Examples

Every example is runnable from the repository root with go run:

Example	Command	What it shows
helloworld	go run ./_example/helloworld	Smallest possible program: add two values on the graph
dataset	go run ./_example/dataset	Dataset workflow: shuffle, split, standardize, batches
xor	go run ./_example/xor	XOR training (MSE + a softmax sanity check)
fizzbuzz	go run ./_example/fizzbuzz	FizzBuzz as classification
spiral	go run ./_example/spiral	3-class spiral classification
iris	go run ./_example/iris	Iris classification
mnist	go run ./_example/mnist	MNIST classifier (-model dense, cnn, or knn) with save/load
charrnn	go run ./_example/charrnn	Character-level LSTM text generation on the autograd engine
tinygpt	GOEXPERIMENT=simd go run ./_example/tinygpt	Character-level transformer trained from scratch on the n-d autograd engine (-gpu trains on the device)
plasma	go run ./_example/plasma	Terminal plasma rendered by a neural network — a live SIMD benchmark
dot	go run ./_example/dot	Graphviz DOT export of the z = x + y graph
tensor	go run ./_example/tensor	Tour of the n-d Tensor: broadcasting, batched MatMul, attention
wgpu	go run -tags wgpu ./_example/wgpu	WebGPU MatMul: adapter info, CPU cross-check, GPU vs CPU sweep
gpt2	GOEXPERIMENT=simd go run ./_example/gpt2	The published GPT-2 (124M) checkpoint generating text in pure Go

The gpt2 example downloads the GPT-2 checkpoint (~550MB) on first run. For instruction-tuned models — nine families, from Qwen2.5-0.5B up to 7B — use the tensai command: see LLM Inference.

MNIST

The MNIST example downloads the standard IDX gzip files into _example/mnist/data when they are missing (set MNIST_DIR to use another cache directory; both raw IDX files and .gz variants are accepted):

go run ./_example/mnist                                  # dense MLP
go run ./_example/mnist -model cnn                       # Conv2D/MaxPool2D/Dropout + AdamW
go run ./_example/mnist -model knn                       # no-training k-NN baseline
go run ./_example/mnist -model cnn -export mnist.tflite  # export to TFLite

On the 5000-sample subset the k-NN baseline scores ~91% against ~92% for the MLP and ~95% for the CNN. Both trained variants finish by saving the model and re-scoring it after a reload, and -export writes a TFLite flatbuffer that scores identically on the LiteRT interpreter — see Model Formats.

charrnn

Trains a character-level LSTM on an embedded public-domain text, saves the parameters with SaveParamsFile, restores them into a fresh model, and generates a sample from the reloaded parameters.

tinygpt

Trains a small character-level transformer -- token and position embeddings, two pre-norm blocks with four-head causal attention and a GELU feed-forward, a final norm and an output projection -- on the same embedded text charrnn uses, then samples from it. About 106k parameters and a minute of training with GOEXPERIMENT=simd, after which it reproduces whole sentences of the corpus. The whole model is written against the n-dimensional autograd engine: activations are (batch, sequence, model) tensors, the per-head split is a Reshape plus a Transpose, and a Tape recycles each step's buffers. Flags: -iters, -lr, -temp, -n, -seed, plus -model, -heads, -blocks, -batch and -seq to change the shape.

-gpu (on a wgpu build) trains the whole block on the device: values, gradients and the Adam update stay there and only the loss comes back each step. Whether it is faster depends on the shape — at the default size the tensors are too small to keep a GPU busy and the AVX2 kernels win, while a wider model crosses over. On an AMD 780M, 24ms/step against 72ms with -gpu at the default size, and 282ms against 129ms at -model 256 -heads 8 -batch 16 -seq 64. The losses match to the digit either way.

plasma

Animates a demoscene-style plasma in the terminal where the plasma function is a randomly weighted network (a CPPN) evaluated for every pixel of every frame as one batch. The status line shows the per-frame network time: ~32 fps portable, ~100 fps with GOEXPERIMENT=simd. Try different -seed values for different effects.

wgpu

Prints the adapter, cross-checks the GPU result against the CPU kernel, and -sweep walks a ladder of matrix sizes marking where the GPU overtakes the CPU — see GPU (WebGPU).