clipping, norms, plus determinant, inverse, and linear solving (Gauss-Jordan with partial pivoting)

Usage

g := goneural.New(
	0.1,
	goneural.MSE(),
	goneural.Layer{Nodes: 2},
	goneural.Layer{Nodes: 4, Activator: goneural.Sigmoid()},
	goneural.Layer{Nodes: 1},
)

g.Train(goneural.SGD(), goneural.DataSet{
	{Inputs: []float64{1, 0}, Targets: []float64{1}},
	{Inputs: []float64{0, 1}, Targets: []float64{1}},
	{Inputs: []float64{1, 1}, Targets: []float64{0}},
	{Inputs: []float64{0, 0}, Targets: []float64{0}},
}, 5000)

g.Predict([]float64{1, 1})

Save and load (filename extension is up to you; .goneural matches the project name):

g.Save("model.goneural")
g, err := goneural.Load("model.goneural")

Compose the training extras — momentum with a cosine-annealed learning rate and gradient clipping:

o := goneural.NewMomentumOptimizer(16, 0.5, 0.9)
o.MaxGradNorm = 5
opt := goneural.WithScheduleFunc(o.Optimize, goneural.CosineAnnealing(0.5, 0.01, 50),
	func(lr float64) { o.LearningRate = lr })

g.InitXavier()
g.Train(opt, data, 200)
fmt.Println(g.Accuracy(data))

Examples

• examples/mnist — handwritten digit classification (softmax + cross-entropy + Adam, ~85-90% test accuracy; see its README for dataset download)
• examples/xor — XOR with a small MLP
• examples/perceptron — single perceptron demo

Requirements

Go 1.21+ (see go.mod).

Clone

git clone https://github.com/BrandonKS05/goneural.git

License

See LICENSE in this repository.